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Advancing semantic search in higher education through the Heterogeneous Student Performance data retrieval model.

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Title: Advancing semantic search in higher education through the Heterogeneous Student Performance data retrieval model.
Authors: Musa, Mohd Hafizan1,2 (AUTHOR) p032220014@student.utem.edu.my, Salam, Sazilah1,3 (AUTHOR) sazilah@utem.edu.my, Norasikin, Mohd Adili1 (AUTHOR) adili@utem.edu.my, Hall, Wendy3 (AUTHOR) wh@ecs.soton.ac.uk, Rusdi, Jack Febrian1,4 (AUTHOR) jack@utem.edu.my
Source: Discover Education. 5/26/2026, Vol. 5 Issue 1, p1-26. 26p.
Subject Terms: *Learning analytics, *Digital learning, *Information retrieval, *Academic achievement, *Higher education, SPARQL (Computer program language), Ontology
Abstract: In recent years, Learning Analytics has become a key discipline for enhancing educational outcomes through the collection, analysis, and interpretation of data related to learners and their learning environments. Modern educational institutions nowadays typically operate multiple e-learning platforms, such as Learning Management Systems, Student Information Systems, Massive Open Online Course platforms, and virtual classrooms, which generate extensive historical records. These records can offer valuable insights into student performance and academic trends if properly analysed. A significant challenge is that each of these e-learning datasets is maintained separately and not integrated with the others. This paper presents a mixed-method data retrieval model designed to support learning analytics and student performance monitoring in Higher Education Institutions. The proposed model, known as the Heterogeneous Student Performance (HSP) data retrieval model, integrates semantic technologies with SPARQL query generation to enable accurate and automated retrieval of student data from heterogeneous sources. Competency Questions (CQs) derived from thematic analysis of domain expert interviews are used to guide ontology development and query formulation. Each CQ is translated into SPARQL queries through structured SPO triples, enabling precise mapping of learning and assessment data. The model comprises ten structured phases, including ontology development or reuse, validation, schema mapping, and SPARQL query validation. Experimental validation demonstrates the model's effectiveness in generating relevant data insights, supporting decision-making for curriculum review, student support, and institutional performance evaluation, with the accuracy, precision, recall, and F1 score of the model being above , , , and , respectively. [ABSTRACT FROM AUTHOR]
Copyright of Discover Education is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: Advancing semantic search in higher education through the Heterogeneous Student Performance data retrieval model.
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  Data: <searchLink fieldCode="AR" term="%22Musa%2C+Mohd+Hafizan%22">Musa, Mohd Hafizan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> p032220014@student.utem.edu.my</i><br /><searchLink fieldCode="AR" term="%22Salam%2C+Sazilah%22">Salam, Sazilah</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<i> sazilah@utem.edu.my</i><br /><searchLink fieldCode="AR" term="%22Norasikin%2C+Mohd+Adili%22">Norasikin, Mohd Adili</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> adili@utem.edu.my</i><br /><searchLink fieldCode="AR" term="%22Hall%2C+Wendy%22">Hall, Wendy</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> wh@ecs.soton.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Rusdi%2C+Jack+Febrian%22">Rusdi, Jack Febrian</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<i> jack@utem.edu.my</i>
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  Data: <searchLink fieldCode="JN" term="%22Discover+Education%22">Discover Education</searchLink>. 5/26/2026, Vol. 5 Issue 1, p1-26. 26p.
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  Data: In recent years, Learning Analytics has become a key discipline for enhancing educational outcomes through the collection, analysis, and interpretation of data related to learners and their learning environments. Modern educational institutions nowadays typically operate multiple e-learning platforms, such as Learning Management Systems, Student Information Systems, Massive Open Online Course platforms, and virtual classrooms, which generate extensive historical records. These records can offer valuable insights into student performance and academic trends if properly analysed. A significant challenge is that each of these e-learning datasets is maintained separately and not integrated with the others. This paper presents a mixed-method data retrieval model designed to support learning analytics and student performance monitoring in Higher Education Institutions. The proposed model, known as the Heterogeneous Student Performance (HSP) data retrieval model, integrates semantic technologies with SPARQL query generation to enable accurate and automated retrieval of student data from heterogeneous sources. Competency Questions (CQs) derived from thematic analysis of domain expert interviews are used to guide ontology development and query formulation. Each CQ is translated into SPARQL queries through structured SPO triples, enabling precise mapping of learning and assessment data. The model comprises ten structured phases, including ontology development or reuse, validation, schema mapping, and SPARQL query validation. Experimental validation demonstrates the model's effectiveness in generating relevant data insights, supporting decision-making for curriculum review, student support, and institutional performance evaluation, with the accuracy, precision, recall, and F1 score of the model being above , , , and , respectively. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Discover Education is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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