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Войцун, О. Є. "Перспективи educational data mining в Україні." Thesis, Cумський державний університет, 2016. http://essuir.sumdu.edu.ua/handle/123456789/47901.
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Актуальність дослідження полягає в тому, що сучасний стан освіти
вимагає використання сучасних методів для імплементації вказаних
вище потреб, і educational data mining (EDM) надає унікальні можливості для дослідників і практиків. Метою роботи було виявлення перективних напрямків ЕDM для України.
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Melgueira, Pedro Miguel Lúcio. "Educational data mining applied to Moodle data from the University of Évora." Master's thesis, Universidade de Évora, 2017. http://hdl.handle.net/10174/21346.
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E-Learning tem vindo a ganhar popularidade como forma de transmissão de conhecimentos a nível educacional
graças aos avanços nas tecnologias, como por exemplo, a Internet. Instituições como universidades
e empresas têm vindo a usar E-Learning para a transmissão de conteúdos educacionais para locais remotos
estendendo o seu alcance a estudantes e colaboradores que estão fisicamente distantes.
Sistemas chamados “Learning Management Systems”, como o Moodle, existem para organizar E-Learning.
Eles oferecem plataformas online onde professores e educadores podem publicar conteúdo, organizar actividades,
fazer avaliações, e outro tipo de ações relacionadas, de modo a que os estudantes possam aprender
e serem avaliados.
Estes sistemas geram e guardam muitos dados relacionados não só com o seu uso, mas também relacionados
com notas de estudantes. Este tipo de dados são frequentemente chamados de Dados Educacionais.
Métodos de Data Mining são aplicados a estes dados de modo a fazer suposições não triviais. As técnicas
aplicadas tomam inspiração de projectos semelhantes no campo da Data Mining Educacional. Este campo
consiste na aplicação de métodos de Data Mining a Dados Educacionais.
Neste projecto, um repositório de dados do Moodle da Universidade de Évora foi explorado. Técnicas de
aprendizagem supervisionada são aplicadas aos dados de modo a mostrar como é possível prever o sucesso
de estudantes a partir do seu uso do Moodle. Métodos de aprendizagem não supervisionada são também
aplicados de modo a mostrar como há divisões nos dados; Abstract:
E-Learning has been rising in popularity as a way to deliver training due to the advancements of technologies,
like the Internet. Institutions such as universities and companies have been making use of E-Learning to
deliver training to remote locations extending their reach to students and employees who are physically
distant.
Systems called Learning Management Systems, like Moodle, exist to organize E-Learning. They provide online
platforms where professors and educators can publish content, organize activities, perform evaluations,
and so on, in order for students to learn and get evaluated.
These systems generate and store lots of data regarding not only their usage, but also regarding the grades
of students. This kind of data is often referred too as Educational Data. Data Mining techniques are
applied to this data in order to make non trivial assumptions. The techniques applied take inspiration from
similar projects within the field of Educational Data Mining. This field consists in applying Data Mining
Techniques to Education Data.
In this project, a data repository from the Moodle of the University of Évora is explored. Supervised learning
techniques are applied to this data in order to show how it is possible to make predictions about the success
of students based on their usage of Moodle. Unsupervised learning techniques are also applied in order to
show how data is divisible.
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Borg, Olivia. "Educational Data Mining : En kvalitativ studie med inriktning på dataanalys för att hitta mönster i närvarostatistik." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-16987.
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Studien fokuserar på att hitta olika mönster i närvarostatistik hos elever som inte närvarar i skolan. Informationen som resultatet ger kan därefter användas som ett beslutsunderlag för skolor eller till andra organisationer som är intresserade av EDM inom närvarostatistik. Arbetet genomförde en kvalitativ metodansats med en fallstudie som bestod utav en litteraturstudie samt en implementation. Litteraturstudien användes för att få en förståelse över vanliga tillvägagångssätt inom EDM, som därefter låg till grund för implementationen som använde arbetssättet CRISP-DM. Resultatet blev fem olika mönster som definieras genom dataanalys. Mönstren visar frånvaro ur ett tidsperspektiv samt per ämne och kan ligga till grund för framtida beslutsunderlag.The study focuses on finding different patterns in attendance statistics for students who are not present at school. The information provided by the results can thereafter be used as a basis for decision-making for schools or for other organizations interested in EDM within attendance statistics. The work carried out a qualitative method approach with a case study that consisted a literature study and an implementation. The literature study was used to gain an understanding of common approaches within EDM, which subsequently formed the basis for the implementation that used the working method CRISP-DM. The project resulted in five different patterns defined by data analysis. The patterns show absence from a time perspective and per subject and can form the basis for future decision-making.
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Mavrikis, Manolis P. "Modelling students' behaviour and affect in ILE through educational data mining." Thesis, University of Edinburgh, 2008. http://hdl.handle.net/1842/15294.
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The long-term objective behind the research presented in this thesis is the improvement of ILEs and particularly those components that take into account students’ behaviour, as well as emotions and motivation. In related research, this is often attempted based on intuition, theoretical perspectives, or guided by results from studies in the isolation of a research lab. In this thesis, an attempt is made to inform the design of adaptation and feedback components by collecting and analysing as realistic data as possible. Guided by the belief that qualitative data analysis results can be enriched by employing statistical and machine learning techniques, the focus of this research is to investigate (a) key aspects of students’ behaviour and their relation to their learning and (b) how their behaviour could be employed to predict students’ affective and motivational characteristics. The first step is to gain an in-depth understanding of students’ behaviours in ILEs when they interact on their own time and location, rather than during a study where the social dynamics are different. Based on results, components of an ILE are redesigned and two Bayesian models are machine-learned; one that predicts when students need help in answering a question and one that predicts if their interaction with the system is beneficial to their learning. In the next step, machine learning is employed in order to derive predictive models of students’ affective and motivational states based on their interaction with ILEs. This is achieved by deriving decision trees based on a dataset of students’ self-reports collected during replays of their interaction. In addition, in order to take tutors’ perspectives into account, two different approaches are followed: The first attempts to elicit tutors’ inferences while they are watching replays of students’ interactions. This was not entirely successful. In the second approach, decision trees are derived from a dataset of tutors’ inferences collected during one-to-one computer-mediated tutorials.
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Alsuwaiket, Mohammed. "Measuring academic performance of students in Higher Education using data mining techniques." Thesis, Loughborough University, 2018. https://dspace.lboro.ac.uk/2134/34680.
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Educational Data Mining (EDM) is a developing discipline, concerned with expanding the classical Data Mining (DM) methods and developing new methods for discovering the data that originate from educational systems. It aims to use those methods to achieve a logical understanding of students, and the educational environment they should have for better learning. These data are characterized by their large size and randomness and this can make it difficult for educators to extract knowledge from these data. Additionally, knowledge extracted from data by means of counting the occurrence of certain events is not always reliable, since the counting process sometimes does not take into consideration other factors and parameters that could affect the extracted knowledge. Student attendance in Higher Education has always been dealt with in a classical way, i.e. educators rely on counting the occurrence of attendance or absence building their knowledge about students as well as modules based on this count. This method is neither credible nor does it necessarily provide a real indication of a student s performance. On other hand, the choice of an effective student assessment method is an issue of interest in Higher Education. Various studies (Romero, et al., 2010) have shown that students tend to get higher marks when assessed through coursework-based assessment methods - which include either modules that are fully assessed through coursework or a mixture of coursework and examinations than assessed by examination alone. There are a large number of Educational Data Mining (EDM) studies that pre-processed data through the conventional Data Mining processes including the data preparation process, but they are using transcript data as it stands without looking at examination and coursework results weighting which could affect prediction accuracy. This thesis explores the above problems and tries to formulate the extracted knowledge in a way that guarantees achieving accurate and credible results. Student attendance data, gathered from the educational system, were first cleaned in order to remove any randomness and noise, then various attributes were studied so as to highlight the most significant ones that affect the real attendance of students. The next step was to derive an equation that measures the Student Attendance s Credibility (SAC) considering the attributes chosen in the previous step. The reliability of the newly developed measure was then evaluated in order to examine its consistency. In term of transcripts data, this thesis proposes a different data preparation process through investigating more than 230,000 student records in order to prepare students marks based on the assessment methods of enrolled modules. The data have been processed through different stages in order to extract a categorical factor through which students module marks are refined during the data preparation process. The results of this work show that students final marks should not be isolated from the nature of the enrolled module s assessment methods; rather they must be investigated thoroughly and considered during EDM s data pre-processing phases. More generally, it is concluded that Educational Data should not be prepared in the same way as exist data due to the differences such as sources of data, applications, and types of errors in them. Therefore, an attribute, Coursework Assessment Ratio (CAR), is proposed to use in order to take the different modules assessment methods into account while preparing student transcript data. The effect of CAR and SAC on prediction process using data mining classification techniques such as Random Forest, Artificial Neural Networks and k-Nears Neighbors have been investigated. The results were generated by applying the DM techniques on our data set and evaluated by measuring the statistical differences between Classification Accuracy (CA) and Root Mean Square Error (RMSE) of all models. Comprehensive evaluation has been carried out for all results in the experiments to compare all DM techniques results, and it has been found that Random forest (RF) has the highest CA and lowest RMSE. The importance of SAC and CAR in increasing the prediction accuracy has been proved in Chapter 5. Finally, the results have been compared with previous studies that predicted students final marks, based on students marks at earlier stages of their study. The comparisons have taken into consideration similar data and attributes, whilst first excluding average CAR and SAC and secondly by including them, and then measuring the prediction accuracy between both. The aim of this comparison is to ensure that the new preparation process stage will positively affect the final results.
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Rajibussalim. "Data Mining for Studying the Impact of Reflection on Learning." Thesis, The University of Sydney, 2010. http://hdl.handle.net/2123/10589.
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Title: Data Mining for Studying the Impact of Reflection on Learning Keywords: educational data mining, Reflect, learning behaviour, impact Abstract On-line Web-based education learning systems generate a large amount of students' log data and profiles that could be useful for educators and students. Hence, data mining techniques that enable the extraction of hidden and potentially useful information in educational databases have been employed to explore educational data. A new promising area of research called educational data mining (EDM) has emerged. Reflect is a Web-based learning system that supports learning by reflection. Reflection is a process in which individuals explore their experiences in order to gain new understanding and appreciation, and research suggests that reflection improves learning. The Reflect system has been used at the University of Sydney’s School of Information Technology for several years as a source of learning and practice in addition to the classroom teaching. Using the data from a system that promotes reflection for learning (such as the Reflect system), this thesis focuses on the investigation of how reflection helps students in their learning. The main objective is to study students' learning behaviour associated with positive and negative outcomes (in exams) by utilising data mining techniques to search for previously unknown, potentially useful hidden information in the database. The approach in this study was, first, to explore the data by means of statistical analyses. Then, popular data mining algorithms such as the K-means and J48 algorithms were utilised to cluster and classify students according to their learning behaviours in using Reflect. The Apriori algorithm was also employed to find associations among the data attributes that lead to success. We were able to group and classify students according to their activities in the Reflect system, and identified some activities associated with student performance and learning outcomes (high, moderate or low exam marks). We concluded that the approach resulted in the identification of some learning behaviours that have important impacts on student performance.
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Xu, Yonghong. "Using data mining in educational research: A comparison of Bayesian network with multiple regression in prediction." Diss., The University of Arizona, 2003. http://hdl.handle.net/10150/280504.
