Using Decision Trees for Discovering Problems on Adaptive Courses

Javier Bravo1, César Vialardi2 and Alvaro Ortigosa1 1Computer Science Department, Universidad Autónoma de

Madrid, Spain2Computer Science Department, Universidad de Lima, Peru

{javier.bravo, alvaro.ortigosa}@uam.escvialar@correo.ulima.edu.pe

IndexImproving an adaptive courseStructure of logsData for the experimentsAnalysis of the dataFirst experimentSecond experimentConclusions and future work

Improving an adaptive course

Students

Instructor

User Model

Authoring ToolCourse Delivering System

Student behavior

Student results

Student paths

<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>

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 experimentsStudents:

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 experimentactivity

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 experimentactivity

=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

Profile Activity

Age=12Experience=Normal

ep1_b1

Age=12 em2_b1

All ec3_a1

ConclusionsThis 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