Use of recommendation models to provide support to dyslexic students

Gianluca Morcianoa (gianluca.morciano@unitus.it), José Manuel Alcalde Llergoa
(jose.alcalde@unitus.it), Andrea Zingonia (andrea.zingoni@unitus.it), Enrique
Yeguas Bolívarb (eyeguas@uco.es), Juri Taborria(juri.taborri@unitus.it),
Giuseppe<EMAIL_ADDRESS>
a Department of Economics, Engineering, Society and Business Organization
(DEIM), University of Tuscia, 01100 Viterbo, Italy
b Department of Computing and Numerical Analysis, Córdoba University, 14071
Córdoba, Spain

Corresponding Author:
Gianluca Morciano
Department of Economics, Engineering, Society and Business Organization
(DEIM), University of Tuscia, 01100 Viterbo, Italy
Email<EMAIL_ADDRESS>
###### Abstract

Dyslexia is the most widespread specific learning disorder and significantly
impair different cognitive domains. This, in turn, negatively affects dyslexic
students during their learning path. Therefore, specific support must be given
to these students. In addition, such a support must be highly personalized,
since the problems generated by the disorder can be very different from one to
another. In this work, we explored the possibility of using AI to suggest the
most suitable supporting tools for dyslexic students, so as to provide a
targeted help that can be of real utility. To do this, we relied on
recommendation algorithms, which are a branch of machine learning, that aim to
detect personal preferences and provide the most suitable suggestions. We
hence implemented and trained three collaborative-filtering recommendation
models, namely an item-based, a user-based and a weighted-hybrid model, and
studied their performance on a large database of 1237 students’ information,
collected with a self-evaluating questionnaire regarding all the most used
supporting strategies and digital tools. Each recommendation model was tested
with three different similarity metrics, namely Pearson correlation, Euclidean
distance and Cosine similarity. The obtained results showed that a
recommendation system is highly effective in suggesting the optimal help
tools/strategies for everyone, with an error less then 12%. As a further
evidence of the effectiveness of the implemented system, its precision was
0.85 and its recall was 0.83. The best performing filter was the hybrid one,
when Pearson’s correlation is used to measure the distance among users and/or
items. In addition, in a final testing performed on 50 students, dyslexic
students who used the recommendation algorithm increased their academic scores
of almost 1 point in a 1 to 10 scale, showing higher learning performance
compared to students who did not use it. This demonstrates that the proposed
approach is successful and can be used as a new and effective methodology to
support students with dyslexia.

###### keywords:

Specific Learning Disorders, Dyslexia, Artificial Intelligence, Machine
Learning, Recommendation Systems, Education.

††journal: Expert Systems with Applications

##  1 Introduction

Dyslexia is a neurodevelopmental disorder that can negatively impact several
cognitive abilities that are crucial for learning. Therefore, a timely
diagnosis and the use of valid support systems are of outmost importance in
order to help the children to cope with their difficulties. The diagnosis of
dyslexia can be done by using standard pen-and-paper tests [Schulte-Körne,
2010] or by using machine learning (ML) algorithms. An example of the use of
ML to diagnose dyslexia can be found in [Palacios et al., 2016]. After the
diagnosis, however, the use of effective support items is crucial to help the
children to overcome some of their difficulties. ML and virtual reality (VR)
represent ways in which support methods can be created and used by dyslexic
children. For instance, in [Maresca et al., 2022] VR was used to enhance
cognitive abilities in dyslexic children while in [Yeguas-Bolívar et al.,
2022] was used to determine the difficulties of each tested student. ML and,
in particular, recommendation systems are used to customize the learning
experience of students with or without cognitive difficulties [Urdaneta-Ponte
et al., 2021, Hoic-Bozic et al., 2015, Rodríguez et al., 2015]. Our work
compares three recommendation systems using three different similarity
metrics. The goal is to select the best model to assist university students
with dyslexia in choosing the most effective learning tools and studying
strategies for their needs. To provide a comprehensive understanding, the
following sections will delve into the relevant literature (Section 2) and the
methodologies employed in this study (Section 3). Subsequently, Section 4 will
present the results, leading to a succinct conclusion in Section 5 summarizing
the key insights gained from our investigation.

##  2 Related works

Specific learning disorders (SLDs) are a set of neurodevelopmental disorders
that affect around 10% of the worldwide population [Narimani et al., 2016] and
impair, to different degrees, specific cognitive skills such as reading,
writing and performing mathematical operations correctly [Shaywitz, 1998].
Depending on the severity and the type of the impaired skill, three categories
of SLDs can be distinguished [Toffalini et al., 2017]: (i) dyslexia, if the
subject has strong difficulties in reading; (ii) dysgraphia, when impaired
writing skills are predominant and (iii) dyscalculia, if the mathematical
skills are the most affected. In addition, there is a high rate of comorbidity
between these SLDs, thus often two or more disorders are present together.
Among them, dyslexia has the highest prevalence [Wagner et al., 2020].
Dyslexia affects not only reading but also attention [Bosse et al., 2007],
working memory [Smith-Spark & Fisk, 2007] and phonological processing
[Galaburda et al., 2006]. Also, abilities such as note-taking, text composing
and organizing the study activity are negatively influenced [Mortimore &
Crozier, 2006]. These deficits can significantly impact the learning
experience of dyslexic students. However, numerous studies show that if
dyslexia is detected early during childhood, it is possible to reduce the
severity of the symptoms by employing supporting methodologies that help in
addressing the specific difficulties of the child [Battistutta et al., 2018].
Therefore, diagnosing dyslexia as early as possible is of paramount
importance.

