Given the limited classroom time available in middle school mathematics classes, teachers are compelled to choose between time spent assisting students' development and time spent assessing students' abilities. To help resolve this dilemma, this team of researchers plans to integrate assistance and assessment by utilizing a web-based system ("Assistment") that will offer instruction to students while providing a more detailed evaluation of their abilities to the teacher than is possible under current approaches. Students will work on the Assistment website for about 20 minutes per week. The Assistment system is an artificial intelligence program. Each week when students work on the website, the system "learns" more about the students' abilities and provides increasingly accurate predictions of how they will do on a standardized mathematics test. The system also identifies the difficulties individual students - and the class as a whole - are having. Teachers will be able to use this detailed feedback to tailor their instruction to focus on the particular difficulties identified by the system. Unlike other assessment systems, the Assistment technology also provides students with intelligent tutoring assistance while the assessment information is being collected.

Project Website: http://www.assistment.org/

Related IES Projects: Making Longitudinal Web-Based Assessments Give Cognitively Diagnostic Reports to Teachers, Parents, and Students While Employing Mastery Learning (R305A070440) and An Efficacy Study of Online Mathematics Homework Support: An Evaluation of the ASSISTments Formative Assessment and Tutoring Platform (R305A120125)

Products and Publications

Book chapter

Ayers, E., and Junker, B.W. (2006). Do Skills Combine Additively to Predict Task Difficulty in Eighth Grade Mathematics?. In J. Beck, E. Aimeur, and T. Barnes (Eds.), Educational Data Mining: Papers From the 2006 AAAI Workshop(pp. 14–20). Menlo Park, CA: AAAI Press.

Cen, H., Koedinger, K., and Junker, B.W. (2007). Is Over Practice Necessary?: Improving Learning Efficiency With the Cognitive Tutor Through Educational Data Mining. In R. Luckin, K. Koedinger, and J. Greer (Eds.), Artificial Intelligence in Education—Building Technology Rich Learning Contexts That Work (pp. 511–518). Amsterdam: IOS Press.

Feng, M., Heffernan, N.T., and Koedinger, K.R. (2005). Looking for Sources of Error in Predicting Students' Knowledge. In J.E. Beck (Ed.), Educational Data Mining: Papers From the 2005 AAAI Workshop (pp. 54–61). Menlo Park, CA: AAAI Press.

Junker, B.W. (2007). Using On-Line Tutoring Records to Predict End-of-Year Exam Scores: Experience With the Assistments Project and MCAS 8th Grade Mathematics. In R.W. Lissitz (Ed.), Assessing and Modeling Cognitive Development in School: Intellectual Growth and Standard Settings (pp. 1–34). Maple Grove, MN: JAM Press.

Nuzzo-Jones, G., Walonoski, J.A., Heffernan, N.T., and Livak, T. (2005). The Extensible Tutor Architecture: A New Foundation for ITS. In C.K. Looi, G. Mccalla, B. Bredeweg, and J. Breuker (Eds.), Artificial Intelligence in Education—Supporting Learning Through Intelligent and Socially Informed Technology (pp. 902–904). Amsterdam: IOS Press.

Pardos, Z., Feng, M., Heffernan, N.T., and Heffernan-Linquist, C. (2007). Analyzing Fine-Grained Skill Models Using Bayesian and Mixed Effect Methods. In R. Luckin, K. Koedinger, and J. Greer (Eds.), Artificial Intelligence in Education—Building Technology Rich Learning Contexts that Work (pp. 626–628). Amsterdam: IOS Press.

Razzaq, L., Feng, M., Heffernan, N.T., Koedinger, K., Nuzzo-Jones, G., Junker, B.W., Macasek, M.A., Rasmussen, K.P., Turner.T.E., and Walonoski, J.A. (2007). A Web-Based Authoring Tool for Intelligent Tutors: Blending Assessment and Instructional Assistance. In N. Nedjah, L.D. Mourelle, M.N. Borges, and N.N. Almeida (Eds.), Intelligent Educational Machines: Methodologies and Experiences (pp. 23–49). New York: Springer.

