Educational robotics can support algorithmic thinking, yet students often struggle to connect intuitive problem-solving with formal algorithms, and senior engineering students frequently report declining engagement in final-year coursework. This study evaluates a comparative pedagogical redesign of the path-planning module in a robotics laboratory course for 43 final-year engineering students. Instead of introducing algorithms first, students first manually navigated robots through self-designed obstacle courses, then received formal algorithm instruction, programmed algorithmic solutions, and explicitly compared both approaches. Apostactivity questionnaire assessed perceived cognitive, metacognitive, and affective outcomes, with scale reliability examined before interpretation. Students reported strong perceived understanding of why the algorithm selected its route, high engagement and enjoyment, exploratory gains in metacognitive awareness, and increased confidence across path planning, programming, robotics, and general problem-solving. Prior experience with the module content was predominantly at beginner level and was not significantly related to outcomes, consistent with a domain-transition interpretation. Because the study used a single-cohort, postactivity design without a control group or pretest, findings reflect self-reported perceptions rather than causal learning gains. The comparative activity offers a replicable framework for redesigning similar modules in senior courses, capstone experiences, and workforce preparation, and highlights the value of making the contrast between intuitive and algorithmic approaches visible to learners.
Contribution: This article presents a systematic taxonomy-based method to define role-playing (RP) activities in software engineering education (SEE), focusing on the categorization of challenges specific to RP and the assignment of roles to students based on their cognitive profile and learning style. Background: SEE aims to enable the acquisition of technical and relational skills by students, and RP activities are frequently used to simulate real-world scenarios and enhance team dynamics. However, role assignment and team formation are often free-form, potentially leading to inefficient distribution of skills and a negative impact on team performance and cohesion. Research Questions: This study investigates how a taxonomy can support the systematic definition of RP activities in SEE and how structured role assignment based on cognitive profile and learning style affects team outcomes. Methodology: A taxonomy-based method was defined considering challenges specific to RP in SEE and the assignment of roles based on students’ cognitive profile and learning style. The approach was initially validated experimentally in undergraduate classes on software requirements elicitation, with seven student teams (three teacher-assigned and four self-assigned), and their outcomes were assessed for product quality, cohesion, and performance. Findings: Initial results show that teacher-assigned teams achieved higher product quality scores than self-assigned teams and showed better cohesion and performance. This suggests high potential for structured role assignments to improve team dynamics and learning outcomes.
Contributions: This study provides evidence from a structured individual-to-collaborative isomorphic task sequence while explicitly distinguishing group-produced technical performance from student-level psychological associations. Background: Collaborative problem solving can support peer reasoning, but group-produced outcomes should not be conflated with individual learning evidence when repeated observations arise from students and groups. Research Questions: The study examines how collaborative group performance compares with precollaboration individual performance within each group and how intrinsic motivation (IM), professional identity (PI), and knowledge-transfer perception are (kT) associated with perceived academic success (PS). Methodology: A repeated classroom design involved 53 eligible students across eight experimental modules. Seventy verified Group-by-Activity observations were summarized descriptively. Repeated activity occasions were aggregated within stable group clusters before directional inference. The upper-median benchmark was used for the primary directional comparison, with the best-member benchmark used as a diagnostic comparison. Repeated questionnaire responses were aggregated within students. Findings: Relative to the upper-median benchmark, 14 group products were higher, 56 equal, and none lower. Across the ten stable group clusters, four were positive, six were tied, and none were negative; the directional exact test did not reach alpha = 0.05 (one-sided p = 0.063; two-sided p = 0.125). Relative to the best-member benchmark, all 70 group products matched the strongest member's archived category, with complete top-category censoring. IM (rho = 0.558), PI (rho = 0.422), and KT (rho = 0.361) were positively associated with PS. The findings characterize group-produced performance but do not establish a collaboration-specific effect or individual learning gain.
Large language models (LLMs) have emerged as a transformative technological force in modern programming education, offering novel solutions to the increasingly diverse and personalized learning demands of students. This systematic review presents a comprehensive synthesis of the integration status and practical applications of LLMs within programming education by analyzing 40 representative studies published between 2019 and September 2025. The overarching analytical framework is structured around three core research dimensions: typical LLM interaction patterns (RQ1), targeted learner adaptation strategies (RQ2), and implementation challenges alongside pedagogical risks (RQ3). In particular, existing applications are unified into four quintessential interaction roles: teaching assistant, feedback partner, task generator, and integrity monitor. To accommodate differentiated instructional needs, a 2-D learner adaptation framework is established, aligning teacher-facing and student-facing service paradigms with three distinct student cohorts: nonengineering majors (experience-oriented), non-CS engineering majors (application-oriented), and computer science majors (competence-oriented). Furthermore, this review critically discusses institutional challenges and instructor cognitive burdens while uncovering long-term risks of LLM-based applications in programming education. Ultimately, this work provides an actionable roadmap for curriculum designers and educators to transition from passive tool adopters to strategic architects of human-artificial intelligence (AI) collaborative learning environments.
Contribution: This article presents the development of a system-oriented electric vehicle (EV) powertrain course module that differs from traditional standalone control unit courses. It emphasizes real-time interaction and practical integration among subsystems, offering a novel framework for EV engineering education and serving as a foundational basis for future EV-related specialized courses, research, or advanced studies. Background: The powertrain and electrical architecture of EVs differ fundamentally from those of conventional fuel-powered vehicles. Therefore, during this industrial transformation, schools urgently need a prerequisite course on EV powertrain systems. However, most institutions currently lack such a course. With both industry experience and a background as an adjunct lecturer, the author developed and refined this course over three years through iterative curriculum development. Intended Outcomes: The course module is designed to provide students with a practical and system-level framework for EV powertrain systems. The teaching objectives are to enable students to understand the architecture and functionality of key EV subsystems and their interactions through in-vehicle network communication. Through the learning process, students will develop core competencies in embedded control, system integration, and teamwork, aligning with current industry practices in the EV sector. Application Design: This study adopts an outcome-based education (OBE)-guided, project-based instructional approach supported by systems-oriented and model-based learning, integrating theoretical instruction, modeling, simulation, and hands-on experimentation on a custom EV powertrain platform, with iterative refinement through formative assessment and statistical evaluation across three cohorts. Findings: Results show significant improvements in student learning outcomes across three cohorts, supported by descriptive statistics, one-way analysis of variance (ANOVA) with moderate to large effect sizes, enhanced performance in summative assessments, and consistent qualitative feedback indicating improved system-level understanding and engagement.
