CAPÍTULO III DISEÑO DE LA RED
3.3. Detalle de la red propuesta
Considered to be the backbone of Content-Based Filtering, Collaboration Filtering and Knowledge-Based
recommendation systems; similarity metrics aid in the prediction of ratings of items which have not been
rated, by deducing the closeness between two featured vectors. This section explains the most widely used
similarity metrics utilised for purposes related to e-learning recommendation techniques, as displayed in
Table 5.3.
Let x= (x1,· · ·,xn) andy= (y1,· · ·,yn)be two vectors in an n-dimensional real vector space, for
Table 2.11: A summary of widely used similarity metrics in approaches related to e-learning recommendation systems
Study Adaptation Euclidean Pearson Jaccard Cosine Manhattan [287] Adaption of learning objects. D
[288] Calculating the degree of similarity
between students. D D
[289] Measurement of similarity between
learners’ rating vectors. D
[151] Compute similarity between learners. D
[290] Calculate the similarity among students. D
[291]
Learning objects self-organisation process using (cosine similarity) and matching the similarity degree between learners and LOs using (Jaccard coefficient).
D D
[260] Calculate the similarity between the
user profile and the course profile D
[292] Measuring the similarity between users
to recommend courses. D
[293] Calculate user similarity for
the suggestion of the course. D
[294]
Recommend individual learners’ elective courses based on comparison of single course template with that of course templates in the same
curriculum to deduce a similarity between learners.
D
[295]
Calculate similarity with other learners (rating and learning styles similarities) to
recommend learning materials.
D D
2.7.1
Euclidean Distance
The Euclidean Distance which is always greater than or equal to zero is the most widely used measure to
compute the distance of two vectorsxandyas defined in by Eq. (2.1) [296]:
D(x,y) = s n
∑
i=1 (xi−yi)2 (2.1)In the case where the vectors are identical, the result would be equal to a value of 0. Furthermore,
when the coordinates of both vectors range from 0 to 1 (which is the case for the feature vectors used
in this study to represent the student Learning Styles and the learning object profiles, see chapter 3), i.e.
0≤xi,yi≤1 for all 1≤i≤n, then the Euclidean distance can be normalised as follows:
d(x,y) = s 1 n n
∑
i=1 (xi−yi)2 (2.2)Note that 0≤d(x,y)≤1 and the greater the value ofd(x,y)the more dissimilarxandyare. Hence, the
derivation of Eq. (2.2) will result in a similarity matrix as defined below.
Simd(x,y) =1−d(x,y) (2.3)
Though the Euclidean Distance is widely used in algorithms such as K-means and Fuzzy C-means [297]
for the purpose of clustering continuous data, it possesses its own limitations, i.e., (i) when two data
vectors possess no common attribute values, they might have a smaller distance than the other pair of data
vectors which contain identical attribute values [298, 299]; and (ii) in the case of Euclidean distances, the
largest-scaled feature would be dominant over others, hence, the normalisation of continuous features
could be a solution to this issue [298].
2.7.2
Pearson Correlation
A measure of the linear dependence between two vectorsxandy, the Pearson correlation is described as
the ratio of the covariance of the two vectors by the product of their standard deviations (which acts as a
normalisation factor) [300], as shown in Eq. (2.4).
P(x,y) = ∑ n i=1(xi−x¯) (yi−y¯) q ∑ni=1(xi−x¯)2 q ∑ni=1(yi−y¯)2 (2.4)
Wherexandyare the mean values ofxandy, respectively.
Usually, the coefficientP(x,y)varies between−1 to 1, where the value 1 represents perfect negative
linear dependence, 0 represents no linear dependence, and 1 represents perfect positive linear dependence.
negative figures represent dissimilarity. Despite being sensitive to outliers, the Person measure is invariant
to linear transformations [298, 301].
2.7.3
Cosine Similarity
A widely used similarity metric, the cosine similarity computes the ratio of the scalar product by the
product of magnitudes by measuring the angle between two vectors [302], as computed in Eq. (2.5).
c(x,y) = x.y
||x||.||y|| (2.5)
Generally, the values ofc(x,y)range from−1 to 1, and from 0 to 1 if thexandycoordinates are non-
negative values. Effectively, this thesis is concerned with values between 0 and 1 where the value 0
represents no similarity and 1 constitutes perfect similarity. The Cosine correlation is invariant to rotation
but is variant to linear transformations in addition to being independent of vector length [298].
