Dissertations / Theses: 'Learning Analytics (LA)'

1

Farrell, Tracie. "Affordances of learning analytics for mediating learning." Thesis, Open University, 2018. http://oro.open.ac.uk/57621/.

Full text

Abstract:

Learning analytics acceptance and adoption is a socio-technological endeavour. Understanding how learning analytics impact practice is an important part of demonstrating their value. In the study presented in this thesis, "Mediated Learning" provides a framework through which to describe how learning analytics can impact psychological, social and material aspects of learning, from the perspective of educators and learners. It also offers a structure through which to make recommendations for improving the mediatory effects of learning analytics. A qualitative research design, based on "Grounded Theory" was implemented and 10 educators from 3 European universities were recruited through convenience and purposive sampling for exploratory interviews. A subsequent case study of the Open University provided critical perspectives from both educators (n=18) and learners (n=22) about the institutional, departmental, domain-related and epistemological factors that broadly influence perceptions of learning analytics. The study applied "Affordance Theory" to identify what participants were most easily able to recognise as beneficial to their own practice. Participant contributions were open-coded to uncover emerging themes and then organised into thematic categories and subcategories. Respondent validation, as well as triangulation of data between the exploratory interviews and focus groups support the validity of the study. Findings suggested that domain-related epistemological assumptions and previous experience influence how and why an individual could make use of learning analytics insights. Gaining stakeholder acceptance involves targeting the right training and opportunities at the appropriate disciplines. Findings also indicate that learning analytics has the strongest mediatory effect for learners when the technology is capable of exposing them to other learners' strategies, or when it assists them personally, and continually in goal orientation adoption. The implications of the study are important for higher education institutions looking to implement large-scale learning analytics initiatives, in particular, those with a diverse student body.

APA, Harvard, Vancouver, ISO, and other styles

2

Komolafe, Tomilayo A. "Data Analytics for Statistical Learning." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/87468.

Full text

Abstract:

The prevalence of big data has rapidly changed the usage and mechanisms of data analytics within organizations. Big data is a widely-used term without a clear definition. The difference between big data and traditional data can be characterized by four Vs: velocity (speed at which data is generated), volume (amount of data generated), variety (the data can take on different forms), and veracity (the data may be of poor/unknown quality). As many industries begin to recognize the value of big data, organizations try to capture it through means such as: side-channel data in a manufacturing operation, unstructured text-data reported by healthcare personnel, various demographic information of households from census surveys, and the range of communication data that define communities and social networks. Big data analytics generally follows this framework: first, a digitized process generates a stream of data, this raw data stream is pre-processed to convert the data into a usable format, the pre-processed data is analyzed using statistical tools. In this stage, called statistical learning of the data, analysts have two main objectives (1) develop a statistical model that captures the behavior of the process from a sample of the data (2) identify anomalies in the process. However, several open challenges still exist in this framework for big data analytics. Recently, data types such as free-text data are also being captured. Although many established processing techniques exist for other data types, free-text data comes from a wide range of individuals and is subject to syntax, grammar, language, and colloquialisms that require substantially different processing approaches. Once the data is processed, open challenges still exist in the statistical learning step of understanding the data. Statistical learning aims to satisfy two objectives, (1) develop a model that highlights general patterns in the data (2) create a signaling mechanism to identify if outliers are present in the data. Statistical modeling is widely utilized as researchers have created a variety of statistical models to explain everyday phenomena such as predicting energy usage behavior, traffic patterns, and stock market behaviors, among others. However, new applications of big data with increasingly varied designs present interesting challenges. Consider the example of free-text analysis posed above. There's a renewed interest in modeling free-text narratives from sources such as online reviews, customer complaints, or patient safety event reports, into intuitive themes or topics. As previously mentioned, documents describing the same phenomena can vary widely in their word usage and structure. Another recent interest area of statistical learning is using the environmental conditions that people live, work, and grow in, to infer their quality of life. It is well established that social factors play a role in overall health outcomes, however, clinical applications of these social determinants of health is a recent and an open problem. These examples are just a few of many examples wherein new applications of big data pose complex challenges requiring thoughtful and inventive approaches to processing, analyzing, and modeling data. Although a large body of research exists in the area of anomaly detection increasingly complicated data sources (such as side-channel related data or network-based data) present equally convoluted challenges. For effective anomaly-detection, analysts define parameters and rules, so that when large collections of raw data are aggregated, pieces of data that do not conform are easily noticed and flagged In this work, I investigate the different steps of the data analytics framework and propose improvements for each step, paired with practical applications, to demonstrate the efficacy of my methods. This paper focuses on the healthcare, manufacturing and social-networking industries, but the materials are broad enough to have wide applications across data analytics generally. My main contributions can be summarized as follows: • In the big data analytics framework, raw data initially goes through a pre-processing step. Although many pre-processing techniques exist, there are several challenges in pre-processing text data and I develop a pre-processing tool for text data. • In the next step of the data analytics framework, there are challenges in both statistical modeling and anomaly detection o I address the research area of statistical modeling in two ways: -There are open challenges in defining models to characterize text data. I introduce a community extraction model that autonomously aggregates text documents into intuitive communities/groups -In health care, it is well established that social factors play a role in overall health outcomes however developing a statistical model that characterizes these relationships is an open research area. I developed statistical models for generalizing relationships between social determinants of health of a cohort and general medical risk factors o I address the research area of anomaly detection in two ways: -A variety of anomaly detection techniques exist already, however, some of these methods lack a rigorous statistical investigation thereby making them ineffective to a practitioner. I identify critical shortcomings to a proposed network based anomaly detection technique and introduce methodological improvements -Manufacturing enterprises which are now more connected than ever are vulnerably to anomalies in the form of cyber-physical attacks. I developed a sensor-based side-channel technique for anomaly detection in a manufacturing process PHD The prevalence of big data has rapidly changed the usage and mechanisms of data analytics within organizations. The fields of manufacturing and healthcare are two examples of industries that are currently undergoing significant transformations due to the rise of big data. The addition of large sensory systems is changing how parts are being manufactured and inspected and the prevalence of Health Information Technology (HIT) systems in healthcare systems is also changing the way healthcare services are delivered. These industries are turning to big data analytics in the hopes of acquiring many of the benefits other sectors are experiencing, including reducing cost, improving safety, and boosting productivity. However, there are many challenges that exist along with the framework of big data analytics, from pre-processing raw data, to statistical modeling of the data, and identifying anomalies present in the data or process. This work offers significant contributions in each of the aforementioned areas and includes practical real-world applications. Big data analytics generally follows this framework: first, a digitized process generates a stream of data, this raw data stream is pre-processed to convert the data into a usable format, the pre-processed data is analyzed using statistical tools. In this stage, called ‘statistical learning of the data’, analysts have two main objectives (1) develop a statistical model that captures the behavior of the process from a sample of the data (2) identify anomalies or outliers in the process. In this work, I investigate the different steps of the data analytics framework and propose improvements for each step, paired with practical applications, to demonstrate the efficacy of my methods. This work focuses on the healthcare and manufacturing industries, but the materials are broad enough to have wide applications across data analytics generally. My main contributions can be summarized as follows: In the big data analytics framework, raw data initially goes through a pre-processing step. Although many pre-processing techniques exist, there are several challenges in pre-processing text data and I develop a pre-processing tool for text data. In the next step of the data analytics framework, there are challenges in both statistical modeling and anomaly detection I address the research area of statistical modeling in two ways: There are open challenges in defining models to characterize text data. I introduce a community extraction model that autonomously aggregates text documents into intuitive communities/groups In health care, it is well established that social factors play a role in overall health outcomes however developing a statistical model that characterizes these relationships is an open research area. I developed statistical models for generalizing relationships between social determinants of health of a cohort and general medical risk factors o I address the research area of anomaly detection in two ways: A variety of anomaly detection techniques exist already, however, some of these methods lack a rigorous statistical investigation thereby making them ineffective to a practitioner. I identify critical shortcomings to a proposed network-based anomaly detection technique and introduce methodological improvements Manufacturing enterprises which are now more connected than ever are vulnerable to anomalies in the form of cyber-physical attacks. I developed a sensor-based side-channel technique for anomaly detection in a manufacturing process.

APA, Harvard, Vancouver, ISO, and other styles

3

Vujovic, Milica. "Studying collaborative learning space design with multimodal learning analytics." Doctoral thesis, Universitat Pompeu Fabra, 2021. http://hdl.handle.net/10803/673315.

Full text

Abstract:

Research has provided relevant advances in evidence-based design for productive learning. For example, in the field of collaborative learning, there is extensive evidence for some key learning design elements, such as methods of structuring activity sequencing, group formation techniques, and technology mediating collaboration. However, progress has been more limited in the area of evidence-based design of collaborative learning physical spaces. Contradictorily, research on learning spaces and their impact on teaching and learning have been a field of inquiry for decades. Existing studies have explored how learning spaces can play a role in inhibiting or encouraging student participation in active learning tasks, such those applying collaborative learning methods. However, the methods used in these studies have provided limited empirical evidence on the effects that specific design elements of collaborative learning spaces have on student behaviour. In this context, technological advances in data capture and analysis tools offer new opportunities and challenges to overcome this lack of evidence. In particular, the potential to advance learning space research through approaches involving Multimodal Learning Analytics (MMLA) is becoming increasingly clear. This dissertation contributes to emerging MMLA research by aiming to disentangle the effects of space design elements and their interaction with other learning design elements in order to help broaden the evidence-based design spectrum with more fruitful learning. In particular, the thesis focuses on the interaction of table shapes in learning spaces with the group size learning design element. The dissertation also explores the relevant, but often neglected, gender perspective. An experimental design methodology is applied with the objective of answering research questions related to: (1) the differences in student behaviour when two table shapes and two group sizes are used; (2) indicators relevant to collaborative learning space research; and (3) data collection, analytical, and visualisation techniques. Contributions include the first empirical evidence about the influence of table shape on student behaviour, including effects arising from the interaction of table shape with group size and student gender. In addition, the dissertation offers a new case that discusses MMLA indicators in this field and explores how motion capture, temporal analysis, and aggregated visualisation can contribute to collaborative learning space research. Ha habido avances importantes en la investigación en el ámbito del diseño para el aprendizaje efectivo basado en evidencias. Por ejemplo, en el ámbito del aprendizaje colaborativo, se han conseguido evidencias sobre algunos elementos importantes de su diseño, como los métodos para estructurar las secuencias de actividades, las técnicas de formación de grupo o la tecnología que media la colaboración. Sin embargo, el avance ha sido más limitado en el área del diseño de los espacios físicos para el aprendizaje colaborativo. Contradictoriamente, la investigación sobre los espacios de aprendizaje y su impacto en la educación han sido objeto de investigación durante décadas. Estudios existentes han explorado cómo los espacios de aprendizaje juegan un papel en inhibir o favorecer la participación de los estudiantes en tareas de aprendizaje activo, como las que aplican métodos de aprendizaje colaborativo. Sin embargo, los métodos utilizados en estos estudios han generado muy pocas evidencias empíricas sobre los efectos que elementos específicos de esos espacios tienen en el comportamiento de los estudiantes. En este contexto, los avances en las tecnologías para la captura y el análisis de datos ofrecen nuevas oportunidades, y retos, para cubrir esta falta de evidencias. En particular, el potencial de la Analítica de Aprendizaje Multimodal (MMLA, de sus siglas en inglés) se está vislumbrando como especialmente prometedor para avanzar la investigación sobre los espacios de aprendizaje. Esta tesis doctoral contribuye al campo emergente de MMLA con el objetivo de desgranar los efectos de los elementos de diseño en los espacios de aprendizaje y su interacción con otros elementos de diseño para el aprendizaje. El objetivo último es ampliar el espectro del diseño basado en evidencias para el aprendizaje efectivo. Para ello, la tesis se centra en estudiar la interacción de las formas de las mesas con el tamaño de grupo, como elementos de diseño sobre el espacio y sobre el método de aprendizaje. La tesis también explora la perspectiva de género, una perspectiva relevante pero no suficientemente considerada en el ámbito. La metodología empleada es de diseño experimental y se plantean preguntas de investigación relacionadas con: (1) las diferencias en el comportamiento de los estudiantes cuando se utilizan dos tipos de mesas y tamaños de grupo; (2) los indicadores de analítica de aprendizaje relevantes en la investigación de espacios de aprendizaje colaborativo; (3) las técnicas para la recogida, el análisis y la visualización de datos. Las contribuciones de la tesis incluyen unas primeras evidencias científicas sobre la influencia de las formas de las mesas en el comportamiento de los estudiantes, considerando la interacción con el tamaño de grupo y el género. Además, la tesis también ofrece un nuevo caso de recogida de datos que permite revisar la validez de indicadores MMLA propuestos en el campo y explorar como aproximaciones de captura de movimiento, análisis temporal y visualización avanzada pueden contribuir a la investigación en espacios para el aprendizaje colaborativo.

