Investigating boredom and engagement during writing using multiple sources of information: The essay, the writer, and keystrokes

Laura K. Allen, Caitlin Mills, Matthew E. Jacovina, Scott Crossley, Sidney D'mello, Danielle McNamara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

Writing training systems have been developed to provide students with instruction and deliberate practice on their writing. Although generally successful in providing accurate scores, a common criticism of these systems is their lack of personalization and adaptive instruction. In particular, these systems tend to place the strongest emphasis on delivering accurate scores, and therefore, tend to overlook additional indices that may contribute to students' success, such as their affective states during writing practice. This study takes an initial step toward addressing this gap by building a predictive model of students' affect using information that can potentially be collected by computer systems. We used individual difference measures, text indices, and keystroke analyses to predict engagement and boredom in 132 writing sessions. The results suggest that these three categories of indices were successful in modeling students' affective states during writing. Taken together, indices related to students' academic abilities, text properties, and keystroke logs were able classify high and low engagement and boredom in writing sessions with accuracies between 76.5% and 77.3%. These results suggest that information readily available in writing training systems can inform affect detectors and ultimately improve student models within intelligent tutoring systems.

Original languageEnglish (US)
Title of host publicationLAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation
PublisherAssociation for Computing Machinery
Pages114-123
Number of pages10
Volume25-29-April-2016
ISBN (Electronic)9781450341905
DOIs
StatePublished - Apr 25 2016
Externally publishedYes
Event6th International Conference on Learning Analytics and Knowledge, LAK 2016 - Edinburgh, United Kingdom
Duration: Apr 25 2016Apr 29 2016

Other

Other6th International Conference on Learning Analytics and Knowledge, LAK 2016
CountryUnited Kingdom
CityEdinburgh
Period4/25/164/29/16

Keywords

  • Corpus linguistics
  • Intelligent tutoring systems
  • Natural language processing
  • Stealth assessment
  • Writing

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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    Allen, L. K., Mills, C., Jacovina, M. E., Crossley, S., D'mello, S., & McNamara, D. (2016). Investigating boredom and engagement during writing using multiple sources of information: The essay, the writer, and keystrokes. In LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation (Vol. 25-29-April-2016, pp. 114-123). Association for Computing Machinery. https://doi.org/10.1145/2883851.2883939