<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Student engagement is an essential device for deepening learning, achieving learning outcomes, developing competencies, and improving academic performance in education settings. It is widely receiving increased attention among various scholars and higher education leaders. However, there are increasing concerns about the academic performance of students in higher education settings. The application of statistical data analytics for mining student engagement datasets is a candidate strategy for discovering essential indicators associated with academic performance. However, widely used data analytic methods like principal component analysis are ineffective when most of the indicators captured are categorical, making them inappropriate for establishing the weighty academic performance indicators. This study’s objective was to investigate the application of multiple correspondence analysis to establish weighty student engagement indicators of academic performance. This study’s findings have indicated that higher-order learning and student-staff interaction are weighty indicators that relate student engagement to academic performance.</p>
</abstract>

Student engagement is an essential device for deepening learning, achieving learning outcomes, developing competencies, and improving academic performance in education settings. It is widely receiving increased attention among various scholars and higher education leaders. However, there are increasing concerns about the academic performance of students in higher education settings. The application of statistical data analytics for mining student engagement datasets is a candidate strategy for discovering essential indicators associated with academic performance. However, widely used data analytic methods like principal component analysis are ineffective when most of the indicators captured are categorical, making them inappropriate for establishing the weighty academic performance indicators. This study’s objective was to investigate the application of multiple correspondence analysis to establish weighty student engagement indicators of academic performance. This study’s findings have indicated that higher-order learning and student-staff interaction are weighty indicators that relate student engagement to academic performance.

Relating Student Engagement Indicators to Academic Performance Using Multiple Correspondence Analysis

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Student engagement is an essential device for deepening learning, achieving learning outcomes, developing competencies, and improving academic performance in...

Relating Student Engagement Indicators to Academic Performance Using Multiple Correspondence Analysis

Online veröffentlicht: 30. März 2021

Seitenbereich: 87 - 102

Eingereicht: 01. Juli 2020

Akzeptiert: 21. Dez. 2020

DOI: https://doi.org/10.2478/cait-2021-0007

Schlüsselwörter
Academic performance, correspondence analysis, national survey, student behaviour, student engagement

© 2021 Ropo E. Ogunsakin et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Relating Student Engagement Indicators to Academic Performance Using Multiple Correspondence Analysis

Online veröffentlicht: 30. März 2021

Seitenbereich: 87 - 102

Eingereicht: 01. Juli 2020

Akzeptiert: 21. Dez. 2020

DOI: https://doi.org/10.2478/cait-2021-0007

SchlüsselwörterAcademic performance, correspondence analysis, national survey, student behaviour, student engagement

© 2021 Ropo E. Ogunsakin et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Academic performance, correspondence analysis, national survey, student behaviour, student engagement