APA, Harvard, Vancouver, ISO, and other styles

4

Gaaw, Stephanie, and Cathleen M. Stützer. "Learning und Academic Analytics in Lernmanagementsystemen (LMS)." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-234425.

Full text

Abstract:

Der Einsatz digitaler Medien hat in der nationalen Hochschullehre Tradition. Lernmanagementsysteme (LMS), E-Learning, Blended Learning, etc. sind Schlagwörter im Hochschulalltag. Allerdings stellt sich die Frage, was LMS und Blended Learning im Zeitalter digitaler Vernetzung und der herangewachsenen Generation der “Digital Natives” leisten (können bzw. sollen)? Die Verbreitung neuer Technologien im Zusammenhang mit neuen Lehr- und Lernkonzepten wie OER, MOOCS, etc. macht zudem die Entwicklung von Analytics-Instrumenten erforderlich. Das ist auch im nationalen Diskurs von großem Interesse und legt neue Handlungsfelder für Hochschulen offen. Doch es stellt sich die Frage, warum Learning Analytics (LA) bzw. Academic Analytics (AA) bisher nur in einem geringfügigen Maße an deutschen Hochschulen erfolgreich zum Einsatz kommen und warum eine Nutzung insbesondere in LMS, wie zum Beispiel OPAL, nicht ohne Weiteres realisierbar erscheint. Hierzu sollen Einflussfaktoren, die die Implementierung von LA- und AA-Instrumenten hemmen, identifiziert und diskutiert werden. Aufbauend darauf werden erste Handlungsfelder vorgestellt, deren Beachtung eine verstärkte Einbettung von LA- und AA Instrumenten in LMS möglich machen soll.

APA, Harvard, Vancouver, ISO, and other styles

5

Kruse, Gustav, Lotta Åhag, Samuel Dahlback, and Albin Åbrink. "Seco Analytics." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-414862.

Full text

Abstract:

Forecasting is a powerful tool that can enable companies to save millions in revenue every year if the forecast is good enough. The problem lies in the good enough part. Many companies today use Excel topredict their future sales and trends. While this is a start it is far from optimal. Seco Analytics aim to solve this issue by forecasting in an informative and easy manner. The web application uses the ARIMA analysis method to accurately calculate the trend given any country and product area selection. It also features external data that allow the user to compare internal data with relevant external data such as GDP and calculate the correlation given the countries and product areas selected. This thesis describes the developing process of the application Seco Analytics.

APA, Harvard, Vancouver, ISO, and other styles

6

Rudzewitz, Björn [Verfasser]. "Learning Analytics in Intelligent Computer-Assisted Language Learning / Björn Rudzewitz." Tübingen : Universitätsbibliothek Tübingen, 2021. http://d-nb.info/1238594751/34.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Bheda, Anuj. "Predictive analytics of active learning based education." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113509.

Full text

Abstract:

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2017. Cataloged from PDF version of thesis. Includes bibliographical references (pages 113-115). Learning Analytics (LA) is defined as the collection, measurement, and analysis of data related to student performance such that the feedback from the analytical insights can be used to optimize student learning and improve student outcomes. Blended Learning (BL) is a teaching paradigm that involves a mix of face-to-face interactions in a classroom based setting along with instructional material distributed through an online medium. In this thesis, we explore the role of a blended learning model coupled with learning analytics in an introductory programming class for non-computer science students. We identify the features that were necessary for setting up the infrastructure of the course. These include discussions on preparing the course content materials and producing assignment exercises. We then talk about the various dynamics that were in play during the duration of the class by describing the interplay between watching video tutorials, listening to mini-lectures and performing active learning exercises that are backed by modern software development practices. Lastly, we spend time analyzing the data collected to create a predictive model that can measure student performance by defining the specifications of a machine learning algorithm along with many of its adjustable parameters. The system thus created will allow instructors to identify possible outliers in teaching efficacy, the feedback from which could then be used to tune course material for the betterment of student outcomes. by Anuj Bheda. S.M. in Engineering and Management

APA, Harvard, Vancouver, ISO, and other styles

8

Teo, Hon Jie. "Knowledge Creation Analytics for Online Engineering Learning." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/64465.

Full text

Abstract:

The ubiquitous use of computers and greater accessibility of the Internet have triggered widespread use of educational innovations such as online discussion forums, Wikis, Open Educational Resources, MOOCs, to name a few. These advances have led to the creation of a wide range of instructional videos, written documents and discussion archives by engineering learners seeking to expand their learning and advance their knowledge beyond the engineering classroom. However, it remains a challenging task to assess the quality of knowledge advancement on these learning platforms particularly due to the informal nature of engagement as a whole and the massive amount of learner-generated data. This research addresses this broad challenge through a research approach based on the examination of the state of knowledge advancement, analysis of relationships between variables indicative of knowledge creation and participation in knowledge creation, and identification of groups of learners. The study site is an online engineering community, All About Circuits, that serves 31,219 electrical and electronics engineering learners who contributed 503,908 messages in 65,209 topics. The knowledge creation metaphor provides the guiding theoretical framework for this research. This metaphor is based on a set of related theories that conceptualizes learning as a collaborative process of developing shared knowledge artifacts for the collective benefit of a community of learners. In a knowledge-creating community, the quality of learning and participation can be evaluated by examining the degree of collaboration and the advancement of knowledge artifacts over an extended period of time. Software routines were written in Python programming language to collect and process more than half a million messages, and to extract user-produced data from 87,263 web pages to examine the use of engineering terms, social networks and engineering artifacts. Descriptive analysis found that state of knowledge advancement varies across discussion topics and the level of engagement in knowledge creating activities varies across individuals. Non-parametric correlation analysis uncovered strong associations between topic length and knowledge creating activities, and between the total interactions experienced by individuals and individual engagement in knowledge creating activities. On the other hand, the variable of individual total membership period has week associations with individual engagement in knowledge creating activities. K-means clustering analysis identified the presence of eight clusters of individuals with varying lengths of participation and membership, and Kruskal-Wallis tests confirmed that significant differences between the clusters. Based on a comparative analysis of Kruskal-Wallis Score Means and the examination of descriptive statistics for each cluster, three groups of learners were identified: Disengaged (88% of all individuals), Transient (10%) and Engaged (2%). A comparison of Spearman Correlations between pairs of variables suggests that variable of individual active membership period exhibits stronger association with knowledge creation activities for the group of Disengaged, whereas the variable of individual total interactions exhibits stronger association with knowledge creation activities for the group of Engaged. Limitations of the study are discussed and recommendations for future work are made. Ph. D.

APA, Harvard, Vancouver, ISO, and other styles

9

Schumacher, Clara [Verfasser], and Dirk [Akademischer Betreuer] Ifenthaler. "Cognitive, metacognitive and motivational perspectives on Learning Analytics : Synthesizing self-regulated learning, assessment, and feedback with Learning Analytics / Clara Schumacher ; Betreuer: Dirk Ifenthaler." Mannheim : Universitätsbibliothek Mannheim, 2020. http://d-nb.info/1204828741/34.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Santiteerakul, Wasana. "Trajectory Analytics." Thesis, University of North Texas, 2015. https://digital.library.unt.edu/ark:/67531/metadc801885/.

Full text

Abstract:

The numerous surveillance videos recorded by a single stationary wide-angle-view camera persuade the use of a moving point as the representation of each small-size object in wide video scene. The sequence of the positions of each moving point can be used to generate a trajectory containing both spatial and temporal information of object's movement. In this study, we investigate how the relationship between two trajectories can be used to recognize multi-agent interactions. For this purpose, we present a simple set of qualitative atomic disjoint trajectory-segment relations which can be utilized to represent the relationships between two trajectories. Given a pair of adjacent concurrent trajectories, we segment the trajectory pair to get the ordered sequence of related trajectory-segments. Each pair of corresponding trajectory-segments then is assigned a token associated with the trajectory-segment relation, which leads to the generation of a string called a pairwise trajectory-segment relationship sequence. From a group of pairwise trajectory-segment relationship sequences, we utilize an unsupervised learning algorithm, particularly the k-medians clustering, to detect interesting patterns that can be used to classify lower-level multi-agent activities. We evaluate the effectiveness of the proposed approach by comparing the activity classes predicted by our method to the actual classes from the ground-truth set obtained using the crowdsourcing technique. The results show that the relationships between a pair of trajectories can signify the low-level multi-agent activities.

APA, Harvard, Vancouver, ISO, and other styles

11

Galaige, Joy. "Supporting Self-Regulated Learning with Student-Facing Learning Analytics: User-centric Design Guidelines." Thesis, Griffith University, 2021. http://hdl.handle.net/10072/401416.