Among the methods used to diagnose dyslexia, the most common are the cognitive
tests [Schulte-Körne, 2010]. These, are used to examine general cognitive
abilities, using the Wechsler Adult Intelligence Scale (WAIS) for adults or
the Wechsler Intelligence Scale for Children (WISC) [Wechsler, 2014] for
children, or to assess specific cognitive abilities such as verbal and
phonological processing [Pothos & Kirk, 2004], generally compromised in
dyslexia.

If the diagnosis of dyslexia does not arrive early in life, during childhood
or adolescence, or if it fails, the difficulties produced by dyslexia tend to
be more accentuated and acting to mitigate them becomes even more difficult
[Battistutta et al., 2018]. In addition, these issues can influence self-
esteem and future aspirations [Ghisi et al., 2016] which increase the risk of
developing generalized anxiety [Mammarella et al., 2016] or depression
[Mugnaini et al., 2009]. In light of this, finding support tools for dyslexic
students is of utmost importance. This support can come in the form of
standard tools or digital tools or a combination of those. For instance in
[MacCullagh et al., 2017], the authors interviewed dyslexic students about the
support tools perceived as most useful. The students reported that one of the
most effective standard support tool was attending face-to-face lectures and
recording them. This was best coupled with the use of visual slides.

An example of digital tool that is currently studied to help dyslexic students
is the VR [Alcalde-Llergo et al., 2023]. For instance, in [Maresca et al.,
2022] dyslexic students were divided and trained through classical
neuropsychological treatments or using VR. The VR treatment was composed of
different exercises aimed at improving cognitive functions such as memory,
attention, language, spatial–temporal orientation and executive functions. The
results show that the students that underwent the VR treatment improved their
abilities across all the cognitive domains analyzed. This study shows the
potential of using VR in supporting dyslexic students.

However, even if providing standard and digital support tools helps dyslexic
students, this may not be enough. As highlighted before, dyslexia has a high
comorbidity with other SLDs. In addition, dyslexia has a comorbidity with
other neurodevelopmental disorders such as attention deficit hyperactivity
disorder (ADHD) [Germanò et al., 2010], executive functioning disorder [Gooch
et al., 2011] or obsessive compulsive disorder [Pauc, 2005]. Therefore, the
presence of two or more disorders translates in two or more cognitive deficits
co-existing and this leads to increased issues during the learning path of
each student. Furthermore, greater deficits in learning can result in higher
risk of developing psychological issues during childhood and adolescence. In
light of this, it appears evident that different dyslexic students can have
different difficulties and thus, the support strategies and tools designed to
help them during their learning path must be personalized. With the recent
advancements in machine learning (ML) algorithms, today it is possible to
develop supporting methodologies that can ameliorate the difficulties
encountered by dyslexic subjects during their life, especially during their
education path [Ciolacu et al., 2017], [Baglama et al., 2018], [Zingoni et
al., 2024]. For instance, in [Hamid et al., 2015], researchers use a hidden
Markov model to predict challenges in learning the Malay language among
primary school students with dyslexia. The model analyzes errors in phonology,
spelling, reading, and writing exercises, emphasizing the necessity for
personalized support interventions. Similarly, [Mpia Ndombo et al., 2013]
adopts a comparable approach but incorporates students’ behavior into the
model training. The outcome is a computer-assisted learning system that, based
on predictions, leads to a substantial 60% improvement in dyslexic students’
skills compared to traditional support tools. Meanwhile, the primary goals of
[Rajapakse et al., 2018, Thelijjagoda et al., 2019] are focused on creating an
assistive learning platform tailored for both primary and secondary school
students, by using object character recognition and a neural network (NN) in
the former or a support vector machine (SVM) in the latter.

Among the ML algorithms, recommendation systems (RS) are a natural candidate
to provide a personalized learning experience to students [Masruroh et al.,
2021] as they are specifically designed to detect user preferences and
recommend items accordingly [Khalid et al., 2022]. RSs can be generally
categorized in collaborative filtering (CF), in which items are recommended
based on the preference of similar users [Herlocker et al., 2004], content-
based (CB), in which items are suggested to the user based on the description
of the item [Marana et al., 2023], knowledge-based (KB), which focuses on the
knowledge of the users’ need for a particular item [Carrer-Neto et al., 2012]
and hybrid system which is a combination of the algorithms described above
[Burke, 2002]. In the field of education, several RSs were proposed to enhance
the learning experience of the students [Nabizadeh et al., 2020] . For
instance, in [Hoic-Bozic et al., 2015] the authors combined a set of RSs such
as knowledge-based, CB and CF along with Web 2.0 tools to provide teachers
with suggestions on how to design a course and students with indications on
what activities to choose such as attending seminars, participating in
e-courses, summaries of subject matter and solving online tests. Thanks to
this method, students achieved better final course results. In [Rodríguez et
al., 2015] the authors created an hybrid model in which a CF, a CB and a KB
algorithm were combined in order to recommend learning objects to students
that have similar learning styles. This hybrid recommendation approach
increased the relevance of the recommended educational material and improved
the students’ learning process.

The studies reported above and in Table 1 show a prominent role of RSs in
education.

