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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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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 Philosophy<br>Department of Mathematics<br>Andrew G. Bennett<br>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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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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Burley, Keith Martin. "Data mining techniques in higher education research : the example of student retention." Thesis, Sheffield Hallam University, 2006. http://shura.shu.ac.uk/19412/.
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Data Mining has been used for more than a decade in a variety of differing environments. It takes an inductive approach to data analysis in that it is concerned with the extraction of patterns from the data often without preconceived ideas. Data mining is part of the field of Business Intelligence, a subject area that the author is familiar with and has taught for many years. He believes that the application of data mining techniques has much to offer within the context of higher education. However, there is little evidence that these well established techniques have previously been applied to the sphere of higher education. Student retention is a hot issue in higher education at the moment. It is for this reason that the author chose to establish the power of data mining techniques in higher education using the examination of student retention issues as a vehicle. The field of student retention has been well documented over the years. Contemporary authors such as McGivney (1996), Moxley et al (2001), Yorke (1999) and Yorke & Longden (2004) have examined strategies and derived intervention techniques aimed at assisting students to adapt to university life. As the proportion of students entering Higher Education has increased there has been an increasing awareness that universities need to adapt to the changing profile of these students. The data was collected via an online questionnaire administered to a large group of computing students at Sheffield Hallam University and similar institutions. The collected data was explored using Data Mining techniques including Decision Trees, Market Basket Analysis and Cluster Analysis. This study sought to explore interrelationships between factors that contribute to student attrition and hence establish the demographics of at-risk students. The use of data mining techniques was found to be highly effective, having found most of the primary issues established in previous research. It went on to find the strongest relationships between them, corresponding well to findings from previous research using standard statistical techniques. The author believes that he has established the power of data mining techniques in higher education and recommends further areas where it could be used profitably.
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Кузіков, Борис Олегович, Борис Олегович Кузиков та Borys Olehovych Kuzikov. "Сучасний стан та напрями розвитку Education Data Mining в Сумському державному університеті". Thesis, Сумський державний університет, 2014. http://essuir.sumdu.edu.ua/handle/123456789/37991.
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Однією з переваг автоматизації бізнес процесів є можливість їх глибокого аналізу з метою пошуку прихованих зв’язків між параметрами моделі і ефективністю процесів. Дослідження у цьому напрямку не пройшли осторонь систем дистанційного навчання, особливо з появою Massive Open Online Courses (MOOC). На ряду із терміном Data Mining – видобуток знань з масивів інформації, розглядають поняття «Education Data Mining» (EDM).
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Pepe, Julie. "STUDENT PERCEPTION OF GENERAL EDUCATION PROGRAM COURSES." Doctoral diss., University of Central Florida, 2010. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3545.
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The purposes of this study were to: (a) determine, for General Education Program (GEP) courses, what individual items on the student form are predictive of the overall instructor rating value; (b) investigate the relationship of instructional mode, class size, GEP foundational area, and GEP theme with the overall instructor rating value; (c) examine what teacher/course qualities are related to a high (Excellent) overall evaluation or a low (Poor) overall evaluation value. The data set used for analysis contained sixteen student response scores (Q1-Q16), response number, class size, term, foundational area (communication, cultural/historical, mathematics, social, or science), GEP theme (yes/no), instructional mode (face-to-face or other), and percent responding (calculated value). All identifying information such as department, course, section, and instructor was removed from the analysis file. The final data set contained 23 variables, 8,065 course sections, and 294,692 student responses. All individual items on the student evaluation form were related to the overall evaluation item score, measured using Spearman s correlation coefficients. None of the examined course variables were selected as significant when the individual form items were included in the modeling process. This indicated students employed a consistent approach to the evaluation process regardless of large or small classes, face-to-face or other instructional modes, foundational area, or percent responding differences. Data mining modeling techniques were used to understand the relationship of individual item responses and additional course information variables to the overall score. Items one to fifteen (Q1 to Q15), class size, instructional mode, foundational area, and GEP theme were the independent variables used to find splits to create homogenous groups in relation to the overall evaluation score. The model results are presented in terms of if-then rules for  Excellent or  Poor overall evaluation scores. The top three rules for  Excellent or  Poor based their classifications on some combination of the following items: communication of ideas and information; facilitation of learning; respect and concern for students; instructor s overall organization of the course; instructor s interest in your learning; instructor s assessment of your progress in the course; and stimulation of interest in the course. Proportion of student responses conforming to the top three rules for  Excellent or  Poor overall evaluation ranged from 0.89 to .60. These findings suggest that students reward, with higher evaluation scores, instructors who they perceive as organized and strive to clearly communicate course content. These characteristics can be improved through mentoring or professional development workshops for instructors. Additionally, instructors of GEP courses need to be informed that students connect respect and concern and having an interest in student learning with the overall score they give the instructor.<br>Ph.D.<br>Department of Educational and Human Sciences<br>Education<br>Education PhD
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Franco, Gaviria María Auxiliadora. "Principled design of evolutionary learning sytems for large scale data mining." Thesis, University of Nottingham, 2013. http://eprints.nottingham.ac.uk/14299/.
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Currently, the data mining and machine learning fields are facing new challenges because of the amount of information that is collected and needs processing. Many sophisticated learning approaches cannot simply cope with large and complex domains, because of the unmanageable execution times or the loss of prediction and generality capacities that occurs when the domains become more complex. Therefore, to cope with the volumes of information of the current realworld problems there is a need to push forward the boundaries of sophisticated data mining techniques. This thesis is focused on improving the efficiency of Evolutionary Learning systems in large scale domains. Specifically the objective of this thesis is improving the efficiency of the Bioinformatic Hierarchical Evolutionary Learning (BioHEL) system, a system designed with the purpose of handling large domains. This is a classifier system that uses an Iterative Rule Learning approach to generate a set of rules one by one using consecutive Genetic Algorithms. This system have shown to be very competitive so far in large and complex domains. In particular, BioHEL has obtained very important results when solving protein structure prediction problems and has won related merits, such as being placed among the best algorithms for this purpose at the Critical Assessment of Techniques for Protein Structure Prediction (CASP) in 2008 and 2010, and winning the bronze medal at the HUMIES Awards for Human-competitive results in 2007. However, there is still a need to analyse this system in a principled way to determine how the current mechanisms work together to solve larger domains and determine the aspects of the system that can be improved towards this aim. To fulfil the objective of this thesis, the work is divided in two parts. In the first part of the thesis exhaustive experimentation was carried out to determine ways in which the system could be improved. From this exhaustive analysis three main weaknesses are pointed out: a) the problem-dependancy of parameters in BioHEL's fitness function, which results in having a system difficult to set up and which requires an extensive preliminary experimentation to determine the adequate values for these parameters; b) the execution time of the learning process, which at the moment does not use any parallelisation techniques and depends on the size of the training sets; and c) the lack of global supervision over the generated solutions which comes from the usage of the Iterative Rule Learning paradigm and produces larger rule sets in which there is no guarantee of minimality or maximal generality. The second part of the thesis is focused on tackling each one of the weaknesses abovementioned to have a system capable of handling larger domains. First a heuristic approach to set parameters within BioHEL's fitness function is developed. Second a new parallel evaluation process that runs on General Purpose Graphic Processing Units was developed. Finally, post-processing operators to tackle the generality and cardinality of the generated solutions are proposed. By means of these enhancements we managed to improve the BioHEL system to reduce both the learning and the preliminary experimentation time, increase the generality of the final solutions and make the system more accessible for end-users. Moreover, as the techniques discussed in this thesis can be easily extended to other Evolutionary Learning systems we consider them important additions to the research in this field towards tackling large scale domains.
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Allègre, Olivier. "Adapting the Prerequisite Structure to the Learner in Student Modeling." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS116.
