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Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 1 Using On-line Tutoring Records to Predict End-of-Year Exam Scores Experience with the Assistments.

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Presentation on theme: "Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 1 Using On-line Tutoring Records to Predict End-of-Year Exam Scores Experience with the Assistments."— Presentation transcript:

1 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 1 Using On-line Tutoring Records to Predict End-of-Year Exam Scores Experience with the Assistments Project and MCAS 8th Grade Mathematics Neil Heffernan, Ken Koedinger, Brian Junker with Mingyu Feng, Beth Ayers, Nathaniel Anozie, Zach Pardos, and many others Funding from US Department of Education, National Science Foundation (NSF), Office of Naval Research, Spencer Foundation, and the US Army

2 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 2 The ASSISTments Project Web-based 8 th grade mathematics tutoring system ASSIST with, and ASSESS, progress toward Massachusetts Comprehensive Assessment System Exam (MCAS) –Guide students through problem solving with MCAS released items –Predict students’ MCAS scores at end of year –Provide feedback to teachers (what to teach next?) (Generalize to other States…) Over 50 workers at Carnegie Mellon, Worcester Polytechnic Institute, Carnegie Learning, Worcester Public Schools

3 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 3 The ASSISTment Tutor Main Items: Released MCAS or “morphs” Incorrect Main  “Scaffold” Items –“One-step” breakdowns of main task –Buggy feedback, hints on request, etc. All items coded by source, teacher-generated “curriculum”, and transfer model (Q-matrix) Student records contain responses, timing data, bugs/hints, etc. System tracks students through time, provides teacher reports per student & per class. –MCAS prediction –Skills learned/not-learned, etc.

4 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 4 The ASSISTment Architectures Extensible Tutor Architecture –Scalable from simple pseudo-tutors with few users to model-tracing tutors and 1000’s of users –Curriculum Unit Items organized into multiple curricula Sections within curriculum: Linear, Random, Experimental, etc. –Problem & Tutoring Strategy Units Task organization & user interaction (e.g. main item & scaffolds, interface widgets, …) Task components mapped to multiple transfer models –Logging Unit Fine-grained human-computer interaction trace Abstracting/coarsening mechanisms Web-based Item Builder –Used by classroom teachers to develop content –Support for building curricula, mapping tasks to transfer models, etc. Relational Database and Network Architecture supports –User Reports (e.g., students, teachers, coaches, administrators) –Research Data Analysis Razzaq et al. (to appear) overview

5 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 5 Goals and Complications Two Assessment Goals –To predict end-of-year MCAS scores –To provide feedback to teachers (what to teach next?) Some Complications –Assessment ongoing throughout the school year as students learn (from teachers & from ASSISTments!) –Multiple skills models for different purposes –Scaffold questions: For tutoring or for measurement? –Deliberate ready-fire-aim user-assisted development

6 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference Data Tutoring tasks –493 main items –1216 scaffold items Students –912 eighth-graders in two middle schools Skills Models (Transfer Models / Q Matrices) –1 “Proficiency”: Unidimensional IRT –5 MCAS “strands”: Number/Operations, Algebra, Geometry, Measurement, Data/Probability –39 MCAS learning standards: nested in the strands –77 active skills: “WPI April 2005” (106 potential)

7 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 7 Static Prediction Models Feng et al. (2006 & to appear): –Online testing metrics Percent correct on main/scaffold/both items “assistance score” = (errors+hints)/(number of scaffolds) Time spent on (in-)correct answers etc. –Compare paper & pencil pre/post benchmark tests Ayers and Junker (2006): –Rasch & LLTM (linear decomps of item difficulty) –Augmented with online testing metrics Pardos et al. (2006); Anozie (2006): –Binary-skills conjunctive Bayes nets –DINA models (Junker & Sijtsma, 2001; Maris, 1999; etc.)

