Modeling Programming Skills with Source Code Embeddings for Context-aware Exercise Recommendation
Departamento de Informática Universidade Federal de Viçosa（信息学院弗拉门多大学）；Instituto Federal de Minas Gerais（矿业联邦机构）；Instituto de Ciências Matemáticas e de Computação Universidade de São Paulo（数学与计算机科学学院圣保罗大学）；Departamento de Ciência da Computação Universidade Federal de Minas Gerais（计算机科学学院联邦矿业大学）
Carlos Eduardo P. Silva, João Pedro M. Sena, Julio C. S. Reis, André G. Santos, Lucas N. Ferreira
备注：
10 pages, 4 figures, to be published in LAK26: 16th International Learning Analytics and Knowledge Conference (LAK 2026)
In this paper, we propose a context-aware recommender system that models students' programming skills using embeddings of the source code they submit throughout a course. These embeddings predict students' skills across multiple programming topics, producing profiles that are matched to the skills required by unseen homework problems. To generate recommendations, we compute the cosine similarity between student profiles and problem skill vectors, ranking exercises according to their alignment with each student's current abilities. We evaluated our approach using real data from students and exercises in an introductory programming course at our university. First, we assessed the effectiveness of our source code embeddings for predicting skills, comparing them with token-based and graph-based alternatives. Results showed that Jina embeddings outperformed TF-IDF, CodeBERT-cpp, and GraphCodeBERT across most skills. Additionally, we evaluated the system's ability to recommend exercises aligned with weekly course content by analyzing student submissions collected over seven course offerings. Our approach consistently produced more suitable recommendations than baselines based on correctness or solution time, indicating that predicted programming skills provide a stronger signal for problem recommendation.
Central China Normal University Wollongong Joint Institute（华中师范大学伍伦贡联合研究院）；Central China Normal University（华中师范大学）；Faculty of Artificial Intelligence in Education, Central China Normal University（华中师范大学人工智能教育学部）
BamaER: A Behavior-Aware Memory-Augmented Model for Exercise Recommendation
Guilin University of Electronic Technology（桂林电子科技大学）；Jinan University（吉林大学）；Beijing University（北京大学）；Xiamen university（厦门大学）；Nanjing University of Science and Technology（南京理工大学）
LiveGraph: Active-Structure Neural Re-ranking for Exercise Recommendation
University of Macau（澳门大学）；University of Pennsylvania（宾夕法尼亚大学）；Tongji University（同济大学）；Juntendo University（立命馆大学）；University of Southampton（南安普顿大学）；University of Bologna（博洛尼亚大学）；Wuhan University（武汉大学）；University of the Chinese Academy of Sciences（中国科学院大学）；Hanyang University（汉阳大学）；Zhejiang University（浙江大学）；The Hong Kong University of Science and Technology（香港科学与技术大学）
Designing and evaluating an online reinforcement learning agent for physical exercise recommendations in N-of-1 trials
Hasso Plattner Institute for Digital Engineering, University of Potsdam（波茨坦大学哈索·普拉特纳数字工程研究所）；Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院西奈山哈索·普拉特纳数字健康研究所）；Harvard University（哈佛大学）
Detecting Unsuccessful Students in Cybersecurity Exercises in Two Different Learning Environments
University of Pennsylvania（宾夕法尼亚大学）；Masaryk University（马萨里克大学）；Georgia Institute of Technology（佐治亚理工学院）；The Evergreen State College（常青州立学院）；Lewis & Clark College（刘易斯与克拉克学院）；Northern Kentucky University（北肯塔基大学）
Prompt Transfer for Dual-Aspect Cross Domain Cognitive Diagnosis
School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机与信息工程学院）；Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China)（大数据知识工程教育部重点实验室）；School of Computer Science and Technology, University of Science and Technology of China（中国科学技术大学计算机科学与技术学院）；School of Big Data And Artificial Intelligence, Hefei University（合肥学院大数据与人工智能学院）