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Advances in technology have altered data collection and popularized large databases in areas including education. To turn the collected data into knowledge, effective analysis tools are required. Traditional statistical approaches have shown some limitations when analyzing large-scale data, especially sets with a large number of variables. This dissertation introduces to educational researchers a new data analysis approach called data mining, an analytic process at the intersection of statistics, databases, machine learning/artificial intelligence (AI), and computer science, that is designed to explore large amounts of data to search for consistent patterns and/or systematic relationships between variables. To examine the usefulness of data mining in educational research, one specific data mining technique--the Bayesian Belief Network (BBN) based in Bayesian probability--is used to construct an analysis model in contrast to the traditional statistical approaches to answer a pseudo research question about faculty salary prediction in postsecondary institutions. Four prediction models--a multiple regression model with theoretical variable selection, a regression model with statistical variable extraction, a data mining BBN model with wrapper feature selection, and a combination model that used variables selected by the BBN in a multiple regression procedure--are expounded to analyze a data set called the National Survey of Postsecondary Faculty 1999 (NSOPF:99) provided by the National Center of Educational Services (NCES). The algorithms, input variables, final models, outputs, and interpretations of the four prediction models are presented and discussed. The results indicate that, with a nonmetric approach, the BBN can effectively handle a large number of variables through a process of stochastic subset selection; uncover dependence relationships among variables; detect hidden patterns in the data set; minimize the sample size as a factor influencing the amount of computations in data modeling; reduce data dimensionality by automatically identifying the most pertinent variable from a group of different but highly correlated measures in the analysis; and select the critical variables related to a core construct in prediction problems. The BBN and other data mining techniques have drawbacks; nonetheless, they are useful tools with unique advantages for analyzing large-scale data in educational research.
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Davoodi, Alireza. "User modeling and data mining in intelligent educational games : Prime Climb a case study." Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/45274.
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Educational games are designed to leverage students’ motivation and engagement in playing games to deliver pedagogical concepts to the players during game play. Adaptive educational games, in addition, utilize students’ models of learning to support personalization of learning experience according to students’ educational needs. A student’s model needs to be capable of making an evaluation of the mastery level of the target skills in the student and providing reliable base for generating tailored interventions to meet the user’s needs. Prime Climb, an adaptive educational game for students in grades 5 or 6 to practice number factorization related skill, provides a test-bed for research on user modeling and personalization in the domain of education games. Prime Climb leverages a student’s model using Dynamic Bayesian Network to implement personalization for assisting the students practice number factorization while playing the game. This thesis presents research conducted to improve the student’s model in Prime Climb by detecting and resolving the issue of degeneracy in the model. The issue of degeneracy is related to a situation in which the model’s accuracy is at its global maximum yet it violates conceptual assumptions about the process being modeled. Several criteria to evaluate the student’s model are introduced. Furthermore, using educational data mining techniques, different patterns of students’ interactions with Prime Climb were investigated to understand how students with higher prior knowledge or higher learning gain behave differently compared to students with lower prior knowledge and lower learning gain.
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Dailey, Matthew D. "Learning the Effectiveness of Content and Methodology in an Intelligent Tutoring System." Digital WPI, 2011. https://digitalcommons.wpi.edu/etd-theses/684.
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Classroom instruction time is a valuable yet scarce resource to teachers, who must decide how to best meet their objectives by selecting which topics to spend time on and when to move forward. Intelligent Tutoring Systems (ITS) are a powerful tool for teachers in this regard, allowing them to measure their students' current level of knowledge, helping them gauge student knowledge acquisition, and providing them with valuable insight into learning methodologies. By using ITS to identify the effectiveness of proven methods of instruction, we can more effectively teach students both in and outside of the classroom. In this paper we review the results and contributions of a new Bayesian data mining method which can be used to identify what works in an ITS and how it can be used to learn from data which is not in the typical randomized controlled trial design. We then discuss modifications to this dataset which use more knowledge about the students to improve accuracy. Lastly we evaluate this model on detecting and predicting long term student retention, and discuss methods to improve its predictive accuracy.
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Xu, Beijie. "Clustering Educational Digital Library Usage Data: Comparisons of Latent Class Analysis and K-Means Algorithms." DigitalCommons@USU, 2011. https://digitalcommons.usu.edu/etd/954.
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There are common pitfalls and neglected areas when using clustering approaches to solve educational problems. A clustering algorithm is often used without the choice being justified. Few comparisons between a selected algorithm and a competing algorithm are presented, and results are presented without validation. Lastly, few studies fully utilize data provided in an educational environment to evaluate their findings. In response to these problems, this thesis describes a rigorous study comparing two clustering algorithms in the context of an educational digital library service, called the Instructional Architect. First, a detailed description of the chosen clustering algorithm, namely, latent class analysis (LCA), is presented. Second, three kinds of preprocessed data are separately applied to both the selected algorithm and a competing algorithm, namely, K-means algorithm. Third, a series of comprehensive evaluations on four aspects of each clustering result, i.e., intra-cluster and inter-cluster distances, Davies-Bouldin index, users' demographic profile, and cluster evolution, are conducted to compare the clustering results of LCA and K-means algorithms. Evaluation results show that LCA outperforms K-means in producing consistent clustering results at different settings, finding compact clusters, and finding connections between users' teaching experience and their effectiveness in using the IA. The implication, contributions, and limitation of this research are discussed.
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Peroutka, Lukáš. "Návrh a implementace Data Mining modelu v technologii MS SQL Server." Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-199081.
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This thesis focuses on design and implementation of a data mining solution with real-world data. The task is analysed, processed and its results evaluated. The mined data set contains study records of students from University of Economics, Prague (VŠE) over the course of past three years. First part of the thesis focuses on theory of data mining, definition of the term, history and development of this particular field. Current best practices and meth-odology are described, as well as methods for determining the quality of data and methods for data pre-processing ahead of the actual data mining task. The most common data mining techniques are introduced, including their basic concepts, advantages and disadvantages. The theoretical basis is then used to implement a concrete data mining solution with educational data. The source data set is described, analysed and some of the data are chosen as input for created models. The solution is based on MS SQL Server data mining platform and it's goal is to find, describe and analyse potential as-sociations and dependencies in data. Results of respective models are evaluated, including their potential added value. Also mentioned are possible extensions and suggestions for further development of the solution.
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Kehrer, Paul H. "Reaching More Students: A Web-based Intelligent Tutoring System with support for Offline Access." Digital WPI, 2012. https://digitalcommons.wpi.edu/etd-theses/336.
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ASSISTments is a web-based intelligent tutoring system that can provide students with immediate feedback when they are doing math homework. Until now, ASSISTments required internet access in order to do nightly homework. Without ASSISTments, students do their work on paper and are not told if they are correct or given help for wrong answers until the next morning at best. We've developed a component that supports 'offline-mode', enabling students without internet access at home to still receive immediate feedback on their responses. Students with laptops download their assignments at school, and then run ASSISTments at home in offline mode, utilizing the browser's application cache and Web Storage API. To evaluate the benefit of having the offline feature, we ran a randomized controlled study that tests the effect of immediate feedback on student learning. Intuition would suggest that providing a student with tutoring and feedback immediately after they submit an answer would lead to better understanding of the material than having them wait until the next day. The results of the study confirmed our hypothesis, and validated the need for 'offline mode.'
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Vigentini, Lorenzo. "From learning to e-learning : mining educational data : a novel, data-driven approach to evaluate individual differences in students' interaction with learning technology." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/5532.
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In recent years, learning technology has become a very important addition to the toolkit of instructors at any level of education and training. Not only offered as a substitute in distance education, but often complementing traditional delivery methods, e-learning is considered an important component of modern pedagogy. Particularly in the last decade, learning technology has seen a very rapid growth following the large-scale development and deployment of e-learning financed by both Governments and commercial enterprises. These turned e-learning into one of the most profitable sectors of the new century, especially in recession times when education and retraining have become even more important and a need to maximise resources is forced by the need for savings. Interestingly, however, evaluation of e-learning has been primarily based on the consideration of users’ satisfaction and usability metrics (i.e. system engineering perspective) or on the outcomes of learning (i.e. gains in grades/task performance). Both of these are too narrow to provide a reliable effect of the real impact of learning technology on the learning processes and lead to inconsistent findings. The key purpose of this thesis is to propose a novel, data-driven framework and methodology to understand the effect of e-learning by evaluating the utility and effectiveness of e-learning systems in the context of higher education, and specifically, in the teaching of psychology courses. The concept of learning is limited to its relevance for students’ learning in courses taught using a mixture of traditional methods and online tools tailored to enhance teaching. The scope of elearning is intended in a blended method of delivery of teaching. A large sample of over 2000 students taking psychology courses in year 1 and year 2 was considered over a span of 5 five years, also providing the scope for the analysis of some longitudinal sub-samples. The analysis is accomplished using a psychologically grounded approach to evaluation, partially informed by a cognitive/ behavioural perspective (online usage) and a differential perspective (measures of cognitive and learning styles). Relations between behaviours, styles and academic performance are also considered, giving an insight and a direct comparison with existing literature. The methodology adopted draws heavily from data mining techniques to provide a rich characterisation of students/users in this particular context from the combination of three types of metrics: cognitive and learning styles, online usage and academic performance. Four different instruments are used to characterise styles: ASSIST (Approaches to learning, Entwistle), CSI (Cognitive Styles Inventory, Allinson & Hayes), TSI (Thinking Styles Inventory and the mental self-government theory, Sternberg) and VICS-WA (Verbal/Imager and Wholistc/Analytic Cognitive style, Riding, Peterson) which were intentionally selected to provide a varied set of tools. Online usage, spanning over the entire academic year for each student, is analysed applying web usage mining (WUM) techniques and is observed through different layers of interpretation accounting for behaviours from the single clicks to a student’s intentions in a single session. Academic performance was collated from the students’ records giving an insight in the end-of-year grades, but also into specific coursework submissions during the whole academic year allowing for a temporal matching of online use and assessment. The varied metrics used and data mining techniques applied provide a novel evaluation framework based on a rich profile of the learner, which in turn offers a valuable alternative to regression methods as a mean to interpret relations between metrics. Patterns emerging from styles and the way online material is used over time, proved to be valuable in discriminating differences in academic performance and useful in this context to identify significant group differences in both usage and academic performance. As a result, the understanding of the relations between e-learning usage, styles and academic performance has important practical implications to enhance students’ learning experience, in the automation of learning systems and to inform policymakers of the effects of learning technology has from a user and learner-centred approach to learning and studying. The success of the application of data mining methods offers an excellent starting point to explore further a data-driven approach to evaluation, support informed design processes of e-learning and to deliver suitable interventions to ensure better learning outcomes and provide an efficient system for institutions and organization to maximise the impact of learning technology for teaching and training.
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Wang, Yutao. "Student Modeling within a Computer Tutor for Mathematics: Using Bayesian Networks and Tabling Methods." Digital WPI, 2015. https://digitalcommons.wpi.edu/etd-dissertations/383.
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"Intelligent tutoring systems rely on student modeling to understand student behavior. The result of student modeling can provide assessment for student knowledge, estimation of student¡¯s current affective states (ie boredom, confusion, concentration, frustration, etc), prediction of student performance, and suggestion of the next tutoring steps. There are three focuses of this dissertation. The first focus is on better predicting student performance by adding more information, such as student identity and information about how many assistance students needed. The second focus is to analyze different performance and feature set for modeling student short-term knowledge and longer-term knowledge. The third focus is on improving the affect detectors by adding more features. In this dissertation I make contributions to the field of data mining as well as educational research. I demonstrate novel Bayesian networks for student modeling, and also compared them with each other. This work contributes to educational research by broadening the task of analyzing student knowledge to student knowledge retention, which is a much more important and interesting question for researchers to look at. Additionally, I showed a set of new useful features as well as how to effectively use these features in real models. For instance, in Chapter 5, I showed that the feature of the number of different days a students has worked on a skill is a more predictive feature for knowledge retention. These features themselves are not a contribution to data mining so much as they are to education research more broadly, which can used by other educational researchers or tutoring systems. "
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Mendes, Renê de Ávila. "Aplicação da arquitetura lambda na construção de um ambiente big data educacional para análise de dados." Universidade Presbiteriana Mackenzie, 2017. http://tede.mackenzie.br/jspui/handle/tede/3441.
