1. Design a personalized e-learning system based on item response theory and artificial neural network approach Ahmad Baylari, Gh.A. Montazer IT Engineering Department, School of Engineering, Tarbiat Modares University, Tehran, Iran Expert Systems with Applications, Volume 36, Issue 4, May 2009, Pages 8013-8021 Reporter ： Yu Chih Lin

2. Outline  Introduction  Item response theory  Test construction process  System architecture  System evaluation  Conclusion

3. Introduction  In web-based educational systems the structure of learning domain and content are presented in the static way  Without taking into  account the learners’ goals  experiences  existing knowledge  ability  interactivity

4. Introduction  Personalization and interactivity will increase the quality of learning  This paper proposes a personalized multi-agent e-learning system  Item response theory (IRT) Presents adaptive tests  Artificial neural network (ANN) Personalized recommendations

5. Item response theory  Item response theory (IRT) was first introduced to provide a formal approach to adaptive testing  Three common models for ICC  One parameter logistic mode(1PL)  Two parameter logistic model(2PL)  Three parameter logistic model (3PL)

6. Item response theory

10.  Item information function (IIF) is the subject of the amount of information  Effectively distinguish between subjects potential ability to reduce the estimation error  Test information function (TIF) Sum of the amount of information the test results for each subject

11. Item response theory  IIF&TIF ： (IIF) (TIF)  P’(θ) is the first derivative of Pi(θ) and Qi(θ) = 1 - Pi(θ)  I (θ) : amount of information for item,1~N

12. Test construction process  Three types of tests pre-test post-test review tests( 延後測 )  All of these tests have 10 items  Use IRT-3PL model to test construction appropriate post-test selection for learners

13. Test construction process  For posttest construction

14. System architecture  Proposed a personalized multi-agent e-learning system  Middle layer contains four agents  Activity agent : records e-learning activities  Planning agent : agent plans the learning process  Test agent : based on the requests of planning agent, presents appropriate test type to the learner  Remediation agent : analyzes the results of review tests, and diagnoses learner’s learning problems

15. System architecture  System architecture

16. System design and development

17.  Experiment the remediation agent  Essentials of information technology management course Divided into several Los A few codes were allocated for all Los

18. System design and development  I1 to I10 columns are item codes  R1 to R10 are corresponding responses which code  1 : correct response  0 : incorrect response

19. System design and development  Use a back-propagation network(BPNN) Learning Data  Use 20 input nodes  Use 5 output neurons

20. System design and development  Use items responses data as input data the neural network  Output neurons are recommended LOs

21. System design and development  Normalization of data within a uniform range 0–1  Prevent larger numbers from overriding smaller ones  Prevent premature saturation of hidden nodes  No one standard procedure  Input  Output

22. System design and development

23.  ANN requires partitioning of the parent database into three subsets  Training  Test  Validation  Training used 60% of all data  Validation 10% for data  Remaining data for testing the network

24. System design and development  Use one or two hidden layer  Trained with various neurons in each layer in MATLAB software  In hidden layer Use sigmoid function as activation function  For output neurons  First use linear activation function  Second use sigmoid activation function

25. System design and development

26.  For example in training a network  15 neurons in one hidden layer With sigmoid activation function  Output layer neurons With linear activation function  MSE error 7.13

27. System design and development  For example in training a network  15 neurons in one hidden layer and 10 neurons in second hidden layer With sigmoid activation function  Output layer neurons With linear activation function  MSE error 0.0158

28. System design and development  Summarizes the results of trained networks with different architectures  Network configuration 20-20-5 (network No. 11)

29. System evaluation  Recommended LOs from the network compared with recommended LOs from a human instructor  Output was exactly the same as the target output, 25 of 30 tests (83.3%)

30. Conclusion  Proposed a personalized multi-agent e-learning system  Estimate learner’s ability using item response theory  Diagnose learner’s learning problems  Recommend appropriate learning materials to the learner  Neural network approach to learning material recommendation