i-Ntervene: Applying an Evidence-Based Learning Analytics Intervention to Support Computer Programming Instruction

Autor/inn/en	Utamachant, Piriya; Anutariya, Chutiporn; Pongnumkul, Suporn
Titel	i-Ntervene: Applying an Evidence-Based Learning Analytics Intervention to Support Computer Programming Instruction
Quelle	In: Smart Learning Environments, 10 (2023), Artikel 37 (30 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Utamachant, Piriya)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
DOI	10.1186/s40561-023-00257-7
Schlagwörter	Intervention; Learning Analytics; Learning Management Systems; Programming; Learner Engagement; Comprehension; At Risk Students; Evidence Based Practice; College Freshmen; Educational Technology; Electronic Learning; Student Motivation + Suchen Sie Ihr Suchwort? Programmierung; Verstehen; Verständnis; Studienanfänger; Unterrichtsmedien; Schulische Motivation
Abstract	Apart from good instructional design and delivery, effective intervention is another key to strengthen student academic performance. However, intervention has been recognized as a great challenge. Most instructors struggle to identify at-risk students, determine a proper intervention approach, trace and evaluate whether the intervention works. This process requires extensive effort and commitment, which is impractical especially for large classes with few instructors. This paper proposes a platform, namely "i-Ntervene," that integrates Learning Management System (LMS) automatic code grader, and learning analytics features which can empower systematic learning intervention for large programming classes. The platform supports instructor-pace courses on both Virtual Learning Environment (VLE) and traditional classroom setting. The platform iteratively assesses student engagement levels through learning activity gaps. It also analyzes subject understanding from programming question practices to identify at-risk students and suggests aspects of intervention based on their lagging in these areas. Students' post-intervention data are traced and evaluated quantitatively to determine effective intervention approaches. This evaluation method aligns with the evidence-based research design. The developed i-Ntervene prototype was tested on a Java programming course with 253 first-year university students during the COVID-19 pandemic in VLE. The result was satisfactory, as the instructors were able to perform and evaluate 12 interventions throughout a semester. For this experimental course, the platform revealed that the approach of sending extrinsic motivation emails had more impact in promoting learning behavior compared to other types of messages. It also showed that providing tutorial sessions was not an effective approach to improving students' subject understanding in complex algorithmic topics. i-Ntervene allows instructors to flexibly trial potential interventions to discover the optimal approach for their course settings which should boost student's learning outcomes in long term. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01