Framework for Detection, Assessment and Assistance of University Students with Dyslexia and/or Reading Difficulties

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. Introduction
2. Proposal of a framework for detection,
assessment and assistance of university
students with dyslexia and/or reading
difficulties
3. Detection
4. Assessment
5. Assistance
6. Integration of the framework with a learning
management system
7. Conclusions and future work
of university students with
reading difficulties

4
Learning Management Systems (LMS)
It is an hypermedia system that
automates the management of
educational processes such as
teaching and learning.
Adaptive Hypermedia Systems (AHS)
It is an hypermedia system which
reflect some features of the learner in
a learner model and apply this model
to 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 of
reading, 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
Motivation
Learning 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.
Motivation
Dyslexia
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 and
spelling (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 orthograpical
processing, 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 questions
Main 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 be
detected?
RQ2. How cognitive traits of the students with dyslexia and/or reading
difficulties can be assessed in order to inquire which cognitive
processes related to reading are failing?
RQ3. How university students with dyslexia and/or reading difficulties can be
assisted?
RQ4. How the detection, assessment and assistance of university students
with dyslexia and/or reading difficulties can be provided through an
LMS?.
Research questions
Subordinate 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.
Objectives
Main objective

14
Objectives
Subordinate objectives
OB.1 Defining a framework for detection, assessment and assistance of university
students with dyslexia and/or reading difficulties that can be integrated into a
LMS.
OB.6 Integrating the tools developed for the detection, assessment and assistance
of 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 in
university 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 learning
style 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.

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
Recommendations
engine
Reading profile tool
Demographics
Learning analytics
engine
Assessment
Assistance
Student
Teacher
Expert
Framework
Forms
PADA
RADA
ADEA
BEDA
ADDAWeb Services L
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Web Services
Web Services

Framework
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LMS
Cognitive traits
Reading profile
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
Recommendations
engine
Demographics
Learning analytics
engine
Assessment
Assistance
Student
Teacher
Expert
Framework
Forms
PADA
RADA
ADEA
BEDA
ADDAWeb Services L
E
A
R
N
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Web Services
Web Services
OB.1
OB.2
OB.3
OB.4
OB.5
OB.6

19
Detection
LMS
Cognitive traits
Reading profile
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
Recommendations
engine
Demographics
Learning analytics
engine
Assessment
Assistance
Student
Teacher
Expert
Framework
Forms
PADA
RADA
ADEA
BEDA
ADDAWeb Services L
E
A
R
N
E
R
M
O
D
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L
A
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Web Services
Web Services
1 ADDA: Autocuestionario de Detección de Dislexia en Adultos
2 ADEA: Autocuestionario de Detección del Estilo de Aprendizaje
1
2

Demographics
20
LMS
Cognitive traits
Reading profile
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
Recommendations
engine
Demographics
Learning analytics
engine
Assessment
Assistance
Student
Teacher
Expert
Framework
Forms
PADA
RADA
ADEA
BEDA
ADDAWeb Services L
E
A
R
N
E
R
M
O
D
E
L
A
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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
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
Recommendations
engine
Demographics
Learning analytics
engine
Assessment
Assistance
Student
Teacher
Expert
Framework
Forms
PADA
RADA
ADEA
BEDA
ADDAWeb Services L
E
A
R
N
E
R
M
O
D
E
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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: 256
M: 257
Age x: 20
Sx: 4,3
Range: 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 and
Economic Sciences
Business Administration
and 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 and
Psychology
Pedagogy 35 3 32 6.8
Psychology 50 14 36 9.7
Social Work 53 5 48 10.3
Total 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
62
59
39
32
14,8 12,1 11,5
7,6
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 25
28,1
35,7 36,5
46,2
35,7
30,4
33,9
28,6
35,7
46,4
0
10
20
30
40
50
60
Sample
Diagnosis
50
46,4
Common reading difficulties
Percentages
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 previous
diagnosis 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 types
of 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 detect
students with dyslexia (Gilger et al., 1991; Gilger, 1992; Lefly & Pennington, 2000).
Case study

