framework for detection, assessment and assistance of university students with dyslexia and/or...

Framework for Detection, Assessment

and Assistance of University Students

with Dyslexia and/or Reading

Difficulties

X

Framework for Detection,

Assessment and Assistance of

University Students with

Dyslexia and/or Reading

Difficulties

Carolina Mejía Corredor

Girona

October 2013

Outline

2

1. Introduction2. Proposal of a framework for detection,

assessment and assistance of university students with dyslexia and/or reading difficulties

3. Detection4. Assessment5. Assistance6. Integration of the framework with a learning

management system7. Conclusions and future work

of university students with reading difficulties

3

Introduction

4

Learning Management Systems (LMS)

It is an hypermedia system thatautomates the management ofeducational processes such asteaching and learning.

Adaptive Hypermedia Systems (AHS)

It is an hypermedia system whichreflect some features of the learner ina learner model and apply this modelto adapt visible aspects of the system

to the learner (Brusilovsky, 1996).

Hypermedia System

Learner Model

Adaptation engine

Adaptation

Learner modeling

Motivation

e-learning

5

Adaptation

Learner modeling AHS

LMS Personalization

Research focus

Overall technological-oriented research focus

Motivation

6

Disorders manifested by significant difficulties in the acquisition and use ofreading, writing, spelling, or mathematical abilities (NJCLD, 1994).

Categories of LD

• Children

• Adolescents

• Adults

Types of LD

• Dyslexia

• Dysgraphia

• Dysorthographia

• Dyscalculia

Most common LD in education

MotivationLearning disabilities (LD)

Population under-explored

(University students)

7

Specific reading difficulties which are characterized by:

• difficulties in word recognition,• poor spelling, and• decoding abilities typically result from a phonological deficit.

MotivationDyslexia

Not all students affected with dyslexia are diagnosed before starting their studies at university (Lindgrén, 2012; Löwe & Schulte-Körne, 2004; Wolff, 2006).

reading comprehension reading experience

May include problems in (Lyon, 2003):

8

Dyslexia

Characteristics

Difficulties in reading (e.g., accuracy, decoding words), writing andspelling (Høien & Lundberg, 2000; Lindgrén, 2012).

Associated difficulties (e.g., memory, attention, pronunciation,automation) (Baumel, 2008; Beatty & Davis, 2007; Marken, 2009; Snowling, 2000).

Background of the difficulties (e.g., medical and family history, school life,reading and writing habits, affective and motivational) (Decker, Vogler, &

Defries, 1989; Giménez de la Peña, Buiza, Luque, & López, 2010; Westwood, 2004).

Compensatory strategies (e.g., coping skills, learning styles) (Firth,

Frydenberg, & Greaves, 2008; Lefly & Pennington, 1991; Mellard, Fall, & Woods, 2010).

Deficits in cognitive processes (e.g., phonological and orthograpicalprocessing, lexical access) (De Vega et al., 1990; Fawcett & Nicolson, 1994; Jiménez &

Hernández-Valle, 2000).

Motivation

Dyslexia

Support process

To affected students with dyslexia by means of enabling:

Detection of difficulties related to reading, associated difficulties,background of these difficulties and compensatory strategies, (Giménez de la

Peña et al., 2010; Coffield et al., 2004).

Assessment of cognitive processes (Díaz, 2007; Gregg, 1998; Kaufman, 2000).

Assistance through awareness of difficulties and self-regulation of learning(Goldberg et al., 2003; Raskind et al., 1999; Reiff et al., 1994; Werner, 1993).

Motivation

9

Adaptation

Learner modeling AHS

LMS

10

Overall technological-oriented research focus, with a specific psychological support

Personalization

• Reading difficulties• Associated difficulties• Background• Compensatory strategies• Cognitive processes

Dyslexia characteristics

• Detection• Assessment• Assistance

Dyslexia support process

University student with dyslexia

Motivation

11

Research questionsMain research question

How to include Spanish-speaking university students with dyslexia and/or

reading difficulties in an e-learning process?

12

RQ1. How university students with dyslexia and/or reading difficulties can bedetected?

RQ2. How cognitive traits of the students with dyslexia and/or readingdifficulties can be assessed in order to inquire which cognitiveprocesses related to reading are failing?

RQ3. How university students with dyslexia and/or reading difficulties can beassisted?

RQ4. How the detection, assessment and assistance of university studentswith dyslexia and/or reading difficulties can be provided through anLMS?.

Research questionsSubordinate research questions

13

Including students with dyslexia and/or reading difficulties in an e-learning process,

so as to define methods and tools to detect, assess and assist them in

overcoming their difficulties during their higher education.

ObjectivesMain objective

14

ObjectivesSubordinate objectives

OB.1 Defining a framework for detection, assessment and assistance of universitystudents with dyslexia and/or reading difficulties that can be integrated into aLMS.

OB.6 Integrating the tools developed for the detection, assessment and assistanceof university students with dyslexia and/or reading difficulties with a LMS

OB.5 Analyzing and developing adaptation methods and tools that can be used to

assist university students with dyslexia and/or reading difficulties.

OB.4 Analyzing cognitive processes associated with reading that can be altered inuniversity students with dyslexia and/or reading difficulties in order to develop

methods and tools needed to assess which specific processes are failing.

OB.3 Analyzing and adopting methods and tools for the detection of the learningstyle of university students with dyslexia and/or reading difficulties.

OB.2 Analyzing and developing methods and tools for the detection of university

students with dyslexia and/or reading difficulties.

15

Proposal

Methodology

16

Detection

Assessment

Assistance

Demographics• Personal details

• Reading difficulties• Associated difficulties• Background

Reading profile

• Compensatory strategies Learning styles

Cognitive traits• Cognitive processes

Learning analytics

Recommendations

• Awareness• Self-regulation

Learner model

Adaptation engines

Framework

17

LMS

Personal details tool

Cognitive traits

Reading profile

Learning analytics’ Dashboard

Learning styles

Recommendations

M

U

L

T

I

M

O

D

A

L

M

E

C

H

A

N

I

S

M

S

Learning style tool

Detection

Assessment battery

Recommendationsengine

Reading profile tool

Demographics

Learning analytics engine

Assessment

Assistance

Student

Teacher

Expert

Framework

Forms

PADA

RADA

ADEA

BEDA

ADDAWeb ServicesLEARNER

MODEL

ADAPTATION

ENGINES

Web Services

Web Services

Framework

18

LMS


Cognitive traits

Reading profile


Learning styles

Recommendations

M

U

L

T

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M

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D

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L

M

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C

H

A

N

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M

S

Learning style tool

Detection

Assessment battery



Demographics


Assessment

Assistance

Student

Teacher

Expert

Framework

Forms

PADA

RADA

ADEA

BEDA


MODEL

ADAPTATION

ENGINES

Web Services

Web Services

OB.1

OB.2

OB.3

OB.4

OB.5

OB.6

19

DetectionLMS


Cognitive traits

Reading profile


Learning styles

Recommendations

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U

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T

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M

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D

A

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C

H

A

N

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M

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Learning style tool

Detection

Assessment battery



Demographics


Assessment

Assistance

Student

Teacher

Expert

Framework

Forms

PADA

RADA

ADEA

BEDA


MODEL

ADAPTATION

PROCESSES

Web Services

Web Services

1 ADDA: Autocuestionario de Detección de Dislexia en Adultos2 ADEA: Autocuestionario de Detección del Estilo de Aprendizaje