Reference Type of ML Description [Hamid et al., 2015] Hidden Markov ML tracks
children’s mistakes in phonology,speling, reading and writing [Hoic-Bozic et
al., 2015] CB, CF, KB RS systems were combined with Web applications in order
to help teachers in design courses and students to choose the activities to
integrate in their learning path [Rodríguez et al., 2015] CB, CF, KB RS
systems used to recommend learning objects to student with similar learning
styles [Sergis & Sampson, 2015] CF RS is used to suggest, to teachers, new
resources to enhance their teaching practices [Bourkoukou & El Bachari, 2016]
CF RS used to select the best learning path based on the learning style of
each user [Meryem et al., 2016] Hybrid RS RS aimed at helping the students in
choosing their career path [Rajapakse et al., 2018] OCR and NN OCR and NN used
to support students while reading difficult words [Thelijjagoda et al., 2019]
SVM SVM used to change the difficulty of video-games and exercises.The
students report improvements in their skills and are more engaged in learning
[Pan et al., 2020] NN Used NN to recommend courses to students and identify
those with learning risks [Suganya et al., 2020] SVM ML used to predict
students performance and to indicate directions for improvement [Zingoni et
al., 2024] SVM, k-NN, LR, RF ML models used to suggest the best digital tools
and learning strategies to dyslexic students

Table 1:  Brief overview of ML studies aimed at helping students with and
without learning difficulties during their academic education. The acronyms in
the table refer to different ML models. In particular, NN: Neural network,
OCR: object-character recognition, SVM: Suppor Vector Machine, k-NN: k-Nearest
Neighbors, LR: Logistic Regression, RF: Random forest

Unfortunately, the majority of RSs focus on students without SLDs such as
dyslexia. Moreover, little to no effort is done in creating study support
tools to dyslexic students at university except for [Zingoni et al., 2021,
2024] that mixes AI and virtual reality to create a platform to support
dyslexic students.

Our work was developed with the goal of exploiting ML to customize the
learning experience of students by recommending specific support tools. In
particular, three RSs were compared to determine which algorithm recommend the
best learning strategies and study tools according to the needs of each
student. Lastly, the best performing algorithm has been used to recommend
learning tools and study strategies to dyslexic and non-dyslexic students and
their learning rate was assessed by experienced professors. This work is
framed within the VRAIlexia project, which aims at provide specific support to
university students with dyslexia, by relying on new technologies, like VR and
AI [vrailexia consortium, ].

##  3 Methodology

In this work, the goal was to compare the efficacy of three RSs, a user-based,
an item-based and a hybrid model, in suggesting the best study tools and
learning strategies to university students with dyslexia. Along with these
RSs, three similarity metrics were tested as well namely, the Euclidean
distance, the Cosine distance and the Pearsons’ correlation. After the
collection of the dataset, the hybrid model was implemented by combining the
user-based and the item-based with different weights, as shown in [Badaro et
al., 2013]. In order to select the best set of weights, the mean absolute
error was computed, and subsequently, the algorithm’s performance using these
weights was further validated using other commonly used metrics, such as
precision and recall at k. Finally, the best performing model was used to help
students with and without dyslexia during their learning path.

###  3.1 Dataset collection

The dataset used in this work was collected from a questionnaire aimed at
assessing the issues that each dyslexic student has experienced and the
learning strategies and study tools they have used and found useful to
ameliorate their difficulties. Questions were also asked about the main
problems caused by dyslexia, on the basis of which the most useful tools were
able to be recommended in [Yeguas-Bolívar et al., 2022]. In addition,
demographic information has been collected in the dataset, but were used for
other purposes such as shown in [Benedetti et al., 2022]. The different
learning methodologies presented in the questions were selected with the help
of experts in SLDs. Here, participants can describe how much the supporting
tools and strategies presented were considered helpful by choosing different
options: “not at all”, “very little”, “little”, “medium”, “much” and “very
much”. However, to include the possibility that users never tried or did not
know one or more items, two other answers (“never tried” and “I don’t know”)
were added. These two responses were used to identify tools and strategies
that were not sufficiently known to users, enabling the promotion of their
application to obtain more accurate ratings in future analyses. In order to
quantify a qualitative answer, and use it profitably in RSs, the different
options have been converted into a range of numerical values between 0 and 5,
were 0 corresponded to “not at all” and 5 to “very much”. Regarding responses
labeled as “never tried” and “I don’t know” they will be treated and
preprocessed as missing values. The algorithm will process these data in the
form of a ratings matrix, where the rows will represent different students,
and the columns will represent support tools and study methodologies that may
be proposed by the system. This representation follows a format similar to the
one illustrated in Figure 1encompassing all the students and the dyslexic
support methodologies under consideration.

Figure 1: Rating matrix representation format.

The questionnaire was created by skilled psychologists by starting from an
interview carried out on 20 dyslexic university students about the best tools
and strategies they have adopted to overcome those issues. The questionnaire
was then administered to other 30 dyslexic students, in order to check for its
accessibility and clearness. The final version, comprising 17 support tools
and 22 learning strategies, totaling 39 items, was distributed to 1,237
dyslexic students, in accordance with the following criteria: having a valid
diagnosis of dyslexia, being native Italian speakers, being 18 years old or
more, and attending university or having finished or abandoned it less than
five years before the filling of the questionnaire. The distribution of the
participants was nearly even, concerning gender (54% female students against
46% male students) and age (an almost uniform distribution was observed within
the selected range). In addition, the above-described information we collected
about the received support, the type of high school attended, the category of
student (full-time student, worker student, commuter etc.) and the family
context allowed us to verify that all these variables were quite uniformly
distributed. This limit potential biases. On the contrary, a surely present
bias is given by the fact that only Italian-speaking participants took part to
the questionnaire but, as reported previously limiting the work only to one
language is strictly necessary, since dyslexia-related issues vary a lot from
a language to another [Miles, 2000] and, thus, it is opportune to analyze each
language by itself. It is worth mentioning that it was not possible to gather
information concerning variables like the socioeconomic situation of the
students and their ethnicity, which may have an impact on the system. However,
at least the second one does not represent a great issue for the considered
Italian university context, since this is less ethnically heterogeneous than
other European countries, with only a 3% presence of non-Italian students. The
acquired data have been treated according to the article 13-14 of the GDPR
2016/679 [Parliament, ] of the European Union. In particular, they have been
processed completely anonymously and used only for research purposes. Table 2
presents the questions related to the issues, the tools and the learning
strategies gathered by the questionnaire. However, it is important to note
that the difficulties will not be taken into account when generating
recommendations by the system.