2.7.4
Manhattan Distance
The sum of the absolute differences Eq. (2.6) between the Cartesian coordinates of two vectors [303],x
andyin an n-dimensional real vector space determines the Manhattan Distance.
M(x,y) =
n
∑
i=1
|xi−yi| (2.6)
In the case wherex andyare identical,M(x,y) =0 would equate to 0, if not, M (x, y) > 0. Similarly,
as defined in Eq. 2.1, for the Euclidean distance, the Manhattan distance could be normalised to be
within values 0 and 1 for vectors whose coordinates are non-negative real numbers less than or equal to
1. Furthermore, the derivation of Eq. (2.7) results in a similarity matrix as below.
m(x,y) =1 n n
∑
i=1 |xi−yi| (2.7)Likewise, a similarity metric can be derived from Eq. (2.8) as follows.
2.8
Summary
This chapter has presented an extensive discussion of the literature review of this thesis. The focal point of
this chapter discussed the relevant benefits, challenges, and theories related to the concept of adaptive e-
Learning recommender systems. Moreover, five main aspects and background information related to the
concepts of adaptive e-learning, learning styles, student profile adaptation, recommendation algorithms
and clustering in the e-learning system have also been discussed upon.
Through this literature review, the research gap or limitations related to the relevant field of study
which are necessary to be addressed in order to develop personalised e-learning systems are revealed.
These limitations are known to be focused around the following points: first, student learning styles ini-
tialisation to overcome the cold start problem; second, mechanisms used to capture and interpret student
learning styles from learning behaviour patterns; third, techniques used to recommend learning objects
according to student adaptive profile.
Nonetheless, a great deal of work has been conducted on adaptive e-learning recommendation sys-
tems, yet there is no comprehensive solution which successfully incorporates the above-mentioned prob-
lems; some solutions concentrate on solving cold-start problems to improve the accuracy of LOs recom-
mendations, others focused on determining student learning styles.
Hence, in this thesis, we aim to overcome the aforementioned limitations by proposing a novel adap-
tive e-learning recommender system aid students in finding appropriate learning materials. This will be
achieved by building a dynamic student profile (learning styles) and developing a new algorithm for rec-
ommending learning objects to suit individual student learning styles. Additional explanation and details
Chapter 3
Ubiquitous LEARNing system (ULEARN)
Architecture
The effectiveness of Adaptive E-Learning Recommender Systems (AERSs) depends on their ability to
suggest appropriate learning content in line with learners’ Learning Styles and preferences. As mentioned
in this thesis’ literature review (Chapter 2), existing AERSs still face several challenges, some of which
are: 1) how to initialise the Learning Style of a new student; 2) how to recommend learning materials
according to Learning Styles of students, and 3) how to identify student’s Learning Styles from their
learning behaviour patterns.
Primarily, this chapter introduces a novel adaptive e-learning recommendation (ULEARN) frame-
work to recommend personalised learning objects according to students’ Learning Styles. The major
innovative features of ULEARN include: (i) A novel algorithm for initialising the student Learning Style
using a dynamic Learning Style questionnaire; (ii) An innovative algorithm for recommending learn-
ing objects according to learners’ Learning Styles; (iii) An algorithm for dynamically adapting student
Learning Styles based on learning behaviour patterns. The proposed ULEARN framework will help in
answering research question 1:
RQ1. What is an appropriate architecture for an adaptive learning object recommender system?
The remainder of this chapter is structured in the following way. The first section reviews the key
concepts of the proposed adaptive e-learning recommendation framework while the proposed ULEARN
architecture and its layers have been presented to the reader in Section 3.2. Next, Section 3.3 presents the
the reader. Lastly, a brief overview of this chapter is described in Section 3.5.
3.1
Proposed adaptive e-learning recommendation
Through the work in this thesis, we propose a novel adaptive e-learning recommendation architecture
for recommending personalised learning objects on the basis of student learning styles. The ULEARN
architecture aims to overcome issues related to cold-start and rating sparsity by incorporating learning
object profiles in addition to students’ learning styles.