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Les modèles d'apprenant basés sur les données visent à représenter et comprendre les connaissances des élèves ainsi que leurs autres caractéristiques métacognitives pour soutenir leur apprentissage en faisant des prédictions sur leurs performances futures. La modélisation de l'apprenant peut être abordée en utilisant différentes modèles de système complexes, chacun offrant une perspective différente sur l'étudiant et le processus d'apprentissage. Les techniques d'apprentissage automatique améliorées par la représentation de la connaissance, telles que les réseaux bayésiens, sont particulièrement adaptées pour intégrer la connaissance de domaine dans le modèle de l'apprenant, ce qui en fait un outil précieux dans la modélisation des élèves. Ce travail explore la modélisation et les applications potentielles d'un nouveau cadre appelé E-PRISM (pour Embedding Prerequisite Relationships in Student Modeling), qui inclut un modèle d'apprenant basé sur les réseaux Bayésiens dynamiques. Il utilise une nouvelle architecture pour les réseaux bayésiens qui repose sur la clause d’indépendance des influences causales (ICI), qui réduit le nombre de paramètres dans le réseau et permet une interprétabilité améliorée. L'étude examine les points forts d'EPRISM, notamment sa capacité à considérer la structure préalable requise entre les composants de connaissances, son nombre limité de paramètres et son interprétabilité améliorée. L'étude introduit également une nouvelle approche pour l'inférence approximative dans les grands réseaux bayésiens basés sur la clause ICI, ainsi qu'un algorithme d'apprentissage de paramètres performant dans les réseaux bayésiens basés sur cette clause. Dans l'ensemble, l'étude démontre le potentiel d'E-PRISM comme outil prometteur pour découvrir la structure préalable requise des connaissances de domaine qui peuvent être adaptées à l'apprenant avec pour objectif d'améliorer l'adaptabilité de la boucle extérieure d’un tuteur intelligent<br>Data-driven learner models aim to represent and understand students' knowledge and other meta-cognitive characteristics to support their learning by making predictions about their future performance. Learner modeling can be approached using various complex system models, each providing a different perspective on the student and the learning process. Knowledge-enhanced machine learning techniques, such as Bayesian networks, are particularly well suited for incorporating domain knowledge into the learner model, making them a valuable tool in student modeling.This work explores the modeling and the potential applications of a new framework, called E-PRISM, for Embedding Prerequisite Relationships In Student Modeling, which includes a learner model based on dynamic Bayesian networks. It uses a new architecture for Bayesian networks that rely on the clause of Independence of Causal Influences (ICI), which reduces the number of parameters in the network and allows enhanced interpretability. The study examines the strengths of E-PRISM, including its ability to consider the prerequisite structure between knowledge components, its limited number of parameters, and its enhanced interpretability. The study also introduces a novel approach for approximate inference in large ICI-based Bayesian networks, as well as a performant parameter learning algorithm in ICI-based Bayesian networks. Overall, the study demonstrates the potential of E-PRISM as a promising tool for discovering the prerequisite structure of domain knowledge that may be adapted to the learner with the perspective of improving the outer-loop adaptivity
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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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Петренко, А. М. "Застосування методів EDM для розробки системи підтримки рішень". Master's thesis, Сумський державний університет, 2020. https://essuir.sumdu.edu.ua/handle/123456789/82374.
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Дана робота присвячена проектуванню моделі, яка б допомогла адміністратору електронної системи прийняти рішення щодо продуктивності сервера та прогнозувала б максимальну кількість унікальних користувачів та запитів при якій сервер буде неспроможний обробляти запити клієнтів.
У ході роботи проведено аналіз вхідних даних, літератури, обрані методи, алгоритм виконання поставленої задачі, реалізовані наглядні графіки, що показують масштабованість електронного ресурсу, розроблено модель для допомоги адміністратору ресурса прийняти рішення щодо його продуктивності. Об’єкт дослідження — Дистанційний навчальний ресурс https://mix.sumdu.edu.ua. В процесі виконання реалізації моделі було застосовано методи категорії прогнозування, закон Літтла та перевірка статистичної гіпотези щодо рівності середніх значень.
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Maisey, Gemma. "Mining for sleep data: An investigation into the sleep of fly-In fly-out shift workers in the mining industry and potential solutions." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2023. https://ro.ecu.edu.au/theses/2618.
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Shift work in the mining industry is a risk factor for sleep loss leading to impaired alertness, which may adversely impact health and safety risks. This risk is being increasingly recognised by leaders and shift workers in the mining industry, however, there is limited knowledge available on the extent of sleep loss and other potential contributing factors. Furthermore, knowledge of the efficacy of individual interventions to assist shift workers to improve their sleep, and the management of risk at an organisational level is scarce. This PhD thesis involved three studies. The first two studies involved the recruitment of 88 shift workers on a fly-in, fly-out (FIFO) mining operation in Western Australia (WA), undertaken within a business-as-usual model. The third study develops a diagnostic tool to support the systematic assessment of an organisation's Fatigue Risk Management System (FRMS).
Study 1 (Chapter 4) investigated sleep behaviours, the prevalence of risk of sleep disorders and the predicted impact on alertness across the roster schedule. Sleep was objectively measured using wrist-activity monitors for the 21-day study period and biomathematical modelling was used to predict alertness across the roster schedule. The prevalence of risk for sleep problems and disorders was determined using scientifically validated sleep questionnaires. We found sleep loss was significantly greater following days shift and night shift compared to days off, which resulted in a 20% reduced alertness across the 14 consecutive shifts at the mining operation. Shift workers reported a high prevalence of risk for sleep disorders including shift work disorder (44%), obstructive sleep apnoea (OSA) (31%) and insomnia (8%); a high proportion of shift workers were obese with a body mass index (BMI) > 30kg/m2 (23%) and consumed hazardous levels of alcohol (36%). All of which may have contributed to sleep loss. In addition, the design of shifts and rosters, specifically, early morning shift start times ( < 06:00) and long shift durations ( > 12 hrs.) may have also adversely impacted sleep duration, as they did not allow for sufficient sleep opportunity.
Study 2 (Chapter 5) was a randomised control trial (RCT) that investigated the efficacy of interventions to improve sleep, which included a two-hour sleep education program and biofeedback on sleep through a smartphone application. Sleep was objectively measured using wrist-activity monitors across two roster cycles (42 days) with an intervention received on day 21. Our results were inconclusive and suggest that further research is required to determine the efficacy of these commonly used interventions in the mining industry. In line with the results from Study 1, our interventions may not have been effective in improving sleep duration as the shift and roster design did not allow adequate time off between shifts for sleep ( ≥ 7 h) and daily routines.
Study 3 (Chapter 6) used a modified Delphi process that involved 16 global experts, with experience and knowledge in sleep science, chronobiology, and applied fatigue risk management within occupational settings, to define and determine the elements considered essential as part of an FRMS. This study resulted in the development of an FRMS diagnostic tool to systematically assist an organisation in assessing its current level of implementation of an FRMS.
The results of the studies within this PhD thesis present several potential benefits for the mining industry. These include an enhanced understanding of the extent of sleep loss and the potential impact on alertness, in addition to contributing factors, including shift and roster design elements and unmanaged sleep disorders. The development of the FRMS diagnostic tool may practically guide mining operations on the elements required to manage risk. These findings may also inform government, occupational health and safety regulatory authorities and shift work organisations more broadly, on the need to identify and manage fatigue, as a result of sleep loss, as a critical risk.
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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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Wang, Shuai. "Data mining techniques and mathematical models for the optimal scholarship allocation problem for a state university." Wright State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=wright1515618183686262.
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Brown, Marvin Lane. "The Impact of Data Imputation Methodologies on Knowledge Discovery." Cleveland State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=csu1227054769.
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Giese, Marco [Verfasser], and Andreas [Akademischer Betreuer] Behr. "An analysis of dropout students in the German higher education system using modern data mining techniques / Marco Giese ; Betreuer: Andreas Behr." Duisburg, 2021. http://d-nb.info/1234911213/34.
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Bahrami, Fahimeh. "Identifying College Students’ Course-Taking Patterns In Stem Fields." ScholarWorks @ UVM, 2019. https://scholarworks.uvm.edu/graddis/1048.
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In spite of substantial investments in science, technology, engineering, and mathematics (STEM) education, low enrollment and high attrition rate among students in these fields remain an unmitigated challenge for higher education institutions. In particular, underrepresentation of women and minority students with STEM-related college degrees replicates itself in the makeup of the workforce, adding another layer to the challenge. While most studies examine the relationship between student characteristics and their outcomes, in this study, I take a new approach to understand academic pathways as a dynamic process of student curricular experiences that influence his/her decision about subsequent course-takings and major field of the study. I leverage data mining techniques to examine the processes leading to degree completion in STEM fields. Specifically, I apply Sequential Pattern Mining and Sequential Clustering to student transcript data from a four-year university to identify frequent academic major trajectories and also the most frequent course-taking patterns in STEM fields. I also investigate whether there are any significant differences between male and female students’ academic major and course-taking patterns in these fields.
The findings suggest that non-STEM majoring paths are the most frequent academic pattern among students, followed by life science trajectories. Engineering and other hard science trajectories are much less frequent. The frequency of all STEM trajectories, however, declines over time as students switch to non-STEM majors. The switching rate from non-STEM to STEM fields overtime is, however, much lower. I also find that male and female students follow different academic pathways, and these gender-based differences are even more significant within STEM fields.
