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Addressing the Testing Challenge with a Web-Based E-Assessment System that Tutors as it Assesses

Addressing the Testing Challenge with a Web-Based E-Assessment System that Tutors as it Assesses. Mingyu Feng, Worcester Polytechnic Institute (WPI) Neil T. Heffernan, Worcester Polytechnic Institute (WPI) Kenneth R. Koedinger, Carnegie Mellon University (CMU). The “ASSISTment” System.

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Addressing the Testing Challenge with a Web-Based E-Assessment System that Tutors as it Assesses

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  1. Addressing the Testing Challenge with a Web-Based E-Assessment System that Tutors as it Assesses Mingyu Feng, Worcester Polytechnic Institute (WPI) Neil T. Heffernan, Worcester Polytechnic Institute (WPI) Kenneth R. Koedinger, Carnegie Mellon University (CMU)

  2. The “ASSISTment” System • An e-assessment and e-learning system that does both ASSISTing of students and assessMENT (movie) • www.assistment.org • Massachusetts Comprehensive Assessment System “MCAS” • Web-based system built on Common Tutoring Object Platform (CTOP) [1] [1] Nuzzo-Jones., G. Macasek M.A., Walonoski, J., Rasmussen K. P., Heffernan, N.T., Common Tutor Object Platform, an e-Learning Software Development Strategy, WPI technical report. WPI-CS-TR-06-08. WWW’06

  3. ASSISTment • We break multi-step problems into “scaffolding questions” • “Hint Messages”: given on demand that give hints about what step to do next • “Buggy Message”: a context sensitive feedback message • “Knowledge Components”: Skills, Strategies, concepts • The state reports to teachers on 5 areas • We seek to report on 100 knowledge components • How does a student work with the ASSISTment? (movie) (Demo/movie) The original question a. Congruence b. Perimeter c. Equation-Solving The 1st scaffolding question Congruence The 2nd scaffolding question Perimeter A buggy message A hint message WWW’06

  4. Goal • Help student Learning (this paper’s goal[2][3]) • Assess students’ performance and present results to teachers. (this work focused on) • Online “Grade book” report [2] 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., Rasmussen, K.P. (2005). The Assistment Project: Blending Assessment and Assisting. In C.K. Looi, G. McCalla, B. Bredeweg, & J. Breuker (Eds.) Proceedings of the 12th International Conference on Artificial Intelligence In Education, 555-562. Amsterdam: ISO Press. [3] Razzaq, L., Heffernan, N.T. (in press). Scaffolding vs. hints in the Assistment System. In Ikeda, Ashley & Chan (Eds.). Proceedings of the Eight International Conference on Intelligent Tutoring Systems. Springer-Verlag: Berlin. pp. 635-644. 2006. WWW’06

  5. Outline for the talk • Part I: Using • Part II: Longitudinal Models tracking student learning over time • Able to tell which schools provide the most learning to students • Can we tell teachers which skills are being learned WWW’06

  6. Data Source • 600+ students of two middle schools • Used the ASSISTment system every other week from Sep. 2004 to June 2005 • Real MCAS score • test taken in May 2005 • 2 paper and pencil based tests, administered in Sep. 2004 and March 2005. WWW’06

  7. Part I: Using Dynamic Measures • Research Questions • Can we do a more accurate job of predicting student's MCAS score using the online assistance information (concerning time, performance on scaffoldings, #attempt, #hint)? • Can we do a better job predicting MCAS in this online assessment system than the tradition paper and pencil test does? WWW’06

  8. Model Independent Variable’s # Variables Entered R2 BIC+ MAD* Model I Paper practice results only 2 .588 -358 6.20881 Model II The single online static metric of percent correct on original questions 1 .567 -343 6.21108 Model III Model II plus all other online measures 5 .663 -423 5.44183 + BIC: Bayesian Information Criterion * MAD: Mean Absolute Deviance Part I: Using Dynamic Measures • Approach • Run forward stepwise linear regression to train up regression models using different independent variables • Result WWW’06

  9. Part I: Using Dynamic Measures Model III • What do we see from Model III? • the more hint, attempt, time a student need to solve a problem, the worse his predicted score would be WWW’06

  10. Part II: Track Learning Longitudinally • Recall the problems of prediction in Grade book • Only based on static measure (discussed in part I) • Time ignored  part II • What if we take time into consideration? • Research Questions • Can our system detect performance improving over time? • Can we tell the difference on learning rate of students from different schools? Teacher? (Who cares?) • Do students show difference on learning different skills? • Approach -- longitudinal data analysis Note:Different from Razzaq, Feng et. al which looks at student performance gain over learning opportunity pairs within the ASSISTment system, here “learning” includes students learning in class too. WWW’06

  11. Longitudinal Data Analysis • What do we get from a longitudinal model? • Average population trajectory for the specified group • Trajectory indicated by two parameters • intercept: slope: • The average estimated score for a group at time j is • One trajectory for every single student • Each student got two parameters to vary from the group average • Intercept: slope: • The estimated score for student i at time j is • Students’ initial knowledge is indicated by intercept, while slope shows the learning rate WWW’06 [4] Singer, J. D. & Willett, J. B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Occurrence. Oxford University Press, New York.