ID Difficulty/Tool/Strategy ID Difficulty/Tool/Strategy P1 Reading T15 Audio
recording of lessons P2 Writing T16 Video lessons P3 Understanding difficult
words T17 Supplementing study material with internet research P4 Understanding
the lessons S1 A person reading for him/her P5 Concentration S2 A map made by
himself/herself P6 Paying attention during presential lessons S3 A scheme made
by himself/herself P7 Paying attention during online lessons S4 A summary made
by himself/herself P8 Memorising recently studied concepts S5 Repeat the
studied material P9 Remembering concepts studied during the exam S6 Marking
keywords P10 Study time management S7 Underlining with different colours P11
Taking notes S8 Having a study group P12 Limited time available to prepare a
task/question/exam S9 Having a tutor T1 Human voice audio book S10 Dyslexic
student group to exchange resources T2 Robotic voice audio book S11 Presential
lessons T3 Different colour words S12 Online lessons available T4 Using the
EasyReading font S13 Taking breaks during lessons T5 Using a smart pen or
tablet to take notes and record voice S14 Lesson slides available T6 Clearer
layout of the study material S15 Recording the lesson T7 Having the key words
of the text highlighted S16 Taking notes T8 Prepared concept maps S17 Having
the lesson plan in advance T9 Prepared schemes S18 Dividing an
examination/task/question into several parts T10 Prepared summaries S19 Only
written tests T11 E-Books S20 Only oral tests T12 Digital tutor S21 Conducting
the exams in the presence of the professor alone T13 Images to help understand
the meaning of difficult words S22 Having an online database with notes made
by other students T14 Images that help to memorise a concept

Table 2: Difficulties (P), support tools (T) and learning strategies (S)
considered.

###  3.2 Algorithms and metrics

In recent years, RSs have been accruing increased significance, extending
their influence into different fields such as education and learning. Among
the various techniques employed in this domain, collaborative RSs and content-
based approaches stand out as two distinctive methodologies, each with its
unique strengths and limitations. Content-based systems require a big amount
of information about items’ features, instead of focusing on the relationships
obtained through feedback from users through ratings. For this reason, the
collaborative systems approach has been considered more appropriate for our
data set. Still, the content-based approach will be considered in future work
to compare performance against collaborative methods, and ultimately provide
the best possible advice to dyslexic students.

Data were analyzed using three types of CF RSs implemented as [Badaro et al.,
2013]. The three types CF systems were: a user-based [Zhao & Shang, 2010] in
which the similarity between users guides the recommendation of the different
methodologies, an item-based [Wang et al., 2006] in which item similarity is
analyzed to suggest the most relevant tools and strategies to the users, and a
hybrid approach [Badaro et al., 2013] which combines the above mentioned
approaches through a weight system. The motivation to use a hybrid model is
given by the differences presented between the user-based and item-based
approaches. User-based approach make recommendations based on the similarity
between users, which means the algorithm will recommend items liked by users
similar to the student. On the contrary, in the item-based approach
recommendations are made based on similarities between items, computing this
similarity according to the given ratings. The differences between the two
approaches are visually depicted in Figure 2.

(a) User-based collaborative filtering.

(b) Item-based collaborative filtering.

Figure 2: User-based approach (a) discovers that Student 1 and Student 3 are
similar because they have chosen some similar learning methodologies, so it
recommends (dashed arrow) to the Student 3 the item that was useful to Student
1 and that the latter is not using. Item-based approach (b) finds that the two
last items have been rating similarly by different users (Student 1 and 2), so
since Student 3 also likes one of these objects, it recommends the other one
to the Student 3.

Hybrid approach was implemented by combining the user-based and item-based
methods with different weights. This involved assigning specific relevance or
weight to each of the predicted ratings using the equation below:

$\widehat{r}=\alpha\widehat{r}_{u}+(1-\alpha)\widehat{r}_{i}$ (1)

where $\alpha$ is the weight and $\widehat{r}_{u}$ is the predicted rating for
the user-based approach, whereas $(1-\alpha)$ is the weight and
$\widehat{r}_{i}$ is the predicted rating for the item-based algorithm.

These weights were used to reduce the error deriving from predicted ratings
and actual ratings as mentioned in [Badaro et al., 2013]. The value of
$\alpha$ has to satisfy:

$\alpha\geq 0\;and\;\alpha\leq 1\;$ (2)

To select the final weights to be assigned to each of the models, a
preliminary experimentation has been carried out. After that, coinciding with
the results obtained by [Sarwar et al., 2001], it was found that higher
weights for the items-based method ($1-\alpha$) provided better results.
Therefore, a scale of values for the weights has been selected to emphasize
higher values for the item-based weight, starting with an user-based weight
value ($\alpha$) twice as large as the item-based one, and concluding with
item-based weight values seven times larger than $\alpha$. All the considered
cases are illustrated in Table 3.

Case | $\alpha$ | $(1-\alpha)$
---|---|---
#1 | $2/3$ | $1/3$
#2 | $1/2$ | $1/2$
#3 | $1/3$ | $2/3$
#4 | $1/4$ | $3/4$
#5 | $1/5$ | $4/5$
#6 | $1/6$ | $5/6$
#7 | $1/7$ | $6/7$
#8 | $1/8$ | $7/8$
Table 3: Configuration of weights for the hybrid approach.