Students’ course-taking patterns also suggest that taking engineering and computer science courses is predominantly a male course-taking behavior, while females are more likely to pursue academic pathways in life science. I also find that STEM introductory courses - particularly Calculus I, Calculus II and Chemistry I – are gateway courses, that serve as potential barriers to pursuing degrees in STEM-related fields for a large number of students who showed an initial interest in STEM courses. Female students were more likely to switch to non-STEM fields after taking these courses, while male students were more likely to drop out of college overall.
In addition to the study’s findings on students’ academic pathways toward attaining a college degree in a STEM-related field, this study also shows how data mining techniques that leverage data about the sequence of courses students take can be used by higher education leaders and researchers to better understand students’ academic progress and explore how students navigate and interact with college curriculum. In particular, this study demonstrates how these analytic approaches might be used to design and structure more effective course taking pathways and develop interventions to improve student retention in STEM fields.
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Hartl, Karin [Verfasser], and G. [Akademischer Betreuer] Nakhaeizadeh. "The Application Potential of Data Mining in Higher Education Management: A Case Study Based on German Universities / Karin Hartl ; Betreuer: G. Nakhaeizadeh." Karlsruhe : KIT-Bibliothek, 2019. http://d-nb.info/1191267350/34.
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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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Wixon, Naomi. "An Inductive Method of Measuring Students’ Cognitive and Affective Processes via Self-Reports in Digital Learning Environments." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-dissertations/504.
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Student affect can play a profoundly important role in students' post-school lives. Understanding students' affective states within online learning environments in particular has become an important matter of research, as digital tutoring systems have the potential to intervene at the moment that students are struggling and becoming frustrated, bored or disengaged. However, despite the importance of assessing students' affective states, there is no clear consensus about what emotions are most important to assess, nor how these emotions can be best measured.
This dissertation investigates students’ self-reports of their emotions and causal attributions of those emotions collected while they are solving math problems within a mathematics tutoring system. These self-reports are collected in two conditions: through limited choice Likert response and through open response text boxes. The conditions are combined with students’ cognitive attributions to describe epistemic (neither purely affective nor purely cognitive) emotions in order to explain the relationship between observable student behaviors in the MathSpring.org tutoring system and student affect. These factors include beliefs, expectations, motivations, and perceptions of ability and control. A special emphasis of this dissertation is on analyzing the role of causal attributions for the events and appraisals of the learning environment, as possible causes of student behaviors, performance, and affect.
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Carbone, Rego Felipe. "Exploring and Identifying Student Engagement and Performance Profiles in A Learning Environment." Thesis, University of Sydney, 2020. https://hdl.handle.net/2123/23250.
Full textAbstract:
Many studies illustrate the potential of utilizing data and advanced analytics techniques in specific and particular educational and learning settings. Much less focus is devoted to applying broader and more general approaches to exploratory data analysis in the field of Learning Analytics (LA). This thesis presents an additional contribution in this space. It demonstrates a general approach to exploring and predicting students’ profiles in an online learning setting. Its intention is to contribute to the growing literature of exploratory research in the field of LA by applying robust approaches to analysing data about students’ behaviours. The thesis describes a case study where data were collected via a naturalistic experiment relating to an actual, real-life group of students from a first semester engineering course. Subjects in this naturalistic experiment were engaging with a learning content through a learning management system (LMS) in a blended-learning environment. The approach in this thesis leverages modern advanced analytical techniques to contribute to the growing body of literature in the field of exploratory analysis in LA. A combination of unsupervised and supervised statistical learning methods are applied to identify, cluster and, subsequently, classify groups of students based on their profiles. The results suggest the existence of distinct groups of students with fairly distinguishable characteristics. Findings also illustrate the application of predictive analytics models to classify students based on their previously identified characteristics. The thesis concludes with discussions on potential implications and limitations which intend, ultimately, to help researchers, educators and institutions alike understand, build and deliver a more adaptive learning experience and environment from an exploratory data analysis perspective.
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Nicholson, Scott. "Creating a Criterion-Based Information Agent Through Data Mining for Automated Identification of Scholarly Research on the World Wide Web." Thesis, University of North Texas, 2000. https://digital.library.unt.edu/ark:/67531/metadc2459/.
Full textAbstract:
This dissertation creates an information agent that correctly identifies Web pages containing scholarly research approximately 96% of the time. It does this by analyzing the Web page with a set of criteria, and then uses a classification tree to arrive at a decision.
The criteria were gathered from the literature on selecting print and electronic materials for academic libraries. A Delphi study was done with an international panel of librarians to expand and refine the criteria until a list of 41 operationalizable criteria was agreed upon. A Perl program was then designed to analyze a Web page and determine a numerical value for each criterion.
A large collection of Web pages was gathered comprising 5,000 pages that contain the full work of scholarly research and 5,000 random pages, representative of user searches, which do not contain scholarly research. Datasets were built by running the Perl program on these Web pages. The datasets were split into model building and testing sets.
Data mining was then used to create different classification models. Four techniques were used: logistic regression, nonparametric discriminant analysis, classification trees, and neural networks. The models were created with the model datasets and then tested against the test dataset. Precision and recall were used to judge the effectiveness of each model. In addition, a set of pages that were difficult to classify because of their similarity to scholarly research was gathered and classified with the models.
The classification tree created the most effective classification model, with a precision ratio of 96% and a recall ratio of 95.6%. However, logistic regression created a model that was able to correctly classify more of the problematic pages.
This agent can be used to create a database of scholarly research published on the Web. In addition, the technique can be used to create a database of any type of structured electronic information.
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Santos, Rodrigo Magalh?es Mota dos. "T?cnicas de aprendizagem de m?quina utilizadas na previs?o de desempenho acad?mico." UFVJM, 2016. http://acervo.ufvjm.edu.br/jspui/handle/1/1327.
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Previous issue date: 2016<br>A tecnologia, presente cada vez mais no ambiente educacional, tem contribu?do para o aumento
da oferta de cursos ? dist?ncia. Grande parte dos cursos ofertados nesta modalidade utilizam os
Ambientes Virtuais de Aprendizagem (AVA). Estes ambientes ganham espa?o no cotidiano dos
educadores devido ao f?cil manuseio e a grande diversidade de ferramentas disponibilizadas.
Tais ferramentas permitem, de forma geral, a administra??o de cursos totalmente ? dist?ncia
com oferta de m?ltiplas m?dias e recursos (f?runs de discuss?o, chats, dentre outros) para intera??es
entre professores e alunos. Tais intera??es criam enormes volumes de dados que podem
ser analisados atrav?s da aplica??o de t?cnicas de Minera??o de Dados Educacionais. Com a
aplica??o destas t?cnicas pode-se realizar a previs?o de desempenho acad?mico que pode ter
grande utilidade para Institui??es de Ensino no sentido de auxili?-las a tomar, de forma antecipada,
decis?es pedag?gicas que possam ajudar os estudantes.
Este trabalho apresenta um estudo de m?todos como Sele??o de Atributos utilizando a abordagem
Wrapper e Classificador em Cascata, ainda n?o empregados em trabalhos correlatos
pesquisados, que visam melhorar os resultados obtidos pelas t?cnicas de Minera??o de Dados
Educacionais utilizadas na previs?o de desempenho acad?mico de estudantes. Os resultados
experimentais indicam uma melhora no desempenho dos algoritmos classificadores utilizados
(alguns alcan?ando a not?vel marca de 90,2% de acur?cia), bem como apontam quais os recursos
utilizados no AVA possuem maior influ?ncia no desempenho dos estudantes.<br>Disserta??o (Mestrado Profissional) ? Programa de P?s-Gradua??o em Educa??o, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2016.<br>The technology, which is being increasingly used in the educational environment, has contributed
for the popularity of distance courses. Much of the courses offered in this mode uses
the so-called Virtual Learning Environments (VLE). These environments are gaining ground
in the daily lives of educators due to its easy handling and the wide variety of available tools.
These tools allow, in general, the administration of fully distance courses with multiple media
and resources (forums, chats, among others) for interactions between teachers and students.
These interactions create huge volumes of data that can be analyzed through the application of
Educational Data Mining techniques. Such techniques can be used to academic performance
prediction that can be very useful for education institutions in order to help them to take, in
advance, pedagogical decisions that can help students.
This work presents a study of methods as Feature Selection using the Wrapper approach and
Classifier Cascade that were not employed in other works, with the aim to improve the results
obtained by Educational Data Mining techniques used in the academic performance prediction.
