Self-Regulation, Scaffolding, and the Illusion of Improvement: A Quasi-Experimental Evaluation of an LLM-Based Programming Help Tool

From criteria to self-regulation: Rubrics guiding pre-class preparation in a higher education Flipped Classroom

Creative Task Cards for Reflection, Self-Efficiency and Self-Regulation in CS1 Introductory Programming: Initial Insights

Computer Science students in introductory programming courses struggle with emotional challenges such as low levels of sustained interest and a lack of self-belief. However, students are typically introduced to disciplinary strategies (such as debugging strategies) and not the metacognitive nor self-regulation strategies that could help them overcome such emotional challenges. This paper addresses this issue by introducing creative task cards designed to support students'reflection, self-efficacy, and self-regulation in programming assignments. To evaluate the cards, twenty-nine students used them to complete a mock coding assignment in-class and participated in surveys and focus groups. Our initial qualitative insights suggest that students: found value in taking breaks, particularly when they could leave the classroom to reflect with peers; could use drawing to better reflect on feelings of cognitive overload; and felt more capable progressing with their work once breaking it into smaller steps.

How Undergraduate and Graduate Students Self-Regulate GenAI Use in Project-Based Learning: Choosing, Creating, Filtering, and Stopping Strategies

Examining a four-process GenAI cycle reveals two distinct metacognitive regulation styles: Exploratory-Simplification and Systematic-Methodical, which show that students use combinations of strategies across the AI-SRL cycle.

The Intention–Implementation Gap: Micro-Cycles and Contextual Factors in First-Year STEM Students’ Self-Regulated Learning

First-year students in STEM programs face significant academic and personal challenges that can undermine retention and success, particularly for those navigating new institutional environments without prior college experience. While self- regulated learning (SRL) theory offers a well-established framework for understanding how students plan and reflect, less attention has been paid to the performance phase, the stage where students must translate plans into action amid real academic and social demands. This qualitative study examines the experiences of 15 first-year life science students across three institution types, a Hispanic-Serving Institution, a predominantly white institution, and a liberal arts college, to investigate what plans students formed at the end of their first semester and what factors facilitated or hindered implementation during their second semester. Using thematic analysis of semi-structured interviews, three major plan themes emerged: help-seeking, internal academic adjustments, and managing social and emotional well-being. Facilitating factors for these plans included small class sizes, anonymized participation tools, approachable instructors, peer and family support, counseling services, and structured planning tools, while hindering factors included fear of judgment, high instructor-student ratios, scheduling conflicts, academic burnout, and unsupportive living environments. The findings reveal that plan implementation depended on the interplay of intersecting psychological, social, and structural factors, which created unique conditions that influenced whether students were able to enact their plans. Importantly, the findings reveal that plan implementation unfolded not as a linear process but through nested micro-cycles of forethought, performance, and reflection within the performance phase, triggered by specific events throughout the semester. These findings have implications for how institutions design learner-centered support for STEM students not only at key transition points, but also throughout the semester, to address the conditions that influence whether students are able to successfully implement, adapt, or abandon their regulatory efforts.

A Comparison of the Relative Effectiveness of Student SelfAssessment/Remediation vs. Using a Self-Assessment Chatbot on Raising Student Performance

In previous research, we have shown that teaching students to self-assess and remediate their own knowledge (gaps) or giving them self-assessment chatbots that take student self-assessments as inputs and use those inputs in answering students’ questions both lead to large gains in student performance. The present study compares the relative effectiveness of each approach to see whether adding a chatbot to aid in remediation provides any benefit to having students selfremediate without the aid of technology. Forty fourth- and fifth-grade students studied a geometry unit on angle relationships and completed identical instruction followed by a Cognitive Structure Analysis (CSA)-based self-assessment that identified strengths and deficiencies in four knowledge categories: facts, strategies, procedures, and rationales. Participants were randomly assigned to one of two remediation conditions. The control group used its self-assessment to guide independent review of the instructional materials, whereas the experimental group submitted the same self-assessment to an LLM-powered chatbot that generated personalized explanations and guidance targeted to each learner's reported knowledge gaps. Learning was measured using parallel pretests and posttests. Both groups demonstrated statistically significant gains from pretest to posttest (both p < .0001). However, students using the self-assessmentinformed chatbot improved by 32.6 percentage points compared with 21.2 percentage points for students performing self-assessment without chatbot support, a statistically significant difference of 11.4 percentage points (t(38) = 4.02, p = .0003). These findings suggest that combining learner-generated knowledge models with LLM-based conversational tutoring produces substantially greater learning gains than self-directed remediation alone.

Mobile Learning for Programming Education: A Case Study of SoloLearn and Self-Directed Learning Skills

The findings suggest that SoloLearn effectively develops foundational SDL skills but requires adaptive features, project-based modules, and improved collaborative tools to support deeper learning.