# Javier Bravo 1, César Vialardi 2 and Alvaro Ortigosa 1 1 Computer Science Department, Universidad Autónoma de Madrid, Spain 2 Computer Science Department,

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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 cvialar@correo.ulima.edu.pe

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

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

name=“John” age=“12” experience=“normal” 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” Structure of logs 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 Profile of the student Type of the activity Name of the activity

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

Analysis of the data

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.

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

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.

Results of second experiment activity =ep1_b1 yes (54/17) no (56/14) yes (15/6) <=12 age yes (8/3) no (26/12) <=12>12 experience =novice yes (12/2) =normal age >=12 yes (81/28)yes (55/19) =ep1_a1=er2_b1=er2_a1 yes (55/19) no (82/22) =ec3_a1=ec3_n1=em2_b1 ProfileActivity Age=12 Experience=Normal ep1_b1 Age=12em2_b1 Allec3_a1

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.

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.

Questions

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