Results showed an improvement in the performance of classifiers (some obtaining the remarkable
mark of 90.2% in accuracy results), as well as pointed out what the resources used in VLE
that have greater influence on student performance.
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Cousino, Andrew. "Using Bayesian learning to classify college algebra students by understanding in real-time." Diss., Kansas State University, 2013. http://hdl.handle.net/2097/15630.
Full textAbstract:
Doctor of Philosophy<br>Department of Mathematics<br>Andrew G. Bennett<br>The goal of this work is to provide instructors with detailed information about their classes at each assignment during the term. The information is both on an individual level and at the aggregate level. We used the large number of grades, which are available online these days, along with data-mining techniques to build our models. This enabled us to profile each student so that we might individualize our approach. From these profiles, we began to investigate what can be done in order to get students to do better, or at least be less frustrated. Regardless, the interactions with our undergraduates will improve as our knowledge about them increases.
We start with a categorization of Studio College Algebra students into groups, or clusters, at some point in time during the semester. In our case, we used the grouping just after the first exam, as described by Dr. Rachel Manspeaker in her PhD. dissertation. From this we built a naive Bayesian model which extends these student clusters from one point in the semester, to a classification at every assignment, attendance score, and exam in the course. A hidden Markov model was then constructed with the transition probabilities being derived from the Bayesian model. With this HMM, we were able to compute the most likely path that students take through the various categories over the semester. We observed that a majority of students settle into a group within the first two weeks of the term.
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Lachi, Ricardo Luís 1977. "Avaliação da qualidade de cursos superiores a distância." [s.n.], 2012. http://repositorio.unicamp.br/jspui/handle/REPOSIP/275685.
Full textAbstract:
Orientador: Heloísa Vieira da Rocha<br>Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Computação<br>Made available in DSpace on 2018-08-21T18:04:31Z (GMT). No. of bitstreams: 1
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Previous issue date: 2012<br>Resumo: O objetivo deste trabalho foi o de demonstrar que os dados armazenados nos Ambientes Virtuais de Aprendizagem (AVAs) podem ser usados como importantes fontes de informação para avaliar a qualidade dos cursos. Para isso foi construído um modelo de avaliação baseado na coleta de respostas para conjuntos de perguntas específicas relacionadas a aspectos que a literatura define como relevantes para a avaliação de um curso online. A validade e a confiabilidade desses conjuntos de perguntas elaborados são discutidas e, especificamente no caso das perguntas subjetivas, foi apresentada uma comprovação estatística de sua confiabilidade por meio do cálculo do valor do indicador de confiabilidade Alfa de Cronbach, a partir de uma amostra de respostas coletadas. A definição desses conjuntos de perguntas específicas permitiu identificar que dados registrados em um AVA devem ser recuperados e que efetivamente trazem informações importantes para a avaliação do curso online. Por fim, foi desenvolvido todo um suporte computacional, tanto para facilitar a aplicação do modelo de avaliação proposto, quanto para a recuperação de dados registrados em um AVA. Isso comprovou a possibilidade de automatizar e resgatar computacionalmente dados registrados em um AVA e que eles são uma fonte de informação relevante para a avaliação de um curso online. Os resultados obtidos neste trabalho abrangem: a definição de um modelo claro e bem detalhado de quais aspectos devem efetivamente ser considerados na avaliação da qualidade de um curso online; a construção de um sistema computacional denominado SAESD (Sistema de Apoio para a Avaliação de cursos Superiores a Distância) para dar suporte e facilitar a aplicação do modelo de avaliação definido; a construção e o projeto de ferramentas computacionais capazes de recuperar informações relevantes para a avaliação da qualidade de um curso online, abrangendo desde a análise de logs do Sistema Operacional até o padrão de acessos dos participantes do curso online<br>Abstract: The goal of this study was to demonstrate that the data stored in Virtual Learning Environments (VLEs) can be used as important sources of information to evaluate the quality of a distance course. This way, it was developed an evaluation model based on collection of answers to specific sets of questions related to aspects that literature defines as relevant to the evaluation of an online course. The validity and reliability of these sets of questions are discussed and developed. Particularly, it was calculated the Cronbach's Alpha coefficient for the set of subjective questions in order to prove statistically its validity. These questions helped to identify which data recorded in a VLE should be recovered and which effectively provide important information for the evaluation of an online course. Finally, we developed an entire computer support, both to facilitate the implementation of the proposed evaluation model, and for the recovery of data recorded in a VLE. This demonstrated the possibility to automate and rescue data recorded in a VLE, besides proving they are a source of relevant information to the evaluation of an online course. The main results reached in this work include: the definition of a clear and well detailed model of what aspects should effectively be considered in evaluating the quality of an online course; building a computer system called SAESD to support and help the implementation of the evaluation model defined; the construction and design of computational tools able to retrieve relevant information to online course assessment, which includes, the log analysis of the operating system and the access pattern of the online course participants<br>Doutorado<br>Ciência da Computação<br>Doutor em Ciência da Computação
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Bhaskaran, Subhashini Sailesh. "An Investigation into the Knowledge Discovery and Data Mining (KDDM) process to generate course taking pattern characterised by contextual factors of students in Higher Education Institution (HEI)." Thesis, Brunel University, 2017. http://bura.brunel.ac.uk/handle/2438/15880.
Full textAbstract:
The Knowledge Discovery and Data Mining (KDDM), a growing field of study argued to be very useful in discovering knowledge hidden in large datasets are slowly finding application in Higher Educational Institutions (HEIs). While literature shows that KDDM processes enable discovery of knowledge useful to improve performance of organisations, limitations surrounding them contradict this argument. While extending the usefulness of KDDM processes to support HEIs, challenges were encountered like the discovery of course taking patterns in educational datasets associated with contextual information. While literature argued that existing KDDM processes suffer from the limitations arising out of their inability to generate patterns associated with contextual information, this research tested this claim and developed an artefact that overcame the limitation. Design Science methodology was used to test and evaluate the KDDM artefact. The research used the CRISP-DM process model to test the educational dataset using attributes namely course taking pattern, course difficulty level, optimum CGPA and time-to-degree by applying clustering, association rule and classification techniques. The results showed that both clustering and association rules did not produce course taking patterns. Classification produced course taking patterns that were partially linked to CGPA and time-to-degree. But optimum CGPA and time-to-degree could not be linked with contextual information. Hence the CRISP-DM process was modified to include three new stages namely contextual data understanding, contextual data preparation and additional data preparation (merging) stage to see whether contextual dataset could be separately mined and associated with course taking pattern. The CRISP-DM model and the modified CRISP-DM model were tested as per the guidelines of Chapman et al. (2000). Process theory was used as basis for the modification of CRISP-DM process. Results showed that course taking pattern contextualised by course difficulty level pattern predicts optimum CGPA and time-to-degree. This research has contributed to knowledge by developing a new artefact (contextual factor mining in the CRISP-DM process) to predict optimum CGPA and optimum time-to-degree using course taking pattern and course difficulty level pattern. Contribution to theory was in extension of the application of a few theories to explain the development, testing and evaluation of the KDDM artefact. Enhancement of genetic algorithm (GA) to mine course difficulty level pattern along with course taking pattern is a contribution and a pseudocode to verify the presence of course difficulty level pattern. Contribution to practise was by demonstrating the usefulness of the modified CRISP-DM process for prediction and simulation of the course taking pattern to predict the optimum CGPA and time-to-degree thereby demonstrating that the artefact can be deployed in practise.
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Папченко, О. І., та А. В. Силюк. "Інтелектуальний аналіз данних використання електронних навчальних матеріалів". Thesis, Сумський державний університет, 2014. http://essuir.sumdu.edu.ua/handle/123456789/38750.
Full textAbstract:
Однією з передумов забезпечення високої якості освіти є здійснення своєчасного та точного її моніторингу. Особливі можливості у цьому напряму з’являються із поглибленням використання електронних засобів навчання. Цінними як поточна статистики, так і аналітика на основі зібраних даних. Нова міждисциплінарна область досліджень, яка займається розробкою методів видобутку даних в освітньому контексті дістала назви Education Data Mining (EDM).
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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.
Full textAbstract:
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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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 Ricarte<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação<br>Made available in DSpace on 2018-08-17T16:59:53Z (GMT). No. of bitstreams: 1
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Previous issue date: 2010<br>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<br>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<br>Mestrado<br>Engenharia de Computação<br>Mestre em Engenharia Elétrica
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Durr, Angel Krystina. "A Text Analysis of Data Science Career Opportunities and U.S. iSchool Curriculum." Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1404565/.
