Mining activity grades to model students' performance

Peña, David de la; Lara, Juan A.; Lizcano, David; Martínez, María A.; Burgos, Concepcion; Campanario, Maria L.

doi:10.1109/icemis.2017.8272963

Cited by 10 publications

(2 citation statements)

References 15 publications

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“…Dropout prediction systems can signal which students need special attention from tutors, and corrective action can be taken to prevent these students from dropping out (Acevedo et al , 2018; Nasiri et al , 2012; de la et al , 2017). The systems can provide an alert regarding student performance and behavior so that interventions become more assertive (Shrestha and Pokharel, 2019) or even systematically suggest interventions that can be adopted (Ortigosa et al , 2019).…”

Section: Analysis Of Research Questionmentioning

confidence: 99%

Educational data mining and learning analytics: a review of educational management in e-learning

Rabelo

Rodrigues

Nobre

et al. 2023

IDD

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Purpose The purpose of this study is to identify the main perspectives and trends in educational data mining (EDM) in the e-learning environment from a managerial perspective. Design/methodology/approach This paper proposes a systematic literature review to identify the main perspectives and trends in EDM in the e-learning environment from a managerial perspective. The study domain of this review is restricted by the educational concepts of e-learning and management. The search for bibliographic material considered articles published in journals and papers published in conferences from 1994 to 2023, totaling 30 years of research in EDM. Findings From this review, it was observed that managers have been concerned about the effectiveness of the platform used by students as it contains the entire learning process and all the interactions performed, which enable the generation of information. From the data collected on these platforms, there are improvements and inferences that can be made about the actions of educators and human tutors (or automatic tutoring systems), curricular optimization or changes related to course content, proposal of evaluation criteria and also increase the understanding of different learning styles. Originality/value This review was conducted from the perspective of the manager, who is responsible for the direction of an institution of higher education, to assist the administration in creating strategies for the use of data mining to improve the learning process. To the best of the authors’ knowledge, this review is original because other contributions do not focus on the manager.

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Section: Analysis Of Research Questionmentioning

confidence: 99%

Educational data mining and learning analytics: a review of educational management in e-learning

Rabelo

Rodrigues

Nobre

et al. 2023

IDD

View full text Add to dashboard Cite

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“…Highlight for the Bayesian Table 3 Articles selected for the research that address dropout in distance education Generalized Linear Model algorithm, accurately predicting the conclusion more significant than the dropout measured by the Stochastic Gradient Boosting algorithm. De La Peña et al (2018) propose the use of data mining techniques, specifically Logistic Regression, to predict whether the student would abandon the course based on the grades of activities obtained by each stored in Moodle. The results obtained from applying the Logit Act tool to more than 100 students in 5 courses were slightly better than the existing proposals in terms of accuracy, especially in the crucial weeks of the semester.…”

Section: Rq1: Has Dropout In Distance Education Been An Object Of Study?mentioning

confidence: 99%

Active Methodology, Educational Data Mining and Learning Analytics: A Systematic Mapping Study

Andrade¹,

Rigo²,

Barbosa³

2021

Informatics in Education

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Distance Learning has enabled educational practices based on digital platforms, generating massive amounts of data. Several initiatives use this data to identify dropout contexts, mainly providing teacher support about student behavior. Approaches such as Active Methodologies are known as having good potential to involve and motivate students. This article presents a systematic mapping aiming to identify current Educational Data Mining and Learning Analytics methods. Besides, we identify Active Methodologies' application to mitigate dropout in Distance Learning. We evaluated 668 papers published from January 2015 to March 2020. The results indicate a growing application of Educational Data Mining and Learning Analytics to identify and mitigate students' abandonment in Distance Learning. However, studies with Active Methodologies to minimize dropout and enhance student permanence are scarce. Some works suggest Active Methods as a possible complement of Learning Analytics in dropout.

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