Razzaq, L., Feng, M., Nuzzo-Jones, G., Heffernan, N.T., Koedinger, K.R., Junker, B., Ritter, S., Knight, A., Aniszczyk, C., Choksey, S., Livak, T., Mercado, E., Turner, T.E., Upalekar, R., Walonoski, J.A., Macasek, M.A., and Rasmussen, K.P. (2005). Blending Assessment and Instructional Assisting. In C.K. Looi, G. Mccalla, B. Bredeweg, and J. Breuker (Eds.), Artificial Intelligence in Education—Supporting Learning Through Intelligent and Socially Informed Technology (pp. 555–562). Amsterdam: IOS Press.

Razzaq, L., Heffernan, N.T., and Lindeman, R.W. (2007). What Level of Tutor Interaction is Best?. In R. Luckin, K. Koedinger, and J. Greer (Eds.), Artificial Intelligence in Education—Building Technology Rich Learning Contexts That Work (pp. 222–229). Amsterdam: IOS Press.

Rose, C., Donmez, P., Gweon, G., Knight, A., Junker, B., Cohen, W., Koedinger, K., and Heffernan, N. (2005). Automatic and Semi-Automatic Skill Coding With a View Towards Supporting On-Line Assessment. In C.K. Looi, G. McCalla, B. Bredeweg, and J. Breuker (Eds.), Artificial Intelligence in Education—Supporting Learning Through Intelligent and Socially Informed Technology (pp. 571–578). Amsterdam: IOS Press.

Turner, T.E., Macasek, M.A., Nuzzo-Jones, G., Heffernan, N.T., and Koedinger, K. (2005). The Assessment Builder: A Rapid Development Tool for ITS. In C.K. Looi, G. Mccalla, B. Bredeweg, and J. Breuker (Eds.), Artificial Intelligence in Education—Supporting Learning Through Intelligent and Socially Informed Technology(pp. 929–931). Amsterdam: IOS Press.

Journal article, monograph, or newsletter

Ayers, E., and Junker, B.W. (2008). IRT Modeling of Tutor Performance to Predict End-of-Year Exam Scores. Educational and Psychological Measurement, 68(6): 972–987.

Baker, R., Walonoski, J., Heffernan, N., Roll, I., Corbett, A., and Koedinger, K.R. (2008). Why Students Engage in "Gaming the System" Behavior in Interactive Learning Environments. Journal of Interactive Learning Research, 19(2): 185–224.

Feng, M, Heffernan, N., Heffernan, C., and Mani, M. (2009). Using Mixed-Effects Modeling to Analyze Different Grain-Sized Skill Models in an Intelligent Tutoring System. IEEE Transactions on Learning Technologies, 2(2): 79–92.

Feng, M., and Heffernan, N.T. (2006). Informing Teachers Live About Student Learning: Reporting in the Assistment System. Technology, Instruction, Cognition, and Learning, 3(1): 115–128.

Feng, M., and Heffernan, N.T. (2007). Towards Live Informing and Automatic Analyzing of Student Learning: Reporting in Assistment System. Journal of Interactive Learning Research, 18(2): 207–230.

Heffernan, N., Koedinger, K., and Razzaq, L. (2008). Expanding the Model-Tracing Architecture: A 3rd Generation Intelligent Tutor for Algebra Symbolization. The International Journal of Artificial Intelligence in Education, 18(2): 153–178.

Koedinger, K.R., McLaughlin, E.A., and Heffernan, N.T. (2010). A Quasi-Experimental Evaluation of an On-Line Formative Assessment and Tutoring System. Journal Of Educational Computing Research, 43(4): 489–510.

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.

Nongovernment report, issue brief, or practice guide

Cen, H., Koedinger, K., and Junker, B. (2005). Automating Cognitive Model Improvement by a Search and Logistic Regression.Menlo Park, CA: AAAI Press. Macasek, M.A., and Heffernan, N.T. (2006). Towards Enabling Collaboration in Intelligent Tutoring Systems WPI Technical Report #CS-TR-06–07.Worcester, MA: Worchester Polytechnic Institute.

Nuzzo-Jones, G., Macasek, M.A., Walonoski, J., Rasmussen K.P., and Heffernan, N.T. (2006). Common Tutor Object Platform: An E-Learning Software Development Strategy (WPI Technical Report #CS-TR-06–08).Worchester, MA: Worchester Polytechnic Institute.

Proceeding

Anozie, N.O., and Junker, B.W. (2006). Predicting End-of-Year Accountability Assessment Scores From Monthly Student Records in an Online Tutoring System. In Proceedings of the 21st National Conference on Artificial Intelligence (pp. 1–6). Menlo Park, CA: AAAI Press.