Full textAbstract:
Data science employment opportunities of varied complexity and environment are in growing demand across the globe. Data science as a discipline potentially offers a wealth of jobs to prospective employees, while traditional information science-based roles continue to decrease as budgets get cut across the U.S. Since data is related closely to information historically, this research will explore the education of U.S. iSchool professionals and compare it to traditional data science roles being advertised within the job market. Through a combination of latent semantic analysis of over 1600 job postings and iSchool course documentation, it is our aim to explore the intersection of library and information science and data science. Hopefully these research findings will guide future directions for library and information science professionals into data science driven roles, while also examining and highlighting the data science techniques currently driven by the education of iSchool professionals. In addition, it is our aim to understand how data science could benefit from a mutually symbiotic relationship with the field of information science as statistically data scientists spend far too much time working on data preparation and not nearly enough time conducting scientific inquiry. The results of this examination will potentially guide future directions of iSchool students and professionals towards more cooperative data science roles and guide future research into the intersection between iSchools and data science and possibilities for partnership.
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Kadikinas, Vaidas. "Nuotolinių mokymo sistemų vartotojų aktyvumo analizės ir valdymo metodai." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2011. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2011~D_20110902_092107-25937.
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Nuotolinio mokymo technologijos įgauna pagreitį visame pasaulyje. Technologijos trūkumai ir silpnosios vietos tampa matomesnės, nei bet kada anksčiau. Siekiant patobulinti mokymo procesą ir sumažinti kurso nebaigiančių studentų dalį, vis svarbesnėmis tampa studentų aktyvumo skatinimo ir mokymosi paramos sistemos. Šio darbo tikslai – apžvelgti esamus studentų aktyvumo analizės ir valdymo metodus naudojamus nuotoliniame mokyme. Taip pat, pagerinti esamus arba pasiūlyti gaires naujų kūrimui. Literatūros analizė parodė duomenų gavybos metodų potencialą mokymo valdymo sistemose. Pasiūlytas studentų aktyvumo skatinimo modelis pagrįstas šiais metodais. Naujasis modelis paremtas automatiškai besireguliuojančia sistema, kuri analizuotų besimokančiųjų mokymosi įpročius. Analizės duomenys būtų naudojami sukurti ir siųsti personalizuotus priminimus ir pranešimus apie mokymosi sistemos įvykius. Ta pati sistema galėtų informuoti kurso vedėją apie neįprastus studentų veiklos pokyčius, padidėjusią nesėkmingo kurso baigimo tikimybę. Atliktas tyrimas patvirtinantis modelio kūrimui pasirinktas prielaidas ir jo reikalingumą.<br>Distance education technology is gaining momentum all around the world. Weaknesses and limitations in the technology are exposed more than ever. Activities, such as, stimulation of students’ activity and development of support systems are becoming important in attempts to lower student dropout rates and improve quality of education. This thesis has goals to review existing methodology of analyzing and encouraging student activity in higher education courses based on online technology. As well as to enhance current methods and set guidelines for development of the new ones. Literature analysis has highlighted the astonishing potential of data mining methods in Learning Management Systems. Based on these methods a new model of students’ activity stimulation has been suggested. The new model is based on automatic self tuning system which would analyze the behavior patterns of course users. The results of this analysis would be used to compose and send notifications of course events which are relevant for and desired by the individual user while respecting their learning patterns. Same system could inform instructor of any abnormalities in student learning behavior, unfavorable odds to successfully complete the course or even drop out. A study has been made, which confirmed the initial assumptions and potential usefulness of the proposed model.
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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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Previous issue date: 2017<br>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.<br>Disserta??o (Mestrado Profissional) ? Programa de P?s-Gradua??o em Educa??o, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2017.<br>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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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.
Full textAbstract:
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.<br>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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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.<br>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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Portal, Cleber. "Estratégias para minimizar a evasão e potencializar a permanência em EAD a partir de sistema que utiliza mineração de dados educacionais e learning analytics." Universidade do Vale do Rio dos Sinos, 2016. http://www.repositorio.jesuita.org.br/handle/UNISINOS/5409.
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Previous issue date: 2016-02-29<br>CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>A presente dissertação de mestrado, desenvolvida no contexto do Grupo de Pesquisa Educação Digital GPe-dU UNISINOS/CNPq, vinculada à Linha de Pesquisa Educação, Desenvolvimento e Tecnologias do Programa de Pós-Graduação em Educação, investigou como são elaboradas as estratégias utilizadas pelos diferentes atores envolvidos no contexto da Educação a Distância (EaD), para minimizar a evasão e potencializar a permanência dos estudantes nessa modalidade, tendo como subsídios um conjunto de informações e indicadores gerados por um sistema, o GVWise, que faz uso de mineração de dados e Learning Analytics. A pesquisa é exploratória, de natureza qualitativa. Fundamenta-se na Teoria Ator-Rede (LATOUR, 2012) e faz uso da metodologia da cartografia das controvérsias. (LATOUR, 2012). Envolve, ainda, uma análise documental nos registros do sistema – ator não humano - ANH e entrevistas semi-estruturadas com os atores humanos - AH, em diferentes instâncias: coordenadores vinculados à gestão da EaD e aos cursos de graduação, professores e tutores dos respectivos cursos. O objetivo principal consistiu em compreender de que forma as informações fornecidas pelo sistema estão sendo compreendidas pelos diferentes atores, bem como perceber se as articulações dessas informações estão sendo eficientes no sentido de contribuir para a criação de estratégias que possam minimizar a evasão e potencializar a permanência dos estudantes nessa modalidade. Os principais resultados obtidos indicam, no que se refere ao ANH – sistema, que esse fornece um conjunto de informações, as quais, quando articuladas, evidenciam que a maior evasão ocorre antes da avaliação, ou seja, da realização dos Graus B e C. No que se refere aos AH da gestão em EaD e dos cursos, bem como os professores e tutores, os resultados evidenciam compreensões distintas e singulares sobre a evasão e a permanência, bem como sobre a forma de articular as informações fornecidas pelo sistema, na criação de estratégias para minimizar a evasão e potencializar a permanência do estudante na EaD, embora esses AH integrem a mesma equipe mas com funções diferentes. Esse resultado se manifesta como controvérsia, as quais são acessadas por meio da abertura das caixas pretas, no momento em que esses atores são instigados a refletir sobre as estratégias utilizadas. As relações dos AH se apresentam distanciadas uns dos outros, principalmente na disciplina de maior evasão. Produzem uma comunicação pouco eficiente ou ineficiente, gerando obstáculos no campo metodológico da disciplina, dificultando possíveis mudanças positivas e restringindo o desenvolvimento dos processos pedagógicos. A estratégia de contatar o AH estudante, se usado de forma adequada, pode colaborar e abrir possibilidades para a melhor compreensão do fenômeno da evasão e ampliação da visão estratégica institucional. Como principal contribuição da dissertação apresenta-se o diagrama das mediações, ou seja, o desenho da distribuição da mobilidade, os movimentos na construção, na busca por uma estratégia que possa minimizar a evasão e potencializar a permanência do estudante em EaD.<br>The currrent dissertation of Master’s Degree, developed in the context of Grupo de Pesquisa Educação Digital GPe-dU UNISINOS/CNPq (Research Group on Digital Education GPe-dU UNISINOS/CNPq) bound to Linha de Pesquisa Educação, Desenvolvimento e Tecnologias do Programa de Pós-Graduação em Educação (Line of Research Education, Development and Technologies of the Program of Postgraduate in Education) investigated how the strategies used were made by different actors, enfolded in the context of Distance Education (EaD) to minimize evasion and potentiate the permanence of students in this modality, having as subsidy a set of information and indexes generated by a system, the oGVWise, which makes use of data mining and Learning Analytics. The research is exploratory, of qualitative nature, having as a basis the Teoria Ator-Rede (the Actor-Net Theory) (LATOUR, 2012) and makes use of the methodology of the cartography of controversies.(LATOUR, 2012). It still enfolds a documental analysis in the registers of the system – non human actor – NHA, and semi-structured interviews with the human actors – HA, in different instances: Coordinators bound to the managements of EaD and to the courses of graduation, professors and tutors of the related courses. The main goal consisted in understanding how information given by the system are being understood by different actors, as well in noticing if the articulations of these information are being efficient in the sense of contributing to the creation of strategies that might minimize the evasion and potentiate the permanence of students in this modality. The main results obtained indicate, in what relates to NHA – system, that this one gives a set of information, which when articulated, give evidence that the major evasion happens before the evaluations, it means, of the accomplishment of Degrees B and C. Relating to HA of management in EaD and of the courses, as well as the professors and tutors, the results give evidence to different and singular comprehensions on evasion and permanence, as well as on the way of articulating the information given by the system, on the making of strategies to minimize evasion and potentiate the permanence of the student in EaD, however these HA integrate the same team. This result is manifested as controversy, which are accessed by means of opening the black boxes, in the moment that these actors are instigated to reflect on the used strategies. There is is a detachment among HA, mainly in the discipline of major evasion, that produce little efficient communication or inefficient generating obstacles in the methodological field of the subject, making difficult possible positive changes and restraining the development of pedagogical processes. The strategy of contacting a HA student, if a proper way is used, might collaborate and open possibilities to the comprehension of the phenomenon of evasion and enlargement of the institutional strategic vision. As main contribution of the dissertation the diagram of mediations is presented, it means, the design of the distribution of mobility, the movements of construction, the search for a strategy that might minimize evasion and potentiate the permanence of the student in EaD.
