The current study introduces a model for measuring student diligence using online behaviors during intelligent tutoring system use. This model is validated using a full academic year dataset to test its predictive validity against long-term academic outcomes including end-of-year grades and total work completed by the end of the year. The model is additionally validated for robustness to time-sample length as well as data sampling frequency. While the model is shown to be predictive and robust to time-sample length, the results are inconclusive for robustness in data sampling frequency. Implications for research on interventions, and understanding the influence of self-control, motivation, metacognition, and cognition are discussed.
%0 Conference Paper
%1 citeulike:14340919
%A Dang, Steven
%A Yudelson, Michael
%A Koedinger, Kenneth R.
%B Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale
%C New York, NY, USA
%D 2017
%I ACM
%K intelligent-tutoring las2017
%P 51--59
%R 10.1145/3051457.3051470
%T Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems
%U http://dx.doi.org/10.1145/3051457.3051470
%X The current study introduces a model for measuring student diligence using online behaviors during intelligent tutoring system use. This model is validated using a full academic year dataset to test its predictive validity against long-term academic outcomes including end-of-year grades and total work completed by the end of the year. The model is additionally validated for robustness to time-sample length as well as data sampling frequency. While the model is shown to be predictive and robust to time-sample length, the results are inconclusive for robustness in data sampling frequency. Implications for research on interventions, and understanding the influence of self-control, motivation, metacognition, and cognition are discussed.
%@ 978-1-4503-4450-0
@inproceedings{citeulike:14340919,
abstract = {{The current study introduces a model for measuring student diligence using online behaviors during intelligent tutoring system use. This model is validated using a full academic year dataset to test its predictive validity against long-term academic outcomes including end-of-year grades and total work completed by the end of the year. The model is additionally validated for robustness to time-sample length as well as data sampling frequency. While the model is shown to be predictive and robust to time-sample length, the results are inconclusive for robustness in data sampling frequency. Implications for research on interventions, and understanding the influence of self-control, motivation, metacognition, and cognition are discussed.}},
added-at = {2017-11-15T17:02:25.000+0100},
address = {New York, NY, USA},
author = {Dang, Steven and Yudelson, Michael and Koedinger, Kenneth R.},
biburl = {https://www.bibsonomy.org/bibtex/2e4222aae416f77940d98c8a29bd74f77/brusilovsky},
booktitle = {Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale},
citeulike-article-id = {14340919},
citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=3051470},
citeulike-linkout-1 = {http://dx.doi.org/10.1145/3051457.3051470},
doi = {10.1145/3051457.3051470},
interhash = {3da307a4aaaa53df029f398d5db9e2fe},
intrahash = {e4222aae416f77940d98c8a29bd74f77},
isbn = {978-1-4503-4450-0},
keywords = {intelligent-tutoring las2017},
location = {Cambridge, Massachusetts, USA},
pages = {51--59},
posted-at = {2017-04-21 14:44:11},
priority = {2},
publisher = {ACM},
series = {L@S '17},
timestamp = {2023-06-05T04:48:31.000+0200},
title = {{Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems}},
url = {http://dx.doi.org/10.1145/3051457.3051470},
year = 2017
}