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To properly deal with volume, velocity and variety data dimensions in educational contexts is a major concern for Educational Institutions and both Educational Data Mining and Learning Analytics Researchers have cooperated to properly address this challenge which is popularly called Big Data. Hardware developments have been made to increase computing power, storage capacity and efficiency in energy use. New technologies in databases, file systems and distributed systems, as well as developments in data transmission techniques, data management, data analysis and visualization have been trying to overcome the challenge of processing, storing and analyzing large volumes of data and the inability to meet simultaneously the requirements of consistency, availability and tolerance of partitions. Although the architecture definition is the main task in a Big Data system design, objective guidelines for the selection of the architecture and the tools for the implementation of Big Data systems were not found in the literature. The present research aims to analyze the main architectures for both batch and stream processing and to use one of them in the construction of a Big Data environment, providing important orientations to Researchers, Technicians and Managers. Academic data and logs of the Virtual Learning Environment Moodle of an Academic Unit of a Higher Education Institution are used.
Lidar adequadamente com as dimensões de volume, velocidade e variedade dos dados no contexto educacional é um importante desafio para as Instituições de Ensino, e Pesquisadores das áreas de Mineração de Dados Educacionais e Learning Analytics têm cooperado para tratar adequadamente este desafio, popularmente chamado de Big Data. Desenvolvimentos em hardware têm sido feitos para aumentar o poder computacional, a capacidade de armazenamento e a eficiência no uso de energia. Novas tecnologias de bancos de dados, sistemas de arquivos e sistemas distribuídos, além do desenvolvimento de técnicas de transmissão, administração, análise e visualização de dados têm tentado vencer o desafio de processar, armazenar e analisar grandes volumes de dados e a impossibilidade de atender simultaneamente os requisitos de consistência, disponibilidade e tolerância a partições. Embora a definição da arquitetura seja a principal tarefa em um projeto de sistema Big Data, não foram encontradas na literatura orientações objetivas para a seleção da arquitetura e das ferramentas para a implementação de aplicações Big Data. A presente pesquisa tem por objetivo analisar as principais arquiteturas para processamento em lote e em fluxo e utilizar uma delas na construção de um ambiente Big Data, fornecendo importantes orientações a Pesquisadores, Técnicos e Gestores. São utilizados dados acadêmicos e logs do Ambiente Virtual de Aprendizagem Moodle de uma Unidade Acadêmica de uma Instituição de Ensino Superior.
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Krasniuk, M. T., and S. O. Krasniuk. "Analysis of teaching experience and prospects of the "Data Mining" and "Data Science" disciplines in light of actual world macroeconomic and educational trends." Thesis, Київський національний університет технологій та дизайну, 2020. https://er.knutd.edu.ua/handle/123456789/16653.
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McKeague-McFadden, Ikaika A. "Identifying Students at Risk of Not Passing Introductory Physics Using Data Mining and Machine Learning." Miami University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=miami1596214863294544.
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Zhu, Linglong. "A Prediction Model Uses the Sequence of Attempts and Hints to Better Predict Knowledge: Better to Attempt the Problem First, Rather Than Ask for A Hint." Digital WPI, 2014. https://digitalcommons.wpi.edu/etd-theses/445.
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Intelligent Tutoring Systems (ITS) have been proven to be efficient in providing students assistance and assessing their performance when they do their homework. Many research projects have been done to analyze how students' knowledge grows and to predict their performance from within intelligent tutoring system. Most of them focus on using correctness of the previous question or the number of hints and attempts students need to predict their future performance, but ignore how they ask for hints and make attempts. In this research work, we build a Sequence of Actions (SOA) model taking advantage of the sequence of hints and attempts a student needed for previous question to predict students' performance. A two step modeling methodology is put forward in the work, which is a combination of Tabling method and the Logistic Regression. We used an ASSISTments dataset of 66 students answering a total of 34,973 problems generated from 5010 questions over the course of two years. The experimental results showed that the Sequence of Action model has reliable predictive accuracy than Knowledge Tracing and Assistance Model and its performance of prediction is improved after combining with Knowledge Tracing.
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Alvarado, Mantecon Jesus Gerardo. "Towards the Automatic Classification of Student Answers to Open-ended Questions." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39093.
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One of the main research challenges nowadays in the context of Massive Open Online Courses (MOOCs) is the automation of the evaluation process of text-based assessments effectively. Text-based assessments, such as essay writing, have been proved to be better indicators of higher level of understanding than machine-scored assessments (E.g. Multiple Choice Questions). Nonetheless, due to the rapid growth of MOOCs, text-based evaluation has become a difficult task for human markers, creating the need of automated systems for grading.
In this thesis, we focus on the automated short answer grading task (ASAG), which automatically assesses natural language answers to open-ended questions into correct and incorrect classes. We propose an ensemble supervised machine learning approach that relies on two types of classifiers: a response-based classifier, which centers around feature extraction from available responses, and a reference-based classifier which considers the relationships between responses, model answers and questions.
For each classifier, we explored a set of features based on words and entities. For the response-based classifier, we tested and compared 5 features: traditional n-gram models, entity URIs (Uniform Resource Identifier) and entity mentions both extracted using a semantic annotation API, entity mention embeddings based on GloVe and entity URI embeddings extracted from Wikipedia. For the reference-based classifier, we explored fourteen features: cosine similarity between sentence embeddings from student answers and model answers, number of overlapping elements (words, entity URI, entity mention) between student answers and model answers or question text, Jaccard similarity coefficient between student answers and model answers or question text (based on words, entity URI or entity mentions) and a sentence embedding representation.
We evaluated our classifiers on three datasets, two of which belong to the SemEval ASAG competition (Dzikovska et al., 2013). Our results show that, in general, reference-based features perform much better than response-based features in terms of accuracy and macro average f1-score. Within the reference-based approach, we observe that the use of S6 embedding representation, which considers question text, student and model answer, generated the best performing models. Nonetheless, their combination with other similarity features helped build more accurate classifiers. As for response-based classifiers, models based on traditional n-gram features remained the best models.
Finally, we combined our best reference-based and response-based classifiers using an ensemble learning model. Our ensemble classifiers combining both approaches achieved the best results for one of the evaluation datasets, but underperformed on the remaining two. We also compared the best two classifiers with some of the main state-of-the-art results on the SemEval competition. Our final embedded meta-classifier outperformed the top-ranking result on the SemEval Beetle dataset and our top classifier on SemEval SciEntBank, trained on reference-based features, obtained the 2nd position.
In conclusion, the reference-based approach, powered mainly by sentence level embeddings and other similarity features, proved to generate the most efficient models in two out of three datasets and the ensemble model was the best on the SemEval Beetle dataset.
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Xu, Beijie. "Understanding Teacher Users of a Digital Library Service: A Clustering Approach." DigitalCommons@USU, 2011. https://digitalcommons.usu.edu/etd/890.
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This research examined teachers' online behaviors while using a digital library service--the Instructional Architect (IA)--through three consecutive studies. In the first two studies, a statistical model called latent class analysis (LCA) was applied to cluster different groups of IA teachers according to their diverse online behaviors. The third study further examined relationships between teachers' demographic characteristics and their usage patterns. Several user clusters emerged from the LCA results of Study I. These clusters were named isolated islanders, lukewarm teachers, goal-oriented brokerswindow shoppers, key brokers, beneficiaries, classroom practitioners, and dedicated sticky users. In Study II, a cleaning process was applied to the clusters discovered in Study I to further refine distinct user groups. Results revealed three clusters, key brokers, insular classroom practitioners, and ineffective islanders. In Study III, the integration of teacher demographic profiles with clustering results revealed that teaching experience and technology knowledge affected teachers' effectiveness in using the IA. The implication, contributions, and limitation of this research are discussed.
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Kane-Sellers, Marjorie Laura. "Predictive models of employee voluntary turnover in a North American professional sales force using data-mining analysis." [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-1486.
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Moro, Luis Fernando de Souza. "Caracterização de alunos em ambientes de ensino online: estendendo o uso da DAMICORE para minerar dados educacionais." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-14092015-164510/.
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Com a popularização do uso de recursos tecnológicos na educação, uma enorme quantidade de dados, relacionados às interações entre alunos e esses recursos, é armazenada. Analisar esses dados, visando caracterizar os alunos, é tarefa muito importante, uma vez que os resultados dessa análise podem auxiliar professores no processo de ensino e aprendizagem. Entretanto, devido ao fato de as ferramentas utilizadas para essa caracterização serem complexas e pouco intuitivas, os profissionais da área de ensino acabam por não utilizá-las, inviabilizando a implementação de tais ferramentas em ambientes educacionais. Dentro desse contexto, a dissertação de mestrado aqui apresentada teve como objetivo analisar os dados provenientes de um sistema tutor inteligente, o MathTutor, que disponibiliza exercícios específicos de matemática, para identificar padrões de comportamento dos alunos que interagiram com esse sistema durante um determinado período. Essa análise foi realizada por meio de um processo de Mineração de Dados Educacionais (EDM), utilizando a ferramenta DAMICORE, com o intuito de possibilitar que fossem geradas, de forma rápida e eficaz, informações úteis à caracterização dos alunos. Durante a realização dessa análise, seguiram-se algumas fases do processo de descobrimento de conhecimento em bases de dados, seleção, pré-processamento, mineração dos dados e avaliação e interpretação. Na fase de mineração de dados, foi utilizada a ferramenta DAMICORE, que encontrou padrões que foram estudados na fase de avaliação e interpretação. A partir dessa análise foram encontrados padrões comportamentais dos alunos, por exemplo, alunos do sexo masculino apresentam rendimento superior ou inferior ao de alunas do sexo feminino e quais alunos terão um bom ou mau rendimento nas etapas finais do processo de ensino. Como principal resultado temos que uma das hipóteses criadas, Alunos que obtiveram bom desempenho no pós-teste imediato apresentaram dois dos três seguintes comportamentos: poucas interações na intervenção, baixo tempo interagindo com o sistema na intervenção e poucos misconceptions no pré-teste, teve sua acurácia comprovada dentre os dados utilizados nessa pesquisa. Assim, por meio desta pesquisa concluiu-se que a utilização da DAMICORE em contexto educacional pode auxiliar o professor a inferir o desempenho dos seus alunos oferecendo a ele a oportunidade de realizar as intervenções pedagógicas que auxiliem alunos com possíveis dificuldades e apresente novos desafios para aqueles com facilidade no tema estudadoWith the popularization of the use of technological resources in education, a huge amount of data, related to the interactions between students and these resources, is stored. Analyzing this data, due to characterize the students, is an important task, since the results of this analysis can help teachers on teaching and learning process. However, due to the fact that the tools used to this characterization are complex and non-intuitive, the educational professionals do not use it, invalidating the implementation of such tools at educational environments. Within this context, this master\'s dissertation aimed analyzing the prevenient data from an educational web system named MathTutor, which offers specific math exercises to identify behavioral patterns of students who interacted with this system during some period. This analysis was performed by a process known as Educational Data Mining, using the tool named DAMICORE, in order to enable quickly and effectively the construction of helpful information to the characterization of the students. During the course of this analysis, some phases of the process of knowledge discovery in databases were followed: \"selection\", \"preprocessing\", \"data mining\" and \"evaluation and interpretation\". In \"data mining\" phase, the tool DAMICORE was used to find behavioral patterns of students which were studied at the \"evaluation and interpretation\" phase. From this analysis, behavioral patterns of students were found, for example, male students have higher or lower yield against the female students and which students are going to have a good or bad yield on the final steps of the educational process. As the main result we have one of the made assumptions, \"Students who get good performance in the \"immediate posttest\" showed two of the following behaviors: few interactions in the \"intervention\", low time interacting with the system in the \"intervention\" and few misconceptions in \"pretest\"\", has proven its accuracy among the data used in this dissertation. Thus, through this research, it was concluded that the use of DAMICORE at educational context can help teacher to infer the performance of their students offering him the opportunity to perform the pedagogical interventions that help students who faces difficulties and show new challenges for those who have facilities in the subject studied.