8 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 8 Static Models: Feng et al. (2006 & to appear) What is related to raw MCAS (0-54 pts)? P&P pre/post benchmark tests Online metrics: –Pct Correct on Mains –Pct Correct on Scaffolds –Seconds Spent on Incorrect Scaffolds –Avg Number of Scaffolds per Minute –Number of Hints Plus Incorrect Main Items –etc. All annual summaries PredictorCorr P & P Tests SEP-TEST 0.75 MARCH-TEST 0.41 ASSISTment Online Metrics MAIN_PERCENT_CORRECT 0.75 MAIN_COUNT 0.47 TOTAL_MINUTES 0.26 PERCENT_CORRECT 0.76 QUESTION_COUNT 0.20 HINT_REQUEST_COUNT AVG_HINT_REQUEST HINT_COUNT AVG_HINT_COUNT BOTTOM_OUT_HINT_COUNT AVG_BOTTOM_HINT ATTEMPT_COUNT 0.08 AVG_ATTEMPT AVG_QUESTION_TIME AVG_ITEM_TIME -0.39

9 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 9 Static Models: Feng et al. (2006 & to appear) Stepwise linear regression Mean Abs Deviation Within-sample MAD = Raw MCAS = 0-54, so Within-sample Pct Err = MAD/54 =10.25% (uses Sept P&P Test) PredictorCoefficient (Const)26.04 Sept_Test0.64 Pct_Correct_All24.21 Avg_Attempts Avg_Hint_Reqs-2.28

10 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 10 Static Models: Ayers & Junker (2006) Compared two IRT models on ASSISTment main questions: –Rasch model for 354 main questions. –LLTM: Constrained Rasch model decompose main question difficulty by skills in the WPI April Transfer Model (77 skills). Replace “Percent Correct” with IRT proficiency score in linear predictions of MCAS

11 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 11 Static Models: Ayers & Junker (2006) Rasch fits much better than LLTM –  BIC = -3,300 –  df = +277 Attributable to –Transfer model? –Linear decomp of item difficulties? Residual and difficulty plots suggest transfer model fixes.

12 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 12 Static Models: Ayers & Junker (2006) Focus on Rasch, predict MCAS with where  = proficiency, Y=online metric 10-fold cross-validation vs. 54-pt raw MCAS: PredictorsVariablesCV- MADCV % Error % Corr Main  (proficiency)  + 5 Online Metrics

13 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 13 Static Models: Pardos et al. (2006); Anozie (2006) Conjunctive binary-skills Bayes Net (Maris, 1999; Junker & Sijtsma DINA, 2001; etc.) P(Congruence) 11 P(Equation-Solving) 22 P(Perimeter) 33 GateP(Question) True1-s 1 Falseg1g1 GateP(Question) True1-s 2 Falseg2g2 GP(Question) True1-s 3 Falseg3g3

14 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 14 Static Models: Pardos et al. (2006) Compared nested versions of binary skills models (coded both ASSISTments and MCAS): g i = 0.10, s i = 0.05, all items;  k = 0.5, all skills Inferred skills from ASSISTments; computed expected score for 30-item MCAS subset MODELMean Absolute Deviance (MAD)% ERROR (30 items) 39 MCAS standards skills (WPI Apr) MCAS strands Binary Skill

15 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 15 Static Models: Anozie (2006) Focused on 77 active skills in WPI April Model Estimated  k ’s, g i ’s and s i ’s using flexible priors Predicted full raw 54-pt MCAS score as linear function of (expected) number of skills learned Months of DataCV MADCV % Err

16 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 16 Static Models: Anozie (2006) Which graph contains the points in the table? 1.Quadrant of (-2,-3)? 2.Quadrant of (-1,-1)? 3.Quadrant of (1,3)? 4.[Repeat main] XY Main Item: Scaffolds: SlipGuess

17 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 17 Dynamic Prediction Models Razzaq et al. (to appear): evidence of learning over time Feng et al. (to appear): student or item covariates plus linear growth curves (a la Singer & Willett, 2003) Anozie and Junker (2006): changing influence of online metrics over time

18 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 18 Dynamic Models: Razzaq et al. (to appear) ASSISTment system is sensitive to learning Not clear what is the source of learning here…

19 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 19 Dynamic Models: Feng et al. (to appear) Growth-Curve Model I: Overall Learning Growth-Curve Model II: Learning in Strands School was a better predictor (BIC) than Class or Teacher; possibly because School demographics dominate the intercept. Sept_Test is a good predictor of baseline proficiency. Baseline and learning rates varied by Strand.

