Measuring Item Similarity in Introductory Programming: Python and Robot Programming Case Studies
This work addresses the need for effective similarity measures in educational technology for programming learners, but it appears incremental as it builds on existing concepts without claiming major breakthroughs.
The paper tackles the problem of measuring item similarity in introductory programming to support personalized learning systems, proposing a general approach and specific measures, and illustrates this with case studies from three programming environments.
A personalized learning system needs a large pool of items for learners to solve. When working with a large pool of items, it is useful to measure the similarity of items. We outline a general approach to measuring the similarity of items and discuss specific measures for items used in introductory programming. Evaluation of quality of similarity measures is difficult. To this end, we propose an evaluation approach utilizing three levels of abstraction. We illustrate our approach to measuring similarity and provide evaluation using items from three diverse programming environments.