Student Competency Assessment and Presentation Methods Based on Algorithm Courses

• URL: http://arxiv.org/abs/2606.00200v1
• Date: Fri, 29 May 2026 17:03:59 GMT
• Title: Student Competency Assessment and Presentation Methods Based on Algorithm Courses
• Abstract summary: competency-based education, which integrates knowledge, skills, and dispositions, has become pivotal in computer science education.<n>The research aims to analyze learning behavior patterns, design methods for competency assessment in algorithm courses, and evaluate the difficulty of course experiments to inform curriculum design.
• Score: 4.332793252500841
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This full research paper describes the assessment and presentation of student competencies in algorithm courses, grounded in the CC2020 competency model. With the growing emphasis on bridging the gap between academic training and industry demands, competency-based education, which integrates knowledge, skills, and dispositions, has become pivotal in computer science education. To bridge the gap, we need to develop a comprehensive framework to evaluate competencies (knowledge, skills, and dispositions) in computer science education. The research aims to analyze learning behavior patterns, design methods for competency assessment in algorithm courses, and evaluate the difficulty of course experiments to inform curriculum design. We collected programming experiment and written assignment data from 169 students, adapting it to the xAPI specification for unified analysis. In this work, Markov process modeling was employed to analyze behavioral sequences, revealing cognitive patterns during programming tasks. Multiple methods were applied to quantify competencies (knowledge, skills, dispositions) and identify distinct student clusters. Course difficulty was quantified using proactiveness metrics derived from submission timeliness. This work contributes a scalable framework for competency assessment in algorithm courses and offers actionable insights for personalized teaching and curriculum optimization. Practically, it enables instructors to tailor interventions based on student clusters and optimize task difficulty. Future work will integrate more students' performance to validate competency models and extend the framework to broader computer science curricula.

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