A Comprehensive Review of Relevant Techniques used in Course Recommendation System.

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 01 | Jan 2023 www.irjet.net p-ISSN: 2395-0072
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A Comprehensive Review of Relevant Techniques used in Course
Recommendation System.
Rehan Ahmed Khan, Piyush Kumar Gupta
Department of Computer Science and Engineering, Jamia Hamdard, New Delhi, India
Assistant Professor, Department of Computer Science and Engineering, Jamia Hamdard, New Delhi, India
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Abstract - Through a variety of internet venues, there has
been a significant increase in online learning tools in recent
years, and COVID lockdown was the cherry on top, making us
more web friendly for learning things through the web,
therefore defining a course recommendationsystemhasnever
been more important. Course recommendation systems try to
simplify the difficulty of identifying relevant courses based on
ranking and set criteria. Despite all of the advantages, we
encounter several difficulties and complexities, such as
accuracy, time consumption, and insufficient data. This
research examines online recommendation systems using a
variety of methods, including Content-Based, Collaborative
Filtering, Knowledge-Based, and Hybrid Systems. Datasets,
methods, evaluation, and outputs were discovered to be
necessary components during the research. And the proposed
way of recommendation system in this paper will be highly
beneficial in recommending courses to students.
Keywords: - e-learning, content based, collaborative
filtering, machine learning, online course recommendation
1. INTRODUCTION
Since evolution, Humans have been building things or
machines that accomplish various tasksinvery simpleways.
There are concepts of learning and making others learn or
train to make people or machines useful. Like, how we train
any algorithm to carryouttasksthatareperformednaturally
and effortlessly by machines as humans do. As, Machine
Learning enables computers to think and decide on their
own like humans do. This is to be one of the noteworthy and
most significant developments in the field of computer
science.
As we move toward a data-centric society, there has been a
massive data explosion in recent years. And a large amount
of data is meaningless unless it is analysed for hidden
patterns. Machine learning approaches allow us to uncover
hidden patterns and information about an issue that may be
utilised to forecast the future and assist us in different
decision-making processes. Learning has undergone a
significant transition, moving from the conventional
classroom to an online learning environment. Consequently,
demonstrating a recommendation system for an online
learning environment is a necessity of the hour. Similarly,
high-speed internet encourages us to study over the
internet; there are a variety of platforms selling a variety of
courses, but advising which is most suited to one's
personality will make the process go more smoothly. The
concept of a recommender system arose from the idea that
people rely on others to make routinedecisionsintheirlives.
The explosion in the volume and diversity of information
available on the internet has aided the development of
recommender systems, which has resulted in a rise in profit,
user benefits, and industry applications. In order to locate
relevant courses for the users, a recommendation technique
must establish that a course needs to besuggested.The most
common recommendationalgorithmtechniquesareContent
Based, Collaborative Filtering, and Hybrid Systems.
The popularity of E-learning has resulted inanabundance of
courses, making it difficult to choose ones that are
appropriate. In contrast to typical learning settings, E-
learning allows for the personalization of the learning
experience. Traditional learning often accommodates just
one learning experience, because in a typical classroom
setting, a teacher is frequently dealing with several pupils at
the same time in the same area. As a result, each student is
forced to get the identical course materials, regardless of
their own requirements, traits, orpreferences.Furthermore,
determining the optimum learning technique for each
learner and implementing it in a real classroom is
exceedingly challenging for a teacher. One approach is to
leverage recommender system (RS) approaches to
personalise the learning process according to each learner's
interests and goals.
This paper presents a comprehensive overview of current
courses recommendation methodologies as well asmachine
learning algorithms employed in RS. In order to identify
research contributions and limits, the emphasis is on
creating categorizationsof recommendationapproaches,ML
approaches, as well as methodologies employed,application
domains, datasets, evaluation, and input/output data.
We used the queries "e-learning recommendation system,"
"course recommender," "online course recommendation
system," "recommendation system," and "e-learning"tofind
relevant publications. We found 30 publications from
journals that were relevant between 2018 and 2020. The
availability of a recommendation system served as the sole
selection criteria at this stage. 'E-learning,' 'course
suggestion,' and 'aims' that addressed one of RS's remaining
issues were the requirements for stage two (i.e., cold-start,

data sparsity etc.). Twenty articles were discarded since
none of the stage-2 requirements weremet.Thefinal studies
indicate an RS for e-learning system.