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Raya, Katia. "Réseaux sociaux et communautés en ligne dans le paysage universitaire libanais (2018-20)." Thesis, Sorbonne université, 2020. http://www.theses.fr/2020SORUL107.
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Pour faire face aux défis et la forte concurrence d’aujourd’hui, l’enseignement supérieur s’est tourné vers la stratégie de communication sur les réseaux sociaux en vue de fidéliser leur public, améliorer leur réputation et renforcer les relations avec la communauté à travers des niveaux d’engagement plus élevés. L’enjeu de cette thèse est d’appréhender cet engagement de la communauté universitaire sur les réseaux sociaux. La recherche s’intéressera dans un premier temps à l’étude des concepts clés reliés à son objectif, à savoir, l’engagement, la communauté et les médias sociaux. Des études empiriques quantitatives et qualitatives seront mises en œuvre par les méthodes du questionnaire et du data mining. Les résultats statistiques, l’analyse du contenu et la détection des communautés ont permis de comprendre le rôle que joue la stratégie contenu et l’impact du profil communautaire sur les niveaux d’engagement sur les publications créées par les principaux établissements d’enseignement supérieur au Liban<br>To face the challenges and strong competition of today, higher education has turned to the strategy of communication on social networks in order to retain their audience, improve their reputation and strengthen relations with the community through higher levels of engagement. The aim of this thesis is to understand this engagement of the university community on social networks. The research is initially interested in the study of key concepts related to its objective, namely, engagement, community and social media. Quantitative and qualitative empirical studies will be implemented using questionnaire and data mining methods. Statistical results, content analysis and community detection helped to understand the role of content strategy and the impact of community profile on engagement levels on posts created by major higher education institutions in Lebanon
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Beese, Elizabeth Brott. "A vision of the curriculum as student self-creation: A philosophy and a system to manage, record, and guide the process." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1345336992.
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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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SANTOS, Danilo Abreu. "Recomendação pedagógica para melhoria da aprendizagem em redações." Universidade Federal de Campina Grande, 2015. http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/550.
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Previous issue date: 2015-08-24<br>A modalidade de educação online tem crescido significativamente nas últimas décadas
em todo o mundo, transformando-se em uma opção viável tanto àqueles que não dispõem de tempo para trabalhar a sua formação acadêmica na forma presencial quanto àqueles que desejam complementá-la. Há também os que buscam ingressar no ensino superior por meio do Exame Nacional do Ensino Médio (ENEM) e utilizam esta modalidade de ensino para complementar os estudos, objetivando sanar lacunas deixadas pela formação escolar. O ENEM é composto por questões objetivas (subdivididas em 4 grandes áreas: Linguagens e Códigos; Matemática; Ciências Humanas; e Ciências Naturais) e a questão subjetiva (redação). Segundo dados do Ministério da Educação (MEC), mais de 50% dos candidatos que fizeram a prova do ENEM em 2014 obtiveram desempenho abaixo de 500 pontos na redação. Esta pesquisa utilizará recomendações pedagógicas baseadas no gênero textual utilizado pelo ENEM, visando prover uma melhoria na escrita da redação dissertativa. Para tanto, foi utilizado, como ferramenta experimental, o ambiente online de aprendizagem MeuTutor. O ambiente possui um módulo de escrita de redação, no qual é utilizada para correção dos textos elaborados pelos alunos, a metodologia de avaliação por pares, cujo pesquisas mostram que os resultados avaliativos são significativos e bastante similares aos obtidos por professores especialistas. Entretanto, apenas apresentar a pontuação da redação por si só, não garante a melhora da produção textual do aluno avaliado. Desta forma, visando um ganho em performance na produção da redação, foi adicionado ao MeuTutor um módulo de recomendação pedagógica baseado em 19 perfis resultados do uso de algoritmos de mineração de dados (DBScan e Kmeans) nos microdados do ENEM 2012 disponibilizado pelo MEC. Estes perfis foram agrupados em 6 blocos que possuíam um conjunto de tarefas nas áreas de escrita, gramática e coerências e concordância textual. A validação destas recomendações foi feita em um experimento de 3 ciclos, onde em cada ciclo o aluno: escreve a redação; avalia os seus pares; realiza a recomendação pedagógica que foi recebida. A partir da análise estatística destes dados,
foi possível constatar que o modelo estratégico de recomendação utilizado nesta pesquisa, possibilitou um ganho mensurável na qualidade da produção textual.<br>Online education has grown significantly in recent years throughout the world, becoming
a viable option for those who don’t have the time to pursuit traditional technical training
or academic degree. In Brazil, people seek to enter higher education through the National
Secondary Education Examination (ENEM) and use online education to complement
their studies, aiming to remedy gaps in their school formation. The ENEM consists of
objective questions (divided into 4 main areas: languages and codes; Mathematics; Social
Sciences, and Natural Sciences), and the subjective questions (the essay). According to
the Brazilian Department of Education (MEC), more than 50% of the candidates who
took the test (ENEM) in 2014, obtained performance below 500 points (out of a 1000
maximum points) for their essays. This research uses educational recommendations based
on the five official correction criteria for the ENEM essays, to improve writing. Thus, this
research used an experimental tool in an online learning environment called MeuTutor.
The mentioned learning environment has an essay writing/correction module. The
correction module uses peer evaluation techniques, for which researches show that the
results are, significantly, similar to those obtained by specialists’ correction. However, to
simply display the scores for the criteria does not guarantee an improvement in students’
writing. Thus, to promote that, an educational recommendation module was added to
MeuTutor. It is based on 19 profiles obtained mining data from the 2012 ENEM. It uses
the algorithms DBSCAN and K-Means, and grouped the profiles into six blocks, to which
a set of tasks were associated to the areas of writing, grammar and coherence, and textual
agreement. The validation of these recommendations was made in an experiment with
three cycles, where students should: (1) write the essay; (2) evaluate their peers; (3)
perform the pedagogical recommendations received. From the analysis of these data, it
was found that the strategic model of recommendation used in this study, enabled a
measurable gain in quality of textual production.
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Peoples, Bruce E. "Méthodologie d'analyse du centre de gravité de normes internationales publiées : une démarche innovante de recommandation." Thesis, Paris 8, 2016. http://www.theses.fr/2016PA080023.
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.../<br>“Standards make a positive contribution to the world we live in. They facilitate trade, spreadknowledge, disseminate innovative advances in technology, and share good management andconformity assessment practices”7. There are a multitude of standard and standard consortiaorganizations producing market relevant standards, specifications, and technical reports in thedomain of Information Communication Technology (ICT). With the number of ICT relatedstandards and specifications numbering in the thousands, it is not readily apparent to users howthese standards inter-relate to form the basis of technical interoperability. There is a need todevelop and document a process to identify how standards inter-relate to form a basis ofinteroperability in multiple contexts; at a general horizontal technology level that covers alldomains, and within specific vertical technology domains and sub-domains. By analyzing whichstandards inter-relate through normative referencing, key standards can be identified as technicalcenters of gravity, allowing identification of specific standards that are required for thesuccessful implementation of standards that normatively reference them, and form a basis forinteroperability across horizontal and vertical technology domains. This Thesis focuses on defining a methodology to analyze ICT standards to identifynormatively referenced standards that form technical centers of gravity utilizing Data Mining(DM) and Social Network Analysis (SNA) graph technologies as a basis of analysis. As a proofof concept, the methodology focuses on the published International Standards (IS) published bythe International Organization of Standards/International Electrotechnical Committee; JointTechnical Committee 1, Sub-committee 36 Learning Education, and Training (ISO/IEC JTC1 SC36). The process is designed to be scalable for larger document sets within ISO/IEC JTC1 that covers all JTC1 Sub-Committees, and possibly other Standard Development Organizations(SDOs).Chapter 1 provides a review of literature of previous standard analysis projects and analysisof components used in this Thesis, such as data mining and graph theory. Identification of adataset for testing the developed methodology containing published International Standardsneeded for analysis and form specific technology domains and sub-domains is the focus ofChapter 2. Chapter 3 describes the specific methodology developed to analyze publishedInternational Standards documents, and to create and analyze the graphs to identify technicalcenters of gravity. Chapter 4 presents analysis of data which identifies technical center of gravitystandards for ICT learning, education, and training standards produced in ISO/IEC JTC1 SC 36.Conclusions of the analysis are contained in Chapter 5. Recommendations for further researchusing the output of the developed methodology are contained in Chapter 6
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Meza, Fernandez Sandra. "Enseigner et apprendre en ligne : vers un modèle de la navigation sur des sites Web de formation universitaire." Phd thesis, Université de Strasbourg, 2013. http://tel.archives-ouvertes.fr/tel-00974481.