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Chapman, John Shadrack. "Task-Level Feedback in Interactive Learning Enivonments Using a Rules Based Grading Engine." BYU ScholarsArchive, 2016. https://scholarsarchive.byu.edu/etd/6605.
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In order to improve the feedback an intelligent tutoring system provides, the grading engine needs to do more than simply indicate whether a student gives a correct answer or not. Good feedback must provide actionable information with diagnostic value. This means the grading system must be able to determine what knowledge gap or misconception may have caused the student to answer a question incorrectly. This research evaluated the quality of a rules-based grading engine in an automated online homework system by comparing grading engine scores with manually graded scores. The research sought to improve the grading engine by assessing student understanding using knowledge component research. Comparing both the current student scores and the new student scores with the manually graded scores led us to believe the grading engine rules were improved. By better aligning grading engine rules with requisite knowledge components and making revisions to task instructions the quality of the feedback provided would likely be enhanced.
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Menon, Akash, and Nahida Islam. "An investigation of the relationship between online activity on Studi.se and academic grades of newly arrived immigrant students : An application of educational data mining." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-211668.
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This study attempts to analyze the impact of an online educational resource on academic performances among newly arrived immigrant students in Sweden between the grade six to nine in the Swedish school system. The study focuses on the web based educational resource called Studi.se made by Komplementskolan AB.The aim of the study was to investigate the relationship between academic performance and using Studi.se. Another purpose was to see what other factors that can impact academic performances.The study made use of the data mining process, Cross Industry Standard for Data Mining (CRISP-DM), to understand and prepare the data and then create a regression model that is evaluated. The regression model tries predict the dependent variable of grade based on the independent variables of Studi.se activity, gender and years in Swedish schools. The used data set includes the grades in mathematics, physics, chemistry, biology and religion of newly arrived students in Sweden from six municipalities that have access to Studi.se. The data used also includes metrics of the student’s activity on Studi.se.The results show negative correlation between grade and gender of the student across all subjects. In this report, the negative correlation means that female students perform better than male students. Furthermore, there was a positive correlation between number of years a student has been in the same school and their academic grade. The study could not conclude a statistically significant relationship between the activity on Studi.se and the students’ academic grade.Additional explanatory independent variables are needed to make a predictive model as well as investigating alternative regression models other than multiple linear regression. In the sample, a majority of the students have little or no activity on Studi.se despite having free access to the resource through the municipality.Denna studie analyserar inverkan som digitala läromedel har på skolbetyg bland nyanlända elever i Sverige mellan årskurs sex och nio i det svenska skolsystemet. Studien fokuserar på den webbaserade pedagogisk resursen Studi.se, gjord av Komplementskolan AB.Målet med studien var att undersöka relationen mellan skolresultat och användandet av Studi.se. Ett annat syfte var att undersöka vad för andra faktorer som kan påverka skolresultat.Studien använder sig av datautvinningsprocessen, Cross Industry Standard for Datamining (CRISP-DM), för att förstå, förbereda och analysera datan i form av en regressionsmodell som sedan evalueras. Datasamlingen som används innehåller bland annat skolbetyg i ämnena matematik, fysik, kemi, biologi och religion från sex kommuner som har tillgång till Studi.se. Aktivitet hos eleverna från dessa kommuner på Studi.se hemsidan användes också för studien.Resultaten visar en negativ korrelation mellan betyg och kön hos eleverna i alla ämnena. Den negativa korrelationen betyder i denna rapport att tjejer får bättre betyg i genomsnitt än killar hos urvalet av nyanlända från de sex kommunerna. Dessutom fanns det en positiv korrelation mellan antal år en elev varit i skolan alternativt i svenska skolsystemet och deras betyg. Studien kunde inte säkerställa ett statistisk signifikant resultat mellan aktivitet på Studi.se och elevernas skolresultat.Ett flertal förklarande oberoende variabler behövs för att kunna skapa en prognastisk modell för skolresultat samt bör en undersökning på alternativa regressions modeller förutom linjär multipel regression göras. I studiens urval av nyanlända elever från kommunerna, har majoriteten inte använt eller knappt använt Studi.se även om dessa kommuner haft tillgång till denna resurs.
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Goldstein, Adam B. "Responding to Moments of Learning." Digital WPI, 2011. https://digitalcommons.wpi.edu/etd-theses/685.
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In the field of Artificial Intelligence in Education, many contributions have been made toward estimating student proficiency in Intelligent Tutoring Systems (cf. Corbett & Anderson, 1995). Although the community is increasingly capable of estimating how much a student knows, this does not shed much light on when the knowledge was acquired. In recent research (Baker, Goldstein, & Heffernan, 2010), we created a model that attempts to answer that exact question. We call the model P(J), for the probability that a student just learned from the last problem they answered. We demonstrated an analysis of changes in P(J) that we call “spikiness", defined as the maximum value of P(J) for a student/knowledge component (KC) pair, divided by the average value of P(J) for that same student/KC pair. Spikiness is directly correlated with final student knowledge, meaning that spikes can be an early predictor of success. It has been shown that both over-practice and under-practice can be detrimental to student learning, so using this model can potentially help bias tutors toward ideal practice schedules. After demonstrating the validity of the P(J) model in both CMU's Cognitive Tutor and WPI's ASSISTments Tutoring System, we conducted a pilot study to test the utility of our model. The experiment included a balanced pre/post-test and three conditions for proficiency assessment tested across 6 knowledge components. In the first condition, students are considered to have mastered a KC after correctly answering 3 questions in a row. The second condition uses Bayesian Knowledge Tracing and accepts a student as proficient once they earn a current knowledge probability (Ln) of 0.95 or higher. Finally, we test P(J), which accepts mastery if a student's P(J) value spikes from one problem and the next first response is correct. In this work, we will discuss the details of deriving P(J), our experiment and its results, as well as potential ways this model could be utilized to improve the effectiveness of cognitive mastery learning.
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Whitlock, Joshua Lee. "Using Data Science and Predictive Analytics to Understand 4-Year University Student Churn." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/etd/3356.
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The purpose of this study was to discover factors about first-time freshmen that began at one of the six 4-year universities in the former Tennessee Board of Regents (TBR) system, transferred to any other institution after their first year, and graduated with a degree or certificate. These factors would be used with predictive models to identify these students prior to their initial departure. Thirty-four variables about students and the institutions that they attended and graduated from were used to perform principal component analysis to examine the factors involved in their decisions. A subset of 18 variables about these students in their first semester were used to perform principal component analysis and produce a set of 4 factors that were used in 5 predictive models. The 4 factors of students who transferred and graduated elsewhere were “Institutional Characteristics,” “Institution’s Focus on Academics,” “Student Aptitude,” and “Student Community.” These 4 factors were combined with the additional demographic variables of gender, race, residency, and initial institution to form a final dataset used in predictive modeling. The predictive models used were a logistic regression, decision tree, random forest, artificial neural network, and support vector machine. All models had predictive power beyond that of random chance. The logistic regression and support vector machine models had the most predictive power, followed by the artificial neural network, random forest, and decision tree models respectively.
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Medina, Erik Cevallos, Claudio Barahona Chunga, Jimmy Armas-Aguirre, and Elizabeth E. Grandon. "Predictive model to reduce the dropout rate of university students in Perú: Bayesian Networks vs. Decision Trees." IEEE Computer Society, 2020. http://hdl.handle.net/10757/656775.
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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.This research proposes a prediction model that might help reducing the dropout rate of university students in Peru. For this, a three-phase predictive analysis model was designed which was combined with the stages proposed by the IBM SPSS Modeler methodology. Bayesian network techniques was compared with decision trees for their level of accuracy over other algorithms in an Educational Data Mining (EDM) scenario. Data were collected from 500 undergraduate students from a private university in Lima. The results indicate that Bayesian networks behave better than decision trees based on metrics of precision, accuracy, specificity, and error rate. Particularly, the accuracy of Bayesian networks reaches 67.10% while the accuracy for decision trees is 61.92% in the training sample for iteration with 8:2 rate. On the other hand, the variables athletic person (0.30%), own house (0.21%), and high school grades (0.13%) are the ones that contribute most to the prediction model for both Bayesian networks and decision trees.
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Mirashrafi, Seyed Bagher [Verfasser], and G. [Akademischer Betreuer] Nakhaeizadeh. "Applying of Data Mining and Statistical Techniques to Analyze the Impact of Socioeconomic Background on University Admission - A Case Study Using the Iranian Educational Data / Seyed Bagher Mirashrafi. Betreuer: G. Nakhaeizadeh." Karlsruhe : KIT-Bibliothek, 2016. http://d-nb.info/1112224521/34.
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Li, Shoujing. "Modeling Student Retention in an Environment with Delayed Testing." Digital WPI, 2013. https://digitalcommons.wpi.edu/etd-theses/266.
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Over the last two decades, the field of educational data mining (EDM) has been focusing on predicting the correctness of the next student response to the question (e.g., [2, 6] and the 2010 KDD Cup), in other words, predicting student short-term performance. Student modeling has been widely used for making such inferences. Although performing well on the immediate next problem is an indicator of mastery, it is by far not the only criteria. For example, the Pittsburgh Science of Learning Center's theoretic framework focuses on robust learning (e.g., [7, 10]), which includes the ability to transfer knowledge to new contexts, preparation for future learning of related skills, and retention - the ability of students to remember the knowledge they learned over a long time period. Especially for a cumulative subject such as mathematics, robust learning, particularly retention, is more important than short-term indicators of mastery. The Automatic Reassessment and Relearning System (ARRS) is a platform we developed and deployed on September 1st, 2012, which is mainly used by middle-school math teachers and their students. This system can help students better retain knowledge through automatically assigning tests to students, giving students opportunity to relearn the skill when necessary and generating reports to teachers. After we deployed and tested the system for about seven months, we have collected 287,424 data points from 6,292 students. We have created several models that predict students' retention performance using a variety of features, and discovered which were important for predicting correctness on a delayed test. We found that the strongest predictor of retention was a student's initial speed of mastering the content. The most striking finding was that students who struggled to master the content (took over 8 practice attempts) showed very poor retention, only 55% correct, after just one week. Our results will help us advance our understanding of learning and potentially improve ITS.
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Molen, Moris Johan van der. "Minería de datos educacionales: modelos de predicción del desempeño escolar en alumnos de enseñanza básica." Tesis, Universidad de Chile, 2013. http://www.repositorio.uchile.cl/handle/2250/113034.
