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Goal-oriented student motivation in learning analytics: How can a requirements-driven approach help?
Omar Talbi1, Abdelkader Ouared1
1Computer Science, Ibn Khaldoun University, Tiaret, Algeria.
This study introduces a conceptual modeling approach to understand and enhance student motivation in Learning Analytics. The Hafezni system effectively helps educators perceive and address student motivation, improving success prediction.
Area of Science:
- Educational Technology
- Computer Science
- Psychology
Background:
- Student motivation is crucial for academic success and achieving educational goals.
- Learning Analytics (LA) provides tools to understand student behavior, but effectively addressing motivation remains a challenge.
- Model-Driven Engineering (MDE) offers potential for automating interventions in educational contexts.
Purpose of the Study:
- To develop a conceptual modeling approach for defining and analyzing student motivation dimensions within LA.
- To create a mechanism for stimulating student engagement and addressing motivation issues.
- To evaluate the effectiveness of the proposed approach and system (Hafezni) in a real-world educational setting.
Main Methods:
- Proposed a Conceptual Modeling Approach to explicitly define student motivation dimensions.
- Developed a guideline for educational stakeholders to monitor changes in student motivation.
- Implemented a stimulation mechanism to address identified motivation issues.
- Utilized the Hafezni system for a global usage scenario with Master's students.
Main Results:
- The approach enabled educational actors to accurately perceive student motivational states.
- The Hafezni mobile app was found useful by both learners and educational stakeholders.
- Student motivation was shown to be a significant factor in predicting academic success/failure, with classification accuracy increasing from 69.23% to 96.13%.
Conclusions:
- The conceptual modeling approach effectively captures and addresses student motivation within Learning Analytics.
- The Hafezni system provides a practical and useful tool for educators to support student engagement.
- Accurate perception of student motivation significantly improves the prediction of academic outcomes.
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