Academic data derived from a university e-government analytic platform: An educational data mining approach

Chytas, Konstantinos; Tsolakidis, Anastasios; Triperina, Evangelia; Karanikolas, Nikitas N.; Skourlas, Christos

doi:10.1016/j.dib.2023.109357

Cited by 2 publications

(1 citation statement)

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“…They compared their model with several other frequently used machine learning algorithms. 60 used EDM to describe academic data gathered from a Greek university e-government analytic platform and determine the quality of academic outputs learning progress through the years. They collected attributes on 4765 students including 1661 unique students using records on 20 courses taught during the COVID-19 pandemic and used EDM for their analysis and gave insights on how to enhance the learning process and the quality of educational services.…”

Section: Literature Reviewmentioning

confidence: 99%

Regularized ensemble learning for prediction and risk factors assessment of students at risk in the post-COVID era

Khan,

Ali,

Khan

et al. 2024

Sci Rep

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The COVID-19 pandemic has had a significant impact on students’ academic performance. The effects of the pandemic have varied among students, but some general trends have emerged. One of the primary challenges for students during the pandemic has been the disruption of their study habits. Students getting used to online learning routines might find it even more challenging to perform well in face to face learning. Therefore, assessing various potential risk factors associated with students low performance and its prediction is important for early intervention. As students’ performance data encompass diverse behaviors, standard machine learning methods find it hard to get useful insights for beneficial practical decision making and early interventions. Therefore, this research explores regularized ensemble learning methods for effectively analyzing students’ performance data and reaching valid conclusions. To this end, three pruning strategies are implemented for the random forest method. These methods are based on out-of-bag sampling, sub-sampling and sub-bagging. The pruning strategies discard trees that are adversely affected by the unusual patterns in the students data forming forests of accurate and diverse trees. The methods are illustrated on an example data collected from university students currently studying on campus in a face-to-face modality, who studied during the COVID-19 pandemic through online learning. The suggested methods outperform all the other methods considered in this paper for predicting students at the risk of academic failure. Moreover, various factors such as class attendance, students interaction, internet connectivity, pre-requisite course(s) during the restrictions, etc., are identified as the most significant features.

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Section: Literature Reviewmentioning

confidence: 99%