Identifying Students at Risk of Academic Struggles Using Learning Analytics for Tailored Tutoring
Keywords:
learning analytics, tailored tutoring, learning adaptation, virtual classroomAbstract
Within the field of educology, learning analytics (LA) has emerged as a crucial area of exploration, offering opportunities for personalized teaching and learning interventions. This study employs LA through a tool named AnalyTIC, designed to pinpoint students facing potential failure in a course and facilitate subsequent tailored tutoring. AnalyTIC furnishes educators and students with essential information for assessing academic performance, employing a risk assessment matrix. This information empowers teachers to customize tutoring for students encountering difficulties and adapt course content accordingly. The tool underwent validation through a study involving 39 students in the first term of the Environmental Engineering program at the Cooperative University of Colombia, all enrolled in an Algorithms course. Our results emphasize the importance of early identification of struggling students to enable timely corrective actions by educators. The initial development of the sensor aligns with the theoretical framework encompassing the phases of LA processes. Following the identification of these phases, a virtual classroom was constructed, and the tool for implementing these phases was subsequently developed. Validation of the tool highlighted the dynamic enhancement of students' educational experiences when educators possess ample information for decision-making, illustrating how tutoring and content adaptation contribute to improved academic performance.
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