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Cette thèse propose de cartographier le parcours de navigation des usagers des EIAH pour le visualiser, visualiser pour interpréter et interpréter pour anticiper. Les profils d'apprentissage ont une influence sur les modes de navigation dans un environnement d'apprentissage en ligne. S'appuyant sur une méthodologie capable de modéliser le parcours de navigation d'un usager et d'anticiper son prochain clic sur une plateforme, notre étude cherche à élargir le champ des connaissances de l'efficacité/performance des styles d'apprentissage. La méthodologie utilisée repose sur l'analyse des traces d'utilisation élaborée à partir de 63 archives logs Web, incluant 4637 lignes de registre et 13 206 possibilités de choix de module. Le travail de recherche s'inscrit dans le cadre d'approches associant sémiologie, des sciences de l'information, psychologie cognitive et sciences de l'éducation. Trois observations ont été menées, générant des informations sur le profil de l'usager, la représentation des parcours et l'impact du style d'apprentissage dans le choix des fonctionnalités de travail offertes disponibles sur la plateforme. Les principaux résultats sont de deux types : d'une part, l'élaboration d'un outil convertissant les traces des fichiers log en parcours de navigation, et d'autre part, la confirmation d'un lien entre style d'apprentissage et mode de navigation. Ce deuxième résultat permet d'élaborer une méthode d'anticipation du nouveau choix de module sur une plateforme numérique de travail. Les applications pratiques visant à rendre exploitables ces traces dans les formations universitaires sont l'élaboration de bilans de qualité (ressources préférées, fonctionnalités moins utilisées) et l'identification des besoins de médiation pédagogique pour la compréhension de la tâche ou du processus (identifié par exemple dans l'insistance sur le module de consignes, le temps investi par un groupe ou par des trajets répétés). Cette thèse s'adresse principalement aux responsables pédagogiques universitaires décideurs de l'intégration des TIC, et par extension, aux étudiants universitaires et aux concepteurs d'outils d'apprentissage.
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Huang, Zhi-Jie, and 黃芷婕. "Data Mining for the Teaching Development in Shadow Education." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/vh4ref.
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碩士<br>淡江大學<br>管理科學學系企業經營碩士在職專班<br>105<br>The last two decades have seen growing importance placed on research in shadow education. The field of the shadow education in Taiwan has undergone many fluctuation and shifts over the years. The high cost of living and the necessity for both parents to work has given rise to notion that children are an unwelcome border. Shadow education in Taiwan is having trouble to get students and that is not the only problem of the situation. Shadow education is getting more and more in current market. However, research which has empirically documented the link between data mining and shadow education is scant. Therefore, the aim of this article attempts to explore how parents feel about “E” institute of education and their preference of subject course are related. This research involved a survey; the sample focuses on parents whose children study English at “E” Learning Institutes across Taiwan. A total of 1,860 questionnaires were distributed and 811 effective questionnaires. The quantitative analysis of the questionnaires was conducted through clustering analysis and association rules of data mining. In order to indicate the customer relationship and preferences of parents between related. To conclude, this study may be of importance in explaining development of CRM and new curriculum create, as well as in providing, manager of the institute with a better understanding of how parents feel about the institute relate to their strategy use.
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Lin, Yi-chun, and 林怡均. "Development of Higher Education Enrollment Decision Support System Using Data Mining Technology." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/zbqv66.
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碩士<br>國立中央大學<br>資訊管理學系<br>103<br>In higher education, the selection of future students are critical to the success of education. Every universities establish their own admission criteria. Using the relevant admission criteria and equally examine applicants’ qualification, hoping to enroll the applicant which has excellent performance. Therefore, this research aims to establish a model for determine the suitable admission criteria for the features of the department. In order to understand the influence between the potential capability of student and specific subject, and further comprehend whether capability of student correspond to the features of the department or not.This paper apply data mining techniques including classification, attribute selection and association to discover the factors of affecting study performance and establish the model. The Decision Support Systems is built based on this model. It support admission committee to enroll students and moderfy the admission criteria.
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Lu, Wen-Jen, and 陸文楨. "Design and Implementation of Media Synchronization and Data Mining Mechanisms for Networked Education." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/10712055154628931242.
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碩士<br>國立臺灣大學<br>電機工程學系研究所<br>86<br>Recently networked education has emerged as an important Internet application.
It not only breaks the limitation of learning space but also keeps the flexib
ility of teaching time. Also, by multimedia mechanisms and technologies of int
eractivity, students can enjoy a multimedia learning environment according to
their need. However, the current network education system just provides basic
functionality and it may n1ot meet our need for full interactivity. In this th
esis, a network education system is developed to improve the original educatio
nal VOD system, and serveral auxiliary components are implemented. Our auxilia
ry components form two packages："Media-Sync" and "EduMiner". By these two pac
kages, we can automate the procedures for making teaching video, conduct data
mining on student behavior, and improve the teaching quality that a network ed
ucation system can provide.
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Krause, Gladys Helena. "An exploratory study of teacher retention using data mining." Thesis, 2014. http://hdl.handle.net/2152/24742.
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The object of this investigation is to report a study of mathematics teacher retention in the Texas Education System by generating a model that allows the identification of crucial factors that are associated with teacher retention in their profession. This study answers the research question: given a new mathematics teacher with little or no service in the Texas Education System, how long might one expect her to remain in the system? The basic categories, used in this study to describe teacher retention are: long term (10 and more years of service), medium term (5 to 9 years of service), and short term (1 to 4 years of service). The research question is addressed by generating a model through data mining techniques and using teacher data and variables from the Texas Public Education Information Management System (PEIMS) that allows a descriptive identification of those factors that are crucial in teacher retention.
Research on mathematics teacher turnover in Texas has not yet focused on teacher characteristics. The literature review presented in this investigation shows that teacher characteristics are important in studying factors that may influence teachers' decisions to stay or to leave the system. This study presents the field of education, and the state of Texas, with an opportunity to isolate those crucial factors that keep mathematics teachers from leaving the teaching profession, which has the potential to inform policy makers and other educators when making decisions that could have an impact on teacher retention. Also, the methodology applied, data mining, allows this study to take full advantage of a collection of valuable resources provided by the Texas Education Agency (TEA) through the Public Education Information Management System (PEIMS), which has not yet been used to study the phenomenon of teacher retention.<br>text
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Lenchner, Erez. "Mining Transactional Student-Level Data to Predict Community College Student Outcomes." Thesis, 2017. https://doi.org/10.7916/D8GH9PM9.
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A longitudinal analysis of transactional data for an entire college cohort was mined from administrative student records systems to identify individual student behaviors and establish correlations between individual students’ behaviors and academic outcomes. Conducted at one large urban community college, this study determined curricular peer association behavior between individual students, and also evaluated late registration and course schedule change behaviors. Findings demonstrated a strong correlation between these three behavioral patterns and a lasting influence on academic outcomes, such as: semestrial GPA and cumulative GPA, credit accumulation, persistence and graduation rates. Finding also indicated a correlation among the three behaviors themselves. Furthermore, conducting a longitudinal analysis of individual students made it possible to identify the temporal tipping-points which differentiated at-risk behavior from otherwise benign behavior. The intrinsic factors associated with individual students’ behaviors were followed over a period of thirteen consecutive semesters. Mining Transactional Student-Level Data at the scale achieved in this study, when compared to traditional methods of data collection, provided the precision needed to determine the actual proximity among specific peers, and the identification of registration behavior patterns. The extraction of transactional data from the records of each student in an entire cohort resulted in a method of inquiry immune to the negative effects of student’s non-response or selection bias. Complimenting previous research, this study provides a detailed descriptive analysis of those behaviors not only at the semestrial level, but also cumulatively across consecutive semesters.
