Adoption of ICT for Performance Enhancement of Higher Education System

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Added on 2024/06/28

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This literature review investigates the adoption of Information and Communication Technology (ICT) in higher education to enhance performance through virtual learning environments. It analyzes five journal papers, focusing on techniques like Web 3.0 and machine learning algorithms such as RTV-SVM, to address challenges such as underdeveloped knowledge systems, the need for flexible learning models, and network security. The review covers the processes, advantages, and limitations of these approaches, considering factors like data analysis, categorization, and the use of synchronous and asynchronous tools. It evaluates the value of these solutions for improving student integration, predicting at-risk students, and facilitating effective learning through semiotics and multimodal approaches. The ultimate aim is to provide insights into the effective use of ICT to create dynamic and secure virtual learning environments that benefit both teachers and students.

Literature Review (Secondary Research)
Student Name & CSU
ID
Project Topic Title Adoption of ICT for performance enhancement of the higher education system through Virtual learning
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Version 1.0 _ Week 1 (5 Journal Papers from CSU Library)
1
Reference in APA format that will be
in 'Reference List'
García-Álvarez, M. T., Novo-Corti, I., & Varela-Candamio, L. (2018). The effects of social networks
on the assessment of virtual learning environments: A study of social sciences degrees. Telematics and
Informatics, 35(4), 1005-1017.
The citation that will be in the
content
Garcia-Alvarez et. al.,2018
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
https://www-sciencedirect-
com.ezproxy.csu.edu.au/science/
article/pii/S0736585317302885
Level of Journal- Q1 The virtual learning environment, Social networks,
Higher education, Social sciences, Spain
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/
... etc )
The Goal (Objective) of this Solution &
What is the Problem that needs to be
solved
What are the components of it?
2

Technique/Algorithm name:
Web 3.0 (semantic web)
Tools:
Social network service
Applied Area:
Virtual learning environment
Problems:
 The underdeveloped knowledge
system of ICT
 Implementation of Bologna
declaration is not properly used
 Need for creation of more flexible
learning model
 Network security is low
Goal: ICTs are the key element for
knowledge thus to promote ICT, Virtual
learning environment arise as the key tool
for the learning prospectus. One
application which had led to the growth in
the fields of the web the SNS. SNS is a
technological tool which allows one social
publication, share and transfer digital
content and broader the engorgement in
online collaboration and social interaction
via chat and blogs.
 Web 3.0
 Social network service
 Virtual learning
 synchronous and asynchronous tools
 chat
 blogs
The Process (Mechanism) of this Work; The process steps of the Technique/system
3

Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1 Data analysing (the identification and
make the label of the variables related to
data phenomenon was made.)
It evaluates the total feature of college
students VLEs assessment.
Not having the right analysis skills
2 Data categorisation
(psychological-social variables that involve
the research challenge).
The easy approach for appropriate data This approach is using a psychological
and social variable which cannot
measure the latent variable
3 Making relationship between categories
(the quantitative research is made with
structural equation modelling (SEM)
Get appropriate data regarding the variable
4 synchronous tools
(allow communication with agents which
not require delivery and receipt )
Facilities discussion forum N/A
5 asynchronous tools
(allow virtually communication at the same
time)
Providing messaging tools N/A
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
Social network services Web 3.0
Virtual learning environment ICT
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Chat and messaging asynchronous tools
Input and Output Critical Thinking: Feature of this
work, and Why (Justify)
Critical Thinking: Limitations of
the research current solution, and
Why (Justify)
Input (Data) Output (View)
 asynchronous tools
& synchronous tools are
the input for the
framework of this
journal
 Corrective feedback is
the major output of the
implicated model with
the presented input.
In this modern era with the
implantation of the telecommunications
and the digitization of information
which lead ITC the key challenge in this
modern era. To formulate the virtual
learning environment Web3.0
(Semantic Web being used with the
tools of Social Network services. An
SNS platform provides a user with the
facility both online course creation
using the social constructionist
pedagogy and further to administer
learning results.
Virtual learning environment and
social network service shall be
susceptible to adequate use of the
information of its customers, since
information collection can impact on
the user’s privacy. The information
targeted to transfer is under security
threat.
(Describe the research/current solution) Evaluation Criteria How this research/current solution
is valuable for your project
García-Álvarez et. al. (2018) investigated the impact of the
virtual learning practices in the ICT to improve the higher
education learning. They offer a solution to the problem that
is various tools of web3.0 (semantic web) through the use of
social network service. This investigation might be valuable
for identifying the necessary policies regarding the ICTS in
higher education intuitions that allows students to be more
The end user of this sample is the
university students who will be
beneficial by the social service network
by the means of sharing, allocating and
transferring the knowledge and creating
a virtual learning environment.
As the social service network service
is provided through the internet
security and the privacy are a major
concern.
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integrated into the learning process. An SNS platform
provides the user with the facility both online course creation
using the social constructionist pedagogy and further to
administer learning results. Through e-learning platform,
matters are created in the segments that can conclude
different asynchronous and synchronous tools, e.g.
assessments tests discussion forums, learning materials, etc.
Diagram/Flowchart
6