20 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 20 Dynamic Models: Anozie and Junker (2006) Look at changing influence of online metrics on MCAS prediction over time –Compute monthly summaries of all online metrics (not just %- correct) –Build linear prediction model for each month, using all current and prev. months’ summaries To enhance interpretation, variable selection –by metric, not by monthly summary –include/exclude metrics simultaneously in all monthly models

21 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 21 Dynamic Models: Anozie and Junker (2006) More months helps more than more metrics First 5 online metrics retained for final model(s)

22 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 22 Dynamic Models: Anozie and Junker (2006)

23 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 23 Dynamic Models: Anozie and Junker (2006) Recent main question performance dominates – proficiency?

24 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 24 Dynamic Models: Anozie and Junker (2006) Older performance on scaffolds similar to recent – learning?

25 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 25 Summary of Prediction Models ModelVariablesCV-MADCV % ErrorCV-RMSE PctCorrMain #Skills of 77 learned 1? Rasch Proficiency 1? PctCorrMain + 4 metrics 35 ( = 5 x 7 ) Rasch Profic + 5 metrics 6? Feng et al. (in press) compute the split-half MAD of the MCAS and estimate ideal % Error ~ 11%, or MAD ~ 6 points. Ayers & Junker (2006) compute reliabilities of the ASSISTment sets seen by all students and estimate upper and lower bounds for optimal MAD: 0.67 MAD 5.21.

26 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 26 New Directions We have some real evidence of learning –We are not yet modeling individual student learning Current teacher report: For each skill, report percent correct on all items for which that skill is hardest. –Can we do better? Approaches now getting underway: –Learning curve models –Knowledge-tracing models

27 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 27 New Directions: Cen, Koedinger & Junker (2005) Inspired by Draney, Pirolli & Wilson (1995) –Logistic regression for successful skill uses –Random intercept (baseline proficiency) –fixed effects for skill and skill*opportunity Difficulty factor: skill but not skill*opportunity Learning factor: skill and skill*opportunity –Part of Data Shop at Feng et al. (to appear) fit similar logistic growth curve models to ASSISTment items

28 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 28 New Directions: Anozie (2006) DINA model can be used to infer skills directly Experimental posterior intervals illustrated above right When students’ data contradicts prior or “borrowed info” from other students, intervals widen

29 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 29 New Directions: Knowledge Tracing Combine knowledge tracing approach of Corbett, Anderson and O’Brien (1995) with DINA model of Junker and Sijtsma (2001) Each skill represented by a two state (unlearned/learned) Markov process with absorbing state at “learned”. Can locate time during school year when each skill is learned. Work just getting underway (Jiang & Junker).

30 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 30 Discussion ASSISTment system –Great testbed for online cognitive modeling and prediction technologies –Didn’t mention reporting and “gaming detection” technologies –Teachers positive, students impressed Ready-Fire-Aim –Important! Got system up and running, lots of user feedback & buy-in –But… E.g. lack of control over content and content- rollout (content balance vs MCAS?) –Given this, perhaps only crude methods needed/possible for MCAS prediction?

31 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 31 Discussion Multiple skill codings for different purposes –Exam prediction vs. teacher feedback; state to state. Scaffolds –Dependence between scaffolds and main items –Forced-scaffolding: main right  scaffolds right –Content sometimes skills-based, sometimes tutorial We are now building some true one-skill decomps to investigate stability of skills across items Student learning over time –Clearly evidence of that! –Some experiments not shown here suggest modest but significant value-added for ASSISTments –Starting to model learning, time-to-mastery, etc.

