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An adaptive hierarchical questionnaire based on the Index of Learning Styles

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

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An adaptive hierarchical questionnaire based on the Index of Learning Styles

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

  2. The Context • AEHS traditional model Student A(E)HS Course definition Adapted Course

  3. The Context • AEHS traditional model Student User Model A(E)HS Course definition Adapted Course

  4. The Context • AEHS traditional model User Model Student Asking the user (or teacher or…) Deducing / inducing from user behavior

  5. Adapting to LS: an example ILS VALUE ON SEQUENTIAL/GLOBAL: Extreme and mild Sequential Extreme and moderate Global Well balanced

  6. Adapting to LS: an example ILS VALUE ON SEQUENTIAL/GLOBAL: Extreme and mild Sequential Extreme and moderate Global Well balanced

  7. 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. 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. 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 Sequential Neutral Global -11 -9 -7 -5 -3 -1 1 3 5 7 9 11

  10. Aha! No, I don’t mean the AH system ;) • 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? 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. The goal • To ask each student as few questions as possible • We don’t even need to ask the same questions!

  12. The goal (II) • Not a new questionnaire, but an adaptive version of the ILS Something I have done In groups Something I have thought a lot about … Alone

  13. 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. Using classification techniques New instances Model Training examples (instances) Learning algorithm Classified Instances

  15. 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? Sequential Neutral Global -11 -9 -7 -5 -3 -1 1 3 5 7 9 11

  16. 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. 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. Data collection (II) Active/reflective Sensing/intuitive Visual/verbal Sequential/global

  19. Results I: Active/Reflective dim

  20. Results II: Sensing/Intuitive dim

  21. Results III: Visual/Verbal dim

  22. Results IV: Sequential/Global dim

  23. 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. 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. 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. Thank you! Questions?

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