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Using Decision Trees for Discovering Problems on Adaptive Courses

Using Decision Trees for Discovering Problems on Adaptive Courses. Javier Bravo 1 , César Vialardi 2 and Alvaro Ortigosa 1 1 Computer Science Department, Universidad Autónoma de Madrid, Spain 2 Computer Science Department, Universidad de Lima, Peru {javier.bravo, alvaro.ortigosa}@uam.es

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Using Decision Trees for Discovering Problems on Adaptive Courses

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  1. Using Decision Trees for Discovering Problems on Adaptive Courses Javier Bravo1, César Vialardi2 and Alvaro Ortigosa1 1Computer Science Department, Universidad Autónoma de Madrid, Spain 2Computer Science Department, Universidad de Lima, Peru {javier.bravo, alvaro.ortigosa}@uam.es cvialar@correo.ulima.edu.pe

  2. Index • Improving an adaptive course • Structure of logs • Data for the experiments • Analysis of the data • First experiment • Second experiment • Conclusions and future work

  3. Improving an adaptive course Course Delivering System Authoring Tool Students Instructor User Model Student behavior Student paths Student results

  4. Structure of logs Profile of the student <log> <profile> name=“John” age=“12” experience=“normal” </profile> <entry> activity=“eo1_n1” activityType=“P” complete=“1.0” grade=“1.0” numvisits=“1” timestamp=“2005-12-14T11:19:50.879+01:00” type=“LEAVE-ATOMIC” </entry> </log> Name of the activity Type of the activity Level of completeness of the activity Score in the activity Time when the student visits the activity Number of times the student has visited the activity Action executed by the student

  5. Data for the experiments • Students: • 24 students. • Age between 12 and 14. • First yearof secondary mandatory education. • Adaptive course: • Introduction to whole numbers. • Seven lessons, 22 practical activities. • Two levels of adaptation: novice and normal.

  6. Analysis of the data

  7. First experiment • Objective: to find potential problems in the adaptation. • Steps: • Select the practical activities of the logs. • Build a decision tree: • Attributes: age, experience and activity. • Classification attribute: success. • Analyze the decision tree: searching from the leaves with not success to the top.

  8. Results of first experiment activity =ep1_b1 =eo1_n1 =ev1_n1 =er1_b1 =es2_n1 =em2_b1 =ec3_a1 age no (28/4) yes (24/3) no (24/6) yes (23/6) no (24/3) no (22/1) <=12 >12 no (18/8) yes (6/2)

  9. Second experiment • Objective: to find accurate information about the potential problems in the adaptation. • Steps: • Analyze the proportion of failures for different profiles of students. • Simulate 100 students with these proportions of failures by using Simulog. • Build a decision tree. • Analyze the decision tree.

  10. Results of second experiment activity =er2_a1 =er2_b1 =ep1_a1 =ep1_b1 =em2_b1 =ec3_n1 =ec3_a1 age experience yes (55/19) yes (81/28) yes (54/17) yes (55/19) no (82/22) =normal =novice <=12 >12 no (26/12) yes (8/3) yes (12/2) age <=12 >=12 no (56/14) yes (15/6)

  11. Conclusions • This work shows the utility of using data mining methods with real student data. • The first experiment obtained less information of profiles of students with problems. • Is related this lack of information with the size of data set? • The second experiment obtained accurate information of profiles of students with problems. • The size of data set influences on the information provided by the decision tree.

  12. Future work • Support the results of decision trees with other learning methods: associations rules and clustering. • Developing a tool for assisting instructors on understanding the results provided by decision trees.

  13. Questions

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