The development and analysis of tutorial dialogues in AutoTutor Lite.
The development and analysis of tutorial dialogues in AutoTutor Lite.
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DOI：
10.3758/s13428-013-0352-z
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发表时间：
2013-09
影响因子：
5.4
通讯作者：
Weil AM
中科院分区：
文献类型：
--
作者：
Wolfe CR;Widmer CL;Reyna VF;Hu X;Cedillos EM;Fisher CR;Brust-Renck PG;Williams TC;Damas Vannucchi I;Weil AM
以自然语言进行交互的智能辅导系统（ITS）的目标是通过解释学生的答案并适当地响应以鼓励阐述，来模仿训练有素的人类导师为学生提供的好处。BRCA Gist是使用AutoTutor Lite（基于Web的AutoTutor版本）开发的ITS。模糊痕迹理论从理论上推动了BRCA Gist的发展，该理论让人们参与指导对话，教他们遗传乳腺癌的风险。我们描述了一个经验的方法来创建教程对话和微调校准的BRCA Gist的语义处理引擎没有一个团队的计算机科学家。我们围绕教学问题创建了五个互动对话，例如“如果某人收到乳腺癌遗传风险的阳性结果，她应该怎么做？”这种方法涉及一个迭代的改进过程，对不同的文本进行重复测试，并不断调整导师的期望和设置，以提高成绩。这种方法的目的是使BRCA Gist能够以最有利于学习的方式解释和回应答案。我们开发了一种方法来分析导师的对话的功效。我们发现，BRCA Gist对参与者答案的评估与训练有素的人类法官使用可靠的规则找到的答案的质量高度相关。用户和BRCA Gist之间的对话质量，预测导师完成后乳腺癌风险知识测试的表现。BRCA Gist反馈的适当性也预测了答案的质量和乳腺癌风险知识测试分数。
The goal of Intelligent Tutoring Systems (ITS) that interact in natural language is to emulate the benefits a well-trained human tutor provides to students, by interpreting student answers and appropriately responding to encourage elaboration. BRCA Gist is an ITS developed using AutoTutor Lite, a web-based version of AutoTutor. Fuzzy-Trace Theory theoretically motivated the development of BRCA Gist, which engages people in tutorial dialogues to teach them about genetic breast cancer risk. We describe an empirical method to create tutorial dialogues and fine-tune the calibration of BRCA Gist’s semantic processing engine without a team of computer scientists. We created five interactive dialogues centered on pedagogic questions, such as “What should someone do if she receives a positive result for genetic risk of breast cancer?” This method involved an iterative refinement process of repeated testing with different texts, and successively making adjustments to the tutor’s expectations and settings to improve performance. The goal of this method was to enable BRCA Gist to interpret and respond to answers in a manner that best facilitates learning. We developed a method to analyze the efficacy of the tutor’s dialogues. We found that BRCA Gist’s assessment of participants’ answers was highly correlated with the quality of answers found by trained human judges using a reliable rubric. Dialogue quality between users and BRCA Gist, predicted performance on a breast cancer risk knowledge test completed after the tutor. The appropriateness of BRCA Gist feedback also predicted the quality of answers and breast cancer risk knowledge test scores.
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