October 7th, 2011

Learning Style Adaptation in Adaptive Educational Systems

October 7th, 2011

October 7th, 2011

People

Alvaro Ortigosa Danilo Spada José Ignacio Quiroga José Martín Pedro Paredes Pilar Rodríguez Rosa M. Carro

October 7th, 2011

Learning Styles

1. Definition

2. Acquisition

3. Use Individual Collaboration

October 7th, 2011

1: LS definition

Learning style: Characteristic strengths and preferences in the ways [people]

take in and process information (Richard Felder, “Matters of Style”)

Each individual learn differently Two approaches:

To overlook the L.S. giving priority to teaching style (teacher- or content-centered teaching)

To identify L.S. and to adapt taking them into consideration (student-centered teaching)

a) Fostering her way of learningb) Helping her to develop new ways of learning

October 7th, 2011

LS: Models

The Myers-Briggs Type Indicator (MBTI) Extraverts-introverts Sensors-intuitors Thinkers-feelers Judgers-perceivers

Kolb’s Learning Style Model Type I (divergers) Type II (assimilators) Type III (convergers) Type IV (accomodators)

Herrmann Brain Dominance Instrument (HBDI) Quadrant A ( left brain, cerebral) Quadrant B (left brain, limbic) Quadrant C (right brain, limbic) Quadrant D (right brain, cerebral)

October 7th, 2011

LS: Felder model

Four dimensions (Five initially): Sensing/Intuitive Visual/Verbal Active/Reflexive Sequential/Global Inductive/Deductive

(removed by pedagogical reasons)

LEARNING AND TEACHING STYLES IN ENGINEERING EDUCATION[Engr. Education, 78(7), 674–681 (1988)]

October 7th, 2011

LS: Felder model: sensing/intuitive

Sensors like facts, data, and experimentation; intuitors prefer principles and theories.

Sensors are patient with detail but do not like complications; intuitors are bored by detail and welcome complications.

Sensors are good at memorizing facts; intuitors are good at grasping new concepts.

Sensors are careful but may be slow; intuitors are quick but may be careless.

October 7th, 2011

LS: Felder model: visual/verbal

Visual learners remember best what they see: pictures, diagrams, flow charts, time lines, films, demonstrations. If something is simply said to them they will probably forget it.

Verbal (auditory) learners remember much of what they read (hear) and more of what they read (hear) and then write (say). They get a lot out of discussion, prefer verbal explanation to visual demonstration, and learn effectively by explaining things to others.

October 7th, 2011

LS: Felder model: active/reflective

An “active learner” is someone who feels more comfortable with, or is better at, active experimentation than reflective observation, and conversely for a reflective learner.

Active learners do not learn much in situations that require them to be passive, and reflective learners do not learn much in situations that provide no opportunity to think about the information being presented.

Active learners work well in groups; reflective learners work better by themselves or with at most one other person.

October 7th, 2011

LS: Felder model: sequential/global

Sequential learners follow linear reasoning processes when solving problems; global learners make intuitive leaps and may be unable to explain how they came up with solutions.

Sequential learners can work with material when they understand it partially or superficially, while global learners may have great difficulty doing so.

Sequential learners learn best when material is presented in a steady progression of complexity and difficulty; global learners sometimes do better by jumping directly to more complex and difficult material.

October 7th, 2011

2: UM adquisition

Asking the user (or teacher or…)Observing user behavior

Student User Model

October 7th, 2011

UM adquisition regarding

Deduction Test / questionnaires (ILS)

Induction User behavior when interacting with the

application or related applications Mouse movements Behavior in social network applications Mouse movements in s.n. applications

Adaptive questionnaires

October 7th, 2011

ILS questionnaire

For each of the four dimensions 11 questions, 2 possible answers (1 or -1) 12 different possible values

It provides a lot of opportunities for adaptation

October 7th, 2011

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

October 7th, 2011

Inducing from mouse movements

Rosa M. Carro, Department of Computer Science, Universidad Autónoma de Madrid

Mouse movements

Learning styles (ILS)

Offlineprocessing

0 5 10 15-11

-9

-7

-5

-3

-1

1

3

5

7

9

11

maximum vertical speed (pixels / ms)

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maximum vertical speed (pixels/ms) maximum vertical speed (pixels/ms)

r = -0.8accuracy = 94.4%

October 7th, 2011

Inducing from behavior in S.N.

Facebook application Users answered two questionnaires:

Personality and L.S. The application took data from user profiles

Personality test: > 74000 users “Only” 20988 users with all data required for

the analysis L.S. test: 680 users

Only 378 users with all data

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L.S. from Facebook behavior: results

Learning style test (n=378)

Tree(friends)

Tree Fisher(friends)

Fisher

Active/Reflexive 46,44% 57,00% 60,98% 57,97%

Visual/Verbal 52,00% 52,66% 52,41% 37,68%

Sensing/Intuitive 40,80% 43,48% 43,09% 41,55%

Sequential/Global 60,80% 57,91% 66,67% 62,80%

October 7th, 2011

L.S. from Facebook behavior: results

If users has posted more than 10 links, more than 6 friends has posted in ther wall during the last year, she has more than 85 friends,

is member of at most 13 groups and has more than 34 posts in her wall, then she has

a preference for the verbal style

October 7th, 2011

Mouse movements in S.N.