1

2

Demographics

20

LMS


Cognitive traits

Reading profile


Learning styles

Recommendations

M

U

L

T

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M

O

D

A

L

M

E

C

H

A

N

I

S

M

S

Learning style tool

Detection

Assessment battery



Demographics


Assessment

Assistance

Student

Teacher

Expert

Framework

Forms

PADA

RADA

ADEA

BEDA


MODEL

ADAPTATION

PROCESSES

Web Services

Web Services

Demographics

21

Descriptive data of the personal details of students.

• Sex

• Age

• Country

• City

• Institution

• Academic level

• Academic program

• Course

Web-based forms to capture demographics

Reading profile

22

LMS


Cognitive traits

Reading profile


Learning styles

Recommendations

M

U

L

T

I

M

O

D

A

L

M

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C

H

A

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I

S

M

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Learning style tool

Detection

Assessment battery



Demographics


Assessment

Assistance

Student

Teacher

Expert

Framework

Forms

PADA

RADA

ADEA

BEDA


MODEL

ADAPTATION

PROCESSES

Web Services

Web Services

Reading profile

23

Set of characteristics related with the dyslexia (Wolff & Lundberg, 2003).

Self-report questionnaires:• Valid and reliable tools (Gilger, 1992; Lefly & Pennington, 2000). • They allow to collect a big amounts of information in a short time

(Gilger, 1992).• Easy and quick-to-use (Decker, Vogler, & Defries, 1989), but they are unable

to provide a diagnosis (Lyytinen et al., 2006).

There is NOT such a tool standardized to the adult Spanish-speaking population (Giménez de la Peña et al., 2010).

ADDA, a self-report questionnaire to detect dyslexia in adults

24

Case study

Study description

1. Proposing the self-report questionnaire.

2. Estimating the percentage of students that inform of having dyslexia.

3. Knowing the most common difficulties presented by students.

4. Testing the usefulness of the self-report questionnaire.

5. Identifying reading profiles of students.

6. Providing feedback to students.

ADDA:Self-report questionnaire to detect dyslexia in adults

25

Method

Participants:First-year students

N: 513

F: 256M: 257

Age x: 20Sx: 4,3Range: 18-58

Faculties and/or Schools Academic program Frequency Gender %M F

Polytechnic School Architecture 5 5 0 1.0

Electrical Engineering 18 17 1 3.5

Industrial Electronics and Automatic Control Engineering

25 22 3 4.9

Computer Engineering 94 78 16 18.3

Mechanical Engineering 31 26 5 6.0

Chemical Engineering 16 12 4 3.1

Total 189 160 29 36.8

Faculty of Tourism Tourism 15 5 10 2.9

Total 15 5 10 2.9

Faculty of Science Biology 13 4 9 2.5

Biotechnology 10 6 4 1.9

Environmental Sciences 6 2 4 1.2

Chemistry 7 5 2 1.4

Total 36 17 19 7

Faculty of Business andEconomic Sciences

Business Administrationand Management

27 9 18 5.3

Economics 23 14 9 4.5

Total 50 23 27 9.8

Faculty of Law Criminology 30 9 21 5.8

Law 55 21 34 10.7

Total 85 30 55 16.5

Faculty of Education andPsychology

Pedagogy 35 3 32 6.8

Psychology 50 14 36 9.7

Social Work 53 5 48 10.3Total 138 22 116 25.8

Total 513 257 256 100.0


Case study

26

Method

Instrument:

1. School and learning to read experience (9 items).

2. History of learning disabilities (6 items).

3. Current reading-writing difficulties (26 items).

4. Associated difficulties (14 items).

5. Family history of learning disabilities (2 items).

6. Reading habits (7 items).

7. Writing habits (3 items).

*Based on ATLAS (Giménez de la Peña et al., 2010).


Case study

67 items

27

Method

Procedure:

Form: paper-based and computer-based.

Target: class attending first-year students.

Application: individual.

Responsible: examiner.

Time needed: 20 minutes.


Case study

Diagnosis N %

Dyslexia 27 5.26

Dysgraphia/dysorthography 29 5.65

Dyscalculia 3 0.58

Total 59 11,5

28

Results

Percentages

0

10

20

30

40

50

60

70

80 76

6259

39

32

14,8 12,1 11,57,6

N %

• High percentages.• Most common:

dyslexia/dysgraphia/dysorthography


Case study

• Few students have been treated.

29

Results

Case study

23,6 23,8 24,6 2528,1

35,7 36,5

46,2

35,730,4

33,928,6

35,7

46,4

0

10

20

30

40

50

60

Sample

Diagnosis

5046,4

Common reading difficulties

Perc

enta

ges

Current reading difficulties

Self-report questionnaire to detect dyslexia in adultsADDA:

30

Results

ADDA:

Reliability

Section Reliability

1. School and learning to read experience. .167

2. History of learning disabilities. .713

3. Current reading-writing difficulties. .842

4. Associated difficulties. .689

5. Family history of learning disabilities. .579

6. Reading habits. .533

7. Writing habits. .576

Total reliability: 0,850

Case study

Self-report questionnaire to detect dyslexia in adults

31

Results

ADDA:

Reading profiles

Profile A: Students reporting current reading difficulties.

Criteria: 5 or more affirmative items in Section 3 (Current difficulties)

Profile B: Normal readers.

Students with profile A were advised to seek assessment to determine whether or not they have dyslexia and to provide specialized help and feedback to overcome their difficulties.

212 (41.3%) Profile A

Case study


32

Discussion

ADDA:

• There was a high percentage of students who reported a previousdiagnosis of learning disabilities (Allor, Fuchs, & Mathes, 2001; Bassi, 2010; Hatcher

et al., 2002; Jameson, 2009; Kalmár, 2011; Madaus, Foley, Mcguire, & Ruban, 2001).