Learning styles
33
LMS
Cognitive traits
Reading profile
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
Recommendations
engine
Demographics
Learning analytics
engine
Assessment
Assistance
Student
Teacher
Expert
Framework
Forms
PADA
RADA
ADEA
BEDA
ADDAWeb Services L
E
A
R
N
E
R
M
O
D
E
L
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Web Services
Web Services

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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 to
identify and develop the most effective compensatory strategies they
could 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 detect
the 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: 19
M: 18
Age x: 26
Sx: 6,0
Range: 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 (detected
with 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
Percentage
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 module
Create register
Complete test
View result
Teacher module
Check test
Activate test
View result
Expert module
Create/edit test
Create/edit section
Create/edit question
Check test
Activate test
View 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
Expert
module
Teacher
module
View results

43
DetectLD:Software Tool to Detect Learning Difficulties
Interfaces
Register
Self-report questionnaire
Student
module

44
Assessment
LMS
Cognitive traits
Reading profile
Dashboard
Learning styles
Recommendations
M
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T
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M
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D
A
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M
E
C
H
A
N
I
S
M
S
Learning style tool
Detection
Assessment battery
Recommendations
engine
Demographics
Learning analytics
engine
Assessment
Assistance
Student
Teacher
Expert
Framework
Forms
PADA
RADA
ADEA
BEDA
ADDAWeb Services L
E
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A
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G
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N
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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 cognitive
processes.
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: 49
M: 57
Age x: 26
Sx: 7,0
Range: 19-50
Faculties and/or
Schools
Academic program Frequency Gender %
M F
Polytechnic School Electrical Engineering 1 1 0 0,9
Industrial Electronics and
Automatic Control Engineering
1 1 0 0,9
Computer Engineering 16 12 4 15,1
Building Engineering 3 2 1 2,8
Chemical Engineering 1 1 0 0,9
Master 9 7 2 8,5
Doctorate 12 11 1 11,3
Total 44 35 9 39,6
Faculty of Tourism Advertising and Public Relations 1 0 1 0,9
Total 1 0 1 0,9
School of Nursing Master 6 0 6 5,7
Total 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 2,8
Economics 2 1 1 1,9
Master 2 1 1 1,9
Total 10 5 5 9,0
Faculty of Law Political Science and Public
Administration
2 1 1 1,9
Law 9 3 6 8,5
Total 11 4 7 9,9
Faculty of Education and
Psychology
Pedagogy 5 1 4 4,7
Pre-School Education 1 0 1 0,9
Primary School Education 7 3 4 6,6
Psychology 8 3 5 7,5
Social Education 5 2 3 4,7
Social Work 5 2 3 4,7
Master 4 2 2 3,8
Total 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 Kurtosis
1.Segmentation into
syllables 0,77 0,92 0,92 1,00 0,00 1,00 0,16 0,39 -1,60 1,73
2.Number of syllables 0,78 0,88 0,83 1,00 0,00 1,00 0,15 0,38 -1,67 1,86
3.Segmentation into
phonemes 0,82 1,00 1,00 1,00 0,00 1,00 0,14 0,37 -2,03 3,36
4.General rhyme 0,72 1,00 1,00 1,00 0,00 1,00 0,19 0,43 -1,10 -0,48
5.Specific rhyme 0,97 1,00 1,00 1,00 0,14 0,86 0,03 0,16 -5,15 35,73
6.Phonemic location 0,88 0,93 0,93 1,00 0,00 1,00 0,07 0,24 -4,34 24,78
7.Omission of phonemes 0,78 0,94 0,94 1,00 0,00 1,00 0,15 0,37 -1,89 3,53
8.Homophone/pseudoho
mophone choice 0,88 0,92 0,92 1,00 0,00 1,00 0,07 0,25 -4,35 28,26
9.Orthographic choice 0,84 0,91 0,88 1,00 0,18 0,82 0,10 0,28 -2,11 7,01
10.Reading words 0,98 1,00 1,00 1,00 0,25 0,75 0,02 0,11 -5,51 43,47
11.Reading pseudowords 0,96 1,00 1,00 1,00 0,00 1,00 0,04 0,18 -6,04 41,57
12.Visual speed of letters
and numbers 0,95 1,00 1,00 1,00 0,00 1,00 0,05 0,21 -4,95 26,72
13.Retaining letters and
words 0,93 1,00 1,00 1,00 0,00 1,00 0,07 0,24 -4,12 19,31
14.Reading narrative text 0,67 0,80 0,80 1,00 0,00 1,00 0,19 0,43 -1,12 1,02
15.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 processing
Scale
score
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 0-1
2 2 5 3 2 - 9 2-3
3 3 6 4 3 - - 4
4 4 - 5 4 15 10 5
5 5 7 6 5 - 11 6-7
6 6 8 - 6 - - 8
7 7 - 7 7 16 12 9
8 8 9 8 8 - - 10-11
9 9 10 9 9 - 13 12
10 10 - 10 10 17 - 13
11 11 11 11 11 - 14 14-15
12 12 12 12 12 18 15 16
Orthographic processing
Scale
score
Homophone/
pseudohomophone
choice
Orthographic choice
1 0-8 0-9
2 - -
3 9 10
4 - 11
5 10 12
6 - -
7 - 13
8 11 14
9 - 15
10 12 -
11 - 16
12 13 17-18
Case study
Number of intervals = 12
Lexical access
Scale
score
Reading
words
Reading
pseudowords
1 0-25 0-35
2 26 36
3 - 37-38
4 27 39
5 28 40
6 - 41
7 29 42
8 - 43
9 30 44-45
10 - 46
11 31 47
12 32 48
Example:
Orthographic processing = 5 + 9 = 14

