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.

翻译：本篇完整研究论文基于CC2020能力模型，探讨了算法课程中学生能力的评估与呈现方法。随着学界培养与行业需求之间差距日益受到重视，融合知识、技能与素养的能力本位教育已成为计算机科学教育领域的核心议题。为弥合这一差距，亟需构建评估计算机科学教育中学生能力（知识、技能和素养）的综合框架。本研究旨在分析学习行为模式，设计算法课程能力评估方法，并评估课程实验难度以指导课程设计。我们收集了169名学生的编程实验与书面作业数据，并将其转换为符合xAPI规范的标准格式以进行统一分析。工作中采用马尔可夫过程建模分析行为序列，揭示了编程任务中的认知模式；通过多种方法量化了知识、技能与素养三个维度的能力，并识别出不同的学生聚类群体；基于提交及时性指标衍生出的主动性度量，量化了课程难度。本研究为算法课程提供了一套可扩展的能力评估框架，并为个性化教学与课程优化提供了可操作性建议。在实际应用中，该框架能使教师根据学生聚类实施差异化干预并优化任务难度。未来研究将整合更多学生表现数据以验证能力模型，并将该框架推广至更广泛的计算机科学课程体系。