Goal-oriented student motivation in learning analytics: How can a requirements-driven approach help?

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 Hafezni. 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%.