Epistemic Network Analysis and Topic Modeling for Chat Data from Collaborative Learning Environment
Epistemic Network Analysis and Topic Modeling for Chat Data from Collaborative Learning Environment
复制标题
DOI：
--
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Zhiqiang Cai;Brendan R. Eagan;Nia Dowell;J. Pennebaker;A. Graesser;D. Shaffer
中科院分区：
文献类型：
--
作者：
Zhiqiang Cai;Brendan R. Eagan;Nia Dowell;J. Pennebaker;A. Graesser;D. Shaffer
本研究探讨了一种使用认知网络分析和主题建模来分析协作学习环境中聊天数据的可能方法。本研究使用了一个由TASA （Touchstone Applied Science Associates）语料库构建的300个主题的一般主题模型。我们计算了聊天数据中15,670个话语中的每个300个主题得分。根据文献总得分选择7个相关主题。虽然综合主题分数在预测学生的学习方面有一定的能力，但使用认知网络分析可以从不同的角度评估数据。结果表明，低收益学生与高收益学生基于主题得分的认知网络存在显著差异（𝑡= 2.00）。总的来说，结果表明这两种分析方法提供了互补的信息，并为成功的协作互动相关的过程提供了新的见解。
This study investigates a possible way to analyze chat data from collaborative learning environments using epistemic network analysis and topic modeling. A 300-topic general topic model built from TASA (Touchstone Applied Science Associates) corpus was used in this study. 300 topic scores for each of the 15,670 utterances in our chat data were computed. Seven relevant topics were selected based on the total document scores. While the aggregated topic scores had some power in predicting students’ learning, using epistemic network analysis enables assessing the data from a different angle. The results showed that the topic score based epistemic networks between low gain students and high gain students were significantly different ( 𝑡 = 2.00 ). Overall, the results suggest these two analytical approaches provide complementary information and afford new insights into the processes related to successful collaborative interactions.