• There was a prevalence of reading and writing as opposed to other typesof disabilities, e.g., mathematics (Díaz, 2007; Gregg, 2007; Roongpraiwan,

Ruangdaraganon, Visudhiphan, & Santikul, 2002; Shaywitz, 2005; Sparks & Lovett, 2010).

• The use of self-report questionnaires could be effective tools to detectstudents with dyslexia (Gilger et al., 1991; Gilger, 1992; Lefly & Pennington, 2000).

Case study


Learning styles

33

LMS


Cognitive traits

Reading profile


Learning styles

Recommendations

M

U

L

T

I

M

O

D

A

L

M

E

C

H

A

N

I

S

M

S

Learning style tool

Detection

Assessment battery



Demographics


Assessment

Assistance

Student

Teacher

Expert

Framework

Forms

PADA

RADA

ADEA

BEDA


MODEL

ADAPTATION

PROCESSES

Web Services

Web Services

34

To understand the ways in which students learn, their strengths, their

weaknesses to develop appropriate strategies (Keefe, 1979).

Detecting the learning styles of students with dyslexia can help them toidentify and develop the most effective compensatory strategies theycould use to learn (Coffield et al., 2004; Mortimore, 2008; G. Reid, 2001; Rodríguez,

2004; Scanlon et al., 1998).

There exists different classification proposals for learning styles and several tools to detect them (Coffield et al., 2004; Mortimore, 2008; Rodríguez, 2004).

ADEA, a self-report questionnaire to detect learning styles based on Felder-Silverman’s Index of Learning Styles (ILS)

Learning styles

35

Study description

ADEA:Self-report questionnaire to detect learning styles

Case study

1. Implementing a web-based self-report questionnaire based on Felder-Silverman’s Index of Learning Styles (ILS) (Felder & Silverman, 2002) to detectthe learning styles.

2. Identifying the most preferred learning styles.

3. Inquiring whether or not students were satisfied with their learning style.

36

Method

Participants:

N: 37

F: 19M: 18

Age x: 26Sx: 6,0Range: 21-53

University Frequency Gender %

M F

University of Girona 26 11 15 70.3

University of Córdoba 11 7 4 29.7

Total 37 18 19 100

• All students had a Reading Profile A (detectedwith ADDA).

• 8 students with diagnosis of dyslexia.

Case study


37

Instrument:

Dimension Learning style

Processing Active

Reflexive

Perception Sensitive

Intuitive

Input Visual

Verbal

Understanding Sequential

Global

The Felder-Silverman’s Index of Learning Styles (ILS) (Felder & Silverman, 2002).

Case study

Method

45 items

Do you agree with your learning style?

44 questions

1 question


38

Procedure:

Form: computer-based.

Target: voluntary students.




Case study

Method


39

Results

Case study

Preferred learning styles:

0

10

20

30

40

50

60

70

80

90

100

Active Reflective Sensitive Intuitive Visual Verbal Sequential Global

Processing Perception Input Understanding

100

0

62,5

37,5

100

0

75

25

65,5

34,5

72,4

27,6

82,8

17,2

58,6

41,4

Dyslexic

Posible-dyslexic

Perc

enta

ge

Learning styles

Do you agree with your learning style?............................... YES 94.6%


40

Discussion

• There was a preference for learning styles Active, Sensitive, Visual, and Sequential (Baldiris, 2012; Graf, 2007; Peña, 2004).

• These results were similar in students with a previous diagnosis of dyslexia (Alty, 2002; Beacham et al., 2003; Mortimore, 2008). They possess a strong visual preference and they process the information actively (Beacham et al., 2003).

• The detection of learning styles could help students with dyslexia to identify effective compensatory strategies (Coffield et al., 2004; Mortimore, 2008; G. Reid,

2001; Rodríguez, 2004; Scanlon et al., 1998).

Case study


41

DetectLD:

A computer-based tool to manage ADDA and ADEA.

detectLD

Database(Postgres)

Student moduleCreate registerComplete testView result

Teacher moduleCheck testActivate testView result

Expert module

Create/edit testCreate/edit sectionCreate/edit questionCheck testActivate testView result

Web server

(Apache)

PHP

Architecture

Student

Teacher

Expert

Software Tool to Detect Learning Difficulties

42

DetectLD: Software Tool to Detect Learning Difficulties

CreateCheck

Edit/delete

Interfaces

Expertmodule

Teachermodule

View results

43

DetectLD: Software Tool to Detect Learning Difficulties

Interfaces

Register

Self-report questionnaire

Studentmodule

44

AssessmentLMS


Cognitive traits

Reading profile


Learning styles

Recommendations

M

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C

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Learning style tool

Detection

Assessment battery



Demographics


Assessment

Assistance

Student

Teacher

Expert

Framework

Forms

PADA

RADA

ADEA

BEDA


MODEL

ADAPTATION

ENGINES

Web Services

Web Services

1

1 BEDA: Batería de Evaluación de Dislexia en Adultos

Cognitive traits

45

Characteristics related with the cognitive processes involved in reading. If it is suspected of dyslexia, it is important to have an assessment of these

processes to better understand the problem (Kaufman, 2000).

Batteries are assessment tests (i.e., exercises) proposed to identify learning disabilities such as dyslexia (Santiuste & González-Pérez, 2005).

There are NOT existing tools for the assessment of the cognitive processes in Spanish-speaking adult dyslexic population (Jiménez et al. , 2004).

BEDA, an assessment battery of dyslexia in Spanish-speaking adults

4646

Study description

1. Proposing an automated battery for the assessment of cognitiveprocesses.

2. Evaluating the assessment tasks in a sample of university students.

3. Performing a descriptive analysis of the sample results.

4. Obtaining score scales for the assessment tasks.

5. Analyzing and debugging of the assessment items.

BEDA:Assessment Battery of Dyslexia in Adults

Case study

4747

Method

Participants:

N: 106

F: 49M: 57

Age x: 26Sx: 7,0Range: 19-50

Faculties and/or Schools

Academic program Frequency Gender %M F

Polytechnic School Electrical Engineering 1 1 0 0,9Industrial Electronics and Automatic Control Engineering

1 1 0 0,9

Computer Engineering 16 12 4 15,1Building Engineering 3 2 1 2,8Chemical Engineering 1 1 0 0,9Master 9 7 2 8,5Doctorate 12 11 1 11,3Total 44 35 9 39,6

Faculty of Tourism Advertising and Public Relations 1 0 1 0,9Total 1 0 1 0,9

School of Nursing Master 6 0 6 5,7Total 6 0 6 5,4

Faculty of Business and Economic Sciences

Business Administration and Management

3 1 2 2,8

Accounting and Finance 3 2 1 2,8Economics 2 1 1 1,9Master 2 1 1 1,9Total 10 5 5 9,0

Faculty of Law Political Science and Public Administration

2 1 1 1,9

Law 9 3 6 8,5Total 11 4 7 9,9

Faculty of Education and Psychology

Pedagogy 5 1 4 4,7Pre-School Education 1 0 1 0,9Primary School Education 7 3 4 6,6Psychology 8 3 5 7,5Social Education 5 2 3 4,7Social Work 5 2 3 4,7Master 4 2 2 3,8Total 39 13 26 35,1

Total 106 57 49 100.0


Case study

4848

Method

Instrument:

*Based on UGA Phonological/Orthographic Battery (Gregg, 1998), adapted from Diaz (2007).