As already mentioned in Chapter 2 of this thesis, the FSLSM Learning Style model has served as
the basis for our proposed framework. The four dimensions (Information Processing(Active/Reflective),
Information Perception (Sensing/Intuitive), Information Input (Visual/Verbal), and Information Under-
standing (Sequential/ Global)) of the FSLSM model are made use of for the representation of student
Learning Styles as well as learning object profiles.
Therefore, we will briefly introduce the concepts of student profile, object profile and course structure,
which served as the basis of our architecture, before describing the proposed architecture itself. The
following sub-sections will describe these profiles in detail.
3.1.1
Student Profile
Student Profile (SP) is the key resource for facilitating our proposed system to represent essential in-
formation about each student’s preferences. The purpose of the SP in the proposed system is to store
the student’s Learning Style. Every student has their own profile which allows the system to provide a
personalised learning experience in accordance with their learning styles.
A student’s Learning Style is comprised of a vector of real values that ranges between 0 to 1 (or from
0% to 100%), as below in Eq. (3.1), where Learning Style attributes are used as place holders.
LS = (Active, Reflective, Visual, Verbal, Sequential, Global, Sensing, Intuitive) (3.1)
Example 1. Demonstrated below in Table 3.1 are few examples of student Learning Style vectors
from ULEARN database (See Sect.7.3) which are determined by making use of the learner’s responses
Table 3.1: Examples of student learning style vectors
Student profile
Active Reflective Visual Verbal Sequential Global Sensing Intuitive
Yousef 0.5 0.5 0.6 0.4 0.8 0.2 0.7 0.3
Clara 0.4 0.6 0.15 0.85 0.7 0.3 0.8 0.2
Ed 0.5 0.5 0.6 0.4 0.8 0.2 0.7 0.3
George 0.5 0.5 0.65 0.35 0 1 0.55 0.45
3.1.2
Learning object Profile
Learning objects for a specific lesson are generated by the organisation of learning content materials. In
an e-learning environment, a learning object (LO) is an indispensable tool used for the preparation of
tutorials for learners in electronic and reusable formats such as lecture notes, presentations, questions,
activities, examples, exercises, etc. In this research, LOs are presented in numerous formats, namely,
text files (pdfs), presentations (PowerPoint slides), pictures and videos, to suit the Learning Styles of
individual learners.
For instance, a visual learner would favour watching a video rather than reading a pdf document,
compared to a verbal learner who would prefer doing exactly the opposite. Thus, an FSLSM vector can
be used to represent a learning Object Profile (OP) to indicate the class of learners that the respective
learning object is appropriate for, as shown in Eq. (3.2).
OP = (Active, Reflective, Visual, Verbal, Sequential, Global, Sensing, Intuitive) (3.2)
It is important to note that unlike student Learning Styles which are computed by means of the ILS
questionnaire or learning behaviour patterns, it is generally presumed that the learning object profile is
created by the teacher or an educational professional.
Example 2. For illustration purposes, few examples of OPs stored in ULEARN database (See Sect.
7.3) created by the teacher presented in Table 3.2.
3.1.3
Course structure
Within this section, we will introduce the course structure, which is an essential component in personal-
Table 3.2: Examples of learning object profile
Object profile
Active Reflective Visual Verbal Sequential Global Sensing Intuitive
LO1 0.7 0.3 0.5 0.5 1 0 0.3 0.7
LO2 0.2 0.8 0.8 0.2 0.7 0.3 0.4 0.6
LO3 0.5 0.5 0.2 0.8 0.1 0.9 0.6 0.4
LO4 0.5 0.5 0.5 0.5 0.5 0.5 0.3 0.7
LO5 0.9 0.1 0.7 0.3 0.3 0.7 0.1 0.9
higher education system. The structure of the course material comprises three levels, namely, courses,
lessons and learning objects. While each course is grouped into several lessons; each lesson is composed
of a set of learning objects in several formats such as text, video, audio, etc, as shown in Figure 3.1.
Figure 3.1: Structure of the course materials in Egyptian higher education system
The next section describes the proposed adaptive e-learning recommendation technique as well as the
components in more detail.