Full text

Abstract:

Universities are investing heavily in online learning in a bid to remain competitive in a globalized world and in harsh economic times. The need to enhance and strengthen online learning is even greater given the current Covid-19 pandemic that makes it difficult to conduct face-to-face learning sessions due to the need for social distancing. Normally, retention and success rates in online courses are much lower as compared to traditional face-to-face courses – a major concern for universities. This issue is attributed to the lack of adequate self-regulated learning (SRL) skills; a situation, which is particularly problematic in online learning where students have greater levels of autonomy and flexibility. SRL skills enable students to actively and independently control their own learning processes and contribute to academic success. The proliferation of online learning in education institutions brings to greater focus on the importance of supporting students’ SRL skills. It is known that SRL skills can be fostered in students and one possible way to achieve this is to embed tools that support the development of SRL in day-to-day online learning tools. Student-facing learning analytics (SFLA) are one possible avenue for supporting SRL in online learning environments. This is attributed to the fact that they present new opportunities for collecting and analysing students’ learning data and reporting it back directly to students. They make use of visual tools such as charts, graphs, and network diagrams to present feedback to students. This feedback can enable learners to gain insight into their learning process and reflect on their learning thereby supporting students’ SRL activities. However, the potential of SFLA to support students’ SRL skills is failing to be realized. This is largely attributed to the current design methods that are flawed and techno-centric, focusing on availability of data with little attention to learning science theory and student needs as confirmed by the exploratory study. As interest in SFLA to foster SRL grows and higher education institutions continue to implement SFLA on a widespread scale, there is an urgent need for design guidelines that are studentcentred and learning science theory-driven. For an emerging field, the need for developing a body of knowledge to address the design, development, and implementation issues in LA systems cannot be underestimated. The work presented in this thesis is a response to this need. Therefore, the central research question addressed in this study is: How can student-facing learning analytics be designed to best support SRL skills among students? This question was broken down into the following specific questions: i. What are the students’ self-regulated learning support needs based on the self-regulated learning theory? ii. What are the students’ perceptions of student-facing learning analytics? iii. What student-facing learning analytics features are most appropriate to support students’ self-regulated learning? To answer these research questions, Zimmerman’s cyclic model of selfregulation was adopted as the theoretical basis and a user-centred design approach was taken. A mixed-methods approach was used to investigate how the design of SFLA for supporting SRL may be improved. The study focused on understanding student's SRL support needs and how they should be addressed while being grounded in learning science theory; establishing students SFLA preferences and concerns; generating both general and specific design guidelines; and proposing an overall framework will support the design of SFLA for supporting SRL that will enhance learning experiences, learning practices and improve the learning process. To achieve the study aim, an exploratory study was first conducted with learning analytics experts to ascertain the relevancy and urgency of the research problem. From the insights gained, a conceptual framework for the optimal design of SFLA for supporting SRL was proposed. The three research questions stated above were formulated based on this conceptual framework. The study was conducted in three phases with each phase addressing one research question as follows: In the first phase, RQN 1 was answered to establish student's SRL support needs. This involved conducting a survey with online students undertaking business courses at an Australian public university to examine student's SRL differences and SRL support needs. Cluster analysis using K-means revealed four SRL profiles (nonself- regulators, basic self-regulators, proficient self-regulators, and expert selfregulators) based on Zimmerman’s SRL framework. Each profile exhibited different characteristics hence differing SRL support needs. The results confirmed that students have low SRL skills as the non-self-regulators constituted the largest profile with 121 students (40%) while the expert self-regulators were the smallest with 20 students (7%) of the study respondents. In the second phase, RQN 2 was answered by examining students’ perceptions of SFLA using a survey with undergraduate university students. The results revealed SFLA features and data considered most important from the student perspective. Notably, students considered data related to their emotional aspects as extremely important, even though current LA applications have given less attention to the emotional aspects of the learning process. Student concerns towards SFLA were also established and these included the loss of autonomy, privacy and security, teacher role, accuracy, and timing of the feedback, and depression, anxiety, and stress. Hence, learning analytics designers, researchers, and educators should address these concerns during the design and implementation process of SFLA for supporting SRL. In phase three, RQN 3 was addressed through an experiment that was conducted with undergraduate students to determine the most appropriate SFLA features to support SRL for students in each of the identified SRL profiles. The findings revealed both positive and negative relationships between students SFLA preferences and SRL profiles. Some students SFLA preferences conflicted with the kind of SRL support they needed. Based on these results, profile-specific SFLA features and generic SFLA features were generated and summarised in the form of design cards. Cumulatively, the investigations yielded student-centred and theory-based guidelines to inform the design of SFLA that will likely support students’ SRL skills. Specifically, the study yielded the following contributions: A conceptual framework for the optimal design of SFLA for supporting SRL; Self-regulated learning profiles, and their SRL support needs. The kinds of user data and SFLA features that students consider important in SFLA; The specific design guidelines with design cards for each of the identified SRL profile; the overall research-based framework for designing SFLA for supporting SRL. Thesis (PhD Doctorate) Doctor of Philosophy (PhD) School of Info & Comm Tech Science, Environment, Engineering and Technology Full Text

APA, Harvard, Vancouver, ISO, and other styles

12

Engström, Linda. "Studenters förväntningar på Learning Analytics inom akademiska utbildningar." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299269.

Full text

Abstract:

Learning Analytics är ett forskningsområde som innefattar insamling, mätning, analysering och rapportering av “big data” om studenter i deras lärmiljö. Syftet är att förstå och optimera studenters lärande, och deras studiemiljöer. Learning Analytics-tjänster kan bland annat hjälpa studenter att få en insikt i hur de bör studera för att vara tidseffektiva eller höja sina studieprestationer. Dessutom kan tjänsterna upptäcka och ge feedback till studenter som riskerar att misslyckas med sina kurser, samt skapa personliga visualiseringar för t.ex. tidsförbrukning per delmoment i en kurs, eller betygsfördelning. Denna studie använder sig av ett forskningsinstrument som kallas Student Expectations of Learning Analytics Questionnaire (SELAQ) och ämnar undersöka studenters attityd till 12 olika påståenden relaterade till Learning Analytics. Deltagarna i studien fick således svara på en enkät där de fick gradera hur mycket de instämde med de givna påståendena, under premissen att deras lärosäte hypotetiskt skulle börja implementera en Learning Analytics-tjänst. Resultaten från studien indikerar att SELAQ ger oss bra insikt i vilka förväntningar studenter på svenska lärosäten har på Learning Analytics. Resultaten visar bland annat också att studenterna har låga förväntningar kring de områden som rör feedbacken från Learning Analytics-tjänsten. Mer specifikt har de låg tillit till att undervisande personal kommer att leverera feedbacken på ett tillfredsställande vis till studenterna. Vidare visar resultatet att studenterna har högre förväntningar i frågor gällande inhämtning av samtycke och hantering av personlig data. Learning Analytics is an area of research which includes collecting, measuring, analysing, and reporting “big data” about students and their learning environment. The purpose is to understand and optimise students’ learning and learning environments. Learning Analytics services can among other things help students gain insight into how they should study to be time-efficient or increase their study performances. Moreover, the services can detect and provide feedback to those students at risk of failing their courses, as well as create personalised visualizations about for example time consumption per parts of a course, or grade distribution. This study uses a research instrument called Student Expectations of Learning Analytics Questionnaire (SELAQ) and aims to examine students' attitudes to 12 different statements related to Learning Analytics. Thus, the participants in the study got to answer a survey where they had to rate how much they agreed with the given statements, based on the hypothetical premise that their university would start to implement a Learning Analytics service. The results from the study indicates that the SELAQ instrument gives us a good understanding about the expectations on Learning Analytics of students in Swedish higher education. The results also show, among other things, that the students have low expectations in areas related to the feedback from the Learning Analytics service. More specifically, they have low confidence that the teaching staff will deliver the feedback to the students in a satisfying way. Furthermore, the results show that the students have higher expectations in matters concerning the obtaining of consent and handling personal data.

APA, Harvard, Vancouver, ISO, and other styles

13

Kovanovic, Vitomir. "Assessing cognitive presence using automated learning analytics methods." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28759.

Full text

Abstract:

With the increasing pace of technological changes in the modern society, there has been a growing interest from educators, business leaders, and policymakers in teaching important higher-order skills which were identified as necessary for thriving in the present-day globalized economy. In this regard, one of the most widely discussed higher order skills is critical thinking, whose importance in shaping problem solving, decision making, and logical thinking has been recognized. Within the domain of distance and online education, the Community of Inquiry (CoI) model provides a pedagogical framework for understanding the critical dimensions of student learning and factors which impact the development of student critical thinking. The CoI model follows the social-constructivist perspective on learning in which learning is seen as happening in both individual minds of learners and through the discourse within the group of learners. Central to the CoI model is the construct of cognitive presence, which captures the student cognitive engagement and the development of critical thinking and deep thinking skills. However, the assessment of cognitive presence is challenging task, particularly given its latent nature and the inherent physical and time separation between students and instructors in distance education settings. One way to address this problem is to make use of the vast amounts of learning data being collected by learning systems. This thesis presents novel methods for understanding and assessing the levels of cognitive presence based on learning analytics techniques and the data collected by learning environments. We first outline a comprehensive model for cognitive presence assessment which builds on the well-established evidence-cantered design (ECD) assessment framework. The proposed assessment model provides a foundation of the thesis, showing how the developed analytical models and their components fit together and how they can be adjusted for new learning contexts. The thesis shows two distinct and complementary analytical methods for assessing students’ cognitive presence and its development. The first method is based on the automated classification of student discussion messages and captures learning as it is observed in the student dialogue. The second analytics method relies on the analysis of log data of students’ use of the learning platform and captures the individual dimension of the learning process. The developed analytics also extend current theoretical understanding of the cognitive presence construct through data-informed operationalization of cognitive presence with different quantitative measures extracted from the student use of online discussions. We also examine methodological challenges of assessing cognitive presence and other forms of cognitive engagement through the analysis of trace data. Finally, with the intent of enabling for the wider adoption of the CoI model for new online learning modalities, the last two chapters examine the use of developed analytics within the context of Massive Open Online Courses (MOOCs). Given the substantial differences between traditional online and MOOC contexts, we first evaluate the suitability of the CoI model for MOOC settings and then assess students’ cognitive presence using the data collected by the MOOC platform. We conclude the thesis with the discussion of practical application and impact of the present work and the directions for the future research.

APA, Harvard, Vancouver, ISO, and other styles

14

Mazzoleni, Mirko (ORCID:0000-0002-7116-135X). "Learning meets control. Data analytics for dynamical systems." Doctoral thesis, Università degli studi di Bergamo, 2018. http://hdl.handle.net/10446/104812.

Full text

Abstract:

System identification has always been one of the main research focuses of the control community, since the early steps of the automatic control field. The development of a dynamical system’s models from experimental data is instrumental for understanding the plant under study and designing its model-based control scheme. In the last decade, a cross-fertilization began between the System Identification and the Statistical Learning communities. This led firstly to the introduction of regularization techniques in system identification, and, more recently, to the application of kernel methods to dynamical system learning. This thesis further investigates the roles that learning methods can have in the control science. In the first part, we lay the theoretical foundations of a new kernel-based regularization method for Nonlinear Finite Impulse Response (NFIR) system identification. The method, called Semi-Supervised Identification (SSI), relies on the manifold spanned by the system’s inputs. This manifold is constructed by using not only the measured input/output data, but also inputs data for which there is no corresponding outputs. The effect of this rationale is to impose prior information on the system structure, in the form of local smoothness assumptions. This differs from standard Tikhonov regularization, which imposes a global smoothness behaviour on the learned function. The second part of this work presents practical applications of how statistical learning methods can be used to face control and estimation problems. The case studies span a variety of different applications, from fault detection of electro-mechanical actuators, to clustering methodologies and pure forecasting challanges.