Case study

Modules Tasks

Phonological processing 1. Segmentation into syllables (12 items)2. Number of syllables (12 items)3. Segmentation into phonemes (12 items)4. General rhyme (4 items)5. Specific rhyme (18 items)6. Phonemic location (15 items)7. Omission of phonemes (16 items)

Orthographic processing 8. Homophone/pseudohomophone choice (13 items)9. Orthographic choice (18 items)

Lexical access 10. Word reading (32 items)11. Pseudoword reading (48 items)

Processing speed 12. Visual speed (35 items)

Working memory 13. Verbal working memory (18 items)

Semantic processing 14. Reading expository (10 items)15. Narrative texts (10 items)

273 items

4949

Method

Procedure:





Time needed: 50-60 minutes.


Case study

5050

Results

Overall distribution:

Task Mean Median Mode Maximum Minimum Range Variance Std. dev. Skewness Kurtosis1.Segmentation into syllables 0,77 0,92 0,92 1,00 0,00 1,00 0,16 0,39 -1,60 1,732.Number of syllables 0,78 0,88 0,83 1,00 0,00 1,00 0,15 0,38 -1,67 1,863.Segmentation into phonemes 0,82 1,00 1,00 1,00 0,00 1,00 0,14 0,37 -2,03 3,364.General rhyme 0,72 1,00 1,00 1,00 0,00 1,00 0,19 0,43 -1,10 -0,485.Specific rhyme 0,97 1,00 1,00 1,00 0,14 0,86 0,03 0,16 -5,15 35,736.Phonemic location 0,88 0,93 0,93 1,00 0,00 1,00 0,07 0,24 -4,34 24,787.Omission of phonemes 0,78 0,94 0,94 1,00 0,00 1,00 0,15 0,37 -1,89 3,538.Homophone/pseudohomophone choice 0,88 0,92 0,92 1,00 0,00 1,00 0,07 0,25 -4,35 28,269.Orthographic choice 0,84 0,91 0,88 1,00 0,18 0,82 0,10 0,28 -2,11 7,0110.Reading words 0,98 1,00 1,00 1,00 0,25 0,75 0,02 0,11 -5,51 43,4711.Reading pseudowords 0,96 1,00 1,00 1,00 0,00 1,00 0,04 0,18 -6,04 41,5712.Visual speed of letters and numbers 0,95 1,00 1,00 1,00 0,00 1,00 0,05 0,21 -4,95 26,7213.Retaining letters and words 0,93 1,00 1,00 1,00 0,00 1,00 0,07 0,24 -4,12 19,3114.Reading narrative text 0,67 0,80 0,80 1,00 0,00 1,00 0,19 0,43 -1,12 1,0215.Reading expository text 0,63 0,70 0,70 1,00 0,00 1,00 0,21 0,46 -0,62 -1,11


Case study

5151

Results

Score scales:

Phonological processingScalescore

Segmentation into syllables

Number of syllables

Segmentation into phonemes

General rhyme

Specific rhyme

Phonemic location

Omission of phonemes

1 0-1 0-4 0-2 0-1 0-14 0-8 0-12 2 5 3 2 - 9 2-33 3 6 4 3 - - 44 4 - 5 4 15 10 55 5 7 6 5 - 11 6-76 6 8 - 6 - - 8

7 7 - 7 7 16 12 98 8 9 8 8 - - 10-119 9 10 9 9 - 13 12

10 10 - 10 10 17 - 1311 11 11 11 11 - 14 14-1512 12 12 12 12 18 15 16

Orthographic processingScalescore

Homophone/pseudohomophone

choice

Orthographic choice

1 0-8 0-92 - -3 9 104 - 115 10 126 - -7 - 138 11 149 - 15

10 12 -11 - 1612 13 17-18


Case study

Number of intervals = 12

Lexical accessScalescore

Reading words

Reading pseudowords

1 0-25 0-352 26 363 - 37-384 27 395 28 406 - 417 29 428 - 439 30 44-45

10 - 4611 31 4712 32 48

Example:Orthographic processing = 5 + 9 = 14

5252

Results

Score scales:

Scalar sumPercentiles Phonological

processingOrthographical

processingLexical access

Processing speed

Working memory

Semantic processing

1 0-8 0-2 0-2 0-1 0-1 0-23 9 - - - - -5 11 3 3 - - 38 13 - - - - -9 14 4 4 2 2 4

12 16 - - - - -14 18 5 5 - - 518 21 6 6 3 3 623 25 7 7 - - 725 26 - - - - -27 28 8 8 4 4 829 29 - - - - -32 32 9 9 - - 934 33 - - - - -36 35 10 10 5 5 1039 37 - - - - -41 39 11 11 - - 1146 42 12 12 6 6 1250 46 13 13 - - 1353 48 - - - - -55 49 14 14 7 7 1457 51 - - - - -59 52 15 15 - - 1562 55 - - - - -64 56 16 16 8 8 1668 59 17 17 - - 1773 63 18 18 9 9 1875 65 - - - - -77 66 19 19 - - 1980 69 - - - - -82 70 20 20 10 10 2084 72 - - - - -86 73 21 21 - - 2188 75 - - - - -91 77 22 22 11 11 2295 80 23 23 - - 2397 82 - - - - -

100 84 24 24 12 12 24


Case study

Poor performance on reading tests

Poor performance on tests of reading comprehension

Example:Percentile > 25There is NOT deficit

5353

Results

Analysis and debugging of the items:

• Successes/Errors

• Missing

• Difficulty Index (p)

• Levels of difficulty

• Discrimination index (D)

• Levels of discrimination

• Correlations (R)


Case study

273 190 items

Task Initial items Final items1.Segmentation into syllables 12 122.Number of syllables 12 113.Segmentation into phonemes 12 124.General rhyme 4 45.Specific rhyme 18 76.Phonemic location 15 107.Omission of phonemes 16 168.Homophone/pseudohomophone choice 13 79.Orthographic choice 18 1210.Reading words 32 711.Reading pseudowords 48 2512.Visual speed of letters and numbers 35 2713.Retaining letters and words 18 1614.Reading narrative text 10 1015.Reading expository text 10 10

5454

Discussion

• Dyslexia may be caused by a combination of phonological, orthographic, lexical, speed, memory and/or semantic deficits (Booth et al., 2000; Bull & Scerif, 2001;

Marslen-Wilson, 1987; Waters et al., 1984).