  12. WWW’06 Sept Oct Nov Dec Jan Feb Mar Apr May

  13. 17 Student from one class % Correct (Y-Axis) over a given month (X Axis) Table 2. Regression Models WWW’06

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  16. Part II: Track Learning Longitudinally • Result • Unconditional model (model A) : no predictors • Growth model (model B) • estimated initial average PredictedScore = 18 • estimated average monthly learning rate = 1.29 • Observation : students were learning over time • Add in school/teacher/class (model D/E/F) • Model D shows statistical significant advantage as measured by BIC • Observation: students from different schools differ on both incoming knowledge and learning rate WWW’06

  17. Part II: Track Learning Longitudinally • The last question • Can we detect difference on learning rate of different skills? WWW’06

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  20. Part II: Track Learning Longitudinally • The last question • Can we detect difference on learning rate of different skills? Yes we can! In this paper we showed that we can the model with 5 skills to do a more accurate prediction of their own data. Even more recent studies we have down have shown even finer grain model (98 skills) are better at non-only predicting our online data, but predicting the students test scores. [7] Pardos, Z. A., Heffernan, N. T., Anderson, B. & Heffernan, C. (in press). Using Fine-Grained Skill Models to Fit Student Performance with Bayesian Networks. Workshop in Educational Data Mining held at the Eight International Conference on Intelligent Tutoring Systems. Taiwan. 2006. [8] Feng, M., Heffernan, N., Mani, M., & Heffernan C. (in press). Using Mixed-Effects Modeling to Compare Different Grain-Sized Skill Models. AAAI'06 Workshop on Educational Data Mining, Boston, 2006. WWW’06

  21. Large Scale : ASSISTment project • ASSISTments are tagged with skills WWW’06

  22. Large Scale : ASSISTment project • Are the skill/knowledge components mapping any good? • Teachers get reports that they think are credible and useful. [6] [6] Feng, M., Heffernan, N.T. (in press). Informing Teachers Live about Student Learning: Reporting in the Assistment System. To be published in Technology, Instruction, Cognition, and Learning Journal Vol. 3. Old City Publishing, Philadelphia, PA. 2006. [7] Pardos, Z. A., Heffernan, N. T., Anderson, B. & Heffernan, C. (in press). Using Fine-Grained Skill Models to Fit Student Performance with Bayesian Networks. Workshop in Educational Data Mining held at the Eight International Conference on Intelligent Tutoring Systems. Taiwan. 2006. [8] Feng, M., Heffernan, N., Mani, M., & Heffernan C. (in press). Using Mixed-Effects Modeling to Compare Different Grain-Sized Skill Models. AAAI'06 Workshop on Educational Data Mining, Boston, 2006. WWW’06

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  25. [5] Heffernan N.T., Turner T.E., Lourenco A.L.N., Macasek M.A., Nuzzo-Jones G., Koedinger K.R., The ASSISTment builder: Towards an Analysis of Cost Effectiveness of ITS creation, Accepted by FLAIRS2006, Florida, USA (2006). Large Scale : ASSISTment project • We built 300 ASSISTments provided ~8 hours of content using the Builder [5] • Are the content we created good at producing learning? • Do students learn from these? [2] • Good enough that its used by 1,500 8th graders in Worcester, every two weeks as part of their math class. (2nd year) WWW’06

  26. Large Scale : ASSISTment project • Other work Using Hints and Attempts and Time • Can detect how is “gaming” and prevent it • Machine learning of user models [9] Walonoski, J., Heffernan, N.T. (accepted). Detection and Analysis of Off-Task Gaming Behavior in Intelligent Tutoring Systems. In Ikeda, Ashley & Chan (Eds.). Proceedings of the Eight International Conference on Intelligent Tutoring Systems. Springer-Verlag: Berlin. pp. 382-391. 2006 [10] Walonoski, J., Heffernan, N. T. (accepted) Prevention of Off-Task Gaming Behavior in Intelligent Tutoring Systems, Proceedings of the Eight International Conference on Intelligent Tutoring Systems. WWW’06

  27. Conclusion • Our online assessment system did a better job of predicting student knowledge by being able to take into consideration how much tutoring assistance was needed. • Promising evidence was found that the online system was able to track students’ learning during a year well. • We found that the system could reliably track students’ learning of individual skills. WWW’06

  28. Some of the ASSISTMENT TEAM * This research was made possible by the US Dept of Education, Institute of Education Science, "Effective Mathematics Education Research" program grant #R305K03140, the Office of Naval Research grant # N00014-03-1-0221, NSF CAREER award to Neil Heffernan, and the Spencer Foundation. Authors Razzaq and Mercado were funded by the National Science Foundation under Grant No. 0231773. All the opinions in this article are those of the authors, and not those of any of the funders. Leena RAZZAQ*, Mingyu FENG, Goss NUZZO-JONES, Neil T. HEFFERNAN, Kenneth KOEDINGER+, Brian JUNKER+, Steven RITTER, Andrea KNIGHT+, Edwin MERCADO*, Terrence E. TURNER, Ruta UPALEKAR, Jason A. WALONOSKI Michael A. MACASEK, Christopher ANISZCZYK, Sanket CHOKSEY, Tom LIVAK, Kai RASMUSSEN Carnegie Learning

  29. Future work • Predict Student State Test Scores • Regression + longitudinal analysis [9] • Incorporate finer grained cognitive models • Item level prediction [8] • Apply the models in current reporting system [9] Feng, M., Heffernan, N.T., & Koedinger, K.R. (in press). Predicting state test scores better with intelligent tutoring systems: developing metrics to measure assistance required. In Ikeda, Ashley & Chan (Eds.). Proceedings of the Eight International Conference on Intelligent Tutoring Systems. Springer-Verlag: Berlin. pp. 31-40. 2006. [8] Feng, M., Heffernan, N., Mani, M., & Heffernan C. (2006, accepted). Using Mixed-Effects Modeling to Compare Different Grain-Sized Skill Models. AAAI'06 Workshop on Educational Data Mining, Boston, 2006. WWW’06

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