APA, Harvard, Vancouver, ISO, and other styles

15

DI, GIACOMO UMBERTO ANTONIO. "Machine learning and formal methods for sport analytics." Doctoral thesis, Università degli studi del Molise, 2022. https://hdl.handle.net/11695/115353.

Full text

Abstract:

L’obiettivo principale del mio periodo di Dottorato riguarda il concetto della Soccer Analytics. Durante gli ultimi anni la Soccer Analytics ha registrato un’enorme diffusione. Di solito, in quest’ambito, i ricercatori si sono concentrati sulla predizione dei risultati delle partite anche se, soprattutto negli ultimi tempi, c’è stata un’attenzione particolare nello studio dei dati posizionali. Il mio periodo di Dottorato può essere diviso in tre parti fondamentali: nella prima parte ho preso in considerazione l’analisi e lo studio delle attività svolte dall’uomo; nella seconda parte, mi sono soffermato sull’applicazione di tecniche di Machine Learning applicate al contesto della Soccer Analytics per poi passare, nell’ultimo periodo, all’utilizzo delle tecniche di verifica formale sugli stessi dati. Alla fine di questo percorso ho potuto confrontare i punti di forza e di debolezza di tutte le tecniche utilizzate. L’analisi delle attività umane (Behavioural Analysis) è di grande interesse per i ricercatori a causa della grande quantità di applicazioni possibili di questa disciplina. Quando si parla di questo tipo di disciplina, il primo problema da affrontare consiste nel dover gestire e modellizzare le attività e gli scenari presi in considerazione che, chiaramente, possono essere di diversa natura. Questi aspetti possono essere molto difficili da risolvere. Dopo aver trattato lo studio e l’analisi delle attività umane, sono passato all’applicazione delle tecniche di Machine Learning su dati relativi al contesto calcistico, in maniera tale da poter gestire la grande mole di dati disponibili. Nello specifico, sono state utilizzate delle tecniche supervisionate per predire il risultato di partite di calcio e per identificare la posizione occupata dai giocatori in campo in diverse situazioni di gioco. L’ultimo step riguarda l’utilizzo di tecniche di verifica formale sugli stessi dati presi in considerazione nella seconda parte del lavoro, cercando di ottenere una migliore “explainability” riguardo ai dati a disposizione. L’obiettivo, in questa ultima parte del mio lavoro, riguarda l’identificazione dello stile di gioco delle squadre prese in esame. Per ogni squadra, che viene rappresentata mediante un automa, cerchiamo di identificare lo stile di gioco (offensivo o difensivo) utilizzando tecniche di Model Checking. Le proprietà che vengono verificate sono definite grazie all’aiuto di un esperto di dominio. Questa informazione può essere utile per l’allenatore, che può verificare, durante lo svolgimento della partita, se la squadra sta rispettando le sue direttive. Tutte le analisi, svolte nei vari step del progetto, hanno portato a risultati molto promettenti se confrontati con quelli presenti in letteratura. The main focus of my PhD period is related to the concept of sport analytics. During the last few years, sports analytics has been growing rapidly. The main usage of this discipline is the prediction of soccer match results, even if it can be applied with interesting results in different areas, such as analysis based on the player position information. This context has been explored through three different steps: the analysis and recognition of human activities, the adoption of machine learning on soccer data and, at the end, the application of formal methods on sport analytics scenario. In this way, I want to explore the strengths and the weaknesses of different techniques. Human activity recognition is attracting interest from researchers and developers in recent years due to its immense applications in wide area of human endeavors. The main issue in human behaviour modeling is represented by the diverse nature of human activities and the scenarios in which they are performed. These factors make this aspects challenging to deal with. Then, machine learning techniques have been used in order to make some consideration on a great amount of data related to soccer matches. Specifically, these type of techniques have been used in order to predict soccer game results and player positions during a match. The last step is about the usage of formal methods in order to provide more explainability and interpretability of the results obtained. With the application of formal methods based approach, I try to detect the playing style of a soccer teams, providing transparency of the results obtained. I model soccer teams in terms of automata and, by exploiting model verification techniques, I verify whether a playing style, expressed by means of a temporal logic formula, is exhibited by the team under analysis. This information can support the coach in determining the strategy of the team while the match is in progress. The experimental analysis confirms the effectiveness of the proposed method in soccer team behaviour detection, obtaining promising results, compared with standard baseline approaches.

APA, Harvard, Vancouver, ISO, and other styles

16

Ricker, Gina Maria. "Student Learning Management System Interactions and Performance via a Learning Analytics Perspective." ScholarWorks, 2019. https://scholarworks.waldenu.edu/dissertations/6656.

Full text

Abstract:

Enrollment in full-time, virtual, K-12 schools is increasing while mathematics performance in these institutions is lacking compared to national averages. Scholarly literature lacks research studies using learning analytics to better predict student outcomes via student learning management system (LMS) interactions, specifically in the low performing area of middle school mathematics. The theoretical framework for this study was a combination of Hrastinski's theory of online learning as online participation and Moore's 3 types of interactions model of online student behavior. The purpose of this study was to address the current research gap in the full-time, K-12 eLearning field and determine whether 2 types of student LMS interactions could predict mathematics course performance. The research questions were developed to determine whether student clicks navigating course content page(s) or the number of times a student accessed resources predicted student performance in a full-time, virtual, mathematics course after student demographic variables were controlled for. This quantitative study used archived data from 238 seventh grade Math 7B students enrolled from January 8th-10th to May 22nd-25th in two Midwestern, virtual, K-12 schools. Hierarchical regressions were used to test the 2 research questions. Student clicks navigating the course content pages were found to predict student performance after the effects of student demographic covariates were controlled for. Similarly, the number of times a student accessed resources also predicted student performance. The findings from this study can be used to advise actionable changes in student support, build informative student activity dashboards, and predict student outcomes for a more insightful, data-driven, learning experience in the future.

APA, Harvard, Vancouver, ISO, and other styles

17

Carpani, Valerio. "CNN-based video analytics." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

Find full text

Abstract:

The content of this thesis illustrates the six months work done during my internship at TKH Security Solutions - Siqura B.V. in Gouda, Netherlands. The aim of this thesis is to investigate on convolutional neural networks possible usage, from two different point of view: first we propose a novel algorithm for person re-identification, second we propose a deployment chain, for bringing research concepts to product ready solutions. In existing works, the person re-identification task is assumed to be independent of the person detection task. In this thesis instead, we consider the two tasks as linked. In fact, features produced by an object detection convolutional neural network (CNN) contain useful information, which is not being used by current re-identification methods. We propose several solutions for learning a metric on CNN features to distinguish between different identities. Then the best of these solutions is compared with state of the art alternatives on the popular Market-1501 dataset. Results show that our method outperforms them in computational efficiency, with only a reasonable loss in accuracy. For this reason, we believe that the proposed method can be more appropriate than current state of the art methods in situations where the computational efficiency is critical, such as embedded applications. The deployment chain we propose in this thesis has two main goals: it must be flexible for introducing new advancement in networks architecture, and it must be able to deploy neural networks both on server and embedded platforms. We tested several frameworks on several platforms and we ended up with a deployment chain that relies on the open source format ONNX.

APA, Harvard, Vancouver, ISO, and other styles

18

Gaaw, Stephanie, and Cathleen M. Stützer. "Learning und Academic Analytics in Lernmanagementsystemen (LMS): Herausforderungen und Handlungsfelder im nationalen Hochschulkontext." TUDpress, 2017. https://tud.qucosa.de/id/qucosa%3A30891.

Full text

Abstract:

Der Einsatz digitaler Medien hat in der nationalen Hochschullehre Tradition. Lernmanagementsysteme (LMS), E-Learning, Blended Learning, etc. sind Schlagwörter im Hochschulalltag. Allerdings stellt sich die Frage, was LMS und Blended Learning im Zeitalter digitaler Vernetzung und der herangewachsenen Generation der “Digital Natives” leisten (können bzw. sollen)? Die Verbreitung neuer Technologien im Zusammenhang mit neuen Lehr- und Lernkonzepten wie OER, MOOCS, etc. macht zudem die Entwicklung von Analytics-Instrumenten erforderlich. Das ist auch im nationalen Diskurs von großem Interesse und legt neue Handlungsfelder für Hochschulen offen. Doch es stellt sich die Frage, warum Learning Analytics (LA) bzw. Academic Analytics (AA) bisher nur in einem geringfügigen Maße an deutschen Hochschulen erfolgreich zum Einsatz kommen und warum eine Nutzung insbesondere in LMS, wie zum Beispiel OPAL, nicht ohne Weiteres realisierbar erscheint. Hierzu sollen Einflussfaktoren, die die Implementierung von LA- und AA-Instrumenten hemmen, identifiziert und diskutiert werden. Aufbauend darauf werden erste Handlungsfelder vorgestellt, deren Beachtung eine verstärkte Einbettung von LA- und AA Instrumenten in LMS möglich machen soll.

APA, Harvard, Vancouver, ISO, and other styles

19

Pienaar, Celia. "Machine learning in predictive analytics on judicial decision-making." Master's thesis, Faculty of Science, 2021. http://hdl.handle.net/11427/33925.

Full text

Abstract:

Legal professionals globally are under pressure to provide ‘more for less' – not an easy challenge in the era of big data, increasingly complex regulatory and legislative frameworks and volatile financial markets. Although largely limited to information retrieval and extraction, Machine Learning applications targeted at the legal domain have to some extent become mainstream. The startup market is rife with legal technology providers with many major law firms encouraging research and development through formal legal technology incubator programs. Experienced legal professionals are expected to become technologically astute as part of their response to the ‘more for less' challenge, while legal professionals on track to enter the legal services industry are encouraged to broaden their skill sets beyond a traditional law degree. Predictive analytics applied to judicial decision-making raise interesting discussions around potential benefits to the general public, over-burdened judicial systems and legal professionals respectively. It is also associated with limitations and challenges around manual input required (in the absence of automatic extraction and prediction) and domain-specific application. While there is no ‘one size fits all' solution when considering predictive analytics across legal domains or different countries' legal systems, this dissertation aims to provide an overview of Machine Learning techniques which could be applied in further research, to start unlocking the benefits associated with predictive analytics on a greater (and hopefully local) scale.

APA, Harvard, Vancouver, ISO, and other styles

20

Lambert, Glenn M. II. "Security Analytics: Using Deep Learning to Detect Cyber Attacks." UNF Digital Commons, 2017. http://digitalcommons.unf.edu/etd/728.

