Smart Supervision: Leveraging ELISTA to Strengthen Student Motivation and Thesis Completion - Dwi Agus Kurniawan; Astalini Astalini; Darmaji Darmaji; Yusnidar Yusnidar (Cahaya Ilmu Cendekia Publisher, 2025)
Purpose of the study: This study aims to examine how lecturer–student interactions within the ELISTA learning management system influence students’ motivation to complete their final research project, focusing specifically on how feedback quality, clarity, and communication frequency contribute to strengthening their academic persistence. Methodology: This study employed an explanatory sequential mixed-methods design using an ex post facto survey with Google Forms (Google LLC) and a Likert-scale questionnaire. Quantitative analysis used SPSS v.26 (IBM Corp.), while qualitative thematic analysis used NVivo 12 Plus (QSR International). Instruments included online surveys, ELISTA activity logs, and semi-structured interviews recorded using Zoom Cloud Meetings. Main Findings: The study found that lecturer–student interaction via ELISTA was high (mean = 4.12), with clarity of instructions and feedback quality as dominant factors. Student motivation for thesis completion was also high (mean = 4.08), driven by intrinsic motivation and extrinsic factors such as degree achievement and career prospects. Regression analysis showed interactions significantly predicted motivation (R² = 0.236), with clarity of instructions (β = 0.341) and feedback quality (β = 0.268) contributing substantially. Novelty/Originality of this study: This study uniquely integrates ELISTA-based thesis supervision with a mixed-methods approach, combining quantitative analysis of interaction metrics and qualitative NVivo analysis of student motivation. Unlike previous LMS studies focused on course engagement, it specifically examines how clarity of instructions and feedback quality influence both intrinsic and extrinsic motivation, providing new empirical insights for designing effective, technology-mediated academic supervision.