Using Sub Skills to Model and Estimate Final Skill Level

Abstract

Skill level estimation is very important since it allows an instructor, a human or an artificial instructor through an intelligent tutoring system, to predict the level of a student and adjust the learning materials accordingly. In this paper, a new approach based on 1-NN (First Nearest Neighbor) is introduced to determine the skill level of a student based on the pattern of skill levels learned over time in the same course. The data over several years are used to determine four clusters of expert, good, average and bad skill level. The advantage of the proposed approach is in its capability to adjust the levels over time based on the new data received each year. Furthermore, it can estimate the skill level after a few homework or project assignments. Consequently it can help an instructor to better conduct its class. The proposed approach has been implemented and tested on an introductory computer programming course and the results prove the validity of the approach.