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Some issues and applications in cognitive diagnosis and educational data mining

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### Some issues and applications in cognitive diagnosis and educational data mining

Brian W. Junker

Department of Statistics

Carnegie Mellon University

Presentation to the International Meeting of the Psychometric Society

Tokyo Japan, July 2007

Rough Outline

What to do when someone comes into my office?

- Cognitive Diagnosis Models (CDM’s) in Psychometrics Models: a partial review
- The Assistments Project: Using CDM’s in a learning-embedded assessment system
- Educational Data Mining

What are CDM’s? How are they related?

- Rupp (2007), Fu & Li (2007), Junker (1999), Roussos (1994), and others
- Many definitions try to characterize what the unique challenges are, but…
- A simple definition of CDM:

“A latent trait measurement model useful for inferences about cognitive states or processes”

“…Measurement Model useful for inferences about cognitive…”

- Unidimensional Item Response Models
- Multidimensional Item Response Models
- Compensatory structure (e.g. Reckase, 1985, 1997)
- Multiplicative structure (e.g. Embretson, 1984, 1997)
- Task difficulty (LLTM; e.g. Fischer, 1995) vs. person attribute modeling (MIRT, e.g. Reckase, 1997)
- (Constrained) Latent Class Models
- Macready and Dayton (1977); Haertel (1989); Maris (1999);
- Bayes Net Models
- Mislevy et al (e.g. Mislevy, Steinberg, Yan & Almond (1999)
- AI and data mining communities (more later…)

Constrained Latent Class Models

- Basic ingredients

- Xij is data (task/item response)
- Qjk is design (Q-matrix, skills, KC’s, transfer model…)
- ik is latent (knowledge state component of examinee)

[ i = (i1, …,iK) is a latent class label ]

Constrained Latent Class Models

- The Q matrix is the incidence matrix of a bipartite graph

- All such models look like (one-layer) discrete-node Bayesian networks.

Constrained Latent Class Models

- Relate ik to Xij probabilistically:

- Now it looks exactly like an IRT model
- What is the form of Pj(i)?
- Conjunctive (many forms!)
- Disjunctive (less common!)
- Other??

Two simple conjunctive forms…

- DINA

- Examined by Junker & Sijtsma (2001)
- Antecedents incl. Macready & Dayton (1977); Haertel (1989); Tatsuoka (1983, 1995)
- Natural choice in educational data mining
- Difficult to assign credit/blame for failure

A second simple conjunctive form

- NIDA

- Also examined by Junker & Sijtsma (2001)
- Antecedents incl. Maris’ MCLCM (1999)
- Maybe more readily assign credit/blame

A generalization of NIDA

- RedRUM

- *j is maximal probability of success
- r*jk is penalty for each attribute not possessed
- Introduced by Hartz (2002); cf. DiBello, Stout & Roussos (1995).

Compensatory & disjunctiveforms are also possible

- Weaver & Junker (2004, unpubl.)

- Looks like multidimensional Rasch model
- Plausible for some multi-strategy settings
- limited proportional reasoning domain
- DINO, NIDO, … (Rupp, 2007)
- Pathological gambling as in DSM-IV (Templin & Henson, 2006)

A Common Framework

Mixed nonlinear logistic regression models

where

- is a coefficient vector;
- h(i,Qj) is a vector of Qjk-weighted main effects and interactions among latent attributes:

ikQjk, ik1Qjk1ik2Qjk2, ik1Qjk1ik2Qjk2ik3Qjk3 , …

Henson et al. (LCDM, 2007); von Davier (GDM, 2005)

LCDM’s / GDM’s

Obtain RedRUM, NIDA, DINA, DINO, etc., by constraining ’s!

- Weaker constraints on ’s: conjunctive -disjunctive blends, etc.
- Potentially powerful
- unifying framework for many CDM’s
- exploratory modeling tool

Many general frameworks, model choices and design choices

- Conceptual: Fu & Li (2007); Rupp (2007)
- Extensions: HO-DINA, MS-DINA and others (de la Torre & Douglas, 2004, 2005); Fusion model system (Roussos et al., in press); Bayes Nets (Mislevy et al., 1999)
- Model Families: Henson et al. (2007); von Davier (2005), etc.

