Learning Analytics in Science Education to Enhance Self-Regulated Learning, Academic Achievement, and Data-Driven Instruction
DOI:
https://doi.org/10.62872/sej.v1i3.552Keywords:
learning analytics, self-regulated learning, academic achievement, data-driven instruction, science educationAbstract
This study aims to analyze the effectiveness of learning analytics in enhancing students’ self-regulated learning (SRL), academic achievement, and supporting data-driven instruction in science education. The research employed a quantitative approach using a quasi-experimental design with a non-equivalent control group. The participants consisted of an experimental group that utilized learning analytics dashboards integrated into a learning management system and a control group that received conventional instruction. Data were collected using a self-regulated learning questionnaire, an academic achievement test, and an observation sheet for data-driven instructional practices. The results showed that the experimental group achieved significantly higher post-test scores compared to the control group across all variables. The normalized gain (N-gain) analysis indicated that the experimental group reached a medium to high level of improvement, while the control group remained in the low to medium category. Statistical testing using an independent sample t-test revealed a significant difference between the two groups (p < 0.05). Furthermore, learning analytics effectively improved students’ ability to regulate their learning, enhanced academic performance, and supported teachers in implementing data-driven instructional strategies. These findings suggest that learning analytics is a powerful and innovative approach to improving both student learning outcomes and instructional quality in science education.
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