Facebook application Read a text Test: choose an answer using radio buttons Answer T/F questions (check boxes) Sort chronologically a list of events Browse through a menu

October 7th, 2011

Mouse movements in S.N. (ii)

Data analyzed: Time to complete the activity Distance (total, horizontal, vertical) Speed (average, maxi) Acceleration (average, max, min)

Users with mouse separated from users with touchpad

October 7th, 2011

Mouse movements in S.N. (iii)Dimension Mouse (n=20) Touchpad(n=13)

Active/Reflexive 60,00% 46,15%

Visual/Verbal 50,00% 61,54%

Sensing/Intuitive 55,00% 53,85%

Sequential/Global 35,00% 46,15%

Dimension Tool Activity Relevant variables

Active/Reflexive Mouse Test, text

Mean horizontal accelerationMax vertical acceleration

Visual/Verbal Touchpad Sorting Time used

Sensing/Intuitive Mouse T/FT/F

Min vertical accelerationMax vertical acceleration

Sensing/Intuitive Touchpad Browsing Max vertical acceleration

October 7th, 2011

Downsizing the ILS

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

October 7th, 2011

Do we need all the questions?

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

October 7th, 2011

The goal

To ask each student as few questions as possible

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

October 7th, 2011

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

…

October 7th, 2011

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

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Using classification techniques

ModelModel

Training examples(instances)

Learning algorithm

Newinstances

Classified Instances

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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

October 7th, 2011

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.

October 7th, 2011

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

October 7th, 2011

Data collection (ii)

Active/reflective Sensing/intuitive

Visual/verbal Sequential/global

October 7th, 2011

Results

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Results (ii)

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.

October 7th, 2011

Analyzing results

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

October 7th, 2011

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

October 7th, 2011

3: Using L.S. for adaptation

Adapting to individual activities Adapting to collaborative activites

October 7th, 2011

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

Extreme and mild Sequential Well balanced

Extreme and moderate Global

October 7th, 2011

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

Extreme and mild Sequential Well balanced

Extreme and moderate Global

October 7th, 2011

Improving collaborative work

Benefits of cooperative learning are well known: Better understanding, more skills are

developed, ... One issue to be decided is group formation

just another little task for the teacher

October 7th, 2011

Group formation

Usual way in face to face education: “Split up in groups and do ...”

October 7th, 2011

Group formation

Usual way in face to face education: “Split up in groups and do ...”

“Rational” groups wouldn’t be better? What about e-learning systems? There is a need for supporting group

formation

October 7th, 2011

What kind of groups?

Some previous work showed that heterogeneous groups perform better than homogeneous Learning styles Abilities, skills Background Etc.

October 7th, 2011

The Wisdom of Crowds

“... the simple fact of making a group diverse makes it better at problem solving.”

“... groups that are too much alike find it harder to keep learning, because each member is bringing less and less new information to the table.”

James Surowiecki

October 7th, 2011

Heterogeneus groups

In previous experiments, we have found that students with different Learning Styles: Learn better (post tests) Produce better results (products of the

collaborative task)

The impact of learning styles on student grouping for collaborative learning: a case study. Alfonseca el al. UMUAI 16(3–4), 377–401

October 7th, 2011

The goal

Building groups as diverse as possible Different student’s features can be

considered L.S. is the focus of our research

Diversity

October 7th, 2011

What we need

Criteria for comparing (distance of) students Criteria for comparing groups Criteria for comparing sets of groups

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The problem

Finding an optimal solution Can be very slow with a brute force

approach Sophisticated algorithms (genetic

algorithms) can make the trick Even then, there is no clear candidate for

optimal solution In 3D area of triangles is good candidate

October 7th, 2011

Supervised method

The Faraway-so-close algorithm, able to build well-balanced heterogeneous groups in “short time” The goodness of the solution depends on

some parameters TOGETHER visualization tool, which

supports the application of the algorithm with different parameters and comparing the results visually

October 7th, 2011

Analyzing Faraway-so-close

It does not look for an optimal solution, but it uses heuristics to approximate a (fairly) good one

Heavy dependency on: Initial order of the students Pair and group thresholds

Good solutions are more likely to be obtained if different configurations are tried Teacher can use her criteria for choosing

“best” solution

October 7th, 2011

TOGETHER

Iterative application of Faraway-so-close with different initial sorting Typically > 100 runs “Best” set of groups is kept

User (teacher?) responsible for: Choosing the criterion to “optimize” Choosing preferred solution

=> VISUALIZATION

October 7th, 2011

Visualizing students

values for dimension 1

values for dimension 2

Visualization of the 2dimensions that showed to be the mostrelevant in previous research

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Visualizing students

S(-3,7) Dim2(S(-3,7))=-7

S(5,-3)

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Visualizing 166 students

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Visualizing groups

1 28

224

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Visualizing groups

1 group

2 groups

3 groups

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Visualizing groups

worse

better

1 group

2 groups

3 groups

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Constrasting solutions

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Constrasting groups

October 7th, 2011

Results

Tested with 165 students (high school and professional formation). Some groups formed by the tool and some decided

by the own students.Groups built by TOGETHER had a mean of 7.86 “points”, vs. 6.61 of the control group.

October 7th, 2011

The future

Are findings (regarding L.S.) still valid when considering on-line collaboration?

Can we improve L.S. (personality) detection through related applications? Text analysis in Facebook

Can L.S. support other type of adaptations?

October 7th, 2011

Thanks! Questions?

7th International Workshop on Authoring of Adaptive and Adaptable Hypermedia

alvaro.ortigosa@uam.es