What to do when someone comes into my office?

Example: ASSISTments Project

- Web-based 8th grade mathematics tutoring system
- ASSIST with, and ASSESS, progress toward Massachusetts Comprehensive Assessment System Exam (MCAS)
- Main statistical/measurement goals
- Predict students’ MCAS scores at end of year
- Provide feedback to teachers
- Ken Koedinger (Carnegie Mellon),

Neil Heffernan (Worcester Polytechnic),

& over 50 others at CMU, WPI and Worcester Public Schools

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.
- Multiple Knowledge Component (Q-matrix) models:
- 1 IRT
- 5 MCAS math strands
- 39 MCAS standards
- 77-106 “expert coded” basic skills
- Goals:
- Predict MCAS Scores
- KC Feedback: learned/not-learned, etc.

Goal: Predicting MCAS

- The exact content of the MCAS exam is not known until months after it is given
- The ASSISTments themselves are ongoing throughout the school year as students learn (from teachers, from ASSISTment interactions, etc.).

Methods: Predicting MCAS

- Regression approaches [Feng et al, 2006; Anozie & Junker, 2006; Ayers & Junker, 2006/2007]:
- Percent Correct on Main Questions
- Percent Correct on Scaffold Questions
- Rasch proficiency on Main Questions
- Online metrics (efficiency and help-seeking; e.g. Campione et al., 1985; Grigorenko & Sternberg, 1998)
- Both end-of-year and “month-by-month” models
- Bayes Net (DINA Model) approaches:
- Predicting KC-coded MCAS questions from Bayes Nets (DINA model) applied to ASSISTments [Pardos, et al., 2006];
- Regression on number of KC’s mastered in DINA model [Anozie 2006]

Results: Predicting MCAS

10-fold cross-validation using:

Results: Predicting MCAS

- Limits of what we can accomplish for prediction
- Feng et al. (in press) estimate best-possible

MAD ¼ 6from split-half experiments with MCAS

- Ayers & Junker (2007) reliability calculation suggests approximate bounds 1.05· MAD · 6.46.
- Best observed MAD ¼ 5.24
- Tradeoff:
- Greater model complexity (DINA) can help [Pardos et al, 2006; Anozie, 2006];
- Accounting for question difficulty (Rasch), plus online metrics, does as well [Ayers & Junker, 2007]

Goal: KC Feedback

- Providing feedback on
- individual students
- groups of students
- Multiple KC (Q-matrix) models:
- 1 IRT
- 5 MCAS math strands
- 39 MCAS standards
- 106 “expert coded” basic skills
- Scaffolding:

Optimal measures of single KC’s?

Optimal tutoring aids?

- When more than one transfer model is involved, scaffolds fail to line up with at least one of them!
- Use DINA Model, 106 KC’s

Results: KC Feedback

- Average percent of KC’s mastered:

30-40%

- February dip reflects a recording error for main questions
- Monthly split-half cross-val accuracy 68-73% on average

Digression: Learning within DINA

- Current model “wakes up reborn” each month; No data ! posterior falls back to prior ignoring previous response behavior.
- Using last month’s posterior as this month’s prior treats previous response behavior too strongly (exchangeable with present).
- Wenyi Jiang (ongoing, CMU) is looking at incorporating a Markov learning model for each KC in DINA.

Digression: Question & KC Model Characteristics

Main Item:

Guess gj (posterior boxplots)

Which graph contains the points in the table?

Slip sj (posterior boxplots)

Scaffolds:

- Quadrant of (-2,-3)?
- Quadrant of (-1,-1)?
- Quadrant of (1,3)?
- [Repeat main]

Some questions driven by ASSISTments

- Different KC models for different purposes seem necessary.
- How deeply meaningful are the KC’s?
- Q-matrix is QC! task design; what about task ! examinee design?
- Henson & Douglas (2005) provide recent developments in KL-based item selection for CDM’s
- Most settings have designed, undesigned missingness
- Interactions between assignment design and learning
- How close to right does the CDM have to be?
- Douglas & Chui (2007) have started mis-specification studies
- Perhaps the Henson/von Davier frameworks can help?
- For ASSISTments and other settings, this is a sparse data model fit question!
- How to design and improve the KC model?

