American International Journal of Research and Innovation

E-ISSN: XXXX-XXXX   •   Impact Factor: -

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 8, Issue 5 (September-October 2026) Submit your research before last 3 days of October to publish your research paper in the issue of September-October.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 8 Issue 5
September-October 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Learning Analytics for the Early Identification of At-Risk Students

Author(s) Dr. Hendrik Drachsler
Country United States
Abstract The increasing availability of digital learning platforms has generated significant opportunities for using data-driven approaches to improve student success and academic support systems. Learning analytics has emerged as an important educational technology approach that enables institutions to collect, analyse, and interpret learning-related data to understand student behaviour and identify learners who may require additional academic assistance.
This study examines the role of learning analytics in the early identification of at-risk students in higher education. The research explores how academic performance indicators, learning platform interactions, attendance patterns, assessment activities, and engagement behaviours can be analysed to predict potential academic difficulties. A conceptual analytical framework supported by simulated observations is developed to evaluate the contribution of learning analytics toward improving early intervention strategies.
The findings indicate that learning analytics can enhance student support by enabling timely identification of academic risks, personalized interventions, and evidence-based decision-making. However, challenges including data privacy concerns, algorithmic accuracy limitations, institutional readiness, and ethical issues continue to influence successful implementation.
The study contributes to educational technology and learning analytics research by explaining how predictive approaches can support student retention and academic achievement. The findings provide practical implications for universities, educators, policymakers, and technology developers seeking responsible approaches for improving student success through data-informed educational practices.
Keywords Learning Analytics, At-Risk Students, Predictive Education, Student Retention, Educational Data Mining, Academic Success, Higher Education.
Field Engineering
Published In Volume 3, Issue 2, March-April 2021
Published On 2021-03-31
View / Download PDF File

Share this



E-ISSN XXXX-XXXX
CrossRef

CrossRef DOI prefix of AIJRI is 10.00000/AIJRI

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About AIJRI
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +1-872-302-3851 editor@aijri.com