+ Doing More with Less : Student Modeling and Performance Prediction with Reduced Content Models Yun...
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+ Doing More with Less : Student Modeling and Performance Prediction with Reduced Content Models Yun Huang, University of Pittsburgh Yanbo Xu, Carnegie Mellon University Peter Brusilovsky, University of Pittsburgh
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Transcript of + Doing More with Less : Student Modeling and Performance Prediction with Reduced Content Models Yun...
+Doing More with Less : Student Modeling and Performance Prediction with Reduced Content Models
Yun Huang, University of PittsburghYanbo Xu, Carnegie Mellon UniversityPeter Brusilovsky, University of Pittsburgh
+This talk… What? More effective student
modeling and performance prediction
How? A novel framework reducing content model without loss of quality
Why? Better and cheaper Reduced to 10%~20% while maintaining
or improving performance (up to 8% better AUC)
Beat expert based reduction
+Outline
Motivation
Content Model Reduction
Experiments and Results
Conclusion and Future Work
+ Motivation
In some domains and some types of learning content, each content problem (item) is
related to large number of domain concepts (Knowledge Component, KCs)
It complicates modeling due to increasing noise and decreasing efficiency
We argue that we only need a subset of the most important KCs!
+Content model
The focus of this study: Java Each problem involves a complete program
and relates to many concepts
Original content model Each problem is indexed by a set of Java
concepts from ontology In our context of study, number of concepts
per problem can range from 9 to 55!
+An example of original content model
1. class definition2. static method3. public class4. public method5. void method6. String array7. int type variable
declaration 8. int type variable
initialization9. for statement10. assignment11. increment12. multiplication13. less or equal14. nested loop
+Challenges
Select best concepts to model problems
Traditional feature selection focuses on selecting a subset of features for all datapoints (a domain).
item level not domain level
+Our intuitions of reduction methods Three types of methods from different information
sources and intuitions:Intuition 1 “for statement” appears 2 times in this problem -- it should be important for this problem! “assignment” appears in a lot of problems -- it should be trivial for this problem!
Intuition 2: When “nested loops” appears, students always get it wrong -- it should be important for this problem!
Intuition 3: Expert labeled “assignment”, “less than” as prerequisite concepts, while “nested loops”, “for statement” as outcome concepts --- outcome concepts should be the important ones for current problem!
+Reduction Methods
Content-based methods A problem = a document, a KC = a word Use IDF and TFIDF keyword weighting approach
to compute KC importance score.
Response-based Method Train a logistic regression (PFA) to predict student
response Use the coefficient representing the initial
easiness (EASINESS-COEF) of a KC.
Expert-based Method Use only the OUTCOME concepts as the KCs for an item.
+Item-level ranking of KC importance
For each method, we define SCORE function assigning a score to a KC in an item The higher the score, the more important a
KC is in an item.
Then, we do item-level ranking : a KC's importance can be differentiated by different score values, or/and by its different ranking positions in different
items
+Reduction Sizes
What is the best number of KCs each method should reduce to? Reducing non-adaptively to items (TopX):
Select x KCs per item with the highest importance scores.
Reducing adaptively to items (TopX%): Select x% KCs per item with the highest importance scores
+ Evaluating Reduction on PFA and KT
We evaluate by the prediction performance of two popular student modeling and performance prediction models Performance Factor Analysis (PFA): logistic
regression model predicting student response Knowledge Tracing (KT): Hidden Markov
Models predicting student response and inferring student knowledge level
*We select a variant that can handle multiple KCs.
+Outline
Motivation
Content Model Reduction
Experiments and Results
Conclusion and Future Work
+Tutoring System
Collected from JavaGuide, a tutor for learning Java programming.
Each question is generated from a template, and students can try multiple attempts
Students give values for a variable or the output
Java code
+Experimental Setup Dataset
19, 809 observations, about 69.3% correct 132 students on 94 question templates (items) A problem is indexed into 9 ~ 55 KCs, 124 KCs in total
Classification metric: Area Under Curve (AUC) 1: perfect classifier, 0.5: random classifier
Cross-validation: Two runs of 5-fold CV where in each run 80% of the users are in train, and the remaining are in test.
We list the mean AUC on test sets across the 10 runs, and use Wilcoxon Signed Ranks Test (alpha = 0.05) to test AUC comparison significance.
+Reduction v.s. original on PFA
Flat (or roughly in bell shapes) with fluctuations Reduction to a moderate size can provide comparable
or even better prediction than using original content models.
Reduction could hurt if the size goes too small (e.g. < 5), possibly because PFA was designed for fitting items with multiple KCs.
+Reduction v.s. original on KT
Reduction provides gain ranging a much bigger span and scale!
KT achieves the best performance when the reduction size is small: it may be more sensitive than PFA to the size!
Our reduction methods have selected promising KCs that are the important ones for KT making predictions!
+Automatic v.s. expert-based (OUTCOME) reduction method
IDF and TFIDF can be comparable to or outperform OUTCOME method!
E-COEF provides much gain on KT than PFA, suggesting PFA coefficients can provide useful extra information for reducing the KT content models.
(+/−: signicantly better/worse than OUTCOME, : the optimal mean AUC)
+Outline
Motivation
Content Model Reduction
Experiments and Results
Conclusion and Future Work
+
“Everything should be made as simple as possible, but not
simpler.”-- Albert Einstein
+Conclusion
“Content model should be made as simple as possible, but not simpler.” Given the proper reduction size, reduction
enables prediction performance better! Different model reacts to reduction differently!
KT is more sensitive to reduction than PFA Different models achieve the best balance
between model complexity and model fit in different ranges
We are the first to explore reduction extensively! More ideas for selecting important KCs? Larger datasets? Other domains?
+Acknowledgement
Advanced Distributed Learning Initiative (http://www.adlnet.gov/).
LearnLab 2013 Summer School at CMU (Dr. Kenneth R. Koedinger, Dr. Jose P. Gonzalez-Brenes, Dr. Zachary A. Pardos for advising and initiating the project)
+
Thank you for listening !
