通过考虑人们如何学习和忘记来改进人工教师
Improving Artificial Teachers by Considering How People Learn and Forget
摘要：
本文提出了一种新的基于模型的智能教学方法，特别强调与人类互动的教学干预措施的选择问题。而以前的工作集中在个性化的教学或优化教学干预序列，建议个性化的基于模型的规划方法代表了这两条研究线的融合。基于模型的规划通过对用户记忆模型参数的交互式学习来挑选最佳干预措施。该方法是新颖的，在其使用的认知模型，可以解释几个关键的个人和材料的具体特征相关的回忆/遗忘，以及规划技术，考虑用户的实践时间表。以基于规则的方法为基线，作者评估了该方法的好处在第二语言词汇学习的人工教学的对照研究（N=53）。
英文摘要：
The paper presents a novel model-based method for intelligent tutoring, with particular emphasis on the problem of selecting teaching interventions in interaction with humans. Whereas previous work has focused on either personalization of teaching or optimization of teaching intervention sequences, the proposed individualized model-based planning approach represents convergence of these two lines of research. Model-based planning picks the best interventions via interactive learning of a user memory model's parameters. The approach is novel in its use of a cognitive model that can account for several key individual- and material-specific characteristics related to recall/forgetting, along with a planning technique that considers users' practice schedules. Taking a rule-based approach as a baseline, the authors evaluated the method's benefits in a controlled study of artificial teaching in second-language vocabulary learning (N=53).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
