学习者理解能力的评估:一个实验结果

Adidah Lajis, N. A. Aziz
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引用次数: 4

摘要

评估学习者的答案对教育者来说是非常耗时的,并且限制了他们参与其他活动。在许多情况下,试卷包括要求学习者至少写一两个句子来表达他们的理解的问题。然而,由于计算机阅卷技术的限制,基于计算机的评估工具并不多。我们的研究试图通过引入一种评估简短的自由文本答案的技术来解决这一限制。它基于自然语言处理、信息提取和人工智能相结合的混合方法。将文本答案转换为节点链接表示,提取隐藏的知识结构。然后,我们应用多余的熵来计算每个模型的已知信息量,然后计算相应的分数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessment of Leaner's Understanding: An Experimental Result
Assessing leaner’s answers is very time consuming for educators and limits them to be involved in other activities. In many cases, exam papers comprise of questions that require learners to write at least one or two sentences toe express their understanding. However, there are not many computer-based assessment tools due to limitations in computerized marking technology. Our research attempts to address this limitation by introducing a technique to evaluate short free text answer. It is based on a hybrid approach that combines natural language processing, information extraction and artificial intelligence. A textual answer is converted into a node link representation to extract the hidden knowledge structure. We then apply excess entropy to compute the amount of known information for each model and later compute the score accordingly.
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