Temporal Data Mining for Educational Applications
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Adaptive context-aware pervasive and ubiquitous learning
International Journal of Technology Enhanced Learning
Mining LMS data to develop an "early warning system" for educators: A proof of concept
Computers & Education
The acceptance and use of computer based assessment
Computers & Education
Computer based assessment: Gender differences in perceptions and acceptance
Computers in Human Behavior
Variable construction for predictive and causal modeling of online education data
Proceedings of the 1st International Conference on Learning Analytics and Knowledge
Using Signals for appropriate feedback: Perceptions and practices
Computers & Education
Computers in Human Behavior
Proceedings of the Third International Conference on Learning Analytics and Knowledge
Proceedings of the Third International Conference on Learning Analytics and Knowledge
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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. 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.