Abstract: Predicting student’s performance is a challenging, yet complicated task for institutions, instructors and learners. Accurate predictions of performance could lead to improved learning outcomes and increased goal achievement. For that reason, prediction of performance is acknowledged as one of the major objectives of Learning Analytics and Educational Data Mining research. In this paper we explore the predictive capabilities of student’s time-spent on answering (in-)correctly each question of a multiple-choice assessment quiz, along with student’s final quiz-score, in the context of computer-based testing. We also explore the correlation between the time-spent factor (as defined here) and goal-expectancy. We present a case study and investigate the value of using this parameter as a learning analytics factor for improving prediction of performance during computer-based testing. Our initial results are encouraging and indicate that the temporal dimension of learning analytics should be further explored.
Overview from the Hub: The preliminary results from this research indicate that temporal learning analytics – that is to say the cumulative duration of time that a student takes to make correct or incorrect answers on a multiple choice quiz - have a statistically significant capability of predicting actual performance (almost 62% of the variance). In their single case study of 96 high school students the report finds that if a learner spends more time to answer correctly then they are more likely to score higher (positive effect of 0.60) and if they spend more time answering incorrectly then it appears they are likely to score lower (0.29). They also report a third finding: that there is a positive effect of (learner pre-stated) goal expectancy on correct answers and a negative effect on the total time spent making incorrect answers. The proposed use of this technique once researched further is as a potential replacement for traditional grading activities where determining grades act as effort overload.