5252
Results
Score scales:
Scalar sum
Percentiles Phonological
processing
Orthographical
processing
Lexical
access
Processing
speed
Working
memory
Semantic
processing
1 0-8 0-2 0-2 0-1 0-1 0-2
3 9 - - - - -
5 11 3 3 - - 3
8 13 - - - - -
9 14 4 4 2 2 4
12 16 - - - - -
14 18 5 5 - - 5
18 21 6 6 3 3 6
23 25 7 7 - - 7
25 26 - - - - -
27 28 8 8 4 4 8
29 29 - - - - -
32 32 9 9 - - 9
34 33 - - - - -
36 35 10 10 5 5 10
39 37 - - - - -
41 39 11 11 - - 11
46 42 12 12 6 6 12
50 46 13 13 - - 13
53 48 - - - - -
55 49 14 14 7 7 14
57 51 - - - - -
59 52 15 15 - - 15
62 55 - - - - -
64 56 16 16 8 8 16
68 59 17 17 - - 17
73 63 18 18 9 9 18
75 65 - - - - -
77 66 19 19 - - 19
80 69 - - - - -
82 70 20 20 10 10 20
84 72 - - - - -
86 73 21 21 - - 21
88 75 - - - - -
91 77 22 22 11 11 22
95 80 23 23 - - 23
97 82 - - - - -
100 84 24 24 12 12 24
Case study
Poor performance
on reading tests
Poor performance
on tests of reading
comprehension
Example:
Percentile > 25
There 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 items
1.Segmentation into syllables 12 12
2.Number of syllables 12 11
3.Segmentation into phonemes 12 12
4.General rhyme 4 4
5.Specific rhyme 18 7
6.Phonemic location 15 10
7.Omission of phonemes 16 16
8.Homophone/pseudohomophone
choice 13 7
9.Orthographic choice 18 12
10.Reading words 32 7
11.Reading pseudowords 48 25
12.Visual speed of letters and
numbers 35 27
13.Retaining letters and words 18 16
14.Reading narrative text 10 10
15.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
M
U
L
T
I
M
O
D
A
L
Student
Teacher
Expert
Output
Text
Graphics
Audio
Input
Speech
Writing
Mouse
Keyboard

5656
Interfaces
Main menu
Register
Assessment modules

5757
Interfaces
Pedagogical
agent
Example item
Assessment item
Assessment modules

5858
Interfaces
Log in
Main menu
Verify item
Management modules

59
Assistance
LMS
Cognitive traits
Reading profile
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
Recommendations
engine
Demographics
Learning analytics
engine
Assessment
Assistance
Student
Teacher
Expert
Framework
Forms
PADA
RADA
ADEA
BEDA
ADDAWeb Services L
E
A
R
N
E
R
M
O
D
E
L
A
D
A
P
T
A
T
I
O
N
P
R
O
C
E
S
S
E
S
Web Services
Web Services
1
2
1 PADA: Panel de Analíticas de Aprendizaje de Dislexia en Adultos
2 RADA: Recomendador de Actividades para la Dislexia en Adultos