1.1 Inclusion and Exclusion criteria:
Papers were chosen for evaluation based on the following
criteria:
1. Papers presenting algorithms for online platform
recommendation.
2. Publications outlining plans to use Machine
Learning to develop RS.
3. Papers containing the outcomes of the technique's
evaluation.
The following were used as exclusion criteria:
1. Papers to which full access was not granted;
2. Papers that lackeddetaileddiscussionandappraisal
of the issue were all eliminated;
2. BACKGROUND DETAILS
Online learning with virtual assistance is in increasing
demand, and the ed-tech industry has taken full advantage.
Personalizing quizzes, courses, andassessmentsforstudents
makes the whole process more convenient, as it overcomes
the dilemma of picking courses by defining our own criteria.
As a response, researchers classified recommendation
systems into four categories: CB, CF, knowledge-based, and
hybrid. It is not, however, highly optimized, but there is still
much opportunity for improvement.
Content Based: It is based on user’s past activities and likes
in order to recommend things. By categorizing items using
particular keywords, content-based filtering aims to learn
what the student like, search up those terms in the database,
and then offer relatedcourses. Two differentsortsofdataare
employed in this filtering. First, the user's preferences, areas
of interest, and personal data like age or, occasionally,
history. The user vector is used to represent this data.
Second, a subject-related piece of information is referred to
as an item vector. The item vector includes all of the
attributes that may be usedtocomparethingstooneanother.
Through Cosine Similarity, Recommendation would be
determined. If ‘A’ is the user vector and ‘B’ is an item vector
then cosine similarity is given by
Collaborative Filtering: Recommendations are made in
accordance with user behavior.Theuser'spastiscrucial.Itis
predicated on the idea that related users and projects may
be prioritized. [1]. The CF system looks for courses that are
comparable based on user feedback. User may provide
explicit or implicit feedback, such as a numerical rating to
reflect how much users valued a certain course, suchas1for
dislike and 5 for extremely like, or implicit, such as internet
browsing history or reading time for a particular course.
Memory-Based and Model Based algorithms are two forms
of collaborative filtering algorithms. In the first, items and
user data are saved in memory, and then calculations are
used to provide estimates based on the data. The Bayesian
network, rule-based, and clustering approaches are only a
few of the ML algorithms that are used to build the model
process.
Knowledge Based: A recommendersystemisconsideredto
be knowledge-based when it delivers recommendations
based on particular queries instead of a user'sratinghistory.
Context-based and ontology-based are two of the
knowledge-based techniques.
Hybrid Model: Hybrid RS incorporates features of both CB
and CF by combining individual forecasts into one, adding
relevant data to the CF model, or generating final
recommendations based on the collective ranks.
Recommender systems (RS) built on one of the
aforementioned architectures, are prevalent in all
recommenders, and serve as a baseline for addressing

specific recommendation tasks. They should be using ML
techniques, and datasets, be susceptible to monitoring and
feedback, have output, according to us. Each of these
components is covered in further detail in the following
sections.
2.1 Data Used
Data is gathered implicitly or explicitly by recommendation
systems. Implicit data is raw data that may be divided into
two types: those that are purposefully obtained from
available data streams andthosethatareby-productsofuser
activity and it might be utilized or not. Through registration
forms and profile information are used to obtain as explicit
data from users. They can also be obtained through internet
user reviews.
3. RELATED WORKS
The major goal of classifying and categorizing previously
completed work is to have a thorough knowledge of the
recommender system's deployment invariousdomains. The
purpose of this paper is to explainhowcourserecommender
systems have improved performance in the currentday.The
current research has been divided into four categories:
Collaborative, CB, Hybrid Solutions, and Knowledge-Based.
3.1 Content- Based Research
Content-based recommendations are based on a user's
previous choices, which are used to provide suggestions to
other users who have similar likes and dislikes. It made use
of historical student data to make predictions regarding
learning materials. Using fuzzy clusteringanddecisiontrees,
learners are categorized as beginner, intermediate, or
master depending on their academic history and learning
habits. The pattern discovered during categorization also
reflects the amount of interest in taking specific courses.[4]
Also, based on a set of rules in [5], Learners are presented
with learning components and learning objects via adaptive
user interface based on rules presented by author. The CB
technique has the drawback of relying on previous user
experience and being unable to offer fresh content, which
may demotivate users and lead to an unwelcome restricted
focus.