• Tasks used to assess each cognitive process were based on related research works in assessing dyslexia in children and adults (Díaz, 2007; E. García, 2004; C. S.

González, Estevez, Muñoz, Moreno, & Alayon, 2004b; D. González et al., 2010; Guzmán et al., 2004; Jiménez

et al., 2004; Jiménez & Ortiz, 1993; Rojas, 2008).

• Debugging of the assessment items was based on correlations, variance,

difficulty index and discrimination index (Díaz, 2007; E. García, 2004).


Case study

55

BEDA

Database(Postgres)

Phonological processing module

Orthographic processing module

Working memory module

Processing speed module

Lexical access module

Semantic processing module

Assessment modules

Management modules

Administration module

Results analysis module

Web server

(Apache)

PHP


Architecture

MUL

T

I

M

O

D

A

L

Student

Teacher

Expert

OutputTextGraphicsAudio

InputSpeechWritingMouseKeyboard

5656


Interfaces

Main menu

Register

Assessment modules

5757


Interfaces

Pedagogical agent

Example itemAssessment item

Assessment modules

5858


Interfaces

Log in

Main menu

Verify item

Management modules

59

AssistanceLMS


Cognitive traits

Reading profile


Learning styles

Recommendations

M

U

L

T

I

M

O

D

A

L

M

E

C

H

A

N

I

S

M

S

Learning style tool

Detection

Assessment battery



Demographics


Assessment

Assistance

Student

Teacher

Expert

Framework

Forms

PADA

RADA

ADEA

BEDA


MODEL

ADAPTATION

PROCESSES

Web Services

Web Services

1

2

1 PADA: Panel de Analíticas de Aprendizaje de Dislexia en Adultos2 RADA: Recomendador de Actividades para la Dislexia en Adultos

60

Learning analytics

LMS


Cognitive traits

Reading profile


Learning styles

Recommendations

M

U

L

T

I

M

O

D

A

L

M

E

C

H

A

N

I

S

M

S

Learning style tool

Detection

Assessment battery



Demographics


Assessment

Assistance

Student

Teacher

Expert

Framework

Forms

PADA

RADA

ADEA

BEDA


MODEL

ADAPTATION

PROCESSES

Web Services

Web Services

Learning analytics

61

Awareness, which leads to reflection on learning, and facilitate self-regulation, are powerful predictors for the academic success (Goldberg et al.,

2003; Raskind et al., 1999; Reiff et al., 1994).

Opening the learner model to students encourages such awareness, reflection and self-regulation of their learning (Bull & Kay, 2008, 2010; Mitrovic &

Martin, 2007).

An emerging technique for the visualization of the learner model is:

Learning Analytics (Hsiao et al., 2010; Verbert et al., 2011).

PADA, a dashboard of learning analytics of dyslexia in adult

62

1. Proposing the dashboard of learning analytics.

2. Answering the next questions:

• Could students view their learner model?

• Could students understand that model?

• Did students agree with the visualizations presented in that model?

• Were students aware on their difficulties, learning styles and cognitive deficits?

• Could PADA support students to perform self-regulated learning?

• Were learning analytics useful for students?

PADA: Dashboard of learning analytics of dyslexia in adults

Case study

Study description

63

N: 26

F: 15M: 11

Age x: 27Sx: 6,8Range: 21-53

•Students had a Reading Profile A (detected with ADDA).•8 students with diagnosis of dyslexia.


Case study

Method

Participants:

64


Case study

Method

Instrument:

Descriptive informationDES.1. Have you been diagnosed with dyslexia?NavigationA.1. to A.4. Did you check graphical and textual visualizations in… Tab 1?, Tab 2?, Tab 3, Tab 4?UnderstandingB.1. to B.4. Was it easy for you to understand the meaning of the visualizations displayed on… Tab 1?, Tab 2?, Tab

3?, Tab 4?InspectionC.1. Do you agree with the visualizations about your reading difficulties?C.2. Do you agree with the visualizations about your associated difficulties (i.e., languages, memory, etc.)?C.3. Do you agree with the visualizations about your reading habits?C.4. Do you agree with the visualizations about your writing habits?C.5. Do you agree with the visualizations about your learning style?C.6. Do you agree with the visualizations about your successes/errors in each cognitive assessment task?C.7. Do you agree with the visualizations about your successes/errors in each cognitive process?C.8. Do you agree with the visualizations about your results in the cognitive assessment tasks?C.9. Do you agree with the visualizations about your cognitive deficits?AwarenessD.1. Was it possible for you to be aware about your reading difficulties?D.1.* The former was possible by means of…D.2. Was it possible for you to be aware about your learning style?D.2.* The former was possible by means of…D.3. Was it possible for you to be aware about your cognitive deficits?D.3.* The former was possible by means of…D.4. Was it helpful for your awareness process to view your learning analytics versus the performance of

others (i.e., “peers” and “class”?D.5. Did you learn more about your difficulties than you knew previously?D.6. to D.9. What other visualizations do you think could improve your experience in… Tab 1?, Tab 2?,Tab 3?, Tab 4?Self-regulationE.1. Do you think that PADA can help you in reflecting and making decisions to self-regulate your learning

process?UsefulnessF.1. Was it useful for you to check the visualizations in multiple views (i.e., graphical and textual)?F.2. Did the presented learning analytics provide feedback on your reading performance?F.3. Do you think PADA helps to recognize strengths and weaknesses in your reading process you could use

to improve your academic performance?F.4. Did you find all the visualizations you expected?RecommendationsREC.1. Finally, if you could have a recommender system in PADA, what kind of recommender do you prefer? ‘1

- advices recommended by dyslexia-affected peers’, ‘2 - activities/tasks recommended by expert’, ‘3 -exercises, games, and other resources recommended by experts’.

CommentsCOM.1. Please, if you have more comments about your experience with PADA ...

1. Demographics forms

2. ADDA

3. ADEA

4. BEDA

PADA

Online survey

65






PADA:Case study

Method

Procedure:

Dashboard of learning analytics of dyslexia in adults

66


Case study

Results

Navigation:

All students navigated through the different learning analytics.

