Representation of Generalized Human Cognitive Abilities in a Sophisticated Student Leaderboard

摘要

Gamification is a popular method for enhancing learners' motivation and thereby strengthening learning efficiency. An example of gamification is the leaderboard, namely an approach showing a ranking of students. Although leader-boards are currently implemented in various domains, previous studies reported that they are solely based on one student characteristic, such as grade. This paper presents a sophisticated leaderboard showing a more reliable ranking of students. This leaderboard is available to both instructors and learners; instructors can be adequately informed by this ranking and redesign their teaching strategies, while learners can be motivated by the ranking and try more to advance their knowledge. The sophistication of this leaderboard lies in the employment of the Weighted Sum Model (WSM), which is the best-known multi-criteria decision analysis technique and responsible for evaluating a number of alternatives in terms of a number of decision criteria. The input of WSM is multiple learners' characteristics, including current and previous knowledge, interaction time and frequency of misconceptions, so that a more robust representation of students is achieved. Our presented model was incorporated in an intelligent tutoring system for the computer programming language C#, and the evaluation results show high accuracy in the values of the leaderboard.