3.2. Collaborative Filtering Based Research
Collaborative filtering (CF) is a way of combining crowd
consumption patterns to generate a mathematical model of
all students and courses. To put it another way, it'sa method
of identifying a group of people who have similar tastes and
filtering things based on the opinions of other users. By
looking at their favorite themes and incorporatingtheminto
a categorized list of ideas, the approach gets over the
limitations of content-based strategies. There are two
strategies from the perspective of students and courses.The
first, known as User-based CF, analyses all students data to
identify comparable customers and anticipate their
preferences for various courses, a process known as Social
Recommendation. The second technique, known as Item-
based CF, begins with course similarities, then analyses
recent best-sellers and offers discounts to target customers.
The Item Recommendation is what it's called. [3] The CF
technique viewed lack of data as a major drawback. As it can
commonly result in the cold start problem, which explains
the difficulty of offering recommendations when the
students or courses are fresh.
3.3. Knowledge Based Research
Knowledge-based recommendation systems offer
recommendations based on information about studentsand
courses. Knowledge-based suggestions aren't based on a
student's rating, and they don't require any information
about that user to make recommendations. To produce
appropriate learning material from the web based on
learners' needs, researchers utilized an Ontology-Based
model with dependency ratios and parse trees. [7] It
comprises several layers, methodologies, and algorithms
that are not ideal for all sizes of e-learning systems,
Recommendation System Content Based Collaborative
Filtering Knowledge Based Hybrid Approach Consequently,
the knowledge-based strategy is both time- and money-
consuming.
3.4. Hybrid Model Based Research
A hybrid model framework based on three models was
presented by the researchers: CF, Content-Based, and CF
with a Self-Organizing map. The approach's main advantage
was that it included all of the scores from all of the models,
allowing you to take advantage of all of their advantages.
Despite the fact that the technique takes longer to suggest
than previous models, the accuracy and performance
improvements were considerable. [6] The researcher
designed a learner learning object predicted (LLOP) matrix
and used collaborative filtering to choose the most
acceptable learning objects. They then usedtheSPMmethod
to identify the most often occurring sequence of learning
objects in the matrix. [2]
The drawbacks of CB and CFareeliminatedbythisapproach.
Despite being more timeconsumingandmoreexpensive,the
hybrid procedure is now the best option because to all the
benefits listed above.

4. DISCUSSION, CHALLENGES AND ISSUES
We evaluated the methodologies and benefits addressed by
researchers and classified the recommender systems
reported in study. The issues of the categorized
recommendation systems were thenanalyzedandgradedas
follows:
i. Cold Start issue: This problem is generally handled by
freshman or courses. This problem develops due to a main
lack of rating, which implies that a freshman may not have
rated any items, or that any new item may not have been
rated by any students. As a result, making the best
recommendations in this circumstance is difficult.
ii. Data Sparsity: Due to the fact that active users only
evaluated a tiny number of things, data sparsity refers tothe
difficulty in locating enough reliable similar users. It's tough
to deliver an appropriate suggestion list to the target user if
the learning item has a low rating. The learner does not rate
existing learning items, resulting in data sparsity.
iii. Privacy: The privacy hazards of collecting and processing
personal data, on the other hand, are frequently
underestimated or neglected. Many users are unaware of if
and how much of their data is gathered, whether or not it is
sold to third parties, or how safely and for how long it is
held.
4.1 Machine Learning Algorithms
Machine learning and Data Mining employ a variety of
statistical approaches and techniques, as well as diverse
algorithms such as classification models, clustering, and
regression models, to extract insights from massive sets of
data. It allows us to forecast the result based on previous
events. Various fusions of data mining methods, such as
classification and association rule algorithms,clusteringand
association rule algorithms, excreta were compared. were
compared.
They compared the results and discovered that the optimal
mixture includes Clustering, Classification, and Association
Rule Algorithms.[8]
According to the research paper studied, clustering
algorithms were the most popular, along with partitioning
approaches such as k-means and k-nearest neighbour.
Researchers evaluated several combinations of data mining
techniques, including classification and association rule
algorithms, clustering and association rule algorithms,
integrating clustering and classification algorithms into an
association rule algorithm, and solely association rule
algorithms. [9]
Researchers discovered that the optimal combination is
clustering with classification and an association rule
approach.