Full text

Abstract:

Security attacks are becoming more prevalent as cyber attackers exploit system vulnerabilities for financial gain. The resulting loss of revenue and reputation can have deleterious effects on governments and businesses alike. Signature recognition and anomaly detection are the most common security detection techniques in use today. These techniques provide a strong defense. However, they fall short of detecting complicated or sophisticated attacks. Recent literature suggests using security analytics to differentiate between normal and malicious user activities. The goal of this research is to develop a repeatable process to detect cyber attacks that is fast, accurate, comprehensive, and scalable. A model was developed and evaluated using several production log files provided by the University of North Florida Information Technology Security department. This model uses security analytics to complement existing security controls to detect suspicious user activity occurring in real time by applying machine learning algorithms to multiple heterogeneous server-side log files. The process is linearly scalable and comprehensive; as such it can be applied to any enterprise environment. The process is composed of three steps. The first step is data collection and transformation which involves identifying the source log files and selecting a feature set from those files. The resulting feature set is then transformed into a time series dataset using a sliding time window representation. Each instance of the dataset is labeled as green, yellow, or red using three different unsupervised learning methods, one of which is Partitioning around Medoids (PAM). The final step uses Deep Learning to train and evaluate the model that will be used for detecting abnormal or suspicious activities. Experiments using datasets of varying sizes of time granularity resulted in a very high accuracy and performance. The time required to train and test the model was surprisingly fast even for large datasets. This is the first research paper that develops a model to detect cyber attacks using security analytics; hence this research builds a foundation on which to expand upon for future research in this subject area.

APA, Harvard, Vancouver, ISO, and other styles

21

Walker, Daniel David. "Bayesian Test Analytics for Document Collections." BYU ScholarsArchive, 2012. https://scholarsarchive.byu.edu/etd/3530.

Full text

Abstract:

Modern document collections are too large to annotate and curate manually. As increasingly large amounts of data become available, historians, librarians and other scholars increasingly need to rely on automated systems to efficiently and accurately analyze the contents of their collections and to find new and interesting patterns therein. Modern techniques in Bayesian text analytics are becoming wide spread and have the potential to revolutionize the way that research is conducted. Much work has been done in the document modeling community towards this end,though most of it is focused on modern, relatively clean text data. We present research for improved modeling of document collections that may contain textual noise or that may include real-valued metadata associated with the documents. This class of documents includes many historical document collections. Indeed, our specific motivation for this work is to help improve the modeling of historical documents, which are often noisy and/or have historical context represented by metadata. Many historical documents are digitized by means of Optical Character Recognition(OCR) from document images of old and degraded original documents. Historical documents also often include associated metadata, such as timestamps,which can be incorporated in an analysis of their topical content. Many techniques, such as topic models, have been developed to automatically discover patterns of meaning in large collections of text. While these methods are useful, they can break down in the presence of OCR errors. We show the extent to which this performance breakdown occurs. The specific types of analyses covered in this dissertation are document clustering, feature selection, unsupervised and supervised topic modeling for documents with and without OCR errors and a new supervised topic model that uses Bayesian nonparametrics to improve the modeling of document metadata. We present results in each of these areas, with an emphasis on studying the effects of noise on the performance of the algorithms and on modeling the metadata associated with the documents. In this research we effectively: improve the state of the art in both document clustering and topic modeling; introduce a useful synthetic dataset for historical document researchers; and present analyses that empirically show how existing algorithms break down in the presence of OCR errors.

APA, Harvard, Vancouver, ISO, and other styles

22

Santhanagopalan, Meena. "Biopsychosocial Data Analytics and Modeling." Thesis, Federation University Australia, 2021. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/177435.

Full text

Abstract:

Sustained customisation of digital health intervention (DHI) programs, in the context of community health engagement, requires strong integration of multi-sourced interdisciplinary biopsychosocial health data. The biopsychosocial model is built upon the idea that biological, psychological and social processes are integrally and interactively involved in physical health and illness. One of the longstanding challenges of dealing with healthcare data is the wide variety of data generated from different sources and the increasing need to learn actionable insights that drive performance improvement. The growth of information and communication technology has led to the increased use of DHI programs. These programs use an observational methodology that helps researchers to study the everyday behaviour of participants during the course of the program by analysing data generated from digital tools such as wearables, online surveys and ecological momentary assessment (EMA). Combined with data reported from biological and psychological tests, this provides rich and unique biopsychosocial data. There is a strong need to review and apply novel approaches to combining biopsychosocial data from a methodological perspective. Although some studies have used data analytics in research on clinical trial data generated from digital interventions, data analytics on biopsychosocial data generated from DHI programs is limited. The study in this thesis develops and implements innovative approaches for analysing the existing unique and rich biopsychosocial data generated from the wellness study, a DHI program conducted by the School of Science, Psychology and Sport at Federation University. The characteristics of variety, value and veracity that usually describe big data are also relevant to the biopsychosocial data handled in this thesis. These historical, retrospective real-life biopsychosocial data provide fertile ground for research through the use of data analytics to discover patterns hidden in the data and to obtain new knowledge. This thesis presents the studies carried out on three aspects of biopsychosocial research. First, we present the salient traits of the three components - biological, psychological and social - of biopsychosocial research. Next, we investigate the challenges of pre-processing biopsychosocial data, placing special emphasis on the time-series data generated from wearable sensor devices. Finally, we present the application of statistical and machine learning (ML) tools to integrate variables from the biopsychosocial disciplines to build a predictive model. The first chapter presents the salient features of the biopsychosocial data for each discipline. The second chapter presents the challenges of pre-processing biopsychosocial data, focusing on the time-series data generated from wearable sensor devices. The third chapter uses statistical and ML tools to integrate variables from the biopsychosocial disciplines to build a predictive model. Among its other important analyses and results, the key contributions of the research described in this thesis include the following: 1. using gamma distribution to model neurocognitive reaction time data that presents interesting properties (skewness and kurtosis for the data distribution) 2. using novel ‘peak heart-rate’ count metric to quantify ‘biological’ stress 3. using the ML approach to evaluate DHIs 4. using a recurrent neural network (RNN) and long short-term memory (LSTM) data prediction model to predict Difficulties in Emotion Regulation Scale (DERS) and primary emotion (PE) using wearable sensor data. Doctor of Philosophy

APA, Harvard, Vancouver, ISO, and other styles

23

Singh, Vivek Kumar. "Essays on Cloud Computing Analytics." Scholar Commons, 2019. https://scholarcommons.usf.edu/etd/7943.

Full text

Abstract:

This dissertation research focuses on two key aspects of cloud computing research – pricing and security using data-driven techniques such as deep learning and econometrics. The first dissertation essay (Chapter 1) examines the adoption of spot market in cloud computing and builds IT investment estimation models for organizations adopting cloud spot market. The second dissertation essay (Chapter 2 and 3) studies proactive threat detection and prediction in cloud computing. The final dissertation essay (Chapter 4) develops a secured cloud files system which protects organizations using cloud computing in accidental data leaks.

APA, Harvard, Vancouver, ISO, and other styles

24

DI, PIETRO ANASTASIA. "Learning analytics, LMS e piattaforme digitali: soluzioni innovative per apprendimenti student-centered." Doctoral thesis, Università di Foggia, 2021. https://hdl.handle.net/11369/425211.

Full text

Abstract:

Allo stato attuale, nonostante l’interconnessione tra i settori educativi ed informatici, assistiamo ad un contesto in cui i sistemi per la didattica digitale e multimediale sono ancora principalmente centrati sul docente o gli amministratori; i modelli culturali di riferimento risultano ancora fortemente tecnocentrici tralasciando il valore e l’importanza delle metodologie didattiche innovative. Il progetto rivolge la sua attenzione al potenziamento degli ambienti digitali di apprendimento con lo scopo di garantire un’offerta sempre più personalizzata, basata su metodologie attive e un approccio user-centered. Collocandosi a metà tra scienze pedagogiche e scienze informatiche, il progetto si pone come obiettivi non solo quello di migliorare la user experience all'interno dei contesti digitali attraverso pratiche orizzontali basate sul coinvolgimento attivo, ma anche una ristrutturazione dei modelli tecnologici esistenti, definendo dunque una piattaforma prototipale per l'apprendimento centrato. Currently, despite the interconnection between the educational and IT sectors, we are witnessing that most of the time digital and multimedia teaching systems are mainly centered on the teacher or administrators. The reference cultural models are still highly technocentric, without taking into account the value and importance of innovative teaching methodologies. The project focuses on enhancing the digital learning environments with the aim of ensuring an increasingly personalized offer, based on active methodologies and on a user-centered approach. Positioning itself halfway between pedagogical sciences and computer sciences, the project not aims to improve the user experience within digital contexts through horizontal practices based on active involvement, but also a restructuring of current technological models, thus defining a prototype platform for user centered learning.

APA, Harvard, Vancouver, ISO, and other styles

25

Sarzosa, Daniela, Jorge Maldonado, Jimmy Pérez, and Daniel Navarrete. "Experiencias exitosas en el uso de learning analytics en educación superior." Universidad Peruana de Ciencias Aplicadas (UPC), 2019. http://hdl.handle.net/10757/624838.

Full text

Abstract:

Índice del video: Daniel Navarrete (00:19 - 23:50); Daniela Sarzosa (24:00 - 47:12); Jorge Maldonado (48:30-1:13:30); Jimmy Pérez (1:13:30 - 1:13:55) Primera meetup del 2019 de la comunidad Learning Analytics Perú, donde se conversó sobre experiencias de éxito en el uso de herramientas, estrategias y métodos que han conducido a resultados satisfactorios en la aplicación estratégica de los datos en Educación Superior, de la mano de profesionales de Perú, Colombia y Ecuador... Evento realizado el 9 de enero de 2019 en la Universidad Peruana de Ciencias Aplicadas, campus San Isidro, auditorio Luis Bustamante.

APA, Harvard, Vancouver, ISO, and other styles

26

Rao, Rashmi Jayathirtha. "Modeling learning behaviour and cognitive bias from web logs." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492560600002105.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Cao, Xi Hang. "On Leveraging Representation Learning Techniques for Data Analytics in Biomedical Informatics." Diss., Temple University Libraries, 2019. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/586006.