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Ingeniero Civil MatemáticoEn los últimos años, se ha abierto una oportunidad de hacer análisis más precisos de las habilidades y desempeños de los estudiantes. De a poco, han comenzado a proliferar sistemas de ejercitación en línea y tutores inteligentes que permiten registrar una gran cantidad de información valiosa referente al aprendizaje de los alumnos. La Minería de Datos Educacionales (MDE), es un campo de estudio dedicado a desarrollar métodos matemáticos para analizar datos provenientes de ambientes relacionados a la educación, y extraer la mayor cantidad de información para tratar de entender mejor a los estudiantes, profesores y actores relacionados, con el fin de mejorar los procesos educativos. En esta memoria se aborda el problema de predecir el desempeño de un alumno dados sus datos históricos recopilados a partir de su interacción en un sistema computacional de ejercitación en línea. Este desafío se ha constituido últimamente como uno de los más importantes dentro de la MDE, tal como evidencia el aumento de publicaciones relacionadas, y el gran interés que ha despertado por parte de universidades y entidades gubernamentales. En este trabajo, se analizan los registros almacenados de más de medio millón de ejercicios en línea realizados semanalmente en el 2011 por 805 estudiantes en 23 cursos de cuarto básico de 13 escuelas vulnerables, explorando varios de los enfoques más usados para enfrentar este problema, y proponiendo nuevas variantes para mejorar los resultados y ayudar a la detección de observaciones anómalas que podrían incluir instancias de "gaming the system". Adicionalmente, se estudia el problema de conocer cómo ciertos contenidos impactan en otros. Se trata de un problema de Minería de Datos Educacionales central en el diseño curricular y la planificación de clases. Usualmente esta red de influencias causales se construye en base a las opiniones de expertos. Algunos contribuyen explicitando la dependencia lógica de los contenidos y otros con sus experiencias personales al enseñar esos contenidos. Sin embargo, es muy importante contrastar esas opiniones con el proceso de aprendizaje que efectivamente ocurre en el aula y construir redes causales en base a la evidencia empírica. Aprovechamos los datos y técnicas de Minería de Datos para generar automáticamente la primera red causal de contenidos de un currículo construida empíricamente. Finalmente, se reporta el análisis del impacto de la ejercitación en línea en el desempeño de la prueba SIMCE. Mediciones en condiciones de laboratorio muestran que la ejercitación aumenta el aprendizaje. Sin embargo, implementaciones escolares no han mostrado impactos positivos. Este trabajo muestra la experiencia con escuelas vulnerables donde los estudiantes hacen decenas de ejercicios matemáticos semanales en un sistema en línea. El SIMCE de matemáticas subió significativamente, más de tres veces el aumento histórico logrado a nivel nacional en 2011. Además, los cursos que realizaron mayor cantidad de ejercicios lograron un mayor aumento en el SIMCE, independiente del efecto del profesor y de la escuela.
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Falci, Júnior Geraldo Ramos. "Metodologia de mineração de dados para ambientes educacionais online." [s.n.], 2010. http://repositorio.unicamp.br/jspui/handle/REPOSIP/259203.
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Orientador: Ivan Luiz Marques RicarteDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação
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Resumo: Educação a distância populariza-se como meio prático de ensino com a expansão de recursos computacionais e da Internet. Apesar disto, ela traz dificuldades ao educador para compreender as necessidades de suas classes. A análise do uso desses Sistemas de Gerência de Aprendizado a distância por meio de técnicas de mineração de dados é uma forma de obter informações relevantes que permitam ao educador observar essas necessidades e modificar seus cursos de acordo. O objetivo deste trabalho é elaborar uma metodologia de trabalho que permita abordar problemas dessa natureza de forma objetiva e flexível, facilitando identificar potenciais problemas na análise e pontos de retorno adequados para correção e retomada do processo. Um conjunto de etapas é elaborado para compor esta metodologia e em seguida colocado à prova com um conjunto de dados reais obtidos através da instância do TIDIA-Ae utilizada pela UNICAMP como auxiliar às aulas presenciais. Os resultados mostram a eficácia do método proposto e permitiram a observação de diversos problemas devido à maneira de utilização do sistema por alunos e professores
Abstract: Computer-based distance education is becoming popular as computational resources and the Internet expand. Nevertheless, educators may have difficulties to understand the necessities of his classes and therefore improve their courses. Usage analysis of these distance Learning Management Systems through data mining techniques is a way of obtaining relevant information that allow the educator to observe some of the classes' needs and modify his courses accordingly. The goal of the work described in this thesis is to elaborate a methodology to allow tackling problems of this nature in an objective and flexible way, easing the identification of potential problems in the analysis and adequate points of feedback to correct and retake the process. A sequence of steps is elaborated to constitute this methodology and test it with real data obtained from the instance of TIDIA-Ae used by UNICAMP as an auxiliary to classes in campus. The results show the efficiency of the proposed method, though some problems surfaced on these results originated from the way the system is employed by students and teachers
Mestrado
Engenharia de Computação
Mestre em Engenharia Elétrica
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CESARETTI, LORENZO. "How students solve problems during Educational Robotics activities: identification and real-time measurement of problem-solving patterns." Doctoral thesis, Università Politecnica delle Marche, 2020. http://hdl.handle.net/11566/274358.
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Questa tesi presenta l’utilizzo di tecniche di data mining e machine learning per la valutazione di attività di Robotica Educativa.
Gli obiettivi di questo lavoro di ricerca sono tre: identificare differenti pattern durante le attività di problem-solving degli studenti; predire il risultato finale ottenuto nella risoluzione delle sfide di programmazione (e annotato dagli educatori) utilizzando tecniche machine learning; analizzare le correlazioni tra i pattern ottenuti e la valutazione assegnata dagli educatori.
Per raggiungere questi obiettivi è stata svolta una sperimentazione con 455 studenti di 16 scuole primarie e secondarie italiane: è stato aggiornato il software del kit Lego Mindstorms EV3 così da registrare le sequenze di programmazione create dagli studenti in una scheda SD all’interno del robot, durante la risoluzione di due esercizi introduttivi di Robotica.
I dati raccolti sono stati analizzati con una metodologia di data mining. Sono state utilizzate cinque tecniche di machine learning (regressione logistica, support vector machines, K-nearest neighbors, classificatore random forests e rete neurale Multilayer perceptron) così da predire la performance ottenuta dagli studenti.
I risultati ottenuti hanno mostrato che la rete neurale MLP ha superato le altre tecniche in termini di predizione e che 3 stili di problem-solving sono emersi all’interno del dataset considerato; questi 3 stili sono stati analizzati in dettaglio sia da un punto di vista educativo che in relazione ai risultati ottenuti dagli studenti nella risoluzione degli esercizi.This dissertation aims to provide the results through the utilisation of data mining and machine learning techniques for the assessment with Educational Robotics (ER). This research work has three main objectives: identify different patterns in the students’ problem-solving trajectories; predict the students’ team final performance, with a particular focus on the identification of learners with difficulties in the resolution of the ER challenges; analyse the correlation of the discovered patterns of students’ problem-solving with the evaluation given by the educators. We analysed the literature on Educational Robotics’ traditional evaluation and Educational Data Mining for assessment in constructionist environments. An experimentation with 455 students in 16 primary and secondary schools from Italy was conducted, through updating Lego Mindstorms EV3 programming blocks in order to record log files containing the coding sequences designed by the students (within team work), during the resolution of two preliminary Robotics’ exercises (Exercise A and B). The collected data were analysed based on data mining methodology. We utilised five machine learning techniques (logistic regression, support vector machine, K-nearest neighbors, random forests and Multilayer perceptron neural network) to predict the students’ performance, comparing two approaches: - a supervised approach, calculating a feature matrix as input for the algorithms characterised by two parts: the team’s past problem-solving activity (thirteen parameters extracted from the log files) and the learners’ current activity (three indicators for Exercise A and four indicators for Exercise B); and - a mixed approach, applying an unsupervised technique (the k-means algorithm) to calculate the team’s past problem-solving activity, and considering the same indicators of the supervised approach representing the students’ current activity. Firstly, we wanted to verify if similar findings emerged comparing younger students and older students, so we divided the entire dataset in two subsets (students younger than 12 years old and students older than 12 years old) and applied the supervised and mixed approach in these two subgroups for the first exercise, and a clustering analysis for the second exercise. This process demonstrated that similar problem-solving strategies were applied by both younger and older students, so we aggregated the dataset and performed the supervised and the mixed approach comparing the performances of these two techniques considering the entire dataset. The results have highlighted that MLP neural network with the mixed approach outperformed the other techniques, and that three learning styles were predominantly emerged from the data mining. Furthermore, we deeply analysed the pedagogical meaning of these three different approaches and the correlation of the discovered patterns with the performance obtained by learners. We denote the added value of data mining and machine learning applied to Educational Robotics research and highlight the significance of further implications. Finally, we discuss the future further development of this work from educational and technical view.
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Schultz, Sarah E. "Tracing Knowledge and Engagement in Parallel by Observing Behavior in Intelligent Tutoring Systems." Digital WPI, 2015. https://digitalcommons.wpi.edu/etd-theses/140.
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Two of the major goals in Educational Data Mining are determining students’ state of knowledge and determining their affective state. It is useful to be able to determine whether a student is engaged with a tutor or task in order to adapt to his/her needs and necessary to have an idea of the students' knowledge state in order to provide material that is appropriately challenging. These two problems are usually examined separately and multiple methods have been proposed to solve each of them. However, little work has been done on examining both of these states in parallel and the combined effect on a student’s performance. The work reported in this thesis explores ways to observe both behavior and performance in order to more fully understand student state.
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Martinez, Maldonado Roberto. "Analysing, visualising and supporting collaborative learning using interactive tabletops." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/10409.
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The key contribution of this thesis is a novel approach to design, implement and evaluate the conceptual and technological infrastructure that captures student’s activity at interactive tabletops and analyses these data through Interaction Data Analytics techniques to provide support to teachers by enhancing their awareness of student’s collaboration. To achieve the above, this thesis presents a series of carefully designed user studies to understand how to capture, analyse and distil indicators of collaborative learning. We perform this in three steps: the exploration of the feasibility of the approach, the construction of a novel solution and the execution of the conceptual proposal, both under controlled conditions and in the wild. A total of eight datasets were analysed for the studies that are described in this thesis. This work pioneered in a number of areas including the application of data mining techniques to study collaboration at the tabletop, a plug-in solution to add user-identification to a regular tabletop using a depth sensor and the first multi-tabletop classroom used to run authentic collaborative activities associated with the curricula. In summary, while the mechanisms, interfaces and studies presented in this thesis were mostly explored in the context of interactive tabletops, the findings are likely to be relevant to other forms of groupware and learning scenarios that can be implemented in real classrooms. Through the mechanisms, the studies conducted and our conceptual framework this thesis provides an important research foundation for the ways in which interactive tabletops, along with data mining and visualisation techniques, can be used to provide support to improve teacher’s understanding about student’s collaboration and learning in small groups.
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Farghally, Mohammed Fawzi Seddik. "Visualizing Algorithm Analysis Topics." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/73539.
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Data Structures and Algorithms (DSA) courses are critical for any computer science curriculum. DSA courses emphasize concepts related to procedural dynamics and Algorithm Analysis (AA). These concepts are hard for students to grasp when conveyed using traditional textbook material relying on text and static images. Algorithm Visualizations (AVs) emerged as a technique for conveying DSA concepts using interactive visual representations. Historically, AVs have dealt with portraying algorithm dynamics, and the AV developer community has decades of successful experience with this. But there exist few visualizations to present algorithm analysis concepts. This content is typically still conveyed using text and static images.
We have devised an approach that we term Algorithm Analysis Visualizations (AAVs), capable of conveying AA concepts visually. In AAVs, analysis is presented as a series of slides where each statement of the explanation is connected to visuals that support the sentence. We developed a pool of AAVs targeting the basic concepts of AA. We also developed AAVs for basic sorting algorithms, providing a concrete depiction about how the running time analysis of these algorithms can be calculated.
To evaluate AAVs, we conducted a quasi-experiment across two offerings of CS3114 at Virginia Tech. By analyzing OpenDSA student interaction logs, we found that intervention group students spent significantly more time viewing the material as compared to control group students who used traditional textual content. Intervention group students gave positive feedback regarding the usefulness of AAVs to help them understand the AA concepts presented in the course. In addition, intervention group students demonstrated better performance than control group students on the AA part of the final exam.