It is worth noting that by assigning $\alpha=1$ or $\alpha=0$ the fully user-
based approach and the fully item-based approach are obtained, respectively.
Thus, by properly tuning $\alpha$ it is possible to include all the analyzed
cases within the formula 1.

To compute similarities among users or items, three widely used metrics in CF
systems were considered and compared: the Pearson correlation
coefficient[Sheugh & Alizadeh, 2015], the Euclidean distance and the Cosine
distance [Vinh et al., 2018, Hofmann, 2004]. These metrics aim to identify the
n most similar users to the test user (in the case of user-based), or the n
most similarly evaluated items according to the ratings given by the test
user(in the case of item-based). These three metrics are defined as follow:

$Pearson\
Correlation=\frac{\sum_{i=1}^{N}(x_{i}-\bar{x})(y_{i}-\bar{y})}{\sqrt{\sum_{i=1}^{N}(x_{i}-\bar{x})^{2}}\sqrt{\sum_{i=1}^{N}(y_{i}-\bar{y})^{2}}}$
(3)

$Euclidean\ distance=\sqrt{\sum_{i=1}^{N}(x_{i}-y_{i})^{2}}$ (4)

$Cosine\
distance=1-\frac{\sum_{i=1}^{N}x_{i}y_{i}}{\sqrt{\sum_{i=1}^{N}x_{i}^{2}}\sqrt{\sum_{i=1}^{N}y_{i}^{2}}}$
(5)

where N is the number of users or items; $x_{i}$ and $y_{i}$ are the
individual ratings; and $\bar{x}$ and $\bar{y}$ are the sample mean.

The Pearson coefficient is employed for direct calculation of similarities
among all users or objects. Then, most similar neighbors are obtained by
taking the n most correlated ones. In contrast, the Euclidean and Cosine
distances are applied as distance metric within a K-Nearest Neighbors (k-NN)
algorithm, and the n nearest neighbors are directly extracted from the
obtained model. Another important aspect to take into account is the number of
nearest neighbors considered in the similarity calculation. After conducting
numerous tests with various potential values for the number of neighbors, we
determined that optimal results are achieved with n values in the range of 3
to 11. Therefore, in the final experimentation, we will consider the number of
neighbors to be 3, 5, 7, and 11. Algorithm 1 contains the pseudocode of the
process for the similarity computation depending on the different considered
metrics. The method returns the n_neighbors most similar users or items sorted
according the computed similarity measure, stored in top_similarities.

Algorithm 1 Compute similarities

1:function ComputeSimilarities(data_similarities, similarity, n_neighbors)

2: if similarity $=$ “pearson” then

3: correlation_matrix $\leftarrow$ correlation matrix computed using pearson
coefficient

4: test_user_corr $\leftarrow$ matrix row corresponding to the test_user

5: similarities $\leftarrow$ sort similar according to test_user_corr

6: top_similarities $\leftarrow$ first n_neighbors from similar_users

7: else

8: knn $\leftarrow$ generated k-Nearest Neighbors model using similarity as
distance metric and considering n_neighbors as the number of neighbors

9: top_similarities $\leftarrow$ nearest neighbors considered by knn

10: end if

11: return top_similarities

12:end function

The objective of the final learning methodologies RS is to predict the ratings
of tools and strategies for a particular user. The prediction is made by
computing the average of the ratings given by the most similar users in the
case of user-based approach or, in the case of item-based approach, the
ratings of the most similarly scored items. In the case of the hybrid method,
the prediction is obtained with the result of the sum of the user-based and
object-based predictions multiplied by their corresponding weights.

The entire process described above is reflected in Algorithm 2 and Algorithm
3, corresponding to the user-based and item-based recommendation systems,
respectively. Both algorithms generate a list of key-value pairs, referred to
as recommendations, comprising the tools and strategies to be recommended
along with their respective ratings assigned by the recommendation system. To
achieve this, both algorithms distinguish the data they will utilize for
similarity computation, data_similarities. In the case of user-based, only the
training data, data_train_items, and the test user’s data, text_user for whom
the recommendation is to be made, are needed. Conversely, in the case of item-
based, it is necessary to consider each test item, from data_test_items,
separately to generate their respective ratings without taking into account
the rest, treated as unknown to the user. Finally, recommendations is
completed with the average rating values obtained for the different test items
from the n_neighbors most similar users or objects.

Algorithm 2 User-based collaborative filtering

1:function UserBasedRecSys(test_user, data_train_items, data_test_items,
similarity, n_neighbors)

2: recommendations $\leftarrow$ empty key-values map

3: data_similarities $\leftarrow$ concatenation of data_train_items and
test_user as row

4: similarities $\leftarrow$ ComputeSimilarities(data_similarities,
similarity, n_neighbors)

5: recommendations $\leftarrow$ mean of ratings for each item taken from
data_test_items

6: return

7:end function

Algorithm 3 Item-based collaborative filtering

1:function ItemBasedRecSys(test_user, data_train_items, data_test_items,
similarity, n_neighbors)

2: recommendations $\leftarrow$ empty key-values map

3: for test_item in data_test_items do

4: data_similarities $\leftarrow$ concatenation of data_train_items and
test_user as row

5: data_similarities $\leftarrow$ concatenation of data_train_items and
test_item as column

6: similarities $\leftarrow$ ComputeSimilarities(data_similarities,
similarity, n_neighbors)

7: rating $\leftarrow$ mean of ratings given to the test item depending on
train items

8: add (test_item, rating) to recommendations

9: end for

10: return recommendations

11:end function

To assess the accuracy of the used methodologies, the mean absolute error
(MAE) between the score predicted by RS and the respective score actually
given by the user will be obtained. MAE is calculated by averaging all the $N$
absolute errors of the rating-pairs ($p_{i}$, $q_{i}$). Since lower MAE
reflects a better accuracy the choice of the weights must be done in order to
minimize it:

$\arg\min(\alpha)(\frac{\sum_{i=1}^{N}\left|p_{i}(\alpha)-q_{i}(\alpha)\right|}{N})$
(6)

where N is the total number of tools and strategies considered to be
recommended, $p_{i}$ is the predicted value and $q_{i}$ the actual value.