They only had problems to understand the meaning of the learning analyticsof cognitive processes.

Understanding:

Inspection: Question Responses (n=26) Possible-dyslexic (n=18) Dyslexic (n=8)Strongly disagree

Disagree Indifferent Agree Strongly Agree

M SD M SD

C.1. 0 2 0 12 12 4.44 0.784 4.00 0.926

C.2. 0 1 3 11 11 4.28 0.752 4.13 0.991

C.3. 0 0 3 14 9 4.22 0.732 4.25 0.463

C.4. 0 2 4 11 9 3.94 1.056 4.25 0.463

C.5. 0 0 0 9 17 4.78 0.428 4.38 0.518

C.6. 0 1 3 16 6 4.11 0.832 3.88 0.354

C.7. 0 2 3 13 8 4.11 1.023 3.88 0.354

C.8. 0 2 3 14 7 4.17 0.857 3.63 0.744

C.9. 1 1 0 17 7 4.28 0.752 3.63 1.061

Cognitive processes

67

Question Responses (n=26) Possible-dyslexic (n=18) Dyslexic (n=8)Never Almost

neverSometimes Almost

alwaysAlways M SD M SD

D.1. 1 2 5 7 11 4.00 1.237 3.88 0.991D.2. 0 0 2 5 19 4.72 0.575 4.50 0.756D.3. 2 3 1 12 8 3.78 1.263 3.88 1.246D.4. 0 3 6 4 13 4.11 1.231 3.88 0.835D.5. 0 2 4 12 8 4.22 0.808 3.50 0.926

Question Responses (n=26) Possible-dyslexic (n=18) Dyslexic (n=8)Never Almost

neverSometimes Almost

alwaysAlways M SD M SD

F.1. 0 0 0 3 23 4.94 0.236 4.75 0.463F.2. 1 0 6 13 6 3.94 1.056 3.75 0.463F.3. 0 6 5 8 7 3.72 1.274 3.38 0.744F.4. 0 0 5 16 5 4.22 0.548 3.50 0.535


Case study

Results

Awareness:

Usefulness:

Expected visualizations

Self-regulation:

61.5% of the students think that PADA could encourage self-regulation in thelearning process.

Increased knowledge

68

• Perceptions of students shown that PADA is reliable, though this claim mayrequire further analysis of the system's confidence (Bull & Pain, 1995; Mabbott &

Bull, 2006).

• It was identified that some dyslexic students did not increase theirawareness because they already knew their particular difficulties sincechildhood (Decker, Vogler & Defries, 1989; Wolff & Lundberg, 2003).


Case study

Discussion

69

Architecture

•SQL QueriesAggregation

rule

•Self

•Peer

•Class

Social Plane Parameter

•Expert -> Class, peer, self

•Teacher -> Class, peer, self

•Student -> self, peer

Perspective Parameter

Aggregator Elements

Ind

icator

Layer

Co

ntro

lLaye

rSe

man

tic Laye

r

LMS

Inte

rfac

e

Activity-based AggregatorsOutcome-based Aggregators

Data Mining

Learning Analytics Solutions

AJA

X C

alls

Monitor Log / Assessment Results

Sen

sor

Layer


*Based on AEEA architecture (Florian, 2013).

Forms, ADDA, ADEA,

and BEDAservices

70


Interfaces

Visualizations

Tabs

71


Interfaces

Activity-basedVisualization

Outcome-basedVisualization

72


Interfaces

73

Recommendations

LMS


Cognitive traits

Reading profile


Learning styles

Recommendations

M

U

L

T

I

M

O

D

A

L

M

E

C

H

A

N

I

S

M

S

Learning style tool

Detection

Assessment battery



Demographics


Assessment

Assistance

Student

Teacher

Expert

Framework

Forms

PADA

RADA

ADEA

BEDA


MODEL

ADAPTATION

PROCESSES

Web Services

Web Services

74

RADA, a recommender of activities for dyslexia in adults

RecommendationsGiving hints, feedback, guidance and/or advice support the self-regulation

of the students (Passano, 2000; Santiuste & González-Pérez, 2005).

Recommender system of activities/tasks fed by experts (Mejía, Florian, Vatrapu,

Bull & Fabregat, 2013).

75

1. Proposing the recommendations for students with cognitive deficits.

2. Answering the next questions:

• Did you check recommendations (textual and auditory) when entering RADA?

• Was it easy to understand the recommendations displayed in RADA?

RADA:Recommender of activities for dyslexia in adults

Study description

Case study

76

N: 20

Age x: 24Sx: 2,1Range: 22-27

36 recommendations

Instrument:


Method

Participants:

Case study

Example of recommendation for training SpeedProcessing:“Use video games involving your quick reactionand action. For example, the game “Tetris” orgames in which have time limits for completing atask”.

77


Method

Procedure:

Case study






78

• All students confirmed they could both hear and read therecommendations.

• Some of the recommendations have to be reviewed and restructured bythe expert psychologists.


Results

Case study

79

IntegrationLMS


Cognitive traits

Reading profile


Learning styles

Recommendations

M

U

L

T

I

M

O

D

A

L

M

E

C

H

A

N

I

S

M

S

Learning style tool

Detection

Assessment battery



Demographics


Assessment

Assistance

Student

Teacher

Expert

Framework

Forms

PADA

RADA

ADEA

BEDA


MODEL

ADAPTATION

PROCESSES

Web Services

Web Services

80

Framework’s software toolkit

LMS


Cognitive traits

Reading profile


Learning styles

Recommendations

M

U

L

T

I

M

O

D

A

L

M

E

C

H

A

N

I

S

M

S

Learning style tool

Detection

Assessment battery



Demographics


Assessment

Assistance

Student

Teacher

Expert

Framework

Forms

PADA

RADA

ADEA

BEDA


MODEL

ADAPTATION

PROCESSES

Web Services

Web Services

Framework’s software toolkit

81

Forms

Tool to capture student’s

demographics

ADEATool to capture

student's learning style

Tool to capture student's

cognitive traits

BEDAADDATool to capture

student's reading profile

PADA

Tool to visualize student's model

RADATool to visualize

student's recommendations

Cognitive processes

Reading aspects

Recommendations

Activity logs

Reading outcomes

Assessment results

UsersRoles &

capabilitiesLearning

style

SOFT

WA

RE

PR

OC

ESS

DA

TAB

ASE

S

Learner Model Adaptation Processes

Registering user, role, age,

academic program, etc.