In [10] researcher proposed a framework where clustering
algorithm were applied to the dataset, and then frequent
pattern mining algorithm applied which recommend the
courses based on historical data.
4.2 In-depth analysis of chosen publications
We have provided brief information of the chosen
publications in Table 2 below, including recommendation
systems, technology employed, target usergroupsfor whom
the proposed RS was produced, nation of study, data used in
the assessment of the RS, and the proposed system's
application area.
It also demonstrates the programming language used in
developing RS, with innovation-based learning systems as
the primary application. The target demographics were
schools, universities and their students. Foreign data was
used by all systems to evaluate their systems.
Categories Techniques Drawbacks Benefits
Content Based RC Utilizes historical
student data and
provide predictions
Depends on the user's history. Recommendations are very
relevant and transparent.
Collaborative Filtering Clustering similar
users based on their
similar likes and
dislikes
Data sparsity,
Cold start Problem
Student can get broader
exposure to the courses.
Knowledge Based Ontology based Models Time Consuming, Expensive It is not based on user rating
Hybrid Model Combination of CB and
CF
Time Consuming, Expensive Overcome the disadvantages of
CF and CB
Table 1. Shows techniques and Drawbacks

References RS System Technology
used
User group
to be
targeted
Area of
application
Studying
country
Data Collected
[1] Optimized
Collaborative Filtering
based on text similarity
N/A Universities
and Colleges
Students
Optimizing
algorithm
China Real world data
[2] Learner learning
objects
recommendation
(LLOR)
Sequential
pattern
mining
Students Optimized
learning
algorithm
Saudi
Arabia
Real world data
[3] Online Course
Multimedia Learning
System (OCMLS)
MATLAB,
Matrix
Students Optimized
learning path
Taiwan Real world data
[4] Personalized group-
basedrecommendation
system
PHP, Java and
MySQL
Student,
Academic
Institutions
Web search in
e-learning
Malaysia Learning
management
System
[5] Rule-based adaptive
user interface
N/A Students Learning
portal
India Learning
Management
System
[6] Hybrid model with CB,
CF and CF with self-
organizing map
Python Students Optimizing
algorithm
Morocco User’s rating
[7] Adaptable and
personalised e-
recommendation
PHP, HTML Students Web based
learning
portal
N/A Data extracted
from web
searches
[8] Combination of
clustering with
classification and
association rule mining
N/A Students Optimizing
algorithm
India Learning
Management
System, i.e.,
Moodle
[9] Customised clustering
algorithm
N/A Students Optimizing
algorithm
N/A Real world data
[10] Customised e-
recommendationbased
on grades
N/A Students Web based
learning
India University
databases,
online
registration
form
Table 2. In-depth analyses of chosen journal articles

Based on the search queries andresearchpapersstudied, fig.
2 demonstrates an assessment of the popularity of various
RS approaches. CF approaches are utilisedinroughly48%of
papers, whereas CB and Knowledge-based are used in
around 18% and 14% of papers, respectively, and hybrid
models are employed in around 20% of papers, despitetheir
significant benefits.
6. CONCLUSIONS AND FUTURE WORKS
The fulfilment of personalised educational resource
recommendations has become the key challenge that has to
be solved in intelligent education. This review is based on e-
learning publications that deal with Recommendation
System. This paper's key contribution is, different ML
methods and a taxonomy of RS systems are presented,
assessment measures, and insights into obstacles and
concerns that need to be considered in future study. This
study found that CF is a common recommendation strategy
in e-learning, with the majorityofstudiesseekingtoincrease
suggestion quality.
Hybrid approaches provide a competitive advantage over
the other four strategies, but their popularity is modest.
Personalization and adaptive user interface have become
important features in e-learning as it will be engaging and
interesting to learn through the web, and the research
obtained from the exploratory study reported that learners
in group four benefited the most from the individualized
recommendation links provided by the personalised web-
based system. [4] They had much superior learning
comprehension than the other three groups since they had
more Web search experiences.
Data sparsity and latency are major challengesthatstill need
to be addressed with RS, as well as privacy and shilling
threats. The hybrid model has been proved to tackle the
majority of contemporarysystemissues.Furthermore,much
research is necessary to improve user confidence and facets
of user engagement.
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