Some options for designing/improving KC model

- Expert Opinion, Iterations
- Rule space method (Tatsuoka 1983, 1995)
- Directly minimizing ij||ij – Xij|| as a function of Q (Barnes 2005, 2006): Boolean regression & variable generation/selection [related: Leenen et al., 2000]
- Learning Factors Analysis (Cen, Koedinger & Junker 2005, 2006): learning curve misfit is a better clue to improving the Q-matrix than static performance misfit

From www.educationaldatamining.org

- Educational Data Mining Workshop, at the 13th International Conference on Artificial Intelligence in Education (AI-ED). Los Angeles, California, USA. July 9, 2007.
- Workshop on Educational Data Mining, at the 7th IEEE International Conference on Advanced Learning Technologies. Niigata, Japan. During the period July 18-20, 2007.
- Workshop on Educational Data Mining at the 21st National Conference on Artificial Intelligence (AAAI 2006). Boston, USA. July 16-17, 2006.
- Workshop on Educational Data Mining at the 8th International Conference on Intelligent Tutoring Systems (ITS 2006). Jhongli, Taiwan, 2006.
- Workshop on Educational Data Mining at the 20th National Conf. on Artificial Intelligence (AAAI 2005). Pittsburgh, USA, 2005.

From AAAI 2005

- Evaluating the Feasibility of Learning Student Models from Data Anders Jonnson, Jeff Johns, Hasmik Mehranian, Ivon Arroyo, Beverly Woolf, Andrew Barto, Donald Fisher, and Sridhar Mahadevan
- Topic Extraction from Item-Level Grades Titus Winters, Christian Shelton, Tom Payne, and Guobiao Mei
- An Educational Data Mining Tool to Browse Tutor-Student Interactions: Time Will Tell! Jack Mostow, Joseph Beck, Hao Cen, Andrew Cuneo, Evandro Gouvea, and Cecily Heiner
- A Data Collection Framework for Capturing ITS Data Based on an Agent Communication Standard Olga Medvedeva, Girish Chavan, and Rebecca S. Crowley
- Data Mining Patterns of Thought Earl Hunt and Tara Madhyastha
- The Q-matrix Method: Mining Student Response Data for Knowledge Tiffany Barnes
- Automating Cognitive Model Improvement by A*Search and Logistic Regression Hao Cen, Kenneth Koedinger, and Brian Junker
- Looking for Sources of Error in Predicting Student’s Knowledge Mingyu Feng, Neil T. Heffernan, and Kenneth R. Koedinger
- Time and Attention: Students, Sessions, and Tasks Andrew Arnold, Richard Scheines, Joseph E. Beck, and Bill Jerome
- Logging Students’ Model-Based Learning and Inquiry Skills in Science Janice Gobert, Paul Horwitz, Barbara Buckley, Amie Mansfield, Edmund Burke, and Dimitry Markman

Educational Data Mining

- Often very clever algorithms & data management, not constrained by quant or measurement traditions
- A strength (open to new approaches)
- A weakness (re-inventing the wheel, failing to see where a well-understood difficulty lies, etc)

Conclusions? Questions…

- Lots of options for CDM’s, not yet much practical experience beyond “my model worked here”
- Significant design questions remain, and seem to admit quantitative solutions
- Need to be connected to real projects
- real world constraints
- real world competitors in EDM
- It would be mutually advantageous to join with EDM and draw EDM (partially?) into our community…

Can we do it? Do we want to?

END

(references follow)

REFERENCES

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- Anozie, N.O. & Junker, B. W. (2007). Investigating the utility of a conjunctive model in Q matrix assessment using monthly student records in an online tutoring system. Paper presented to the Annual Meeting of the National Council on Research in Education. Chicago, IL.
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Barnes, T., J. Stamper, T. Madhyastha. (2006). Comparative analysis of concept derivation using the q-matrix method and facets. Proceedings of the AAAI 21st National Conference on Artificial Intelligence Educational Data Mining Workshop (AAAI2006), Boston, MA, July 17, 2006.

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