Full text

Abstract:

Computer and Information Science Ph.D. Representation Learning is ubiquitous in state-of-the-art machine learning workflow, including data exploration/visualization, data preprocessing, data model learning, and model interpretations. However, the majority of the newly proposed Representation Learning methods are more suitable for problems with a large amount of data. Applying these methods to problems with a limited amount of data may lead to unsatisfactory performance. Therefore, there is a need for developing Representation Learning methods which are tailored for problems with ``small data", such as, clinical and biomedical data analytics. In this dissertation, we describe our studies of tackling the challenging clinical and biomedical data analytics problem from four perspectives: data preprocessing, temporal data representation learning, output representation learning, and joint input-output representation learning. Data scaling is an important component in data preprocessing. The objective in data scaling is to scale/transform the raw features into reasonable ranges such that each feature of an instance will be equally exploited by the machine learning model. For example, in a credit flaw detection task, a machine learning model may utilize a person's credit score and annual income as features, but because the ranges of these two features are different, a machine learning model may consider one more heavily than another. In this dissertation, I thoroughly introduce the problem in data scaling and describe an approach for data scaling which can intrinsically handle the outlier problem and lead to better model prediction performance. Learning new representations for data in the unstandardized form is a common task in data analytics and data science applications. Usually, data come in a tubular form, namely, the data is represented by a table in which each row is a feature (row) vector of an instance. However, it is also common that the data are not in this form; for example, texts, images, and video/audio records. In this dissertation, I describe the challenge of analyzing imperfect multivariate time series data in healthcare and biomedical research and show that the proposed method can learn a powerful representation to encounter various imperfections and lead to an improvement of prediction performance. Learning output representations is a new aspect of Representation Learning, and its applications have shown promising results in complex tasks, including computer vision and recommendation systems. The main objective of an output representation algorithm is to explore the relationship among the target variables, such that a prediction model can efficiently exploit the similarities and potentially improve prediction performance. In this dissertation, I describe a learning framework which incorporates output representation learning to time-to-event estimation. Particularly, the approach learns the model parameters and time vectors simultaneously. Experimental results do not only show the effectiveness of this approach but also show the interpretability of this approach from the visualizations of the time vectors in 2-D space. Learning the input (feature) representation, output representation, and predictive modeling are closely related to each other. Therefore, it is a very natural extension of the state-of-the-art by considering them together in a joint framework. In this dissertation, I describe a large-margin ranking-based learning framework for time-to-event estimation with joint input embedding learning, output embedding learning, and model parameter learning. In the framework, I cast the functional learning problem to a kernel learning problem, and by adopting the theories in Multiple Kernel Learning, I propose an efficient optimization algorithm. Empirical results also show its effectiveness on several benchmark datasets. Temple University--Theses

APA, Harvard, Vancouver, ISO, and other styles

28

Lukarov, Vlatko [Verfasser], Ulrik [Akademischer Betreuer] Schroeder, and Katrien [Akademischer Betreuer] Verbert. "Scaling up learning analytics in blended learning scenarios / Vlatko Lukarov ; Ulrik Schroeder, Katrien Verbert." Aachen : Universitätsbibliothek der RWTH Aachen, 2019. http://d-nb.info/1194184243/34.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Amarasinghe, Ishari. "The Orchestration of computer-supported collaboration scripts with learning analytics." Doctoral thesis, Universitat Pompeu Fabra, 2020. http://hdl.handle.net/10803/670420.

Full text

Abstract:

Computer-supported collaborative learning (CSCL) creates avenues for productive collaboration between students. In CSCL, collaborative learning flow patterns (CLFPs) provide pedagogical rationale and constraints for structuring the collaboration process. While structured collaboration facilitates the design of favourable learning conditions, orchestration of collaboration becomes an important factor, as learner participation and real-world constraints can create deviations in real time. On the one hand, limited research has examined the orchestration challenges related to collaborative learning situations scripted according to CLFPs in authentic educational contexts to resolve collaboration at different scales. On the other hand, learning analytics (LA) can be used to provide proper technological tooling, infrastructure and support to orchestrate collaboration. To this end, this dissertation addresses the following research question: How can LA support orchestration mechanisms for scripted CSCL? To address this question, this dissertation first focuses on studying the orchestration challenges associated with scripted CSCL situations on small scales (in the classroom learning context) and large scales (in the distance learning context, specifically in massive open online courses [MOOCs]). In the classroom learning context, lack of teacher access to activity regulation mechanisms constituted a key challenge. In MOOCs, sustained student participation in multiple phases of the script was a primary challenge. The dissertation also focuses on studying the design of LA interventions that might address the orchestration challenges under examination. The proposed LA interventions range from human-in-control to machine-in-control in nature given the feasibility and regulation needs of the learning contexts under investigation. Following a design-based research (DBR) methodology, evaluation studies were conducted in naturalistic classrooms and in MOOCs to evaluate the effects of the proposed LA interventions and to understand the conditions for their successful implementation. The results of the evaluation studies conducted in the classroom context shed light on how teachers interpret LA data and how they action the resulting knowledge in authentic collaborative learning situations. In the distance learning context, the proposed interventions were critical in sustaining continuous flows of collaboration. The practical benefits and limitations of deploying LA solutions in real-world settings, as well as future research directions, are outlined. El aprendizaje colaborativo asistido por ordenador (CSCL) ofrece oportunidades para la colaboración productiva entre estudiantes. En CSCL, los patrones de flujo de aprendizaje colaborativo (CLFP) proporcionan un fundamento pedagógico y restricciones para estructurar el proceso de colaboración. Si bien la colaboración estructurada facilita el diseño de condiciones de aprendizaje favorables, la orquestación de dicha colaboración estructurada se convierte en un factor importante, ya que la participación del alumno y los condicionantes del mundo real pueden crear desviaciones en el momento de su realización. Por un lado, existe una investigación limitada sobre los desafíos de la orquestación de aprendizaje colaborativo guiado según los CLFP en contextos educativos auténticos a diferentes escalas. Por otro lado, la analítica del aprendizaje (LA) se puede utilizar para proporcionar las herramientas tecnológicas, la infraestructura y el apoyo adecuados para orquestar la colaboración. Con este fin, esta tesis doctoral plantea la siguiente pregunta de investigación: ¿Cómo puede LA apoyar los mecanismos de orquestación de guiones de CSCL? Para abordar esta pregunta, la tesis doctoral se centra, primero, en estudiar los desafíos de la orquestación en situaciones CSCL guiadas a pequeña escala (en el contexto del aula) y a gran escala (en el contexto de aprendizaje a distancia, específicamente en cursos masivos abiertos en línea [MOOC]). En el contexto del aula, un reto imporante es la falta de acceso de los docentes a los mecanismos de regulación de la actividad. En los MOOC, el reto principal es sostener la participación de los estudiantes a lo largo de las diversas fases del guión. La tesis doctoral también se centra en estudiar el diseño de intervenciones de LA que podrían abordar los retos de orquestación detectados. Dadas las necesidades de viabilidad y regulación de los contextos de aprendizaje investigados, las intervenciones de LA propuestas van desde acciones automáticas donde la “máquina está en control” a intervenciones que implican “control por humanos”. Siguiendo una metodología de investigación basada en el diseño (DBR), se han realizado estudios en aulas y en MOOCs para evaluar los efectos de las intervenciones de LA propuestas y comprender las condiciones para su buena implementación. Los resultados de la evaluación realizada en el contexto del aula arrojan luz sobre cómo los profesores interpretan los datos de LA y cómo actúan en consecuencia en situaciones auténticas de aprendizaje colaborativo. En el contexto de la educación a distancia, las intervenciones propuestas fueron fundamentales para mantener flujos continuos de colaboración. La tesis docotral describe los beneficios prácticos y las limitaciones a la hora de implementar soluciones de LA en entornos reales, así como las direcciones de investigación futuras.

APA, Harvard, Vancouver, ISO, and other styles

30

Singhal, Mudita. "Improving protein interactions prediction using machine learning and visual analytics." Online access for everyone, 2007. http://www.dissertations.wsu.edu/Dissertations/Fall2007/m_singhal_112707.pdf.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Aberg, Cobo Ignacio. "Using behavioral analytics and machine learning to improve churn management." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111464.

Full text

Abstract:

Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, 2017. Cataloged from PDF version of thesis. Includes bibliographical references (page 36). New trends are shaping the telecommunications, media and technology (TMT) industries. Consumers are demanding to be connected anytime to hundreds of thousands of applications that are one click away. In addition, loyalty levels are decreasing and customers do not hesitate to switch providers if they do not receive value for their money. Because of this, churn management is a key driver of profits. However, few companies excel at churn management and most underestimate its impact. The thesis is focused on describing a technological solution targeted to improve churn management capabilities within companies that belong to the TMT sector and explore the opportunities and hurdles of selling this kind of solution in a B2B context. The hypothesis is that a world class churn management solution can effectively deploy statistical models to score customers by their likelihood to churn and execute targeted treatments for each segment through the operator service channels. The study will focus on how behavioral analytics and machine learning can increase customer's life time value and boost margins in TMT companies. Throughout the research, I will describe the best practices within the industry to establish a state of the art churn management solution. by Ignacio Aberg Cobo. S.M.

APA, Harvard, Vancouver, ISO, and other styles

32

Wang, Junpeng. "Interpreting and Diagnosing Deep Learning Models: A Visual Analytics Approach." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1555499299957829.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Anne, Chaitanya. "Advanced Text Analytics and Machine Learning Approach for Document Classification." ScholarWorks@UNO, 2017. http://scholarworks.uno.edu/td/2292.

Full text

Abstract:

Text classification is used in information extraction and retrieval from a given text, and text classification has been considered as an important step to manage a vast number of records given in digital form that is far-reaching and expanding. This thesis addresses patent document classification problem into fifteen different categories or classes, where some classes overlap with other classes for practical reasons. For the development of the classification model using machine learning techniques, useful features have been extracted from the given documents. The features are used to classify patent document as well as to generate useful tag-words. The overall objective of this work is to systematize NASA’s patent management, by developing a set of automated tools that can assist NASA to manage and market its portfolio of intellectual properties (IP), and to enable easier discovery of relevant IP by users. We have identified an array of methods that can be applied such as k-Nearest Neighbors (kNN), two variations of the Support Vector Machine (SVM) algorithms, and two tree based classification algorithms: Random Forest and J48. The major research steps in this work consist of filtering techniques for variable selection, information gain and feature correlation analysis, and training and testing potential models using effective classifiers. Further, the obstacles associated with the imbalanced data were mitigated by adding synthetic data wherever appropriate, which resulted in a superior SVM classifier based model.

APA, Harvard, Vancouver, ISO, and other styles

34

Bodily, Robert Gordon. "Designing, Developing, and Implementing Real-Time Learning Analytics Student Dashboards." BYU ScholarsArchive, 2018. https://scholarsarchive.byu.edu/etd/7258.

Full text

Abstract:

This document is a multiple-article format dissertation that discusses the iterative design, development, and evaluation processes necessary to create high quality learning analytics dashboard systems. With the growth of online and blended learning environments, the amount of data that researchers and practitioners collect from learning experiences has also grown. The field of learning analytics is concerned with using this data to improve teaching and learning. Many learning analytics systems focus on instructors or administrators, but these tools fail to involve students in the data-driven decision-making process. Providing feedback to students and involving students in this decision-making process can increase intrinsic motivation and help students succeed in online and blended environments. To support online and blended teaching and learning, the focus of this document is student-facing learning analytics dashboards. The first article in this dissertation is a literature review on student-facing learning analytics reporting systems. This includes any system that tracks learning analytics data and reports it directly to students. The second article in this dissertation is a design and development research article that used a practice-centered approach to iteratively design and develop a real-time student-facing dashboard. The third article in this dissertation is a design-based research article focused on improving student use of learning analytics dashboard tools.

