Mobile Application Adoption and Self-Regulated Learning in Chemistry Instruction: Pathways to Enhanced Engagement and Autonomy in General Chemistry
DOI:
https://doi.org/10.15294/jpii.v15i3.46190Keywords:
Independent Learning Plan, mobile learning, STEM students, Technology Acceptance ModelAbstract
This study examines the relationship between mobile application adoption and self-regulated learning (SRL) among senior high school students enrolled in General Chemistry. Guided by the Technology Acceptance Model (TAM) and Zimmerman's Self-Regulated Learning Theory, the study examined the associations between students' perceived usefulness and ease of use of mobile applications and their self-regulated learning practices. The study used a descriptive-correlational design involving 87 STEM students. Data were collected using adapted questionnaires on mobile application adoption and self-regulated learning and analyzed using descriptive statistics and Pearson product-moment correlation. Results showed that students reported high levels of mobile application adoption, particularly in terms of perceived usefulness and perceived ease of use. Likewise, students demonstrated high levels of self-regulated learning across all dimensions, with engagement obtaining the highest mean score. Mobile application adoption was positively and significantly associated with all self-regulated learning dimensions. Perceived usefulness exhibited the strongest association with self-efficacy, while perceived ease of use showed its strongest association with self-efficacy. Based on the findings, an Independent Learning Plan (ILP) for General Chemistry was developed to support students' goal setting, self-monitoring, self-reflection, engagement, and self-efficacy. The findings suggest that students who perceive mobile applications as useful and easy to use tend to report stronger self-regulated learning practices. However, the study was limited by its relatively small sample size, reliance on self-reported data, and descriptive-correlational design, which precludes causal inferences. Future research may use larger, more diverse samples, employ predictive analytical techniques, and evaluate the effectiveness of the proposed ILP.
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