Abstract
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.
Original language | English (US) |
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Title of host publication | LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference |
Subtitle of host publication | Understanding, Informing and Improving Learning with Data |
Publisher | Association for Computing Machinery |
Pages | 373-382 |
Number of pages | 10 |
ISBN (Electronic) | 9781450348706 |
DOIs | |
State | Published - Mar 13 2017 |
Externally published | Yes |
Event | 7th International Conference on Learning Analytics and Knowledge, LAK 2017 - Vancouver, Canada Duration: Mar 13 2017 → Mar 17 2017 |
Publication series
Name | ACM International Conference Proceeding Series |
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Other
Other | 7th International Conference on Learning Analytics and Knowledge, LAK 2017 |
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Country/Territory | Canada |
City | Vancouver |
Period | 3/13/17 → 3/17/17 |
Bibliographical note
Funding Information:This research was supported in part by: IES R305G020018-02, IES R305G040046, IES R305A080589, and NSF REC0241133, and NSF IIS-0735682. Opinions, conclusions, or recommendations do not necessarily reflect the views of the IES or NSF. We also thank Matt Jacovina, Scott Crossley, Rod Roscoe and Jianmin Dai for their help with the data collection and developing the ideas found in this paper.
Publisher Copyright:
© 2017 ACM.
Keywords
- Corpus linguistics
- Dynamics
- Intelligent tutoring systems
- Natural language processing
- Reading
- Stealth assessment