=Paper=
{{Paper
|id=Vol-2869/PAPER_02
|storemode=property
|title=Comparative Analysis of Various Techniques used for Predicting Student's Performance
|pdfUrl=https://ceur-ws.org/Vol-2869/PAPER_02.pdf
|volume=Vol-2869
|authors=Amita Dhankhar,Kamna Solanki
}}
==Comparative Analysis of Various Techniques used for Predicting Student's Performance==
https://ceur-ws.org/Vol-2869/PAPER_02.pdf
Comparative Analysis of Various Techniques used for Predicting
Student’s Performance
Amita Dhankhara, Kamna Solankib
a
University Institute of Engineering and Technology, Maharshi Dayanand University, Rohtak, India
b
University Institute of Engineering and Technology, Maharshi Dayanand University, Rohtak, India
Abstract
Digitization is transforming all aspects of education. Learner’s interactions with their online
and offline learning environment lead to a trail of data that can be used for the purpose of
analysis. Learning analytics (LA) and Educational data mining and (EDM) are emerging fields
that attempt to develop methods to confront an abundance of data from the educational domain
in order to optimize learning and leveraging decisions related to learning, teaching, and
educational management. EDM/LA techniques interpret such enormous data and turn it into
useful action. It provides insight to teachers to improve teaching, to understand learners, to
identify difficulties faced by learners, and to provide meaningful feedback to learners thereby
improving the learner’s performance. This paper aims to compare different EDM/LA
techniques and to identify their potential strength and weaknesses that are applied in the
educational domain to predict the student’s performance.
Keywords 1
Educational data mining, learning analytics, machine learning, supervised learning,
unsupervised learning.
1. Introduction
Technology is evolving rapidly [1]. This technological advancement leads to the generation of
tremendous amounts of data and it becomes an integral part of all sectors [2]. The educational sector is
no exception. Big data in the field of the education sector provides unprecedented opportunities for
teachers and educational institutes. The exploration and analysis of an enormous amount of data so that
significant patterns can be discovered is called Data mining (DM). It can also be defined as “a non-
trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns
from data” [3]. The DM techniques when applied to the data gathered from the educational domain to
extract knowledge is called Educational data mining [4]. One of the significant areas of interest for
researchers in EDM is the prediction of student’s performance. Timely predicting student’s
performance helps in identifying poorly performing students thereby helping teachers to provide early
intervene. EDM/LA techniques like classification, clustering, association analysis, prediction are used
to transform raw data into significant information. Computational advancements in data mining and
learning analytics have helped this effort significantly [5]. Considering the importance of various
techniques for predicting student’s performance detailed comparative analysis of these techniques
would be valuable. The sections that follow are listed as methodology is described in Section-2; Results
are summarized in section 3; the conclusion is summarized in section 4.
2. Methodology
This paper performed a comparative analysis of various techniques used for predicting student
performance.
WTEK-2021: Workshop on Technological Innovations in Education and Knowledge Dissemination, May 01, 2021, Chennai, India.
EMAIL: amita.infotech@gmail.com (Amita Dhankhar)
ORCID: 0000-0002-9305-4088 (Amita Dhankhar)
© 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
10
For this purpose, relevant articles were identified, selected, evaluated critically using several criteria,
and then finding were integrated. Few Research questions were formulated to streamline our
contribution, which are:
RQ-1 What EDM/LA techniques are used for predicting student performance?
RQ-2 Comparative analysis of various techniques on the different facet that includes their strength,
weaknesses, and accuracy.
To assess and address the above-mentioned Research Questions, we have adopted the PICO model
[6] that consists of 4 key components namely population, intervention, comparison, and outcomes.
Details of the PICO components of this paper are given in the Table 1. We have searched three databases
namely Scopus, IEEE, and Science Direct for the articles published from 2016 to 2020.
Population Articles predicting student’s
performance
Intervention EDM/LA techniques
Comparison Comparative analysis of EDM/LA
techniques
outcomes Effectiveness, the accuracy of the
techniques
The search string used for the search is
(Prediction OR forecast OR predict) AND (techniques OR methods OR framework) AND
(student’s performance OR retention OR at-risk) AND (Engineering OR Higher education) AND (data
mining OR machine learning OR Learning analytics)
To obtain relevant results, the syntax of the string was modified slightly for each database. The
articles identified through database searching were evaluated using inclusion and exclusion criteria.
Inclusion criteria included articles that explicitly predict student’s performance/predictive
models/techniques/methods, considered only journal articles, full text is available for analysis, focus on
empirical studies, articles in the domain of higher education. Articles not written in English, conference
articles, full text not available were excluded.
Figure 1: PRISMA flow chart of methodology [7].
11
3. Results
In this section, we describe the details of the reviewed articles, EDM/LA techniques used for
predicting student’s performance, and comparative analysis of various techniques on the different facets
that include their strength, weaknesses, and accuracy. Regression and Classification techniques are the
most commonly used techniques in educational data mining and learning analytics. It is the supervised
learning method that analyzes a set of data and classifies data into a different predefined set of classes.
In the context of higher education, this approach has been used to determine or predict student’s success
or failure by identifying the patterns from the student’s learning activities with online learning
resources. Classification techniques can be used to predict student’s performance, to predict students
at-risk or retention [8-10], students dropout prediction [11,12], predict student’s achievement [13],
predict which students would likely submit their assignments [14], assessing student’s engagement
during the course [15]. In this section, we have discussed various techniques used for predicting
student’s performance.