60
Learning analytics
LMS
Cognitive traits
Reading profile
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
Recommendations
engine
Demographics
Learning analytics
engine
Assessment
Assistance
Student
Teacher
Expert
Framework
Forms
PADA
RADA
ADEA
BEDA
ADDAWeb Services L
E
A
R
N
E
R
M
O
D
E
L
A
D
A
P
T
A
T
I
O
N
P
R
O
C
E
S
S
E
S
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: 15
M: 11
Age x: 27
Sx: 6,8
Range: 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 information
DES.1. Have you been diagnosed with dyslexia?
Navigation
A.1. to A.4. Did you check graphical and textual visualizations in… Tab 1?, Tab 2?, Tab 3, Tab 4?
Understanding
B.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?
Inspection
C.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?
Awareness
D.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.3. Was it possible for you to be aware about your cognitive deficits?
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-regulation
E.1. Do you think that PADA can help you in reflecting and making decisions to self-regulate your learning
process?
Usefulness
F.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?
Recommendations
REC.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’.
Comments
COM.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 analytics
of 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
never
Sometimes Almost
always
Always M SD M SD
D.1. 1 2 5 7 11 4.00 1.237 3.88 0.991
D.2. 0 0 2 5 19 4.72 0.575 4.50 0.756
D.3. 2 3 1 12 8 3.78 1.263 3.88 1.246
D.4. 0 3 6 4 13 4.11 1.231 3.88 0.835
D.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
never
Sometimes Almost
always
Always M SD M SD
F.1. 0 0 0 3 23 4.94 0.236 4.75 0.463
F.2. 1 0 6 13 6 3.94 1.056 3.75 0.463
F.3. 0 6 5 8 7 3.72 1.274 3.38 0.744
F.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 the
learning process.
Increased
knowledge

68
• Perceptions of students shown that PADA is reliable, though this claim may
require further analysis of the system's confidence (Bull & Pain, 1995; Mabbott &
Bull, 2006).
• It was identified that some dyslexic students did not increase their
awareness because they already knew their particular difficulties since
childhood (Decker, Vogler & Defries, 1989; Wolff & Lundberg, 2003).
Case study
Discussion

69
Architecture
•SQL Queries
Aggregation
rule
•Self
•Peer
•Class
Social Plane
Parameter
•Expert -> Class, peer, self
•Teacher -> Class, peer, self
•Student -> self, peer
Perspective
Parameter
Aggregator Elements
Indicator
Layer
Control
Layer
Semantic
Layer
LMSInterface
Activity-based Aggregators
Outcome-based Aggregators
Data Mining
Learning Analytics Solutions
AJAXCalls
Monitor Log / Assessment Results
Sensor
Layer
*Based on AEEA architecture (Florian, 2013).
Forms,
ADDA, ADEA,
and BEDA
services

70
Interfaces
Visualizations
Tabs

71
Interfaces
Activity-based
Visualization
Outcome-based
Visualization

72
Interfaces

73
Recommendations
LMS
Cognitive traits
Reading profile
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
Recommendations
engine
Demographics
Learning analytics
engine
Assessment
Assistance
Student
Teacher
Expert
Framework
Forms
PADA
RADA
ADEA
BEDA
ADDAWeb Services L
E
A
R
N
E
R
M
O
D
E
L
A
D
A
P
T
A
T
I
O
N
P
R
O
C
E
S
S
E
S
Web Services
Web Services

74
RADA, a recommender of activities for dyslexia in adults
Recommendations
Giving 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: 24
Sx: 2,1
Range: 22-27
36 recommendations
Instrument:
Method
Participants:
Case study
Example of recommendation for training Speed
Processing:
“Use video games involving your quick reaction
and action. For example, the game “Tetris” or
games in which have time limits for completing a
task”.