Finally, one of the main challenges faced by RSs is the cold-start problem,
which can be presented as a user cold-start or item cold-start [Sejwal &
Abulaish, 2022]. The user cold-start problem stems from the challenge of
addressing new users for whom the system lacks any pre-existing information
[Cosley et al., 2003]. On the other hand, the item cold-start appears when a
new item is being introduced to the dataset [Schein et al., 2002]. The former
concern has been addressed through the solicitation of opinions from new users
regarding the system’s methodologies for profile generation, which are
subsequently compared with those of other users. Regarding the item cold-
start, it is not expected to pose a problem for our existing system. Indeed,
new learning methodologies are not implemented very often due to this kind of
strategies occasionally appears. The developed system will demonstrate its
robustness by considering both test users and items when evaluating its
performance.

###  3.3 Data analysis

From the 39 items used in the questionnaire, T4 support tool (Using the Easy
Reading font) was discarded since more than 48% of the participants did not
know it. Therefore, the actual number of support tools used was 16, to which
are added 22 learning strategies for a total of 38 items. The data were
partitioned, with 75% (947 users) utilized for training the model and the
remaining 25% (290 users) designated for the test set. Subsequently, a 10-fold
cross-validation was applied to the training dataset to optimize the
configuration parameters of the model before validating its performance on the
test set. In addition, another separation of the data has been carried out to
get some test learning methodologies and use them for the evaluation of the
system. This split has been done taking randomly a 20% of the learning
methodologies along different epochs to check the robustness of the system in
making predictions for different combinations of unknown items.

###  3.4 Assessment

Finally, to test the algorithm on a real case, the best performing model found
was used to assess the efficacy of recommending specific learning tools and
strategies to students with or without dyslexia. 50 subjects, 53% male and 47%
female, participated to the testing campaign. Among them, 40% are dyslexic,
whereas the remaining are not affected by any learning disorders. The subjects
are university students or students that finished university but that are
still completing their academic formation with a master, a doctorate or
corporate training courses. The test consisted in providing specific textbooks
of 3 disciplines (political science, communication, and economics) to students
currently enrolled in affine degree, PhD or master courses, and ask them to
study a portion of a book each. 50% of the students, equally divided in
dyslexics and non-dyslexics, tried the RS that was found to perform best, and
obtained personalized suggestions about tools and strategies that should help
them in studying. The other 50% received random recommendations, instead of
the specific ones output by the RS. Then, all the students were asked to
follow the suggested methodologies during the study phase and the level of
knowledge of the studied disciplines was assessed by university professors, on
a scale from 0 to 10, 6 being the minimum sufficient score. A comparison was
made between the results of the ones that benefited of the RS and the ones
that do not. Differences among dyslexic and non-dyslexic students were also
examined.

##  4 Results

In this section, the outcomes acquired from the experimentation will be
presented. Initially, a comparison of the results obtained with the different
weights used in the hybrid RS will be conducted. During this initial
experiment, the optimal number of neighbors to be considered to compute the
similarities will be also calculated. Additionally, this evaluation will
incorporate the results obtained with the three different metrics employed for
calculating similarities. Subsequently, we will perform a comparative analysis
of the three considered approaches: user-based, item-based, and hybrid RSs.

Table 4 shows all the results obtained by the different configuration of the
system, where n is the number of considered neighbors to compute the
similarity and MAE_x is the MAE obtained using the hybrid version with an
$\alpha$ value equal to x.

similarity n MAE_0 MAE_1/8 MAE_1/7 MAE_1/6 MAE_1/5 MAE_1/4 MAE_1/3 MAE_1/2
MAE_2/3 MAE_1 Euclidean 3 1.1832 1.1315 1.1245 1.1152 1.1027 1.0846 1.0563
1.0049 0.9594 0.8920 Euclidean 5 1.1866 1.1347 1.1275 1.1179 1.1050 1.0863
1.0568 1.0015 0.9522 0.8750 Euclidean 7 1.1944 1.1412 1.1342 1.1250 1.1123
1.0939 1.0643 1.0085 0.9562 0.8735 Euclidean 11 1.2193 1.1650 1.1576 1.1479
1.1343 1.1146 1.0829 1.0244 0.9690 0.8782 Cosine 3 1.1839 1.1425 1.1371 1.1301
1.1212 1.1086 1.0895 1.0580 1.0309 0.9876 Cosine 5 1.2505 1.2020 1.1954 1.1867
1.1749 1.1576 1.1301 1.0815 1.0381 0.9647 Cosine 7 1.2754 1.2242 1.2177 1.2091
1.1972 1.1796 1.1511 1.0972 1.0486 0.9636 Cosine 11 1.3106 1.2550 1.2476
1.2380 1.2248 1.2054 1.1737 1.1136 1.0594 0.9659 Pearson 3 0.8217 0.8128
0.8118 0.8107 0.8096 0.8093 0.8128 0.8314 0.8648 0.9661 Pearson 5 0.9092
0.8870 0.8843 0.8809 0.8766 0.8708 0.8639 0.8614 0.8735 0.9329 Pearson 7
0.9810 0.9494 0.9452 0.9398 0.9326 0.9227 0.9080 0.8913 0.8889 0.9222 Pearson
11 1.0868 1.0398 1.0336 1.0256 1.0147 0.9992 0.9756 0.9383 0.9160 0.9148

Table 4: Experimentation results considering the different similarity
measures, number of neighbors and weights for the hybrid implementation. The
best result (highlighted in the table) has been obtained by the hybrid method
with an alpha value equals to 1/4, using Pearson as similarity measure and 3
neighbors.