APA, Harvard, Vancouver, ISO, and other styles

35

Chen, Xin. "Be the Data: Embodied Visual Analytics." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/72287.

Full text

Abstract:

With the rise of big data, it is becoming increasingly important to educate students about data analytics. In particular, students without a strong mathematical background usually have an unenthusiastic attitude towards high-dimensional data and find it challenging to understand relevant complex analytical methods, such as dimension reduction. In this thesis, we present an embodied approach for visual analytics designed to teach students exploring alternative 2D projections of high dimensional data points using weighted multidimensional scaling. We proposed a novel application, Be the Data, to explore the possibilities of using human's embodied resources to learn from high dimensional data. In our system, each student embodies a data point and the position of students in a physical space represents a 2D projection of the high-dimensional data. Students physically moves in a room with respect to others to interact with alternative projections and receive visual feedback. We conducted educational workshops with students inexperienced in relevant data analytical methods. Our findings indicate that the students were able to learn about high-dimensional data and data analysis process despite their low level of knowledge about the complex analytical methods. We also applied the same techniques into social meetings to explain social gatherings and facilitate interactions. Master of Science

APA, Harvard, Vancouver, ISO, and other styles

36

Ng, Wartini. "Contemporary data analytics for soil spectroscopy." Thesis, The University of Sydney, 2019. http://hdl.handle.net/2123/21071.

Full text

Abstract:

No soil, no life. Know soil, know life. Soil provides the basis for life. To promote soil security, soil monitoring is essential. However, conventional methods of soil analysis are costly and time-consuming. This thesis explores contemporary data analytics for analyzing soil infrared spectroscopy data. New data analytics take soil infrared spectral data and convert them to soil properties that are useful for assessing its conditions. This thesis deals with issues of sampling, spectral reduction techniques, deep learning models, and application in soil contamination assessment. Soil spectral data has to be trained using machine learning models to provide predictions for soil properties. The effect of sampling size and designs on the model performance were evaluated. Various ways of spectra data dimension reductions were explored using variable selection techniques to prevent model overfitting when a limited number of samples was available. To deal with large data collected from regional and national soil spectral libraries, deep learning techniques were explored. The convolutional neural network (CNN) was demonstrated as a highly accurate method for predicting soil properties on a large database. A method was derived to enable the interpretability of the CNN model. The application of infrared spectroscopy in screening soil contaminants (microplastics and petroleum hydrocarbons) were illustrated. Collectively, this thesis provides novel data analytics that enabled enhanced applications of infrared spectroscopy in soil science.

APA, Harvard, Vancouver, ISO, and other styles

37

Gibson, Andrew P. "Reflective writing analytics and transepistemic abduction." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/106952/1/Andrew_Gibson_Thesis.pdf.

Full text

Abstract:

This thesis presents a model of Reflective Writing Analytics which brings together two distinct ways of knowing: the human world of individuals in society, and the machine world of computers and mathematics. The thesis presents a specialised mode of reasoning called Transepistemic Abduction which provides a way of justifying intuition and heuristic approaches to computational analysis of reflective writing.

APA, Harvard, Vancouver, ISO, and other styles

38

Chen, Jingjing. "Enhancing student engagement and interaction in e-learning environments through learning analytics and wearable sensing." HKBU Institutional Repository, 2016. https://repository.hkbu.edu.hk/etd_oa/287.

Full text

Abstract:

E-learning refers to computer-based learning experiences, self-paced or instructor-led, supported and enabled by information technology. Virtual Learning Environments (VLEs), as a major form of e-learning systems, are increasingly adopted in universities and educational institutions for supporting various types of learning. Student engagement is critical for successful teaching and learning in VLEs. In existing VLEs, feeling isolated without adequate supervision from teachers may cause negative emotions such as anxiety. Such emotions may in turn significantly weaken students'motivation to engage in learning activities. In addition, the lack of effective interaction in learning activities also results in poor performance and engagement, even dropouts from online courses. In this thesis, we explore a set of approaches and tools to enhance student engagement and interaction in e-learning environments: (1) extract valuable information from the user posts in online course forums to advise the content organization of web pages; (2) instantly monitor and visualize students' interaction statuses in instructor-led learning; (3) identify and highlight the hotspot time slots and contents of the lecture recordings; (4) dynamically provide biofeedback-based visualization via wearable devices to reduce students' anxiety in self-paced learning.;We present a page-segmentation-based wrapper (eCF-wrapper) designed for extracting learner-posted data in online course forums. It consists of a novel page segmentation algorithm and a decision tree classifier. We also develop a web-based interaction-aware VLE (WebIntera-classroom), which employs a ubiquitous interactive interface to enhance the learner-to-content interactions, and a learning analytics tool to instantly visualize learners'interactions in learning activities. Additionally, we propose a high--granularity Learning Analytics Engine (hgLAE) to play a lecture recording, identify hotspots in a lecture recording and raise students'awareness of these hotspots. A questionnaire survey, interview and case study were conducted to investigate the instruction effect of WebIntera-classroom. Besides, we develop a physiologically-state-aware self-paced learning environment (FishBuddy) to alleviate anxiety and promote student engagement in self-paced learning by using wearable technology. The between-groups evaluation result shows that FishBuddy is useful in promoting student engagement (i.e., the consistency of engagement), and the students' self-reports indicate that FishBuddy is helpful for reducing anxiety and experience of isolation during the self-paced learning exercises.;Finally, the thesis is concluded with a discussion on the future work. Keywords: Virtual Learning Environment; Learning Analytics; Interaction; Engagement; Wearable Technology.

APA, Harvard, Vancouver, ISO, and other styles

39

Jofre, Alegria Maria Paz. "Fighting Accounting Fraud through Forensic Analytics." Thesis, The University of Sydney, 2017. http://hdl.handle.net/2123/17826.

Full text

Abstract:

Accounting Fraud is one of the most harmful financial crimes as it often results in massive corporate collapses, commonly silenced by powerful high-status executives and managers. Accounting fraud represents a significant threat to the financial system stability due to the resulting diminishing of the market confidence and trust of regulatory authorities. Its catastrophic consequences expose how vulnerable and unprotected the community is in regards to this matter, since most damage is inflicted to investors, employees, customers and government. Accounting fraud is defined as the calculated misrepresentation of the financial statement information disclosed by a company in order to mislead stakeholders regarding the firm’s true financial position. Different fraudulent tricks can be used to commit accounting fraud, either direct manipulation of financial items or creative methods of accounting, hence the need for non-static regulatory interventions that take into account different fraudulent patterns. Accordingly, this study aims to identify signs of accounting fraud occurrence to be used to, first, identify companies that are more likely to be manipulating financial statement reports, and second, assist the task of examination within the riskier firms by evaluating relevant financial red-flags, as to efficiently recognise irregular accounting malpractices. To achieve this, a thorough forensic data analytic approach is proposed that includes all pertinent steps of a data-driven methodology. First, data collection and preparation is required to present pertinent information related to fraud offences and financial statements. The compiled sample of known fraudulent companies is identified considering all Accounting Series Releases and Accounting and Auditing Enforcement Releases issued by the U.S. Securities and Exchange Commission between 1990 and 2012, procedure that resulted in 1,594 fraud-year observations. Then, an in-depth financial ratio analysis is performed in order to evaluate publicly available financial statement data and to preserve only meaningful predictors of accounting fraud. In particular, two commonly used statistical approaches, including non-parametric hypothesis testing and correlation analysis, are proposed to assess significant differences between corrupted and genuine reports as well as to identify associations between the considered ratios. The selection of a smaller subset of explanatory variables is later reinforced by the implementation of a complete subset logistic regression methodology. Finally, statistical modelling of fraudulent and non-fraudulent instances is performed by implementing several machine learning methods. Classical classifiers are considered first as benchmark frameworks, including logistic regression and discriminant analysis. More complex techniques are implemented next based on decision trees bagging and boosting, including bagged trees, AdaBoost and random forests. In general, it can be said that a clear enhancement in the understanding of the fraud phenomenon is achieved by the implementation of financial ratio analysis, mainly due to the interesting exposure of distinctive characteristics of falsified reporting and the selection of meaningful ratios as predictors of accounting fraud, later validated using a combination of logistic regression models. Interestingly, using only significant explanatory variables leads to similar results obtained when no selection is performed. Furthermore, better performance is accomplished in some cases, which strongly evidences the convenience of employing less but significant information when detecting accounting fraud offences. Moreover, out-of-sample results suggest there is a great potential in detecting falsified accounting records through statistical modelling and analysis of publicly available accounting information. It has been shown good performance of classic models used as benchmark and better performance of more advanced methods, which supports the usefulness of machine learning models as they appropriately meet the criteria of accuracy, interpretability and cost-efficiency required for a successful detection methodology. This study contributes in the improvement of accounting fraud detection in several ways, including the collection of a comprehensive sample of fraud and non-fraud firms concerning all financial industries, an extensive analysis of financial information and significant differences between genuine and fraudulent reporting, selection of relevant predictors of accounting fraud, contingent analytical modelling for better differentiate between non-fraud and fraud cases, and identification of industry-specific indicators of falsified records. The proposed methodology can be easily used by public auditors and regulatory agencies in order to assess the likelihood of accounting fraud and to be adopted in combination with the experience and instinct of experts to lead to better examination of accounting reports. In addition, the proposed methodological framework could be of assistance to many other interested parties, such as investors, creditors, financial and economic analysts, the stock exchange, law firms and to the banking system, amongst others.

APA, Harvard, Vancouver, ISO, and other styles

40

Navarrete, Daniel. "Learning Analytics Perú: Plataforma de desarrollo para la Analítica del Aprendizaje en el Perú." Universidad Peruana de Ciencias Aplicadas (UPC), 2019. http://hdl.handle.net/10757/624844.

Full text

Abstract:

Accede a la filmación de la conferencia en : http://hdl.handle.net/10757/624838 Presentación realizada en el marco de la primera meetup del 2019 de la comunidad Learning Analytics Perú, donde se conversó sobre experiencias de éxito en el uso de herramientas, estrategias y métodos que han conducido a resultados satisfactorios en la aplicación estratégica de los datos en Educación Superior, de la mano de profesionales de Perú, Colombia y Ecuador.

APA, Harvard, Vancouver, ISO, and other styles

41

Siameh, Theophilus. "Graph Analytics Methods In Feature Engineering." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etd/3307.

Full text

Abstract:

High-dimensional data sets can be difficult to visualize and analyze, while data in low-dimensional space tend to be more accessible. In order to aid visualization of the underlying structure of a dataset, the dimension of the dataset is reduced. The simplest approach to accomplish this task of dimensionality reduction is by a random projection of the data. Even though this approach allows some degree of visualization of the underlying structure, it is possible to lose more interesting underlying structure within the data. In order to address this concern, various supervised and unsupervised linear dimensionality reduction algorithms have been designed, such as Principal Component Analysis and Linear Discriminant Analysis. These methods can be powerful, but often miss important non-linear structure in the data. In this thesis, manifold learning approaches to dimensionality reduction are developed. These approaches combine both linear and non-linear methods of dimension reduction.

