Journal
SEVENTH INTERNATIONAL LEARNING ANALYTICS & KNOWLEDGE CONFERENCE (LAK'17)
Volume -, Issue -, Pages 373-382Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3027385.3027445
Keywords
Intelligent Tutoring Systems; Natural Language Processing; stealth assessment; corpus linguistics; dynamics; reading
Categories
Funding
- NSF [REC0241133, IIS-0735682]
- [IES R305G020018-02]
- [IES R305G040046]
- [IES R305A080589]
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In this study, we investigated the degree to which the cognitive processes in which students engage during reading comprehension could be examined through dynamical analyses of their natural language responses to texts. High school students (n = 142) generated typed self-explanations while reading a science text. They then completed a comprehension test that measured their comprehension at both surface and deep levels. The recurrent patterns of the words in students' self-explanations were first visualized in recurrence plots. These visualizations allowed us to qualitatively analyze the different self-explanation processes of skilled and less skilled readers. These recurrence plots then allowed us to calculate recurrence indices, which represented the properties of these temporal word patterns. Results of correlation and regression analyses revealed that these recurrence indices were significantly related to the students' comprehension scores at both surface- and deep levels. Additionally, when combined with summative metrics of word use, these indices were able to account for 32% of the variance in students' overall text comprehension scores. Overall, our results suggest that recurrence quantification analysis can be utilized to guide both qualitative and quantitative assessments of students' comprehension.
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