TY - GEN
T1 - Investigating boredom and engagement during writing using multiple sources of information
T2 - 6th International Conference on Learning Analytics and Knowledge, LAK 2016
AU - Allen, Laura K.
AU - Mills, Caitlin
AU - Jacovina, Matthew E.
AU - Crossley, Scott
AU - D'mello, Sidney
AU - Mcnamara, Danielle S.
N1 - Funding Information:
This research was supported in part by: NSF ITR 0325428, HCC 0834847, DRL 1235958, DRL 1417997, IIS 1523091, and IES R305A120707. Opinions, conclusions, or recommendations do not necessarily reflect the views of the IES or NSF. We also thank Rod Roscoe, Cecile Perret, and Jianmin Dai for their help with the data collection and analysis and developing the ideas found in this paper.
Publisher Copyright:
© 2016 ACM.
PY - 2016/4/25
Y1 - 2016/4/25
N2 - 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.
AB - 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.
KW - Corpus linguistics
KW - Intelligent tutoring systems
KW - Natural language processing
KW - Stealth assessment
KW - Writing
UR - http://www.scopus.com/inward/record.url?scp=84976473432&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84976473432&partnerID=8YFLogxK
U2 - 10.1145/2883851.2883939
DO - 10.1145/2883851.2883939
M3 - Conference contribution
AN - SCOPUS:84976473432
T3 - ACM International Conference Proceeding Series
SP - 114
EP - 123
BT - LAK 2016 Conference Proceedings, 6th International Learning Analytics and Knowledge Conference - Enhancing Impact
PB - Association for Computing Machinery
Y2 - 25 April 2016 through 29 April 2016
ER -