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Bayesian Network Models for Student Knowledge Tracking in Large Classes

机译:大班上学生知识跟踪的贝叶斯网络模型

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Arguably, the post secondary educational system is currently going through a major transition. On one end, the demand on Universities and colleges is growing while budgets are being reduced1. On the other hand, open access initiatives are making available a considerable amount of material to students and instructors2'3. This translates to higher demands on instructors with limited resources. This is of particular importance in a time when the cost of higher education has risen much faster than the average inflation4. In this landscape, the instructor is forced to optimize any available resources. One of the most important resources for instructors is time. Effective instructors not only help students in the learning process but also use meaningful evaluation strategies and provide targeted feedback to the student. However, providing good quality evaluation and feedback can become challenging, especially in large classes. In some cases, instructors might tend to over-test in an effort to give students feedback but the result could be overworked faculty and overloaded students. In other cases instructors might choose less assessment, depriving students of valuable feedback in the learning process.
机译:可以说,后职教育系统目前正在经历重大过渡。一端,大学和大学的需求正在增长,而预算正在减少1。另一方面,开放式访问举措正在为学生和教练2'3提供相当数量的材料。这意味着对资源有限的教师的要求更高。这在高等教育成本上升得比平均通货膨胀速度快得多的时间是特别重要的。在这种景观中,教师被迫优化任何可用资源。教师最重要的资源之一是时间。有效的教师不仅帮助学生在学习过程中,而且还使用有意义的评估策略并为学生提供有针对性的反馈。然而,提供良好的质量评估和反馈可以挑战,特别是在大班上。在某些情况下,教师可能倾向于过度测试,以便为学生提供反馈,但结果可能会过度劳累,学生过载。在其他情况下,教练可能会选择更少的评估,剥夺学生在学习过程中的有价值的反馈。

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