APA, Harvard, Vancouver, ISO, and other styles

42

Di, Silvestro Lorenzo Paolo. "Data Mining and Visual Analytics Techniques." Doctoral thesis, Università di Catania, 2014. http://hdl.handle.net/10761/1559.

Full text

Abstract:

With the beginning of the Information Age and the following spread of the information overload phenomenon, it has been mandatory to develop a means to simply explore, analyze and summarize large quantity of data. To achieve this purposes a data mining techniques and information visualization methods are used since decades. In the last years a new research field is gaining importance: Visual Analytics, an outgrowth of the fields of scientific and information visualization but includes technologies from many other fields, including knowledge management, statistical analysis, cognitive science and decision science. In this dissertation the combined effort of the mentioned research fields will be analyzed, pointing out different way to combine them following the best practice according to several application cases.

APA, Harvard, Vancouver, ISO, and other styles

43

Sarzosa, Daniela. "Laboratoria: Talent that transforms." Universidad Peruana de Ciencias Aplicadas (UPC), 2019. http://hdl.handle.net/10757/624845.

Full text

Abstract:

Accede a la filmación de la conferencia en : http://hdl.handle.net/10757/624838 Presentación realizada en el marco de la primera meetup del 2019 de la comunidad Learning Analytics Perú, donde se conversó sobre experiencias de éxito en el uso de herramientas, estrategias y métodos que han conducido a resultados satisfactorios en la aplicación estratégica de los datos en Educación Superior, de la mano de profesionales de Perú, Colombia y Ecuador.

APA, Harvard, Vancouver, ISO, and other styles

44

Pérez, Jimmy. "Uso Estratégico de la Información de estudiantes de Educación Superior." Universidad Peruana de Ciencias Aplicadas (UPC), 2019. http://hdl.handle.net/10757/624846.

Full text

Abstract:

Parte de la conferencia: Experiencias exitosas en el uso de learning analytics en educación superior. Accede a la filmación de la conferencia en : http://hdl.handle.net/10757/624838 Presentación realizada en el marco de la primera meetup del 2019 de la comunidad Learning Analytics Perú, donde se conversó sobre experiencias de éxito en el uso de herramientas, estrategias y métodos que han conducido a resultados satisfactorios en la aplicación estratégica de los datos en Educación Superior, de la mano de profesionales de Perú, Colombia y Ecuador.

APA, Harvard, Vancouver, ISO, and other styles

45

Maldonado, Jorge. "LALA Project: Building capacity to use a Learning Analytics to improve higher education in Latin América." Universidad Peruana de Ciencias Aplicadas (UPC), 2019. http://hdl.handle.net/10757/624847.

Full text

Abstract:

Parte de la conferencia: Experiencias exitosas en el uso de learning analytics en educación superior. Accede a la filmación de la conferencia en : http://hdl.handle.net/10757/624838 Presentación realizada en el marco de la primera meetup del 2019 de la comunidad Learning Analytics Perú, donde se conversó sobre experiencias de éxito en el uso de herramientas, estrategias y métodos que han conducido a resultados satisfactorios en la aplicación estratégica de los datos en Educación Superior, de la mano de profesionales de Perú, Colombia y Ecuador.

APA, Harvard, Vancouver, ISO, and other styles

46

Janmohammadi, Siamak. "Classifying Pairwise Object Interactions: A Trajectory Analytics Approach." Thesis, University of North Texas, 2015. https://digital.library.unt.edu/ark:/67531/metadc801901/.

Full text

Abstract:

We have a huge amount of video data from extensively available surveillance cameras and increasingly growing technology to record the motion of a moving object in the form of trajectory data. With proliferation of location-enabled devices and ongoing growth in smartphone penetration as well as advancements in exploiting image processing techniques, tracking moving objects is more flawlessly achievable. In this work, we explore some domain-independent qualitative and quantitative features in raw trajectory (spatio-temporal) data in videos captured by a fixed single wide-angle view camera sensor in outdoor areas. We study the efficacy of those features in classifying four basic high level actions by employing two supervised learning algorithms and show how each of the features affect the learning algorithms’ overall accuracy as a single factor or confounded with others.

APA, Harvard, Vancouver, ISO, and other styles

47

Malazgirt, Gorker Alp. "Advanced analytics through FPGA based query processing and deep reinforcement learning." Doctoral thesis, Universitat Politècnica de Catalunya, 2019. http://hdl.handle.net/10803/665699.

Full text

Abstract:

Today, vast streams of structured and unstructured data have been incorporated in databases, and analytical processes are applied to discover patterns, correlations, trends and other useful relationships that help to take part in a broad range of decision-making processes. The amount of generated data has grown very large over the years, and conventional database processing methods from previous generations have not been sufficient to provide satisfactory results regarding analytics performance and prediction accuracy metrics. Thus, new methods are needed in a wide array of fields from computer architectures, storage systems, network design to statistics and physics. This thesis proposes two methods to address the current challenges and meet the future demands of advanced analytics. First, we present AxleDB, a Field Programmable Gate Array based query processing system which constitutes the frontend of an advanced analytics system. AxleDB melds highly-efficient accelerators with memory, storage and provides a unified programmable environment. AxleDB is capable of offloading complex Structured Query Language queries from host CPU. The experiments have shown that running a set of TPC-H queries, AxleDB can perform full queries between 1.8x and 34.2x faster and 2.8x to 62.1x more energy efficient compared to MonetDB, and PostgreSQL on a single workstation node. Second, we introduce TauRieL, a novel deep reinforcement learning (DRL) based method for combinatorial problems. The design idea behind combining DRL and combinatorial problems is to apply the prediction capabilities of deep reinforcement learning and to use the universality of combinatorial optimization problems to explore general purpose predictive methods. TauRieL utilizes an actor-critic inspired DRL architecture that adopts ordinary feedforward nets. Furthermore, TauRieL performs online training which unifies training and state space exploration. The experiments show that TauRieL can generate solutions two orders of magnitude faster and performs within 3% of accuracy compared to the state-of-the-art DRL on the Traveling Salesman Problem while searching for the shortest tour. Also, we present that TauRieL can be adapted to the Knapsack combinatorial problem. With a very minimal problem specific modification, TauRieL can outperform a Knapsack specific greedy heuristics. Hoy en día, se han incorporado grandes cantidades de datos estructurados y no estructurados en las bases de datos, y se les aplican procesos analíticos para descubrir patrones, correlaciones, tendencias y otras relaciones útiles que se utilizan mayormente para la toma de decisiones. La cantidad de datos generados ha crecido enormemente a lo largo de los años, y los métodos de procesamiento de bases de datos convencionales utilizados en las generaciones anteriores no son suficientes para proporcionar resultados satisfactorios respecto al rendimiento del análisis y respecto de la precisión de las predicciones. Por lo tanto, se necesitan nuevos métodos en una amplia gama de campos, desde arquitecturas de computadoras, sistemas de almacenamiento, diseño de redes hasta estadísticas y física. Esta tesis propone dos métodos para abordar los desafíos actuales y satisfacer las demandas futuras de análisis avanzado. Primero, presentamos AxleDB, un sistema de procesamiento de consultas basado en FPGAs (Field Programmable Gate Array) que constituye la interfaz de un sistema de análisis avanzado. AxleDB combina aceleradores altamente eficientes con memoria, almacenamiento y proporciona un entorno programable unificado. AxleDB es capaz de descargar consultas complejas de lenguaje de consulta estructurado desde la CPU del host. Los experimentos han demostrado que al ejecutar un conjunto de consultas TPC-H, AxleDB puede realizar consultas completas entre 1.8x y 34.2x más rápido y 2.8x a 62.1x más eficiente energéticamente que MonetDB, y PostgreSQL en un solo nodo de una estación de trabajo. En segundo lugar, presentamos TauRieL, un nuevo método basado en Deep Reinforcement Learning (DRL) para problemas combinatorios. La idea central que está detrás de la combinación de DRL y problemas combinatorios, es aplicar las capacidades de predicción del aprendizaje de refuerzo profundo y el uso de la universalidad de los problemas de optimización combinatoria para explorar métodos predictivos de propósito general. TauRieL utiliza una arquitectura DRL inspirada en el actor-crítico que se adapta a redes feedforward. Además, TauRieL realiza el entrenamieton en línea que unifica el entrenamiento y la exploración espacial de los estados. Los experimentos muestran que TauRieL puede generar soluciones dos órdenes de magnitud más rápido y funciona con un 3% de precisión en comparación con el estado del arte en DRL aplicado al problema del viajante mientras busca el recorrido más corto. Además, presentamos que TauRieL puede adaptarse al problema de la Mochila. Con una modificación específica muy mínima del problema, TauRieL puede superar a una heurística codiciosa de Knapsack Problem.

APA, Harvard, Vancouver, ISO, and other styles

48

Dyckhoff, Anna Lea [Verfasser]. "Action research and learning analytics in higher education / Anna Lea Dyckhoff." Aachen : Hochschulbibliothek der Rheinisch-Westfälischen Technischen Hochschule Aachen, 2014. http://d-nb.info/1065353847/34.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Parikh, Neena (Neena S. ). "Interactive tools for fantasy football analytics and predictions using machine learning." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100687.

Full text

Abstract:

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015. Cataloged from PDF version of thesis. Includes bibliographical references (pages 83-84). The focus of this project is multifaceted: we aim to construct robust predictive models to project the performance of individual football players, and we plan to integrate these projections into a web-based application for in-depth fantasy football analytics. Most existing statistical tools for the NFL are limited to the use of macro-level data; this research looks to explore statistics at a finer granularity. We explore various machine learning techniques to develop predictive models for different player positions including quarterbacks, running backs, wide receivers, tight ends, and kickers. We also develop an interactive interface that will assist fantasy football participants in making informed decisions when managing their fantasy teams. We hope that this research will not only result in a well-received and widely used application, but also help pave the way for a transformation in the field of football analytics. by Neena Parikh. M. Eng.

APA, Harvard, Vancouver, ISO, and other styles

50

Fosca, Gamarra Almudena. "Predicción de precios de commoditties empleando data analytics y machine learning." Bachelor's thesis, Pontificia Universidad Católica del Perú, 2019. http://hdl.handle.net/20.500.12404/17338.

Full text

Abstract:

Esta investigación explorará las aplicaciones de herramientas de Machine Learning en el campo financiero. Se incluyen trabajos previos para el pronóstico de acciones, índices bursátiles y commodities, pudiendo comparar y contrastar los resultados obtenidos al aplicar diversos algoritmos. De esta forma, se emplean los estudios previos presentados como base para la elaboración de una tesis de bachillerato que tiene como objetivo pronosticar el precio del cobre empleando modelos de Machine Learning. Trabajo de investigación

APA, Harvard, Vancouver, ISO, and other styles

Dissertations / Theses on the topic 'Learning Analytics (LA)'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles