Holistic approach to predicting student's performance in higher education institutions: a conceptual framework

Olugbenga Adejo, Thomas Connolly

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Accurate prediction and early identification of student at-risk of attrition are of high concern for higher educational institutions (HEIs). It is of a great importance not only to the students but also to the educational administrators and the institutions in the areas of improving academic quality and efficient utilisation of the available resources for effective intervention. However, despite the different frameworks and models that various researchers have used across institutions for predicting performance, only negligible success has been recorded in terms of accuracy, efficiency and reduction of student attrition. This has been attributed to the inadequate and selective use of variables for the predictive models. This paper presents a multidimensional and holistic framework for predicting student academic performance and intervention in HEIs. The purpose and functionality of the framework are to produce a comprehensive, unbiased and efficient way of predicting student performance that its implementation is based upon multi-sources data and database system. The proposed approach will be generalizable and possibly give a prediction at a higher level of accuracy that educational administrators can rely on for providing timely intervention to students.
Original languageEnglish
Title of host publicationComputer Science and Information Technology
EditorsDavid Wyld, Natarajan Meghanathan
PublisherAIRCC Publishing Corporation
Number of pages8
ISBN (Print)9781921987663
Publication statusPublished - 27 May 2017
EventInternational Conference on Data Mining and Database - Vienna, Austria
Duration: 27 May 201728 May 2017
Conference number: 4

Publication series

NameComputer Science and Information Technology
PublisherAIRCC Publishing Corporation
ISSN (Electronic)2231-5403


ConferenceInternational Conference on Data Mining and Database
Abbreviated titleDMDB 2017


  • Prediction
  • Student performance
  • higher education
  • holistic framework


Dive into the research topics of 'Holistic approach to predicting student's performance in higher education institutions: a conceptual framework'. Together they form a unique fingerprint.

Cite this