🤖 AI Summary
This study addresses the challenges of assessing students’ comprehensive competencies in algorithm courses and the disconnect between academic instruction and industry needs. Grounded in the CC2020 competency model, it proposes a multidimensional assessment framework that integrates knowledge, skills, and professional dispositions. Behavioral data from programming assignments and written coursework of 169 students were collected using the xAPI specification. Learning behavior sequences were modeled via Markov processes, and cluster analysis was employed to identify distinct competency profiles. Additionally, a timeliness metric for submissions was introduced to quantify task difficulty. The framework not only enables computable representations of student competencies but also provides empirical support for personalized instructional interventions and curriculum refinement, thereby effectively bridging the gap between academic training and industry requirements.
📝 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.