The final exam taken by both the control and intervention groups was based on a pilot version of the Algorithm Analysis Concept Inventory (AACI) that was developed to target fundamental AA concepts and probe students' misconceptions about these concepts. The pilot AACI was developed using a Delphi process involving a group of DSA instructors, and was shown to be a valid and reliable instrument to gauge students' understanding of the basic AA topics.Ph. D.
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Sherzad, Abdul Rahman [Verfasser], Uwe [Akademischer Betreuer] Nestmann, Uwe [Gutachter] Nestmann, Niels [Gutachter] Pinkwart, Sebastian [Gutachter] Bab, and Nazir [Gutachter] Peroz. "Shaping the selection of fields of study in Afghanistan through educational data mining approaches / Abdul Rahman Sherzad ; Gutachter: Uwe Nestmann, Niels Pinkwart, Sebastian Bab, Nazir Peroz ; Betreuer: Uwe Nestmann." Berlin : Technische Universität Berlin, 2018. http://d-nb.info/1164076450/34.
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Fernandes, Warley Leite. "Aplica??o do algoritmo de classifica??o associativa (CBA) em bases educacionais para predi??o de desempenho." UFVJM, 2017. http://acervo.ufvjm.edu.br/jspui/handle/1/1726.
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A Educa??o a Dist?ncia (EAD) tem-se confirmado como importante ferramenta de capacita??o a qualquer tempo e dist?ncia. Por?m, a maioria das Institui??es de Ensino tem encontrado dificuldades relacionadas ao grande n?mero de abandono dos cursos. Avan?os recentes em diversas ?reas da tecnologia possibilitaram o surgimento das Tecnologias da Informa??o e Comunica??o que se tornaram essenciais ? condu??o dos processos educacionais. Assim, imensos volumes de dados s?o gerados pela intera??o de usu?rios em Ambientes Virtuais de Aprendizagem (AVA). Esses dados ?escondem? informa??es ricas. Contudo, manipular tamanha quantidade de dados n?o ? uma tarefa simples. Neste sentido, uma solu??o promissora para extra??o de informa??o ? a Minera??o de Dados, que pode ser entendida como a transforma??o de dados brutos em conhecimento. Essa pesquisa apresenta um estudo para compreender os motivos do baixo desempenho dos alunos em cursos t?cnicos da EAD aplicando, para isto, o algoritmo de Classifica??o Associativa (CBA) em Minera??o de Dados Educacionais (EDM). Com o objetivo de gerar os melhores resultados preditivos de Classifica??o Associativa obtidos pelo CBA, aplicou-se o algoritmo de Regras de Associa??o denominado Predictive Apriori,ainda n?o empregados em trabalhos correlatos. Os resultados experimentais apontam que o CBA aplicado a Bases de Dados Educacionais atinge melhores resultados que os algoritmos de classifica??o tradicionais (alcan?ando uma marca de 85% de acur?cia). Mostrou-se tamb?m que o uso das ferramentas f?rum, quiz e folder t?m uma grande influ?ncia no desempenho dos estudantes.
Disserta??o (Mestrado Profissional) ? Programa de P?s-Gradua??o em Educa??o, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2017.
Distance Education (EAD) has been confirmed as an important training tool at any time and distance. However, most educational institutions have encountered difficulties related to the large number of dropouts. Recent advances in several areas of technology have enabled the emergence of Information and Communication Technologies that have become essential to the conduct of educational processes. Thus, immense data volumes are generated by the interaction of users in Virtual Learning Environments (AVA). These data "hide" rich information. However, handling such a large amount of data is not a simple task. In this sense, a promising solution for information extraction is Data Mining, which can be understood as the transformation of raw data into knowledge. This research presents a study to understand the reasons of the low performance of students in technical courses of the EAD applying, to this, the Association Classification (CBA) algorithm in Educational Data Mining (EDM). In order to further improve the results obtained by the CBA, the Association Rules algorithm called Predictive Apriori, not yet employed in related works, was applied in order to generate the best predictive results of Associative Classification. The experimental results point out that the CBA applied to Educational Databases achieves better results than traditional classification algorithms (reaching a mark of 85% accuracy). It was also shown that the use of the forum, quiz and folder tools have a great influence on student performance.
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Pardos, Zachary Alexander. "Predictive Models of Student Learning." Digital WPI, 2012. https://digitalcommons.wpi.edu/etd-dissertations/185.
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In this dissertation, several approaches I have taken to build upon the student learning model are described. There are two focuses of this dissertation. The first focus is on improving the accuracy with which future student knowledge and performance can be predicted by individualizing the model to each student. The second focus is to predict how different educational content and tutorial strategies will influence student learning. The two focuses are complimentary but are approached from slightly different directions. I have found that Bayesian Networks, based on belief propagation, are strong at achieving the goals of both focuses. In prediction, they excel at capturing the temporal nature of data produced where student knowledge is changing over time. This concept of state change over time is very difficult to capture with classical machine learning approaches. Interpretability is also hard to come by with classical machine learning approaches; however, it is one of the strengths of Bayesian models and aids in studying the direct influence of various factors on learning. The domain in which these models are being studied is the domain of computer tutoring systems, software which uses artificial intelligence to enhance computer based tutorial instruction. These systems are growing in relevance. At their best they have been shown to achieve the same educational gain as one on one human interaction. Computer tutors have also received the attention of White House, which mentioned an tutoring platform called ASSISTments in its National Educational Technology Plan. With the fast paced adoption of these data driven systems it is important to learn how to improve the educational effectiveness of these systems by making sense of the data that is being generated from them. The studies in this proposal use data from these educational systems which primarily teach topics of Geometry and Algebra but can be applied to any domain with clearly defined sub-skills and dichotomous student response data. One of the intended impacts of this work is for these knowledge modeling contributions to facilitate the move towards computer adaptive learning in much the same way that Item Response Theory models facilitated the move towards computer adaptive testing.
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Miled, Mahdi. "Ressources et parcours pour l'apprentissage du langage Python : aide à la navigation individualisée dans un hypermédia épistémique à partir de traces." Thesis, Cachan, Ecole normale supérieure, 2014. http://www.theses.fr/2014DENS0045/document.
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Les travaux de recherche de cette thèse concernent principalement l‘aide à la navigation individualisée dans un hypermédia épistémique. Nous disposons d‘un certain nombre de ressources qui peut se formaliser à l‘aide d‘un graphe acyclique orienté (DAG) : le graphe des épistèmes. Après avoir cerné les environnements de ressources et de parcours, les modalités de visualisation et de navigation, de traçage, d‘adaptation et de fouille de données, nous avons présenté une approche consistant à corréler les activités de conception ou d‘édition à celles dédiées à l‘utilisation et la navigation dans les ressources. Cette approche a pour objectif de fournir des mécanismes d‘individualisation de la navigation dans un environnement qui se veut évolutif. Nous avons alors construit des prototypes appropriés pour mettre à l‘épreuve le graphe des épistèmes. L‘un de ces prototypes a été intégré à une plateforme existante. Cet hypermédia épistémique baptisé HiPPY propose des ressources et des parcours portant sur l‘apprentissage du langage Python. Il s‘appuie sur un graphe des épistèmes, une navigation dynamique et un bilan de connaissances personnalisé. Ce prototype a fait l‘objet d‘une expérimentation qui nous a donné la possibilité d‘évaluer les principes introduits et d‘analyser certains usagesThis research work mainly concerns means of assistance in individualized navigation through an epistemic hypermedia. We have a number of resources that can be formalized by a directed acyclic graph (DAG) called the graph of epistemes. After identifying resources and pathways environments, methods of visualization and navigation, tracking, adaptation and data mining, we presented an approach correlating activities of design or editing with those dedicated to resources‘ use and navigation. This provides ways of navigation‘s individualization in an environment which aims to be evolutive. Then, we built prototypes to test the graph of epistemes. One of these prototypes was integrated into an existing platform. This epistemic hypermedia called HiPPY provides resources and pathways on Python language. It is based on a graph of epistemes, a dynamic navigation and a personalized knowledge diagnosis. This prototype, which was experimented, gave us the opportunity to evaluate the introduced principles and analyze certain uses
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Borges, Vanessa Araujo. "Definição de um modelo de referência de dados educacionais para a descoberta de conhecimento." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-09112018-103702/.
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Sistemas educacionais possuem diversas funcionalidades capazes de apoiar a interação entre alunos e professores de maneira dinâmica, síncrona e assíncrona. Uma das formas de monitorar a eficácia do processo educacional e por meio da utilização dos dados armazenados nesses sistemas como fonte de informação. Pesquisas em Learning Analytics, Academic Analytics e Mineração de Dados Educacionais, buscam explorar os dados de sistemas educacionais utilizando processamento analítico e técnicas de mineração de dados. No entanto, há uma serie de fatores que dificultam a gestão eficiente do processo educacional a partir dos dados de sistemas educacionais. A transformação de dados provenientes de diferentes tipos de sistemas educacionais, como Sistemas de Gestão de Aprendizagem e Sistemas Acadêmicos, e uma tarefa complexa devido a natureza heterogênea dos dados. Dados provenientes desses sistemas podem ser analisados considerando diferentes stakeholders, sob varias perspectivas e níveis de granularidade. Neste cenário, um modelo de referência para a descoberta de conhecimento a partir de dados de sistemas educacionais, denominado Modelo de Referência de Dados Educacionais (EDRM), foi desenvolvido neste trabalho. O EDRM e um modelo dimensional no formato star schema, estruturado em um Data Warehouse, projetado para ser uma fonte única de dados integrados e correlacionados voltada a tomada de decisão. Assim, e possível armazenar dados de diversas fontes, combina-los e, por fim, realizar analises que levem as instituições a desenvolver uma melhor compreensão, rastrear tendências e descobrir lacunas e ineficiências acerca do processo educacional. Neste trabalho, o EDRM foi validado por meio de um estudo de caso, utilizando bases de dados reais coletadas de diferentes sistemas educacionais. Os resultados mostram que o EDRM e eficiente em tarefas com diferentes objetivos, utilizando processamento analítico e mineração de dados.Educational systems support dynamic, synchronous and asynchronous interaction between students and educators. Researches in Learning Analytics, Academic Analytics and Educational Data Mining explore data from educational systems for knowledge discovery through analytical processing, statistical analysis and data mining. However, there are some factors that hinder an efficient management of the educational process. The transformation of data from different kinds of educational system, as Learning Management Systems and Student Information Systems, can be even more difficult due to data heterogeneity. Data from these systems can be analyzed considering different stakeholders, under different perspectives and under different granularities. Motivated by this scenario, in this work we propose Modelo de Referência de Dados Educacionais (EDRM), a reference data model for knowledge discovery in data from educational systems. EDRM is an analytical model structured under a Data Warehouse architecture following a multidimensional data model. EDRM is projected for being an resource of integrated and correlated data focused in decision taking in the educational process. EDRM was developed considering a deep analysis of data and functionalities from different educational systems. In this sense, data from different kinds of systems and sources can be used unified, integrated and consistently. This allows institutions to better comprehend their data, as well as discover patterns, gaps and inefficiencies about their educational process. In this work, EDRM was validated in a case study using real-world databases from different educational systems. The results indicate that EDRM is efficient in tasks with different objectives, using Learning Analytics and Educational Data Mining techniques, and analyzing different perspectives.
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MELO, Allan Sales da Costa. "Previsão automática de evasão estudantil: um estudo de caso na UFCG." Universidade Federal de Campina Grande, 2016. http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/800.
