The AIED community has frequently used L-stat for studying students’ affective dynamics. In this paper, TDs are analysed in a digital simulation-based Predict- Observe- Explain (POE) learning environment by using the likelihood statistic ( L-stat). Lastly, we assess whether students’ sequences of TDs can be indicative of their learning outcomes.
Further, we observe how TDs vary in a simulation-based learning environment (e.g., is it more probable for TDs to transition from easy to hard or vice-versa). This, then, engenders the question: how can instructors design optimal learning conditions where students get challenged but feel confident in accomplishing the task? To address this question, we analyse the relation of task difficulties (TDs) with students’ learning outcomes. In contrast, students may not engage in a task if they repeatedly fail at it. For example, when a task is challenging yet attainable, students may invest effort and persist at it. Students’ perceptions of tasks can influence their learning behaviours. It can also help in designing and sequencing the tasks for the development of effective teaching strategies that can maximize students’ learning. Understanding how task difficulties manifest over time and how they impact students’ learning outcomes is useful, especially when designing for real-time educational interventions, where the difficulty of the tasks could be optimised for students. By contrast, difficulty level medium followed by a medium may lead to better learning outcomes. In terms of TD transitions, difficulty level hard followed by a hard may lead to poorer learning outcomes. The findings suggest that if students perceive the TDs as easy or hard, it may lead to poorer learning outcomes, while the medium or moderate TDs may result in better learning outcomes. We examine the task difficulty sequence data of 236 undergraduate students in a simulation-based Predict- Observe- Explain environment. Task difficulty (TD) reflects students’ subjective judgement on the complexity of a task.
Book series (LNCS, volume 12163) Abstract
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