CoChat
Sign Up
Cited by 51Online Learning and AnalyticsEducational Technology and PedagogyArtificial Intelligence in Healthcare

Predicting Student Performance and Enhancing Learning Outcomes: A Data-Driven Approach Using Educational Data Mining Techniques

Open access
Angeioplastis, Athanasios, Aliprantis, John, Konstantakis, Markos, Tsimpiris, Alkiviadis·2025·Computers
DOI 10.3390/computers14030083
Open this paper in CoChat — free account

Abstract

This study investigates the use of educational data mining (EDM) techniques to predict student performance and enhance learning outcomes in higher education. Leveraging data from Moodle, a widely used learning management system (LMS), we analyzed 450 students’ academic records spanning nine semesters. Five machine learning algorithms—k-nearest neighbors, random forest, logistic regression, decision trees, and neural networks—were applied to identify correlations between courses and predict grades. The results indicated that courses with strong correlations (+0.3 and above) significantly enhanced predictive accuracy, particularly in binary classification tasks. kNN and neural networks emerged as the most robust models, achieving F1 scores exceeding 0.8. These findings underscore the potential of EDM to optimize instructional strategies and support personalized learning pathways. This study offers insights into the effective application of data-driven approaches to improve educational outcomes and foster student success.

Integrity signals

Registry

Crossref · 10.3390/computers14030083

Cites

21 of 21 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Our recent study [1] applied multiple classifiers (including k-nearest neighbors, random forests, and neural networks) to Moodle log data from 450 students, achieving high predictive performance (F1 scores exceeding 0.8) for early identification of at-risk learners.

    “Building on this line of work, our recent study [1] applied multiple classifiers (including k-nearest neighbors, random forests, and neural networks) to Moodle log data from 450 students, achieving high predictive performance (F1 scores exceeding 0.8) for early identification of at-risk learners.”

    From AI for All: Adaptive, Accessible, and Inclusive Learning Experiences in the Age of Intelligent LMSs (2026) · cites this paper as [1]

  • Such rigidity can result in disengagement and inequitable learning experiences, particularly for learners with diverse cognitive preferences, accessibility needs, or varying levels of prior knowledge.

    “Such rigidity can result in disengagement and inequitable learning experiences, particularly for learners with diverse cognitive preferences, accessibility needs, or varying levels of prior knowledge [1,2].”

    From AI for All: Adaptive, Accessible, and Inclusive Learning Experiences in the Age of Intelligent LMSs (2026) · cites this paper as [1]

  • Learning Management Systems (LMSs) remain largely static and administrative, often failing to support personalization and inclusive access to learning resources.

    “Learning Management Systems (LMSs) remain largely static and administrative, often failing to support personalization and inclusive access to learning resources.”

    From AI for All: Adaptive, Accessible, and Inclusive Learning Experiences in the Age of Intelligent LMSs (2026) · cites this paper as [1]

  • Traditional LMS platforms typically follow a one-size-fits-all paradigm that inadequately accommodates individual learner differences.

    “traditional LMS platforms typically follow a one-size-fits-all paradigm that inadequately accommodates individual learner differences.”

    From AI for All: Adaptive, Accessible, and Inclusive Learning Experiences in the Age of Intelligent LMSs (2026) · cites this paper as [1]

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

Authors
Angeioplastis, A., Aliprantis, J., Konstantakis, M., & Tsimpiris, A.
Affiliations
  • International Hellenic University (GR)
  • University of the Aegean (GR)
Corresponding author
Konstantakis, Markos
ORCID
Venue
Computers
Publisher
MDPI AG
Published
2025-02-27
Volume
14
Issue
3
Pages
83
ISSN
2073-431X (ISSN-L)
DOI
10.3390/computers14030083
OpenAlex
W4408026792
Cited by
50
References
21
Citations by year
2026: 29 · 2025: 21
FWCI
44.69
Type
journal article
Language
en
Open access
Licence
Locations
  • Computers (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
  • Physical Sciences › Computer Science › Artificial Intelligence › Educational Technology and Pedagogy
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
student performance prediction, learning outcomes, educational data mining, Moodle, learning management system data, grade prediction, k-nearest neighbors, random forest, artificial neural networks, personalized learning, higher education

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review