77
Method
Procedure:
Case study

78
• All students confirmed they could both hear and read the
recommendations.
• Some of the recommendations have to be reviewed and restructured by
the expert psychologists.
Results
Case study

79
Integration
LMS
Cognitive traits
Reading profile
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
Recommendations
engine
Demographics
Learning analytics
engine
Assessment
Assistance
Student
Teacher
Expert
Framework
Forms
PADA
RADA
ADEA
BEDA
ADDAWeb Services L
E
A
R
N
E
R
M
O
D
E
L
A
D
A
P
T
A
T
I
O
N
P
R
O
C
E
S
S
E
S
Web Services
Web Services

80
Framework’s software toolkit
LMS
Cognitive traits
Reading profile
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
Recommendations
engine
Demographics
Learning analytics
engine
Assessment
Assistance
Student
Teacher
Expert
Framework
Forms
PADA
RADA
ADEA
BEDA
ADDAWeb Services L
E
A
R
N
E
R
M
O
D
E
L
A
D
A
P
T
A
T
I
O
N
P
R
O
C
E
S
S
E
S
Web Services
Web Services

Framework’s software toolkit
81
Forms
Tool to capture
student’s
demographics
ADEA
Tool to capture
student's
learning style
Tool to capture
student's
cognitive traits
BEDAADDA
Tool to capture
student's
reading profile
PADA
Tool to visualize
student's model
RADA
Tool to visualize
student's
recommendations
Cognitive
processes
Reading
aspects
Recommen
dations
Activity
logs
Reading
outcomes
Assessment
results
Users
Roles &
capabilities
Learning
style
SOFTWAREPROCESSDATABASES
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
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
Recommendations
engine
Demographics
Learning analytics
engine
Assessment
Assistance
Student
Teacher
Expert
Framework
Forms
PADA
RADA
ADEA
BEDA
ADDAWeb Services L
E
A
R
N
E
R
M
O
D
E
L
A
D
A
P
T
A
T
I
O
N
P
R
O
C
E
S
S
E
S
Web Services
Web Services
PIADA: Plataforma de
Intervención y Asistencia
de Dislexia en Adultos

PIADA’s block
83
Module created in Moodle to integrate the the framework's software
toolkit 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 block
Web services
Forms
PADA
RADA
ADEA
BEDA
ADDA

85
Tools
Notifications
PIADA’s block
Interfaces
Student

86
Access to PADA
Access to RADA
PIADA’s block
Interfaces
Teacher

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 difficulties
be 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 reading
difficulties be assessed in order to inquire which cognitive processes related
to 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 be
assisted?
 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 university
students 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 students
with dyslexia.
• Replicating the findings and validating them in other university contexts.
• Developing improvements of functionalities.
• Creating a tutorial that explains theoretical foundations for teachers and
students.
• Providing adapted assistance resources and services through an LMS.

Future work
93
• ADDA (Self-report questionnaire to detect dyslexia in adults): studying the
influence of each section for defining the profiles, considering
motivational and affective aspects, creating a standardized procedure.
• ADEA (Self-report questionnaire to detect learning styles): identifying
detailed patterns about the preferences of students with dyslexia.
• BEDA (Assessment Battery of Dyslexia in Adults): converting on a
psychometric test standardized.
• PADA (Assessment Battery of Dyslexia in Adults): creating visualizations
that combine the different aspects of the learner model.
• RADA (Recommender of activities for dyslexia in adults): creating decision
algorithms 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 in
adults”. Journal of Procedia-Social and Behavioral Sciencies, Volume 46, Pages 1795–1800. Published by
Elsevier 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 y
Educativas para la Intervención en el Ámbito Escolar. GEU Editorial.
• Mejía, C., Díaz, A., Jiménez, J., Fabregat, R. (2011). “Considering Cognitive Traits of University Students with
Dyslexia 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 among
Open 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 las
Comunicaciones 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 an
Adaptation 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 dotLRN
throught 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 User
Learning Styles”. In L. Gómez Chova, D. Martí Belenguer & I. Candel Torres (Eds.), International Conference of
Education, 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 - Competencias
para 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 from
the University of Birmingham (UK), and the Department of Education at the University of La Palmas de Gran
Canarias (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 the
Department 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

Framework for Detection, Assessment and Assistance of University Students with Dyslexia and/or Reading Difficulties

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