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A evasão estudantil é uma das maiores preocupações dos institutos de ensino superior brasileiros já que ela pode ser uma das causas de desperdício de recursos da Universidade. A previsão dos estudantes com alta probabilidade de evasão, assim como o entendimento das causas que os levaram a evadir, são fatores cruciais para a definição mais efetiva de ações preventivas para o problema. Nesta dissertação, o problema da detecção de evasão foi abordado como um problema de aprendizagem de máquina supervisionada. Utilizou-se uma amostra de registros acadêmicos de estudantes considerando-se todos os 76 cursos da Universidade Federal de Campina Grande com o objetivo de obter e selecionar atributos informativos para os modelos de classificação e foram criados dois tipos de modelos, um que separa os estudantes por cursos e outro que não faz distinção de cursos. Os dois modelos criados foram comparados e pôde-se concluir que não fazer distinção de alunos por curso resulta em melhores resultados que fazer distinção de alunos por curso.
Students’ dropout is a major concern of the Brazilian higher education institutions as it may cause waste of resources. The early detection of students with high probability of dropping out, as well as understanding the underlying causes, are crucial for defining more effective actions toward preventing this problem. In this paper, we cast the dropout detection problem as a supervised learning problem. We use a large sample of academic records of students across 76 courses from a public university in Brazil in order to derive and select informative features for the employed classifiers. We create two classification models that either consider the course to which the target student is formally committed or not consider it, respectively. We contrast both models and show that not considering the course leads to better results.
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Gottardo, Ernani. "Estimativa de desempenho acadêmico de estudantes em um AVA utilizando técnicas de mineração de dados." Universidade Tecnológica Federal do Paraná, 2012. http://repositorio.utfpr.edu.br/jspui/handle/1/439.
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Alguns ambientes educacionais têm incorporado softwares que são utilizados como apoio ou, em alguns casos, como condição básica para a disponibilização de cursos. Neste cenário, destacam-se os Ambientes Virtuais de Aprendizagem (AVA) usados para apoiar o desenvolvimento de cursos presenciais, semipresenciais e a distância. Os AVA caracterizam-se por armazenar um grande volume de dados. Contudo,
esses ambientes carecem de ferramentas que permitam extrair informações úteis para o desenvolvimento de processos de acompanhamento eficiente dos estudantes. Diante disso, esta pesquisa investiga como os dados armazenados em um AVA poderiam ser processados para geração de informações relacionadas a estimativas de desempenho acadêmico futuro de estudantes. Para obter essas informações, primeiramente fez-se necessário a seleção de um conjunto de atributos para representar estudantes em um curso a distância (EAD) utilizando um AVA. O
conjunto de atributos foi escolhido considerando-se três dimensões, selecionadas
partir da análise de referências teóricas da literatura sobre cursos EAD: perfil de uso do AVA, interação estudante-estudante e interação bidirecional estudante-professor. Aplicando-se técnicas de mineração de dados sobre o conjunto de atributos selecionados, foi possível então a obter estimativas sobre o desempenho futuro de estudantes. Essas estimativas poderiam apoiar o desenvolvimento de processos de acompanhamento efetivo dos estudantes, atividade de fundamental importância em cursos EAD. Neste trabalho, um estudo com sete experimentos foram realizados e apresentam diferentes cenários em que as estimativas sobre o desempenho podem ser obtidas. Os resultados desses experimentos apontam para a viabilidade desta proposta, tendo em vista os índices promissores de acurácia obtidos na classificação de estudantes quanto ao seu desempenho final nos cursos.Some educational environments have incorporated software to support or, in some cases, as a basic condition to the availability of courses. In this scenario, stand out Learning Management Systems (LMS) used to support the development of classroom, blended or distance courses. Learning Management System are characterized by storing a large volume of data. However, these environments lack tools to extract useful information for the development of efficient processes for monitoring students’. Thus, this research investigates how data stored in a LMS could be processed to generate information regarding estimates of students’ future academic performance. To obtain this information, first became necessary to select a set of attributes to represent students in an online course using a LMS. This set of attributes was chosen considering three dimensions, selected through the analysis of theoretical bases about online courses: LMS use profile, student-student interaction and bidirectional student-teacher interaction. Applying data mining techniques on the set of selected attributes, it was possible to obtain estimates of students’ future performance. These estimates can support the development of effective processes for monitoring students, activity of fundamental importance in distance learning. In this research, a study with seven experiments were conducted and present different scenarios where estimates of performance can be obtained. The results of these experiments indicate the viability of this proposal, given the promising accuracy rates obtained in the classification of students regarding their final performance in courses.
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AMARAL, Marcelo Gomes do. "Mineração de dados aplicada à classificação do risco de evasão de discentes ingressantes em instituições federais de ensino superior." Universidade Federal de Pernambuco, 2016. https://repositorio.ufpe.br/handle/123456789/19502.
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As Instituições Federais de Ensino Superior (IFES) possuem um importante papel no desenvolvimento social e econômico do país, contribuindo para o avanço tecnológico e cientifico e fomentando investimentos. Nesse sentido, entende-se que um melhor aproveitamento dos recursos educacionais ofertados pelas IFES contribui para a evolução da educação superior, como um todo. Uma maneira eficaz de atender esta necessidade é analisar o perfil dos estudantes ingressos e procurar prever, com antecedência, casos indesejáveis de evasão que, quanto mais cedo identificados, melhor poderão ser estudados e tratados pela administração. Neste trabalho, propõe-se a definição de uma abordagem para aplicação de técnicas diretas de Mineração de Dados objetivando a classificação dos discentes ingressos de acordo com o risco de evasão que apresentam. Como prova de conceito, a análise dos aspectos inerentes ao processo de Mineração de Dados proposto se deu por meio de experimentações conduzidas no ambiente da Universidade Federal de Pernambuco (UFPE). Para alguns dos algoritmos classificadores, foi possível obter uma acurácia de classificação de 73,9%, utilizando apenas dados socioeconômicos disponíveis quando do ingresso do discente na instituição, sem a utilização de nenhum dado dependente do histórico acadêmico.
The Brazilian's Federal Institutions of Higher Education have an important role in the social and economic development of the country, contributing to the technological and scientific advances and encouraging investments. Therefore, it is possible to infer that a better use of the educational resources offered by those institutions contributes to the evolution of higher education as a whole. An effective way to meet this need is to analyze the profile of the freshmen students and try to predict, as soon as possible, undesirable cases of dropout that when earlier identified can be examined and addressed by the institution's administration. This work propose the development of a approach for direct application of Data Mining techniques to classify newcomer students according to their dropout risk. As a viability proof, the proposed Data Mining approach was evaluated through experimentations conducted in the Federal University of Pernambuco. Some of the classification algorithms tested had an classification accuracy of 73.9% using only socioeconomic data available since the student's admission to the institution, without the use of any academic related data.
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Silva, Sandro José Ribeiro da. "TRILUA: um ambiente gamificado para apoio ao ensino de lógica de programação." Universidade do Vale do Rio dos Sinos, 2016. http://www.repositorio.jesuita.org.br/handle/UNISINOS/6050.
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Submitted by Silvana Teresinha Dornelles Studzinski (sstudzinski) on 2017-03-03T16:51:21Z
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O desenvolvimento de habilidades de programação de sistemas computacionais é uma necessidade crescente, devido ao amplo uso de recursos computacionais nas mais diversas áreas. Ao mesmo tempo, é conhecida a deficiência existente quanto à quantidade de profissionais sendo graduados nesta área. Alguns estudos indicam dificuldades dos estudantes e ao mesmo tempo falta de metodologias adequadas como possíveis elementos contribuindo para este contexto, corroborando a necessidade de desenvolvimento de pesquisas sobre o aprendizado de linguagens de programação. Entre as possíveis soluções para este problema de motivação, o desenvolvimento de um ambiente gamificado como ferramenta de ensino para linguagens de programação vem sendo explorado em projetos de pesquisa e também em opções comerciais. Uma das deficiências observadas nestas inciativas é justamente a falta de suporte aos professores para acompanhamento da evolução dos alunos. Buscando atender esta necessidade, o presente trabalho propõe um ambiente de apoio ao ensino de lógica de programação cujo diferencial é a inclusão de recursos de análise do comportamento dos alunos, voltados para o apoio ao professor. Desta forma, o trabalho proposto alia aos jogos eletrônicos o monitoramento on-line de suas etapas, através do uso de técnicas de mineração de dados educacionais. Com base em um framework para Gamificação, foi definido e desenvolvido um ambiente Web para ensino da linguagem Lua, com aspectos de Gamificação e Mineração de Dados Educacionais. Este ambiente foi utilizado em avaliações com alunos do ensino técnico, tendo sido observados resultados promissores nos aspectos motivacionais. As avaliações envolvendo a identificação de vantagens geradas para os professores com uso dos dados sobre o comportamento dos alunos também foram positivas e indicam um bom potencial para esta abordagem.
The development of computer systems programming skills is a growing necessity, due to the wide use of computational resources in different areas. At the same time, it is known the deficiency with respect to the amount of professionals being graduated in this area. Some studies indicates difficulties of students and lack of adequate methodologies as possible elements contributing to this context, supporting the need to develop research on learning programming languages. As a possible solution to this problem of motivation, the development of a gamified environment as a teaching tool for programming languages is being explored in research projects and also commercial options. One of the deficiencies observed in these initiatives is precisely the lack of support to teachers to follow up of the evolution of students, which consists in one of the differentials of the proposed work. In this way, the work integrates to electronic games the online monitoring through the use of educational data mining techniques. Based on the framework for gamification, has been defined and developed a web environment to the Lua language teaching, with aspects of gamification and education data mining. This environment has already been tested preliminarily with technical education students, being observed promising results. A new stage of development and testing is foreseen to deepening the identification of advantages generated for teachers with the use data on the behavior of students.
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Xiong, Xiaolu. "Theory and Practice: Improving Retention Performance through Student Modeling and System Building." Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-dissertations/139.
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The goal of Intelligent Tutoring systems (ITSs) is to engage the students in sustained reasoning activity and to interact with students based on a deep understanding of student behavior. In order to understand student behavior, ITSs rely on student modeling methods to observes student actions in the tutor and creates a quantitative representation of student knowledge, interests, affective states. Good student models are going to effectively help ITSs customize instructions, engage student's interest and then promote learning. Thus, the work of building ITSs and advancing student modeling should be considered as two interconnected components of one system rather than two separate topics. In this work, we utilized the theoretical support of a well-known learning science theory, the spacing effect, to guide the development of an ITS, called Automatic Reassessment and Relearning System (ARRS). ARRS not only validated the effectiveness of spacing effect, but it also served as a testing field which allowed us to find out new approaches to improve student learning by conducting large-scale randomized controlled trials (RCTs). The rich data set we gathered from ARRS has advanced our understanding of robust learning and helped us build student models with advanced data mining methods. At the end, we designed a set of API that supports the development of ARRS in next generation ASSISTments platform and adopted deep learning algorithms to further improve retention performance prediction. We believe our work is a successful example of combining theory and practice to advance science and address real- world problems.
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Santana, Marcelo Almeida. "Um estudo comparativo das técnicas de predição na identificação de insucesso acadêmico dos estudantes durante cursos de programação introdutória." Universidade Federal de Alagoas, 2015. http://www.repositorio.ufal.br/handle/riufal/1722.