Upon examining Table 4, it can be seen that the hybrid model, utilizing an
$\alpha$ value of 1/4, and employing Pearson’s correlation with 3 neighbors,
yielded the most favorable outcome with respect to the other analyzed cases.
The achieved MAE by this model is 0.8093. To have a comparable measure, the
relative error was calculated as

$\varepsilon_{r}=\frac{1}{N}\sum_{i,j}(\frac{\left|p_{i,j}-q_{i,j}\right|}{q_{i,j}})$
(7)

where i indicates the i-th tool or strategy and j the j-th student tested, N
is the total number of evaluations, $p_{i,j}$ is the real score given by the
j-th student to the i-th methodology and $q_{i,j}$ is the real score given by
the j-th student to the i-th methodology. The obtained result is 11.93%.

There is currently no system that provides recommendations for this type of
tools and study methodologies or anything similar. Nevertheless, the obtained
error indicates the feasibility of utilizing the hybrid RS in suggesting the
best supporting methodologies to dyslexic students.Indeed, an error of less
than one point on the 5-point scale considered in the ratings was obtained.
This implies that, for instance, when the RS proposes a methodology with a
rating of 1 (indicating very limited usefulness), the actual rating would fall
within the range of 0.2 (not useful at all) to 1.8 (somewhat useful), which
would still be considered acceptable recommendations. Likewise, if the RS
recommends a methodology with a rating of 4 (indicating high usefulness), this
would imply that the actual rating would range between 3.2 (useful) and 4.8
(very much useful), again providing a reasonably accurate recommendation.

In addition, according to the reflected results, it appears that $n=3$ is the
best number of neighbors for the algorithm, with an average MAE equals to
1.1816, whereas higher numbers of neighbors lead to worse performance in most
cases.This behavior can be explained, since using a smaller number of
neighbors allows for the recommendation of less popular items within the
general population, thereby avoiding the spread of missinformative items
[Fernández et al., 2021].

An additional noteworthy investigation within the scope of this research
involved the determination of the optimal configurations for every single
weight of the filter. To achieve this objective, each of the considered weight
variants was subjected individual analysis, where the optimal configuration
and MAE associated with each weight were identified. These results are
summarized in Table 5.

alpha | Best configuration | Best MAE
---|---|---
0 | Pearson; n=3 | 0.8217
1/8 | Pearson; n=3 | 0.8128
1/7 | Pearson; n=3 | 0.8118
1/6 | Pearson; n=3 | 0.8107
1/5 | Pearson; n=3 | 0.8096
1/4 | Pearson; n=3 | 0.8093
1/3 | Pearson; n=3 | 0.8128
1/2 | Pearson; n=3 | 0.8313
2/3 | Pearson; n=3 | 0.8648
1 | Euclidean; n=7 | 0.8734
Table 5: Best configurations and MAEs obtained for the different weights in
the hybrid model.

Another interesting observation from Table 5 is that most of the time the best
performance is achieved using the Pearson metric. Indeed, for each weight, the
Pearson configuration consistently outperforms the others, except for the
purely user-based filter, which is optimized using the Euclidean distance.
Furthermore, in this case, it should be noted that the optimum number of
neighbors is 7, instead of 3 as in the other cases. Nevertheless, the
variations observed among the different results are not particularly
substantial, spanning from 0.8734 to 0.8093. This disparity further diminishes
when considering the hybrid models that prioritize the user-based methodology,
where the MAE falls within the range of 0.8093 to 0.8217. Furthermore, it can
be observed that an alpha value of 1/4 serves as the inflection point at which
the system’s performance begins to deteriorate in both directions, with a more
pronounced decline as its value increases. These differences among the results
of the optimal configurations for each weight are illustrated more clearly in
Figure 3.

Figure 3: Best MAE for the different hybrid RS weight configurations.

The distinctions in performance among the different metrics are more evident
in Figure 4. It shows the results obtained by the different similarity
measures for the selected weights, considering the best performance of each
metric. Among the similarities used, the Cosine distance had the highest
average MAE (1.1432) considering all the different conducted experiments,
indicating a poorer performance. The Euclidean distance yielded results
similar to those of the Cosine distance, with an average MAE of 1.0723.
Consequently, it may not be a suitable choice for our system. However, it’s
worth noting that, as demonstrated earlier, it performs better when applied in
a user-based filter context. Finally, in comparison Pearson correlation
exhibited lower MAE values being the best performance metric. It is also
interesting to note that as the value of the parameter $\alpha$ increases,
both Euclidean and Cosine similarities exhibit a decrease, while the Pearson
similarity remains relatively stable until the point of transitioning to a
fully user-based algorithm, at which juncture it undergoes an increase.

Figure 4:  Comparison among different weight-pairs of the hybrid approach
according MAE.