Figure- Results of the structural equation model.
7

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2
Reference in APA format that will be
in 'Reference List'
Chui, K. T., Fung, D. C. L., Lytras, M. D., & Lam, T. M. (2018). Predicting At-risk University
Students in a Virtual Learning Environment via a Machine Learning Algorithm. Computers in Human
Behavior.
The citation that will be in the
content
(Chui et al., 2018)
URL of the Reference Level of Journal (Q1, Q2, …Qn) Keywords in this Reference
https://www-sciencedirect-
com.ezproxy.csu.edu.au/science/article/
pii/S0747563218303091
Level of the journal- Q1 Academic performance, At-risk students, Event
prediction, Higher education, Machine learning
Virtual learning environments
The Name of the Current Solution
(Technique/ Method/ Scheme/
Algorithm/ Model/ Tool/ Framework/
... etc )
The Goal (Objective) of this Solution &
What is the Problem that needs to be
solved
What are the components of it?
Technique/Algorithm name:
Reduced training vector-based support
vector machine (RTV-SVM) & Support
vector machine (SVM)
Tools:
Machine learning, Data processing,
visualization, educational data mining,
technology
Problem:
 achievement in data mining
approaches is not attained due to
underdeveloped use of artificial
intelligence
 large training vectors are required
 big data framework is not well
defined
 Machine learning
 logistic regression
 support vector machine (SVM)
 Educational data mining
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Applied Area:
predicting at-risk and marginal students
Goal:
Reducing the training vectors, time, and
conserving the support vector with a
motive to avoid a decline in categorising
the accuracy. To evaluate and determine
the prediction of risk of marginal students.
To determine the role of RTV-SVM
methodology in predicting accurate
prediction.
The Process (Mechanism) of this Work; The process steps of the Technique/system
Process Steps Advantage (Purpose of this step) Disadvantage (Limitation/Challenge)
1 RTV-SVM inputs
(Determining N set of training vector)
Helps to define the basic input of the RTV-
SVM
Lack of transparency
2 Construction of SVM
(After eliminating both vectors denote
remaining vector as Xremaining.)
Getting the appropriate vector from
eliminating the various factors
Lack of accuracy of the evaluated
remaining vectors
3 OULA database
(predication on the basis of demography
basis)
Getting the prediction of the at risk and
marginal students on the demography basis
N/A
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4 Performance evaluation
(two class were used for the prediction in
binary classification)
Generally, it gains a high rate of accuracy
and doesn't require more time for training
and testing.
Still, there is room for classifying the
accuracy of training vectors.
5
Validation Criteria (Measurement Criteria)
Dependent Variable Independent Variable
Support vector machine Logistic regression
Data mining, data processing Learning analytics
Input and Output Critical Thinking: Feature of this work, and
Why (Justify)
Critical Thinking: Limitations of the
research current solution, and Why
(Justify)
Input (Data) Output (View)
 A set of N
vector
 sequential
minimal
optimisation
 Redundant
training
vector
 discriminant
function
As per the advancement of the artificial
intelligence and the education data mining it is
difficult for ensuring the students who had
passed the course. To overcome this issue
machine learning algorithm technique of the
Support Vector Machine of the redundant
training based. RTV-SVM is the technique
which can help to predict at risk and marginal
student. This technique also provides the
Still, there is a gap or room for the
improvement of the accuracy. Another
shortcoming of this project is the speed and
sizes both in the case of training and testing
and high algorithm complexity.
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accurate training vector and helps
(Describe the research/current solution) Evaluation Criteria How this research/current solution is
valuable for your project
Chui et al. (2018) in this investigation had
evaluated the concern that it is difficult to
ensure about the students who will pass the
course, this may be due to latent education
data mining and development of artificial
intelligence. To predict the number of
students who can pass the course they had
developed the solution through machine
learning algorithm that is reduced training
vector-based support vector machine (RTV-
SVM) which is proficient to predict the at
risk of being marginal.
The end users of this research are at risk and
the marginal student of the university students.
The machine learning algorithm Support
training vector can help in predicting these
students.
The machine learning algorithm reduced
training vector-based support vector machine
(RTV-SVM) is a cable for the forecast of at-
risk of being a marginal student. The RTV-
SVM also helps to remove the reduced
training vector- for reducing the training and
testing time.
Diagram/Flowchart
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