32 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 32 References Anozie, N. (2006). Investigating the utility of a conjunctive model in Q-matrix assessment using monthly student records in an online tutoring system. Proposal submitted to the National Council on Measurement in Education 2007 Annual Meeting. Anozie, N.O. & Junker, B. W. (2006). Predicting end-of-year accountability assessment scores from monthly student records in an online tutoring system. American Association for Artificial Intelligence Workshop on Educational Data Mining (AAAI-06), July 17, 2006, Boston, MA. Ayers, E. & Junker, B.W. (2006). Do skills combine additively to predict task difficulty in eighth-grade mathematics? American Association for Artificial Intelligence Workshop on Educational Data Mining (AAAI-06), July 17, 2006, Boston, MA. Ayers, E. & Junker, B. W. (2006). IRT modeling of tutor performance to predict end of year exam scores. Working paper. Corbett, A. T., Anderson, J. R., & O'Brien, A. T. (1995) Student modeling in the ACT programming tutor. Chapter 2 in P. Nichols, S. Chipman, & R. Brennan, Cognitively Diagnostic Assessment. Hillsdale, NJ: Erlbaum. Draney, K. L., Pirolli, P., & Wilson, M. (1995). A measurement model for a complex cognitive skill. In P. Nichols, S. Chipman, & R. Brennan, Cognitively Diagnostic Assessment. Hillsdale, NJ: Erlbaum. Feng, M., Heffernan, N. T., & Koedinger, K. R. (2006). Predicting state test scores better with intelligent tutoring systems: developing metrocs to measure assistance required. In Ikeda, Ashley & Chan (Eds.) Proceedings of the Eighth International Conference on Intelligent Tutoring Systems. Springer-Verlag: Berlin. pp Feng, M., Heffernan, N., Mani, M., & Heffernan, C. (2006). Using mixed effects modeling to compare different grain-sized skill models. AAAI06 Workshop on Educational Data Mining, Boston MA. Feng, M., Heffernan, N. T., & Koedinger, K. R. (in press). Addressing the testing challenge with a web-based E-assessment system that tutors as it assesses. Proceedings of the 15 th Annual World Wide Web Conference. ACM Press (Anticipated): New York, Hao C., Koedinger K., & Junker B. (2005). Automating Cognitive Model Improvement by A*Search and Logistic Regression. In Technical Report (WS-05-02) of the AAAI-05 Workshop on Educational Data Mining, Pittsburgh, Junker, B.W. & Sijtsma K. (2001). Cognitive assessment models with few assumptions, and connections with nonparametric item response theory. Applied Psychological Measurement 25: Maris, E. (1999). Estimating multiple classification latent class models. Psychometrika 64, Pardos, Z. A., Heffernan, N. T., Anderson, B., & Heffernan, C. L. (2006). Using Fine Grained Skill Models to Fit Student Performance with Bayesian Networks. Workshop in Educational Data Mining held at the Eighth International Conference on Intelligent Tutoring Systems. Taiwan 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 Artificial Intelligence In Education. Amsterdam: ISO Press. pp Razzaq, L., Feng, M., Heffernan, N. T., Koedinger, K. R., Junker, B., Nuzzo-Jones, G., Macasek, N., Rasmussen, K. P., Turner, T. E. & Walonoski, J. (to appear). A web-based authoring tool for intelligent tutors: blending assessment and instructional assistance. In Nedjah, N., et al. (Eds). Intelligent Educational Machines within the Intelligent Systems Engineering Book Series (see Singer, J. D. & Willett, J. B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Occurrence. Oxford University Press, New York. Websites:

33 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 33

34 Brian Junker Carnegie Mellon 2006 MSDE / MARCES Conference 34 Full Set of Online Metrics


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