Since it achieved the lowest MAE, the hybrid model with $\alpha=1/4$ using
Pearson’s correlation and 3 neighbors to compute similarities was chosen as
optimal to made recommendations about learning strategies and support tools to
dyslexics student. To prove the efficacy of the procedure, this optimal
recommendation system was also evaluated using precision and recall at k
metrics [Said et al., 2013]. In this context, a threshold of 3 on the rating
scale was employed to deem a tool or strategy as relevant. This threshold was
established by a panel of experts in methodologies for supporting dyslexic
students, and simultaneously represents a typical threshold in literature when
utilizing Likert-scale ratings within the 0 to 5 range [Joorabloo et al.,
2021]. These metrics can be defined as follows:

  * •

Precision@k: is the proportion of recommended items in the top-k set that are
relevant. It can be calculated as:

$Precision@k=\frac{V@k}{R@k}$ (8)

where V@k is the number of recommended items at k that are relevant for an
user and R@k is the total number of recommended items at k.

  * •

Recall@k: is the proportion of relevant items found in the top-k
recommendations. It can be obtained through:

$Recall@k=\frac{V@k}{T}$ (9)

where V@k is the number of recommended items at k that are relevant for an
user and T is the total number of relevant items.

Similarly to the MAE calculation, precision@k and recall@k have been computed
for the 290 users comprising the test set. Consequently, the average precision
of the proposed hybrid recommendation system is 0.8524, while the average
recall is 0.8278. According to precision, this signifies that 85% of the
methodologies recommended by the recommendation system to a specific student
are relevant support tools or strategies for them. Furthermore, regarding the
recall these results entail that the recommended methodologies by the system
represent approximately 83% of all tools relevant to that student. These
results align with those obtained through MAE, reaffirming the utility of the
proposed recommendation system.

Once the utility of the proposed hybrid recommendation model was verified, its
optimal version was applied to assess the effectiveness of the algorithm in
enhancing the learning experience of students in a real case. The experiment
described in Section 3.4 was carried out and gave the results reported in
Table 6, which show the average score obtained by the 4 groups (i) non-
dyslexic students that received suggestion by the RS, (ii) non-dyslexic
students that do not received suggestion by the RS, (iii) dyslexic students
that received suggestion by the RS, (iv) dyslexic students that do not
received suggestion by the RS.

Table 6: Score received by dyslexic and non-dyslexic students that adopted or not the RS system suggestions | Received RS suggestions | Not received RS suggestions
---|---|---
Non-dyslexic | 8.6 | 8.2
Dyslexic | 8.2 | 7.1

Such results clearly demonstrated how the adoption of the supporting tools and
strategies suggested by the implemented RS increases the score obtained by
dyslexic students considerably (more than 1 point), confirming that the
proposed procedure can actually help them in their learning experience. In
addition, also non-dyslexic students showed an improvement of their average
score (0.4 points), opening to interesting scenarios for future research about
the general usefulness of support methodologies suggested by AIs. A very
outstanding result in our opinion is represented by the fact that, thanks to
the RS, dyslexic students can achieve the same performance of non-dyslexic
students. This represents a step toward the real concept of inclusivity.

##  5 Conclusion

Dyslexia can negatively impair multiple cognitive domains and has a high
comorbidity with other disorders, therefore providing a customized support can
be highly beneficial to people affected by it. In this work we wanted to
explore the possibility of using machine learning, via recommendation systems,
to suggest to dyslexic students the best study support methodologies in terms
of learning tools and studying strategies in a personalized manner.

In order to perform this task, a collaborative-filtering recommendation system
was chosen, evaluating the user-based, item-based and hybrid types, which have
been adopted by different works in the education field. In addition three
widely used metrics, namely the Euclidean distance, Cosine distance and
Pearson distance, have been used in conjunction with the algorithms, to
further increase their accuracies. The algorithms were trained, tested and
validated on a self-evaluating questionnaire in which 1237 dyslexic students
had to rate the most useful studying items among 17 support tools and 22
learning strategies.

The results obtained show that the best performing recommendation system was
able to recommend the most useful studying items to dyslexic students with an
error of 11.93%. This system exploits a hybrid filter with weight,
$\alpha=1/4$ and a Pearson correlation as similarity metric.

To effectively evaluate the benefits of introducing a recommendation system to
support students during their learning path, 50 students with and without
dyslexia used the suggestions from the machine learning model whereas other
students studied with randomized suggestions. The results show that the
students using the recommendation system improved their performance, in
particular dyslexic students obtained the highest benefits compared to non-
dyslexic students.

These results obtained show that the recommendation system can be effectively
used in education to support students with learning disorders during their
education path. However, further experiments are needed to further optimize
the recommendation models for dyslexic students. For instance, a larger
dataset will improve the analysis since it can increase the statistical power
of our analysis. Moreover, recommendation systems can be applied to students
without specific learning disorders in order to highlight possible differences
in the learning tools used students without learning disorders and students
with learning disorders. Indeed, these methodologies can significantly
ameliorate the difficulties of students with specific learning disorders but
can also benefit any student by providing a personalized way to approach
learning.

##  6 Acknowledgements

Gianluca Morciano is a PhD student enrolled in the National PhD in Artificial
Intelligence, XXXVII cycle, course on Health and life sciences, organized by
Università Campus Bio-Medico di Roma.

José Manuel Alcalde Llergo is a PhD student enrolled in the National PhD in
Artificial Intelligence, XXXVIII cycle, course on Health and life sciences,
organized by Università Campus Bio-Medico di Roma.

These results are framed in VRAIlexia project funded by the European Union
Committee within the Erasmus+ Programme 2014-2020 – Key Action 2: Strategic
Partnership Project (Agreement n. 2020-1-IT02-K203-080006 – P.I.: Prof.
Giuseppe Calabrò).

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