This study demonstrates that curricular peer association can be measured directly from common, ubiquitous, transactional records. The rates of Peer Association among individual students was very dynamic: While the majority of students had some peer associations while enrolled, in the aggregate two thirds of students had no peer association (were soloists) at some point in time, while more than a quarter of all students were soloists for at least half of their entire enrollment period.
Soloists differed from students with peer associations. They were likely to be older, international students, African Americans, transfer students, or those entering fully prepared for college level coursework (no remedial coursework). Peer association was positively correlated, both in the semester in which it occurred and cumulatively, with: GPA, credits earned, and retention or graduation rates. These correlations to academic outcomes varied with the number of peer associations established, and the intensity of peer encounters.
The study revealed that nearly a quarter of all students practiced late registration at least once; and more than 10 percent have registered late multiple times during their studies. Nearly three quarters of students made modifications to their course schedule at least once after the semester began. Overall, two fifths of students changed their initial schedule every semester. These behaviors were unrecorded in previous studies that were limited in the evaluation of longitudinal behaviors, used subsets of students and were subject to non-response bias. Late registration and student schedule changes was correlated with lower semestrial and cumulative academic outcomes. Late registration behavior subsequently increased the likelihood of a student being a soloist. When compared to previous studies, the analysis conducted here not only accounted for academic, demographic and financial variables at baseline, but went on to perform updates at key points in time each semester to reflect changes over time. The exhaustive revisiting of the covariates each semester provided enhanced control to the ‘order of time’ influence. All covariates were re-measured each semester allowing to better evaluate the correlation of student behavioral indicators for a given semester, and cumulatively. This enhanced the study’s ability to account for common unobserved variables inherent to academic, demographic and financial attributes that might influence student outcomes correlated with peer association, late registration and schedule changes.
This study contributes to the literature by showing that peer association can be evaluated in the setting of an open admission commuter institution, and that peer association has consistent and positive correlation with academic outcomes. It provides new insights regarding the magnitude of late registration and schedule changes, as well as their negative immediate and longitudinal correlation with student outcomes. Further implications to community colleges’ faculty, administrators, researchers and policymakers, as well as future directions for research employing transactional level data are discussed.
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Yeh, Chien, and 葉倩. "A Study on Applying Data Mining Techniques to Discover Potential Customers for a Computer Education Center." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/71587188641067158204.
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碩士<br>世新大學<br>資訊傳播學研究所(含碩專班)<br>99<br>This thesis through the data mining techniques explores target customers' base and analyzes characteristics of the customers' consumption for computer education center. This thesis expects to provide students' need of curriculum in computer education centers, and the curriculum of various categories in the attribute of students. Thus, we hope to reduce marketing cost, raise the chance of successful marketing, and benefit computer education centers.
First, this thesis is as a result of the curriculum category of computer education center through the connection rule to analyze students' learning preferences and their personal data. This thesis find out high connection is the most notable which is between curriculum category and the net diagram, the connection of webpage designing, vision design, multi-media curriculum category, another is program, database, office application curriculum category. Secondly, by hierarchical cluster analysis and non-hierarchical cluster analysis which are according to the student characteristic establishments, this thesis set compartment estimate model to understand the main preferences and attributes of the main cluster participants. Having information group, beginner group, and professional group. Lastly, this thesis makes use of the decision tree of the classification analysis to find out the influence on students. The result may include more important characteristics, such as webpage designing, 3D animation, marital status, vision design, office application, and age. They are the key attributes of how the factor of the future influences students' set .
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ZHU, SHI-LI, and 朱世立. "Employee education and training from the perspective of data mining: taking a natural gas company for example." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/z6k5np.
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碩士<br>正修科技大學<br>經營管理研究所<br>101<br>The natural gas industry has distinguishing characteristics in terms of its political, public, and social irreplacebility. Owing to its influence on a nation’s energy policy, the rights of users, and the people’s livelihoods, it is dubbed as a form of private utilities, also distinguished by the monopoly and the absence of competitors.
With the rise of consumer awareness, customers now have increasingly high expectations of the natural gas industry. To cater to a market-oriented trend, it is imperative that natural gas companies (AKA gas companies) be more dedicated to upgrading their service and bettering their training in order to improve customer satisfaction and staff quality.
Even with a comprehensive training program in place, the company’s current status leaves something to be desired. For example, instructors hired for the training program do not have a complete understanding of the program itself and its requirements, which leads to the poor quality of courses and handouts that fail to meet the employees’ need. The trainees, as a result, are left to swim or sink on their own. As for execution, with the lack of assessment and evaluation during training, the attitude and learning of trainees are anything but satisfactory. All the above shows the challenge facing the training program of an industry that has such a huge impact on public safety.
To address the above-mentioned issues, this reaserach aims to examine the data in gas meter installation, user security check, decadal renewal, leakage check as well as the statistics collected from customer service, proposing further methods in the planning, execution and assessment of training program. The research concludes by making proposals in training course designing, instructor recruiting, internal assessment, comprehensive course, and motivating trainees. It aims to help the company maintain its high quality of customer service as well as its competence by passing down time-tested skills and experiences.
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林文義. "The Application of Data Mining Techniques for Analyzing Course Design and Learning Effectiveness in Advanced Vocational Education." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/88028293002364367398.
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碩士<br>樹德科技大學<br>資訊管理研究所<br>92<br>With the increasing demands on health and medical care from the public, to improve the quality of health care education is an important issue. In Taiwan, the system of advanced vocational education provides the major part of human resources on the market of nurse-practitioners. It is more than important to make sure that the education and training in these schools are of high quality. In this thesis, we study an advanced vocational college of health care. We try to explore the relations among courses and students’ background using the techniques and tools of data-mining. Techniques for finding association rules and sequential patterns are applied to explore the performance of students attending to different courses in each semester. The techniques for clustering are employed for finding the characteristics of students who have specific performance on specific courses. We expect the analytic results of this research can help similar colleges on course design and student counseling.
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TU, HUI-WEN, and 杜惠文. "Using Data Mining Techniques in community extention education course planning—— An Example of the LS Community College in Changhua." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/r3axcz.
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碩士<br>建國科技大學<br>服務與科技管理研究所<br>104<br>In a rapid growing of the knowledge blooming era, people pay more attention to the lifetime learning in current society. Many colleges and universitys have established affiliated extension education centers, public and private careering training centers, community colleges and active aging colleges. According to government policies, these policies promote many public and private community colleges to open courses of local communities in various counties. Community college becomes a place that provides an opportunity for people in keeping learning and enriching their knowledge. This is different from getting a formal degree. Everyone who wants to learn new knowledge can go to the community college and surround by learning environment. It can also enhance national competitiveness and people educational quality.
Data are collected from people who enrolled the LS community college in this study. The SPSS 18.0 and IBM SPSS Clementine are adopted in data analysis. First, data are grouped by the two-stage cluster method. Then, the Bayesian network is used in rule generationing for different clusters. Results show joint probabilities of course category registration and course graduation dependent on students’ background for different clusters. The purpose of this research is to predict which course is suitable for registration and whether enrollment students can graduate successfully based on their background. This research can also provide valuable suggestions for course designs and filter potential graduation conditions to enhance community college school performance and encourage students’ learning motivations.
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Ko, Mei-Chu, and 柯美珠. "Application of Data Mining Technology in the Education of Public Servants - A Case Study of the Demand for Courses." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/x7bbdc.
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碩士<br>南臺科技大學<br>資訊管理系<br>104<br>Data mining technology is widely used in various fields to find out the valuable information from a large number of database and knowledge. Furthermore, people pay more and more attention to the management and development of competency of human resources in recent years, they have been discussing the organization of competency model or the evaluation of effectiveness of the working training in most of the literature on the subject. But there are few discussion further about relationship and trend of competency of one member or one team with the actual database of competency. Therefore, this research-“analysis and discussion by Association Rules” is mainly to study the overall demand of classes in the competency development by data mining technology with the database of “some domestic civil servants education”, demonstrate the relation that one member or one team builds the demand of competency in English classes and non-English classes, and establish forecasting the demand of education and training. Use association rules to analyze the potential variables between the result of this study , it follows that finding that English classes are associated with the ability and the job of the civil servants.