Detecting particular reading

difficulties

Detecting learning styles

Assessing cognitive processes

Delivering personalized

learning analytics

Delivering personalized

recommendations

Detection Assessment Assistance

82

PIADA’s block

LMS


Cognitive traits

Reading profile


Learning styles

Recommendations

M

U

L

T

I

M

O

D

A

L

M

E

C

H

A

N

I

S

M

S

Learning style tool

Detection

Assessment battery



Demographics


Assessment

Assistance

Student

Teacher

Expert

Framework

Forms

PADA

RADA

ADEA

BEDA


MODEL

ADAPTATION

PROCESSES

Web Services

Web Services

PIADA: Plataforma de Intervención y Asistenciade Dislexia en Adultos

PIADA’s block

83

Module created in Moodle to integrate the the framework's softwaretoolkit with an LMS.

Moodle:• Great pedagogical and technological flexibility and usability.• Supported by a large community of developers and users.• Developed as an open source educational application.• Simple interface, lightweight, and efficient, which can manage great

amounts of educational resources.• Easy to install.

LMS used at the University of Girona, as well as other universities that have contributed in the development of this research work.

84

MOODLE Framework’s software toolkit

SOAP COMMUNICATION

Remote call

(SOAP libraries)

Publish service

(SOAP libraries)

PIADA block

PIADA’s blockWeb services

Forms

PADA

RADA

ADEA

BEDA

ADDA

85

Tools

Notifications

PIADA’s blockInterfaces

Student

86

Access to PADA

Access to RADA

PIADA’s blockInterfaces

Teacher

Conclusions

How to include Spanish-speaking university students with dyslexia and/or reading difficulties in an e-learning process?

e-Learning

Learning Management System (LMS)

Dyslexia and/or reading

difficulties

Personalization

Learner modeling and adaptation

88

1. A learner model made up of demographics, reading profile, learning styles, and cognitive traits

2. Adaptation engines to deliver learning analytics and specialized recommendations

3. Mechanisms to integrate into an LMS

General summary

Contributions

89

1 Framework

2

3 Software tools

4 Psychometric tools

5 Datasets

•DetectLD

•BEDA, PADA, RADA, and PIADA

•Self-report questionnaire ADDA

•Battery BEDA

•513 university students after ADDA

•119 university students after BEDA

Web-based architectures

General summary

Conclusions

90

RQ.1. How can university students with dyslexia and/or reading difficultiesbe detected?

• Three parallel ways in which the detection could be made.• Self-report questionnaires are useful for detecting students with dyslexia.• ADDA: Self-report questionnaire to detect dyslexia in adults.• Two reading profiles namely: students with and without current difficulties.• Learning styles are useful for identifying compensatory strategies.• Felder-Silverman’s Index of Learning Styles (ILS).

RQ.2. How can cognitive traits of the students with dyslexia and/or readingdifficulties be assessed in order to inquire which cognitive processes relatedto reading are failing?

• Cognitive processes associated with reading.• Batteries useful tools for assessing cognitive processes.• BEDA: Assessment Battery of Dyslexia in Adults.• Valid in terms of content.• First scope of standardization.

91

RQ.3. How can students with dyslexia and/or reading difficulties beassisted? Awareness and self-regulation for the academic successful. Learning analytics for opening the learner model. Dashboards are useful tools for visualizing learning analytics. PADA: Assessment Battery of Dyslexia in Adults. Giving hints, feedback and advice for facilitating self-regulation. RADA: Recommender of activities for dyslexia in adults.

RQ.4. How can the detection, assessment and assistance of universitystudents with dyslexia and/or reading difficulties be provided in a LMS?.

Web services can be used independently from a LMS. Moodle useful tool for integrating the framework. PIADA's block: Block of the Platform for Intervening and Assisting Dyslexia in

Adults.

Conclusions

Future work

92

• Analyzing the tools effectiveness with large samples of university studentswith dyslexia.

• Replicating the findings and validating them in other university contexts.

• Developing improvements of functionalities.

• Creating a tutorial that explains theoretical foundations for teachers andstudents.

• Providing adapted assistance resources and services through an LMS.

Future work

93

• ADDA (Self-report questionnaire to detect dyslexia in adults): studying theinfluence of each section for defining the profiles, consideringmotivational and affective aspects, creating a standardized procedure.

• ADEA (Self-report questionnaire to detect learning styles): identifyingdetailed patterns about the preferences of students with dyslexia.

• BEDA (Assessment Battery of Dyslexia in Adults): converting on apsychometric test standardized.

• PADA (Assessment Battery of Dyslexia in Adults): creating visualizationsthat combine the different aspects of the learner model.

• RADA (Recommender of activities for dyslexia in adults): creating decisionalgorithms for the recommendations engine.

Publications

94

Journal papers• Mejía, C., Florian, B., Vatrapu, R., Bull, S., Fabregat, R. (2013). “A novel web-based approach for visualization

and inspection of reading difficulties on university students”. Computers & Education (Impact Factor: 2.621).Submitted (May 2013).

• Mejía, C., Giménez, A., Fabregat, R. (2013). “Evidence for Reading Disabilities in Spanish University Students– Applying ADDA”. The Scientific World Journal (Impact Factor: 1.730). Submitted (August 2013).

• Mejía, C., Díaz, A., Jiménez, J., Fabregat, R. (2012). “BEDA: a computarized assessment battery for dyslexia inadults”. Journal of Procedia-Social and Behavioral Sciencies, Volume 46, Pages 1795–1800. Published byElsevier Ltd., doi: 10.1016/j.sbspro.2012.05.381.

Book chapters• Díaz, A., Jiménez, J., Mejía, C., Fabregat, R. (2013). “Estandarización de la Batería de Evaluación de la Dislexia

en Adultos (BEDA)”. In M. del C. Pérez Fuentes & M. del M. Molero Jurado (Eds.), Variables Psicológicas yEducativas para la Intervención en el Ámbito Escolar. GEU Editorial.

• Mejía, C., Díaz, A., Jiménez, J., Fabregat, R. (2011). “Considering Cognitive Traits of University Students withDyslexia in the Context of a Learning Management System”. In D.D. Schmorrow and C.M. Fidopiastis (Eds.),Lecture Notes in Computer Science, Volume 6780/2011, Pages 432-441. Published by Springer, doi:10.1007/978-3-642-21852-1_50.

• Baldiris, S., Fabregat, R., Mejía, C., Gómez, S. (2009). “Adaptation Decisions and Profiles Interchange amongOpen Learning Management Systems based on Agent Negotiations and Machine Learning Techniques”. In J.Jacko (Ed.), Lecture Notes in Computer Science (Vol. 5613, pp. 12-20). Springer Berlin / Heidelberg.doi:10.1007/978-3-642-02583-9_2.