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The high failure rates of students in the introductory programming course within the universities worldwide have alarmed and worried many educators. Those rates can lead to losses of various types and interests. Thus, there are important reasons to try to clarify the main factors that possibly influence such failures. Furthermore, one of the major challenges is on how to early identify the students likely to in the introductory programming course, eventually allowing effective pedagogical interventions. Thus, in this study we aim to explore educational data mining techniques, in order to compare the effectiveness of prediction algorithms capable of identifying students likely to fail, in a timely manner suitable for pedagogical intervention. This study evaluated the efficacy of prediction algorithms in two different and independent data sources one in the classroom teaching mode and the other in the distance education mode in the disciplines in the introductory programming. The results showed that the techniques discussed in this study are effective in this task of prediction. In addition, it was shown also that after the completion of the pre-processing and adjustments to the parameters of the algorithms analyzed had an improvement in their results. At the end of the process, the Supported Vector Machine (SVM) algorithm showed the best results, both in the classroom teaching mode as in the distance, reaching an f-measure rate of 83% and 93% respectively.As altas taxas de insucesso nas universidades nos cursos que contemplam a disciplina de programação introdutória na sua grade curricular tem alarmado e preocupado muitos educadores, pois o insucesso dos estudantes podem gerar prejuízos dos mais diversos tipos e interesses. Assim, há relevantes motivos para se tentar esclarecer eventuais fatores que afetam tal insucesso. Ainda neste contexto, um dos desafios importantes é o de identificar antecipadamente os estudantes propensos ao insucessos na disciplina de programação introdutória, assumindo-se em tempo hábil para permitir intervenção pedagógica eficaz. Deste modo, buscou-se neste trabalho um estudo em técnicas de mineração de dados educacionais , objetivando-se comparar a eficácia dos algoritmos de predição capazes de identificar, em tempo hábil para intervenção pedagógica, os estudantes propensos ao insucesso. Neste estudo, avaliou-se a eficácia de algoritmos de predição em duas fontes de dados diferentes e independentes, uma na modalidade presencial e outra na modalidade de ensino a distância sobre as disciplinas de programação introdutória. Os resultados mostraram que as técnicas analisadas no estudo são eficazes na identificação dos estudantes propensos ao insucesso no início da disciplina. Além disso, mostrou-se também que após a realização das etapas de pré-processamento e ajustes nos parâmetros de algoritmos, tais algoritmos analisados tiveram uma melhora em seus resultados. Ao fim do processo, o algoritmo máquina de vetor de suporte (SVM: Support Vector Machine) apresentou os melhores resultados, tanto na modalidade de ensino presencial quanto na modalidade a distância, alcançando uma taxa de f-measure de 83% e 92%, respectivamente.
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Cambruzzi, Wagner Luiz. "GVwise: uma aplicação de learning analytics para a redução da evasão na educação à distância." Universidade do Vale do Rio dos Sinos, 2014. http://www.repositorio.jesuita.org.br/handle/UNISINOS/4646.
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Submitted by William Justo Figueiro (williamjf) on 2015-07-28T20:32:18Z
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Aplicações que fazem uso de tecnologias como Mineração de Dados Educacionais (MDE) e Learning Analytics (LA) vêm sendo adotadas na mitigação da evasão escolar, disponibilizando informações sobre os alunos que são utilizadas em intervenções pedagógicas. Os trabalhos estudados sobre a implementação destas aplicações priorizam a descrição das técnicas empregadas e existem poucas avaliações da sua utilização em larga escala, além da falta de detalhamento sobre as causas da evasão. Este trabalho apresenta um estudo de fatores envolvidos no fenô- meno de evasão escolar e descreve a utilização de um sistema para MDE e LA durante 18 meses em cursos de graduação na modalidade de Educação a Distância. É ampliada a análise dos fatores tradicionalmente monitorados e utilizados nos sistemas de MDA e LA, com a inclusão de elementos associados ao papel exercido pelos docentes e pelo conjunto de aspectos metodológicos de cada instituição. O sistema possui como diferencial a flexibilidade na integração e utilização dos dados gerados no processo de mediação digital, o que permite que necessidades de diferentes ferramentas de apoio sejam disponibilizadas. Resultados positivos destacados são a identificação de perfis de alunos evasores e a realização de intervenções pedagógicas, com redução das médias da evasão.
Educational Data mining (EDM) and Learning Analytics (LA) applications have been adopted in mitigation of dropout, providing information about students who are employed in pedagogical interventions. The most papers about the implementation of these systems describe the techniques employed, there are few evaluations of their large-scale use, apart from the lack of detail about the causes of dropout. This work presents a study of factors involved in dropout and describes the use of a system for EDM and LA during 18 months for undergraduate courses in distance education. The analysis of the factors traditionally monitored and used in EDM and LA systems is extended, with the inclusion of elements associated with the role exercised by the teachers and by institutional methodological aspects. The system has flexibility in integration and use of data generated in the process of digital mediation, which allows different support tools to be available. Some results are the identification of evaders students profiles and the realization of pedagogical actions with reducing evasion.
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Sao, Pedro Michael A. "Real-time Assessment, Prediction, and Scaffolding of Middle School Students’ Data Collection Skills within Physical Science Simulations." Digital WPI, 2013. https://digitalcommons.wpi.edu/etd-dissertations/168.
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Despite widespread recognition by science educators, researchers and K-12 frameworks that scientific inquiry should be an essential part of science education, typical classrooms and assessments still emphasize rote vocabulary, facts, and formulas. One of several reasons for this is that the rigorous assessment of complex inquiry skills is still in its infancy. Though progress has been made, there are still many challenges that hinder inquiry from being assessed in a meaningful, scalable, reliable and timely manner. To address some of these challenges and to realize the possibility of formative assessment of inquiry, we describe a novel approach for evaluating, tracking, and scaffolding inquiry process skills. These skills are demonstrated as students experiment with computer-based simulations. In this work, we focus on two skills related to data collection, designing controlled experiments and testing stated hypotheses. Central to this approach is the use and extension of techniques developed in the Intelligent Tutoring Systems and Educational Data Mining communities to handle the variety of ways in which students can demonstrate skills. To evaluate students' skills, we iteratively developed data-mined models (detectors) that can discern when students test their articulated hypotheses and design controlled experiments. To aggregate and track students' developing latent skill across activities, we use and extend the Bayesian Knowledge-Tracing framework (Corbett & Anderson, 1995). As part of this work, we directly address the scalability and reliability of these models' predictions because we tested how well they predict for student data not used to build them. When doing so, we found that these models demonstrate the potential to scale because they can correctly evaluate and track students' inquiry skills. The ability to evaluate students' inquiry also enables the system to provide automated, individualized feedback to students as they experiment. As part of this work, we also describe an approach to provide such scaffolding to students. We also tested the efficacy of these scaffolds by conducting a study to determine how scaffolding impacts acquisition and transfer of skill across science topics. When doing so, we found that students who received scaffolding versus students who did not were better able to acquire skills in the topic in which they practiced, and also transfer skills to a second topic when was scaffolding removed. Our overall findings suggest that computer-based simulations augmented with real-time feedback can be used to reliably measure the inquiry skills of interest and can help students learn how to demonstrate these skills. As such, our assessment approach and system as a whole shows promise as a way to formatively assess students' inquiry.
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Manspeaker, Rachel Bechtel. "Using data mining to differentiate instruction in college algebra." Diss., Kansas State University, 2011. http://hdl.handle.net/2097/8542.
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Doctor of PhilosophyDepartment of Mathematics
Andrew G. Bennett
The main objective of the study is to identify the general characteristics of groups within a typical Studio College Algebra class and then adapt aspects of the course to best suit their needs. In a College Algebra class of 1,200 students, like those at most state funded universities, the greatest obstacle to providing personalized, effective education is the anonymity of the students. Data mining provides a method for describing students by making sense of the large amounts of information they generate. Instructors may then take advantage of this expedient analysis to adjust instruction to meet their students’ needs. Using exam problem grades, attendance points, and homework scores from the first four weeks of a Studio College Algebra class, the researchers were able to identify five distinct clusters of students. Interviews of prototypical students from each group revealed their motivations, level of conceptual understanding, and attitudes about mathematics. The student groups where then given the following descriptive names: Overachievers, Underachievers, Employees, Rote Memorizers, and Sisyphean Strivers. In order to improve placement of incoming students, new student services and student advisors across campus have been given profiles of the student clusters and placement suggestions. Preliminary evidence shows that advisors have been able to effectively identify members of these groups during their consultations and suggest the most appropriate math course for those students. In addition to placement suggestions, several targeted interventions are currently being developed to benefit underperforming groups of students. Each student group reacts differently to various elements of the course and assistance strategies. By identifying students who are likely to struggle within the first month of classes, and the recovery strategy that would be most effective, instructors can intercede in time to improve performance.
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Stützer, Cathleen M. "Informations- und Wissenstransfer in kollaborativen Lernsystemen." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-130139.
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In der Netzwerkgesellschaft des 21. Jahrhunderts gilt die kollaborative Verteilung und Nutzung von Information und Wissen als Schlüsselstrategie für den webbasierten Informations- und Wissenstransfer. Durch die technologischen Möglichkeiten werden technische Zugangsbarrieren weitestgehend überwunden und traditionelle Formen der Wissensvermittlung durch moderne webbasierte Lernumgebungen ergänzt. Der Umgang mit kollaborativen Lehr- und Lernszenarien im dynamischen Informations- und Wissenstransfer bildet die Grundlage für den soziokulturellen Fortschritt innerhalb der Bildungsforschung. Der Schwerpunkt dieser Arbeit lag auf der strukturellen und relationalen Analyse sozialer Organisationsstrukturen innerhalb von Wissensnetzwerken. Ziel war es, Einflussfaktoren offenzulegen, die sich auf das Innovations- und Distributionspotential von Information und Wissen innerhalb von kollaborativen Wissensnetzwerken auswirken. Es wurden dazu Interaktionsprozesse von Teilnehmern innerhalb von Diskussionsforen am Beispiel der Lernplattform OPAL – dem aktuell populärsten Lernmanagementsystem in der Hochschulbildung Sachsens, Deutschland – untersucht. Unter der Annahme, dass soziale Interaktion besonders im Umgang mit kollaborativen Medien den Bildungsablauf und der Aufbau von Wissensnetzwerken die Lehr- und Lernprozesse beeinflusst, wurden in dieser Arbeit die strukturellen Bedingungen des kollaborativen Wissensnetzwerkes in OPAL exploriert und soziale Rollenkonstrukte relational identifiziert, um die Auswirkungen kollaborativer Aktivitäten auf den Informations- und Wissenstransfer in Wissensnetzwerken zu erklären. Es wurden vornehmlich beziehungsorientierte kommunikationstheoretische Modelle zugrunde gelegt und relationale Forschungsmethoden wie SNA (Social Network Analysis) und DNA (Dynamic Network Analysis) angewandt, um eine Basis für die weiterführende Implementierung sozial vernetzter Lehr- und Lernstrategien in der Bildungsforschung zu schaffen. […]In the network society of the 21st century, a key strategy for web-based exchange of information and knowledge is their collaborative distribution and use. Technical hurdles of access are mostly being overcome with technological advances and traditional forms of passing on knowledge are being complemented by modern, e-learning environments. Within research into education, the foundation for socio-cultural progress is formed by involvement with collaborative teaching and learning scenarios in a dynamic exchange of information and knowledge. The emphasis of this work lay in the analysis of structures and relationships of social organisations within knowledge networks. The aim was to describe the exchange of information and knowledge in collaborative learning systems and to explore its influence on the potential for innovation and distribution of information and knowledge. A study was undertaken of the interaction of participants in discussion forums as exemplified by the learning platform OPAL – currently the most popular learning management system in secondary school education in Saxony, Germany. On the assumption that social interaction, particularly involving collaborative media, the progress of education and the construction of knowledge networks do influence teaching and learning processes, this work explored the structural conditions of OPAL's collaborative knowledge network and identified relationships between social role constructs in order to explain the effect of collaborative activities on the process of diffusion of information and knowledge in knowledge networks. Primarily the study was based on relationship oriented sociological models and communication theory models, and research methods for relationships, including SNA (Social Network Analysis) and DNA (Dynamic Network Analysis) were applied, so as to create a basis for further implementation of social network teaching and learning strategies in educational research. [...]