Analysis of Task Difficulty Sequences in a Simulation-Based POE Environment

被引:4
作者
Nawaz, Sadia [1 ]
Srivastava, Namrata [1 ]
Yu, Ji Hyun [2 ]
Baker, Ryan S. [3 ]
Kennedy, Gregor [1 ]
Bailey, James [1 ]
机构
[1] Univ Melbourne, Parkville, Vic 3010, Australia
[2] Univ Michigan, Ann Arbor, MI 48109 USA
[3] Univ Penn, Philadelphia, PA 19104 USA
来源
ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2020), PT I | 2020年 / 12163卷
关键词
Task difficulty; Task complexity; Predict-Observe-Explain; Learning outcomes; L-statistic; Intervention; Flow; Zone of proximal development; AFFECTIVE STATES; SELF-EFFICACY; COMPLEXITY; DYNAMICS; PERFORMANCE; STRATEGIES; FEEDBACK;
D O I
10.1007/978-3-030-52237-7_34
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Task difficulty (TD) reflects students' subjective judgement on the complexity of a task. We examine the task difficulty sequence data of 236 undergraduate students in a simulation-based Predict-Observe-Explain environment. 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. In terms of TD transitions, difficulty level hard followed by a hard may lead to poorer learning outcomes. By contrast, difficulty level medium followed by a medium may lead to better learning outcomes. 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. It can also help in designing and sequencing the tasks for the development of effective teaching strategies that can maximize students' learning.
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页码:423 / 436
页数:14
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