Publications

95

Conference papers• Mejía, C., Bull, S., Vatrapu, R., Florian, B., Fabregat, R. (2012). “PADA: a Dashboard of Learning Analytics for

University Students with Dyslexia”. Proceedings of the Last ScandLE Seminar in Copenhagen.

• Mejía, C., Díaz, A., Florian, B., Fabregat, R. (2012). “El uso de las TICs en la construcción de analíticas de aprendizaje para fomentar la autorregulación en estudiantes universitarios con dislexia”. Proceedings of Congreso Internacional EDUTEC 2012, Canarias en tres continentes digitales: educación, TIC, NET-Coaching.

• Mejía, C., Giménez, A., Fabregat, R. (2012). “ATLAS versión 2: una experiencia en la Universitat de Girona”. Proceedings of the XXVIII Congreso Internacional AELFA: Asociación Española de Logopedia, Foniatría y Audiología.

• Mejía, C., Fabregat, R. (2012). “Framework for Intervention and Assistance in University Students with Dyslexia”. In Bob Werner (Eds). Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies (ICALT 2012), Volume 2012, pp. 342-343. Rome, Italy.

• Mejía, C., Clara, J., Fabregat, R. (2011). “detectLD: Detecting University Students with Learning Disabilities in Reading and Writing in the Spanish Language”. In T. Bastiaens & M. Ebner (Eds.), Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2011 (ED-MEDIA 2011), Volume 2011, Issue 1, pp. 1122-1131, Chesapeake, VA: AACE. Lisboa, Portugal.

• Gelvez, L., Mejía, C., Peña, C.I., Fabregat, R. (2010). “Metodología de Gestión de Proyectos aplicada al Desarrollo de Objetos de Aprendizaje”. In J. Sánchez, Congreso Iberoamericano de Informática Educativa (Vol. 1, pp. 690-697). Santiago de Chile, Chile.

• Mejía, C., Fabregat, R., Marzo, J.L. (2010). “Including Student's Learning Difficulties in the User Model of a Learning Management System”. XXXVI Conferencia Latinoamericana de Informática (CLEI 2010) (pp. 845-858). Asunción, Paraguay.

Publications

96

Conference papers• Mejía, C., Fabregat, R. (2010). “Towards a Learning Management System that Supports Learning Difficulties

of the Students”. In P. Rodriguez (Ed.), XI Simposio Nacional de Tecnologías de la Información y lasComunicaciones en la Educación (ADIE), SINTICE 2010 (pp. 37-44). Ibergarceta Publicaciones , S.L. Valencia,Spain.

• Mejía, C., Baldiris, S., Gómez, S., Fabregat, R. (2009). “Personalization of E-Learning Platforms Based On anAdaptation Process Supported on IMS-LIP and IMS-LD”. In I. Gibson, R. Weber, K. McFerrin, R. Carlsen, & D. A.Willis (Eds.), Society for Information Technology & Teacher Education International Conference 2009 (pp.2882-2887). Charleston, SC, USA: AACE.

• Mejía, C., Mancera, L., Gómez, S., Baldiris, S., Fabregat, R. (2008). “Supporting Competence upon dotLRNthrought Personalization”. 7th OpenACS / .LRN conference (pp. 104-110). Valencia, Spain.

• Mejía, C., Baldiris, S., Gómez, S., Fabregat, R. (2008). “Adaptation Process to Deliver Content based on UserLearning Styles”. In L. Gómez Chova, D. Martí Belenguer & I. Candel Torres (Eds.), International Conference ofEducation, Research and Innovation (ICERI 2008) (pp. 5091-5100). International Association of Technology,Education and Development (IATED). Madrid, Spain.

Guides & reports• Díaz, A., Mejía, C., Jiménez, J., Fabregat, R. (2012). “Manual de uso e instrucciones de la batería de

evaluación de dislexia en adultos (BEDA)”. Universitat de Girona (27 p.), unpublished, Girona (Spain).

• Mejía, C., Díaz, A., Jiménez, J., Fabregat, R. (2012). “Manual de instalación de la Batería de Evaluación de Dislexia en Adultos (BEDA)”. Universitat de Girona (5 p.), unpublished, Girona (Spain).

Publications

97

Final thesis reports • Co-director of the bachelor’s degree project: “Integration of a framework for intervention and assistance of

students with reading difficulties with the e-learning platform MOODLE”, developed by Marco Caballero, Randy Espitia, Julio Martinez. University of Córdoba, Colombia, 2013.

• Co-director of the bachelor’s degree project: “Design and implementation of a system for detection of students with learning disabilities in reading and identification of cognitive processes deficient”, developed by Jonathan Clara. University of Girona, Spain, 2011.

Invited talks • Mejía, C. “Framework per a personalitzar la intervenció i assistència per a estudiants amb dislèxia a través

d’un sistema de gestió de l’aprenentatge”. In FEDER project reports – Clúster TIC MEDIA de Girona,presented at Jornades de Creació d'Objectes d'Aprenentatge Adaptatius: l’Ajuntament de Girona. 2011.Girona, Spain.

• Gómez, S., Mejía, C. Construcción de Unidades de Aprendizaje Adaptativas basada en el Contexto de Acceso.I Congreso Internacional de Ambientes Virtuales de Aprendizaje Adaptativos y Accesibles - Competenciaspara Todos (CAVA3). 2009. Montería, Colombia.

• Mejia, C., Gomez, S., Huerva, D. Adaptation Process in E-Learning Platforms. BCDS International Workshop.2008. Girona, Spain.

Publications

98

Scientific collaborations • Collaborative work initiative for the development of PADA with the Computational Social Science Laboratory

(CSSL) from the Copenhagen Business School (Denmark), the Open Learner Modeling Research Group fromthe University of Birmingham (UK), and the Department of Education at the University of La Palmas de GranCanarias (Spain). 2013.

• Collaborative work initiative for the development of BEDA with the Research Group on Learning Disabilities,Psycholinguistics and New Technologies (DEA&NT) from University of La Laguna (Spain). 2012.

• Collaborative work initiative for the development of ADDA with the University of Girona (Spain), and theDepartment of Psychology from University of Malaga (Spain). 2011.




Difficulties

Framework for Detection, Assessment and

Assistance of University Students with Dyslexia

and/or Reading Difficulties

THANK YOU

Girona

October 2013




Difficulties

Framework for Detection, Assessment and

Assistance of University Students with Dyslexia

and/or Reading Difficulties

QUESTIONS

Girona

October 2013