Predicting Short- and Long-Term Vocabulary Learning via Semantic Features of Partial Word Knowledge
Predicting Short- and Long-Term Vocabulary Learning via Semantic Features of Partial Word Knowledge
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2017-06
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通讯作者：
Sungjin Nam;G. Frishkoff;Kevyn Collins-Thompson
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作者：
Sungjin Nam;G. Frishkoff;Kevyn Collins-Thompson
我们展示了如何新的使用的语义表示的基础上Osgood的语义差异量表可以导致有效的功能，在预测短期和长期的学习，学生使用的词汇学习系统。以往关于学生在词汇习得过程中的中间知识状态的研究并没有提供太多关于学生在词汇学习实践中获得了哪些语义知识的信息。此外，这些研究依赖于人类评级来评估学生的反应。为了解决这个问题，我们提出了一个基于Osgood的词汇语义分解的词的语义表示[16]。为了证明我们的方法可以有效地代表学生的知识在词汇习得中，我们建立模型来预测学生的短期词汇习得和长期保留。我们将我们基于Osgood的语义表示的有效性与Word2Vec神经词嵌入提供的有效性进行了比较[13]，并发现使用基于Osgood基于尺度的分数（OSG）的特征的预测模型比基线表现得更好，并且在准确性方面与使用基于Word2Vec分数的模型（W2V）相当。通过使用更具可解释性的基于奥斯古德的量表，我们的研究结果可以帮助更好地了解学生正在进行的学习状态，并设计个性化的学习系统，以解决个人在词汇习得方面的弱点。
We show how the novel use of a semantic representation based on Osgood’s semantic differential scales can lead to effective features in predicting short-and long-term learning in students using a vocabulary learning system. Previous studies in students’ intermediate knowledge states during vocabulary acquisition did not provide much information on which semantic knowledge students gained during word learning practice. Moreover, these studies relied on human ratings to evaluate the students’ responses. To solve this problem, we propose a semantic representation for words based on Osgood’s semantic decomposition of vocabulary [16]. To demonstrate our method can effectively represent students’ knowledge in vocabulary acquisition, we build models for predicting the student’s short-term vocabulary acquisition and long-term retention. We compare the effectiveness of our Osgood-based semantic representation to that provided by Word2Vec neural word embedding [13], and find that prediction models using features based on Osgood scale-based scores (OSG) perform better than the baseline and are comparable in accuracy to those using Word2Vec score-based models (W2V). By using more interpretable Osgood-based scales, our study results can help with better understanding of students’ ongoing learning states and designing personalized learning systems that can address an individual’s weak points in vocabulary acquisition.