an adaptive hierarchical questionnaire based on the index of learning styles

An adaptive hierarchical questionnaire based on the Index of Learning Styles

Alvaro Ortigosa, Pedro Paredes, Pilar Rodriguez

Universidad Autónoma de Madrid

[email protected]

OPAH Research grouphttp://tangow.ii.uam.es/opah

OPAH

Alvaro Ortigosa – Universidad Autonoma de Madrid

Student

The Context

AEHS traditional model

AdaptedCourse

AdaptedCourse

Course definitionCourse

definition

A(E)HS


Student

The Context


AdaptedCourse

AdaptedCourse

Course definitionCourse

definition

User Model

A(E)HS


The Context


Asking the user (or teacher or…)Deducing / inducing from user behavior

Student User Model


Adapting to LS: an exampleILS VALUE ON SEQUENTIAL/GLOBAL:

Extreme and mild Sequential Well balanced

Extreme and moderate Global


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


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


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


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 through2) I would rather be considered a) realistic b) innovative


The goal

To ask each student as few questions as possible

We don’t even need to ask the same questions!


The goal (II)

Not a new questionnaire, but an adaptive version of the ILS

In groups

Alone

Something Ihave done

Something Ihave thoughta lot about

…


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


Using classification techniques

ModelModel

Training examples(instances)

Learning algorithm

Newinstances

Classified Instances


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

Sequential Neutral Global


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.


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


Data collection (II)

Active/reflective Sensing/intuitive

Visual/verbal Sequential/global


Results I: Active/Reflective dim


Results II: Sensing/Intuitive dim


Results III: Visual/Verbal dim


Results IV: Sequential/Global dim


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.


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


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


Thank you! Questions?

an adaptive hierarchical questionnaire based on the index of learning styles

Documents

relevant questions

dimensions11 questions

fewer questions

number of questions

madridthe ideausing

madridthe goalto

classifier model

questionnaires44q x