Conference Agenda

Students frequently choose correct answers for incorrect reasons, leaving traditional formative assessments unable to surface the misconceptions that matter most for learning. We introduce Structured Post-Evaluation Interviews and Remediation (SPEIR), a workflow that extends two-tier Justified Multiple-Choice Questions (JMCQs) with guided discussions and targeted recovery opportunities to surface and address those hidden misunderstandings. Implemented across ten course sections and compared with a traditional MCQ control, SPEIR showed that correctness alone substantially overestimates understanding, while per-question analyses revealed higher rates of fully correct reasoning in SPEIR sections. Students who completed recovery quizzes demonstrated notable gains, and instructors reported that SPEIR enabled efficient, focused feedback. These results suggest that SPEIR is a scalable approach for integrating diagnostic assessment with timely remediation.

This paper is a case-study on utilizing learning analytics to evaluate the success of integrating Smart Learning Content (SLC) into a project-based undergraduate Python programming course delivered through an online learning platform. We showcase how integration of the logging capabilities of SLC with the platform enabled us to study students' engagement with the SLC activities, and examine connection between the SLC usage and student learning outcomes. There are large publicly available repositories of SLC materials, yet their integration into a specific course and evaluation of its success are often not straightforward. By providing access to several SLC types, such as program construction examples, code animations, and parsons puzzles, we aimed to bridge the gap between static conceptual reading and programming projects already present in the course. To evaluate the integration of SLC, we released the augmented course in the Spring 2025 semester to 252 students at 20 community colleges across the US and analyzed collected interaction logs.