1. An adaptive hierarchical questionnaire based on the Index of Learning Styles Alvaro Ortigosa, Pedro Paredes, Pilar Rodriguez Universidad Autónoma de Madrid alvaro.ortigosa@uam.es OPAH Research group http://tangow.ii.uam.es/opah OPAH

2. Alvaro Ortigosa – Universidad Autonoma de Madrid Student The Context AEHS traditional model Adapted Course Adapted Course Course definition A(E)HS

3. Alvaro Ortigosa – Universidad Autonoma de Madrid Student The Context AEHS traditional model Adapted Course Adapted Course Course definition User Model A(E)HS

4. Alvaro Ortigosa – Universidad Autonoma de Madrid The Context AEHS traditional model Asking the user (or teacher or…) Deducing / inducing from user behavior StudentUser Model

5. Alvaro Ortigosa – Universidad Autonoma de Madrid Adapting to LS: an example ILS VALUE ON SEQUENTIAL/GLOBAL: Extreme and mild Sequential Well balanced Extreme and moderate Global

6. Alvaro Ortigosa – Universidad Autonoma de Madrid Adapting to LS: an example ILS VALUE ON SEQUENTIAL/GLOBAL: Extreme and mild Sequential Well balanced Extreme and moderate Global

7. Alvaro Ortigosa – Universidad Autonoma de Madrid Context: ILS questionnaire For each of the four dimensions 11 questions, 2 possible answers 12 different possible values It provides a lot of opportunities for adaptation

8. Alvaro Ortigosa – Universidad Autonoma de Madrid But… (At least in Engineering fields) Students are not motivated to fulfill questionnaires 44Q x LS + 60Q x Personality + 15’ test x IQ Surveys about teacher performance, workload, “Bologna system”, etc. etc. “Is it part of the evaluation?” Students tend to answer more careless as they go through the questions  As the number of questions grows, answers become less reliable

9. Alvaro Ortigosa – Universidad Autonoma de Madrid However… In our experience with teachers, most of the times they just require categorization -11 -9 -7 -5 -3 -1 1 3 5 7 9 11 SequentialNeutralGlobal -11 -9 -7 -5 -3 -1 1 3 5 7 9 11

10. Alvaro Ortigosa – Universidad Autonoma de Madrid Aha! If only three categories are needed, would it be possible to ask fewer questions? If possible, which questions (among the 11 for a given dimension) would provide more (enough) information about the student learning style? No, I don’t mean the AH system ;) 1) I understand something better after I a) try it out b) think it through 2) I would rather be considered a) realistic b) innovative

11. Alvaro Ortigosa – Universidad Autonoma de Madrid The goal To ask each student as few questions as possible We don’t even need to ask the same questions!

12. Alvaro Ortigosa – Universidad Autonoma de Madrid The goal (II) Not a new questionnaire, but an adaptive version of the ILS I prefer to study I more easily remember …… In classes I have taken In groups Alone Something I have done Something I have thought a lot about …

13. Alvaro Ortigosa – Universidad Autonoma de Madrid The idea Using a database of actual answers from real students To use machine learning techniques in order To find most relevant questions for each dimension Depending on previous answers

14. Alvaro Ortigosa – Universidad Autonoma de Madrid Using classification techniques Model Training examples (instances) Learning algorithm New instances Classified Instances

15. Alvaro Ortigosa – Universidad Autonoma de Madrid How does a classifier work? Each instance is represented by a set of attribute values. Training examples are (usually) already classified. Classifier model (usually) uses a subset of attributes (conditions, linear combinations, etc.) Each student represented by her answers to the 11 questions The class is the category she belongs Which attributes (questions) does the learnt model use? -11 -9 -7 -5 -3 -1 1 3 5 7 9 11 SequentialNeutralGlobal

16. Alvaro Ortigosa – Universidad Autonoma de Madrid Classification trees In classification trees, each node tests a single attribute (question). Classification trees explicitly shows the learnt model. It points to the relevant questions. Different branches on a classification tree can test different attributes. Tree construction aimed to get shorter paths C4.5 algorithm chooses next attribute (question) based on the information gain.

17. Alvaro Ortigosa – Universidad Autonoma de Madrid Data collection Three different samples: 42 secondary school level students. 88 post-secondary level students. 200 university level students Between 15 and 30 years old 101 women and 229 men

18. Alvaro Ortigosa – Universidad Autonoma de Madrid Data collection (II) Active/reflectiveSensing/intuitive Visual/verbalSequential/global

19. Alvaro Ortigosa – Universidad Autonoma de Madrid Results I: Active/Reflective dim

20. Alvaro Ortigosa – Universidad Autonoma de Madrid Results II: Sensing/Intuitive dim

21. Alvaro Ortigosa – Universidad Autonoma de Madrid Results III: Visual/Verbal dim

22. Alvaro Ortigosa – Universidad Autonoma de Madrid Results IV: Sequential/Global dim

23. Alvaro Ortigosa – Universidad Autonoma de Madrid Results V: the four dimensions Other results seem to indicate: a) The relevance of a question does not vary significantly with the age of the student. b) The trees seem to converge to a common tree, independently from the origin of the sample, or at least to a common subset of questions.

24. Alvaro Ortigosa – Universidad Autonoma de Madrid Conclusions Some questions of the ILS provide more information than others. We were able to build dynamic (shorter) questionnaires with high precision. On the average, 4-5 questions needed for each dimension. The size of the sample (>300) enough for providing good information about 11 questions. Ad-hoc trees would be better only if the sample is large enough. Gender does not seem to affect the outcome

25. Alvaro Ortigosa – Universidad Autonoma de Madrid Some limitations More categories will require more questions and larger training sets The approach is not useful when the exact value for each dimension is needed For example, automatic grouping

26. Alvaro Ortigosa – Universidad Autonoma de Madrid Thank you! Questions?