Project Activities
The researchers developed and refined components to the ASSISTments system and conducted an initial evaluation to assess the potential efficacy of the ASSISTments system on mathematics learning. The ASSISTments system is a web-based assessment program that provides tutoring on mathematics questions that students answered incorrectly. In this project, the system offered tutoring associated with each of the 300 released eighth-grade Massachusetts state mathematics assessment items. The researchers developed a mastery learning tracking component to the program so that teachers received instantaneous feedback on their students' progress with multiple types of web-based reports, including individual tracking for 98 different skills. In addition, the researchers developed an automated component to the ASSISTments system that delivered weekly reports to parents detailing what their children learned, as well as what specific skills they struggled with.
Structured Abstract
Setting
The schools were located in Massachusetts.
Sample
Participants were middle school math students and their parents and teachers.
The ASSISTments system is a web-based assessment program that provides tutoring on mathematics questions that students answered incorrectly. In this project, the system offered tutoring associated with each of the 300 released eighth-grade Massachusetts state mathematics assessment items. The researchers developed a mastery learning tracking component to the program so that teachers received instantaneous feedback on their students' progress with multiple types of web-based reports, including individual tracking for 98 different skills. In addition, the researchers developed an automated component to the ASSISTments system that delivered weekly reports to parents detailing what their children learned, as well as what specific skills they struggled with.
Research design and methods
In addition to developing and refining the components of the ASSISTments system, the researchers performed a series of studies to investigate the use of parent and teacher reports, using convenience samples of middle school classrooms. For the study on parent reports, a subset of parents were selected to receive emails reporting on student progress and classroom activities.
Control condition
Depending on the intervention, classrooms in the control condition either did not use the ASSISTments system at all or used it without the parental notification component.
Key measures
The measures of interest were engagement with parent reports, homework completion rates, and a parent survey.
Key outcomes
- Teacher and parent reports were developed within ASSISTments to provide timely information about student progress and skills.
- Weekly parent notifications increased homework completion rates and parents’ feelings of connectedness (Broderick et al 2011).
- Several studies explored the models needed to support intelligent tutoring (Gong, Beck, and Heffernan 2011) and mastery learning (Baker, Goldstein, and Heffernan 2011).
People and institutions involved
IES program contact(s)
Products and publications
The products from this project included a web-based tutoring system in mathematics for sixth- and seventh-grade students and teachers and published reports on the use of parent and teacher reports.
Project website:
Publications:
Book chapter
Baker, R., Pardos, Z., Gowda, S., Nooraei, B., and Heffernan, N. (2011). Ensembling Predictions of Student Knowledge Within Intelligent Tutoring Systems. In J.A. Konstan, R. Conejo, J.L. Marzo, and N. Oliver (Eds.), User Modeling, Adaption and Personalization, Volume 6787 (pp. 13-24). Heidelberg: Springer.
Heffernan, N., Heffernan, C., Decoteau, M., and Militello, M. (2012). Effective and Meaningful Use of Educational Technology: Three Cases From the Classroom. In C. Dede, and J. Richards (Eds.), Digital Teaching Platforms (pp. 88-102). New York: Teacher's College Press.
Singh, R., Saleem, M., Pradhan, P., Heffernan, C., Heffernan, N., Razzaq, L. and Dailey, M. (2011). Feedback During Web-Based Homework: The Role of Hints. In G. Biswas, S. Bull, J. Kay, and A. Mitrovic (Eds.), Artificial Intelligence in Education: 15th International Conference, AIED 2011, Volume 6738 (pp. 328-336). Heidelberg: Springer.
Journal article, monograph, or newsletter
Baker, R.D., Goldstein, A.B., and Heffernan, N.T. (2011). Detecting Learning Moment-By-Moment. International Journal of Artificial Intelligence in Education, 21(1): 5-25.
Broderick, Z., DeNolf, K., Dufault, J., Heffernan, N., and Heffernan, C. (2011). Increasing Parent Engagement in Student Learning Using an Intelligent Tutoring System. Journal of Interactive Learning Research, 22(4): 523-550.
Feng, M., Heffernan, N., and Koedinger, K. (2009). Addressing the Assessment Challenge With an Online System That Tutors as it Assesses: User Modeling and User-Adapted Interaction. The Journal of Personalization Research, 19(3): 243-266.
Gong, Y., Beck, J.E., and Heffernan, N.T. (2011). How to Construct More Accurate Student Models: Comparing and Optimizing Knowledge Tracing and Performance Factor Analysis. International Journal of Artificial Intelligence in Education, 21(1): 27-46.
Mendicino, M., Razzaq, L., and Heffernan, N.T. (2009). A Comparison of Traditional Homework to Computer-Supported Homework. Journal of Research on Technology in Education, 41(3): 331-359.
Ostrow, K.S., Heffernan, N.T., and Williams, J.J. (2017). Tomorrow's EdTech Today: Establishing a Learning Platform as a Collaborative Research Tool for Sound Science. Teachers College Record,, 119(3): 1-36.
Proceeding
Bahador, N., Pardos, Z., Heffernan, and Baker, R. (2011). Less is More: Improving the Speed and Prediction Power of Knowledge Tracing by Using Less Data. In Proceedings of the 4th International Conference on Educational Data Mining (pp. 101-110). Eindhoven, Netherlands: Educational Data Mining.
Feng, M., Beck, J., Heffernan, N., and Koedinger, K. (2008). Can an Intelligent Tutoring System Predict Math Proficiency as Well as a Standardized Test?. In Proceedings of the 1st International Conference on Education Data Mining (pp. 107-116). Montreal, Canada: Educational Data Mining.
Pardos, Z., Gowda, S., Baker, R., and Heffernan, N. (2011). Ensembling Predictions of Student Post-Test Scores for an Intelligent Tutoring System. In Proceedings of the 4th International Conference on Educational Data Mining (pp. 189-198). Eindhoven, Netherlands: Education Data Mining.
Qiu, Y., Qi, Y., Lu, H., Pardos, Z., and Heffernan, N. (2011). Does Time Matter? Modeling the Effect of Time With Bayesian Knowledge Tracing. In Proceedings of the 4th International Conference on Educational Data Mining (pp. 139-148). Eindhoven, Netherlands: Educational Data Mining.
Razzaq, L., and Heffernan, N. (2009). To Tutor or Not to Tutor: That is the Question. In Proceedings of the 2009 Artificial Intelligence in Education Conference (pp. 457-464). Brighton, UK: IOS Press.
Singh, R., Saleem, M., Pradhan, P., Heffernan, C., Heffernan, N., Razzaq, L. Dailey, M. O'Connor, C., and Mulchay, C. (2011). Feedback During Web-Based Homework: The Role of Hints. In Proceedings of the Artificial Intelligence in Education Conference 2011 (pp. 328-336). Heidelberg, Germany: Springer.
Trivedi, S., Pardos, Z., Sarkozy, G., and Heffernan, N. (2011). Spectral Clustering in Educational Data Mining. In Proceedings of the 4th International Conference on Educational Data Mining (pp. 129-138). Eindhoven, Netherlands: Educational Data Mining.
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