AI Article Synopsis
- Understanding student motivation is crucial for helping them achieve their educational goals, and our study focuses on using Learning Analytics to assess and enhance this motivation.
- We developed a Conceptual Modeling Approach that creates a unified framework for defining various aspects of student motivation, which is supported by guidelines for stakeholders to track changes in motivation levels.
- Our evaluation, involving sixteen Master's students from Ibn Khaldoun University, demonstrated that our system, particularly the Hafezni mobile app, significantly improved the ability to identify students' motivational states, resulting in a notable increase in classification accuracy from 69.23% to 96.13%.
Article Abstract
Determining student motivation within the context of Learning Analytics is fundamental for academic students to realize their educational goals. We aim to perceive the student's motivation state at a high level of abstraction and act accordingly to deal with motivation issues. We investigate how Model-Driven Engineering paradigms capture the essence of a motivation domain and provide deep automation in stimulating students' tasks. In this paper, first, we propose a Conceptual Modeling Approach that provides a unified environment in which all dimensions of students' motivation are explicitly defined. Secondly, a guideline, allows educational stakeholders to perceive the states of change in students' motivation. Third, the issue of student motivation is addressed by making a mechanism that stimulates students. Finally, to stress our approach and to prove how it is useful, we present a global usage scenario for our system called . Sixteen Master's students of the computer science department of the Ibn Khaldoun University of Algeria participated in the experiment. Results showed that our approach allows educational actors to perceive the motivational state of the student. The Hafezni mobile app is useful according to learners and educational stakeholders. Finally, the student motivation makes sense on the causality of failure/success with an acceptable percentage of correctly classified instances increased from 69.23% to 96.13%.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9130997 | PMC |
| http://dx.doi.org/10.1007/s10639-022-11091-8 | DOI Listing |
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