Educational data mining and data analysis for optimal learning content management: Applied in moodle for undergraduate engineering studies

Charitopoulos, Angelos; Rangoussi, Maria; Koulouriotis, Dimitrios E.

doi:10.1109/educon.2017.7942969

Cited by 9 publications

(12 citation statements)

References 18 publications

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“…A custom plug-in for moodle has been coded and embedded in the platform, to extract the specific set of variables and to format and preprocess them for further analysis, as detailed in [27]. It has been put to pilot use with the student cohorts of academic years 2015-16 and 2016-17, for verification purposes; preliminary results can be found in [28], [29].…”

Section: Methods and Toolsmentioning

confidence: 99%

Blending E-Learning with Hands-on Laboratory Instruction in Engineering Education

Charitopoulos

Rangoussi

Koulouriotis

2022

Int. J. Emerg. Technol. Learn.

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Among the various information sources exploited for the improvement of the learning process and outcomes, access and usage data from the interaction of students with e-learning platforms along with (past) student performance data are established as the two most meaningful and informative groups of variables. In the present study, these two groups of variables are jointly investigated as to their efficiency in providing both accurate and early prediction of student performance and behavior. The relevant educational intervention is designed and implemented as a quasi-experiment with undergraduate Electrical and Electronics Engineering students, under a novel approach that blends e-learning (asynchronous e-study and synchronous e-assessment) with a hands-on laboratory component. Can educational data mining algorithms provide both early and accurate prediction of student performance and student behavior under this scenario? If yes, how much prediction accuracy can be traded for prediction timeliness in order to allow a proactive class instructor take supportive measures for weak/marginal students, implementing a ‘self-contained’ strategy? To answer these questions, real data from the interaction of 3 academic year student cohorts with moodle are collected and analyzed. Results reveal that the proposed scenario can afford both accurate and early prediction of student performance and behavior, on the basis of data collected within the running academic term. The middle of the term is indicated as the earliest time point for getting meaningful predictions. Moreover, clustering of the data in the selected feature space reveals a consistent and therefore exploitable behavior of students along the term.

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Section: Methods and Toolsmentioning

confidence: 99%

Blending E-Learning with Hands-on Laboratory Instruction in Engineering Education

Charitopoulos

Rangoussi

Koulouriotis

2022

Int. J. Emerg. Technol. Learn.

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“…9. Constructing course content: The objective is to help instructors and developers automatically develop the course and learning content [43]. It also promotes the exchange of existing learning resources among different systems-the clustering DM technique used to develop personalized courseware by building a personalized web tutor tree [8].…”

Section: Applications Of Big Educational Datamentioning

confidence: 99%

Big Data Application and its Impact on Education

Khan

Alqahtani

2020

Int. J. Emerg. Technol. Learn.

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Big data is employed in widely different fields; we here study how education uses big data. We review the literature of the research about big data in education in the time interval from 2010 to 2020 then review the process of big educational data mining, the tools, and the applications of big data in education. This paper, with the help of these applications, explores the idea to improve the education process. Two methods are applied to validate education process and many parameters are discussed to complete the research.

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“…The approach has been used to develop a system that will alert educators regarding the students that plausibly failed the course [17]. LMS log has also been used to estimate learners' performance [18], model learners' approach to studying [19], [20], and how learners interact with the LMS [21]. It is also used as the basis for learning analytics, to predict learners' performance [18].…”

Section: Introductionmentioning

confidence: 99%

Mixed-type Variables Clustering for Learners’ Behavior in Flipped Classroom Implementation

Sengkey

Masengi

2023

IJEEI

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Numerous approaches have been developed to group learners' behavior in an online/blended learning environment. However, most clustering analyses in this particular field only consider numeric features despite the existence of categoric features that are found important in other studies. In this study, we compare K-Means and K-Prototypes algorithms to cluster learners' behavior in a flipped classroom implementation. From the model selection, we found that the model produced by the K-Prototypes algorithmwhich included categoric featuresis a better one. The statistical analysis of the clustering results of the selected K-Prototypes model shows significant differences in most of the inter-cluster comparisons, implying a good separation of the data. More importantly, we can identify the behavior in each cluster which then can be used to help learners in achieving better results in learning.

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Educational data mining and data analysis for optimal learning content management: Applied in moodle for undergraduate engineering studies

Cited by 9 publications

References 18 publications

Blending E-Learning with Hands-on Laboratory Instruction in Engineering Education

Blending E-Learning with Hands-on Laboratory Instruction in Engineering Education

Big Data Application and its Impact on Education

Mixed-type Variables Clustering for Learners’ Behavior in Flipped Classroom Implementation

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