2018/05/24 by Radek Pelánek, Pelánek, Radek, Tomáš Effenberger +7
Computer Science · #Artificial Intelligence (cs.AI) #Computers and Society (cs.CY) #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #Intelligent Tutoring Systems and Adaptive Learning #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Software Engineering Research #Teaching and Learning Programming #Topic Modeling
paper · pdf · doi:10.48550/arxiv.1806.03240
openalex publication_date 2018/05/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A personalized learning system needs a large pool of items for learners to\nsolve. When working with a large pool of items, it is useful to measure the\nsimilarity of items. We outline a general approach to measuring the similarity\nof items and discuss specific measures for items used in introductory\nprogramming. Evaluation of quality of similarity measures is difficult. To this\nend, we propose an evaluation approach utilizing three levels of abstraction.\nWe illustrate our approach to measuring similarity and provide evaluation using\nitems from three diverse programming environments.\n