Cen, H., Koedinger, K.R., and Junker, B. (2006). Learning Factors Analysis: A General Method for Cognitive Model Evaluation and Improvement. In Proceedings of the 8th International Conference on Intelligent Tutoring Systems (pp. 164–175). Berlin, Germany: Springer-Verlag.

Feng, M., Beck, J., and Heffernan, N.T. (2009). Using Learning Decomposition and Bootstrapping With Randomization to Compare the Impact of Different Educational Interventions on Learning. In Proceedings of the 2nd International Conference on Educational Data Mining (pp. 51–60). Cordoba, Spain: Educational Data Mining.

Feng, M., Beck, J., Heffernan, N., Beck, J., and Koedinger, K. (2008). Can we Predict Which Groups of Questions Students will Learn From?. In Proceedings of the 1st International Conference on Education Data Mining(pp. 218–225). Montreal, Canada: Educational Data Mining.

Feng, M., Heffernan, N.T., and Beck, J. (2009). Using Learning Decomposition to Analyze Instructional Effectiveness in the Assistment System. In Proceedings of the 14th International Conference on Artificial Intelligence in Education (AIED-2009) (pp. 523–530). Amsterdam: IOS Press.

Feng, M., Heffernan, N.T., and Koedinger, K.R. (2006). Addressing the Testing Challenge With a Web-Based E-Assessment System That Tutors as it Assesses. In Proceedings of the 15th International World Wide Web Conference (pp. 307–316). Edinburgh, Scotland: World Wide Web Conference Committee (IW3C2).

Feng, M., Heffernan, N.T., and Koedinger, K.R. (2006). Predicting State Test Scores Better With Intelligent Tutoring Systems: Developing Metrics to Measure Assistance Required. In Proceedings of the 8th International Conference on Intelligent Tutoring Systems(pp. 31–40). Berlin, Germany: Springer-Verlag.

Kardian, K., and Heffernan, N.T. (2006). Knowledge Engineering for Intelligent Tutoring Systems: Assessing Semi-Automatic Skill Encoding Methods. In Proceedings of the 8th International Conference on Intelligent Tutoring Systems (pp. 735–737). Berlin, Germany: Springer-Verlag.

Mendicini, M., Heffernan, N., and Razzaq, L. (2008). Comparing Classroom Problem-Solving With No Feedback to Web-Based Homework Assistance. In Proceedings of the 9th International Conference on Intelligent Tutoring Systems (pp. 426–437). Berlin, Germany: Springer-Verlag.

Pardos, Z.A., Heffernan, N.T., Anderson, B., and Heffernan, C. (2006). Using Fine-Grained Skill Models to Fit Student Performance With Bayesian Networks. In On-Line Proceedings of the Workshop on Educational Data Mining at the 8th International Conference on Intelligent Tutoring Systems (pp. 5–12). New York: Springer.

Pardos, Z.A., Heffernan, N.T., Anderson, B., and Heffernan, C.L. (2007). The Effect of Model Granularity on Student Performance Prediction Using Bayesian Networks. In Proceedings of the 11th International Conference on User Modeling (pp. 435–439). Berlin Heidelberg: Springer.

Razzaq, L., and Heffernan, N.T. (2006). Scaffolding vs. Hints in the Assistment System. In Proceedings of the 8th International Conference on Intelligent Tutoring Systems (pp. 635–644). Berlin, Germany: Springer-Verlag.

Razzaq, L., and Heffernan, N.T. (2008). Towards Designing a User-Adaptive Web-Based E-Learning System. In Proceedings of the 2008 Conference on Human Factors in Computing Systems(pp. 3525–3530). Florence, Italy: ACM 2008.

Walonoski, J., and Heffernan, N.T. (2006). Detection and Analysis of Off-Task Gaming Behavior in Intelligent Tutoring Systems. In Proceedings of the 8th International Conference on Intelligent Tutoring Systems(pp. 382–391). Berlin, Germany: Springer-Verlag.

Walonoski, J., and Heffernan, N.T. (2006). Prevention of Off-Task Gaming Behavior in Intelligent Tutoring Systems. In Proceedings of the 8th International Conference on Intelligent Tutoring Systems (pp. 722–724). Berlin, Germany: Springer-Verlag.