Dang, X., & Ghergulescu, I. (2018). Effective Learning Recommendations Powered by AI Engine. In Proceeding International Workshop on Personalization Approaches in Learning Environments (PALE). 19th International Conference on Artificial Intelligence in Education.
This paper presents the learning effectiveness evaluation of a recommender system powered by Adaptemy’s AI Engine in terms of average lesson success rate and improvement per lesson. The data from over 80k lessons were used in this analysis. Three main cases are considered based on the level of teachers’ guidance. The first case is when the system makes recommendation with no input from the teacher, the second case is when the system recommendations are loosely-guided by teacher input through assignment in a topic, and the third case is when the lessons are done on concepts that are specified by teachers while the systemgiven recommendation is ignored. In each case the results are compared between the lessons done on system-recommended concepts and the lessons done on other concepts. The results have shown that both the learning success-rate and the improvement per lesson are higher if the system-based recommendations are followed, in all the three cases.