Figure 2: Distribution of techniques used for Predicting students’ performance
Figure 3: Distribution of datasets used for predicting student’s performance
12
3.1. k-NN
K- nearest neighbor is supervised machine learning algorithm. It is the simplest yet powerful
technique that can be used for both classification and regression predictive problems. The basic concept
of KNN is to classify the test data in a given dataset by using feature similarity. It calculates the distance
(closeness or proximity) between the test data and each training data in the dataset. Then it performs
the majority voting and classifies the test data by the majority votes of neighbor classes. The distance
can be calculated by using various distance functions like Euclidean, Cosine, Chi-square, Minkowsky,
etc [38-42].
3.2. Naive Bayes
Naive Bayes is a classification algorithm that assumes that the predictor variables are independent
of each other. The base of the naive Bayes is the Baye's theorem which is derived from the conditional
probability. Bayesian theorem gives an equation for computing posterior probability P1(c1|x1) from
P1(c1), P1(x1), and P1(x1|c1).
𝑝1(𝑥1|𝑐1)𝑝1(𝑐1)
𝑝1(𝑐1|𝑥1) =
𝑃1(𝑐1)
P1(c1|x1): the posterior probability of type (c, target) provided predictor (x, attributes), P1(c1): the
previous probability of a class, P1(x1|c1): the perspective, which is the probability of predictor given
class, P1(1x): the previous probability of predictor. It classifies the test data by computing conditional
probability with feature vectors x1, x2…., xn which belong to particular class Ci. Naive Bayes
algorithms can be applied in recommendation system spam filtering, sentiment analysis [43-48].
3.3. Logistic Regression
LR is a statistical method that can be used for binary classification problems. It assumes that classes
are almost linearly separable. It uses a logistic function also called the sigmoid function which is used
to map predicted values to probabilities. It utilizes a logit function for predicting the probability of
occurrences of a binary event [49-53].
3.4. Linear Regression
It is a supervised learning process. It finds the function which predicts for given X predicts Y where
Y is continuous.
F(X)→ Y
Many types of functions can be used. The simplest type of function is a linear function. X can
comprise a single feature or multiple features. The basic concept of linear regression is to find a line
that best fits data. The best fit line means the total prediction error for all data points is as small as
possible. The error is the distance between the point to the regression line [54-58].
3.5. Support Vector Machine
It is a very popular machine learning technique. It can be used to perform both classification and
regression. The core idea of SVM is that it tries to find out a hyperplane that separates two classes as
widely as possible. In other words, it finds the hyperplane that maximizes the margin. As margin
increases the generalization accuracy increases. The points through which the hyperplane passes are
called support vectors. The variations to SVM are linear SVM, Polynomial kernel SVM, Radial Basis
Function SVM [24][25][38][58][59].
13
3.6. Decision Trees
A decision tree is not a distance-based method. It can be used for both regression and classification
both. Though, it is mostly used for classification. DT naturally extended to do multi-class classification.
The structure of DT is in the form of a tree. Decision nodes and leaf nodes are the two types of nodes
in DT. Starting with the root node, it checks the conditions and accordingly goes to the matching branch
and continues till it reaches the leaf node. The predicted value will be at the leaf node [60-69].
3.7. Random Forest
Random Forest is basically a bagging technique. In this, some of the row samples and feature
samples are taken and given to one of the many base learners. In a random forest base, learners are
decision trees. This step is basically bootstrap. After this aggregation is done by using majority voting
[70-73].
Table 1: Papers on prediction of student’s performance
Paper Objective Predictive Evaluation Data Set Mode
No. Model/Technique used
/Method
[9] Identifies the students who Logistic Deep ANN Open Online
are at-risk of a course Regression classificatio University (VLE)
failure, early prediction of SVM n model Learning
the students who are at-risk Deep ANN achieved Analytics
and withdrawal from the classification 93% (OULA)
course and identifies model accuracy.
patterns of students who
pass with distinction
[11] The objective is to predict LOGIT_Act LOGIT_Act Activity MOODLE
whether a student will drop knowledge Knowledge data from
out of a course discovery system. System Moodle
It uses logistic achieves an DB of
regression accuracy of Madrid
modeling and 97.13% Open
classification. University
[12] Predict dropout by using an FSPred Framework F1 score of XuetangX MOOC
integrated framework with which uses FSPred is for KDD
feature selection, feature FEATURE 84.69 CUP 2015
generation. SELECTION +
logistic regression
model
[13] The objective is to design a SVM, NB, LR, MLP, F1 score of University Tradition
student achievement MLP- Neural MLP Neural al
predicting framework using Network-based Network
A layer-supervised multi- method. based
layer perceptron (MLP) method is
Neural Network-based 81.3%
method.
14
[24] An innovative two-stage Gaussian RBF 95.53% Higher Moodle
approach is proposed and kernel and the accuracy education learning
evaluated the effectiveness polynomial kernel achieved by data set manage
of it by applying the were applied to Deep Neural ment
approach using two the RF, Deep Network system
different but Neural Network,
complementary datasets. SVM.
[25] Simple model Gradual At- Support Vector SVM Universita UOC LMS
risk (GAR) is presented, to (SV), K-Nearest achieved an t Oberta
identify at-risk students. Neighbors (KNN), accuracy of de
Decision Tree 92.41% Catalunya
(DT)-CART, Naïve
Bayes (NB)
[26] Two models have proposed Generalized Linear Gradient Harvard VLE
naming the learning Model (GLM) and Boosting University
achievement model and the Gradient Boosting Machine and
at-risk student model Machine (GBM)AdaB Massachu
(GBM)AdaBosst osst algo setts
algo, Multi-Layer achieved Institute
Perceptron the highest of
(NNET2), accuracy Technolog
Feedforward that is 89.4% y online
Neural Network courses,
with a single Open
hidden layer University
(NNET1), Random online
Forest (RF). courses.
[27] Predict the possibility of logistic regression, Accuracy=7 University Universit
drop out students by a multilayer 7%, in Taiwan y’s
implementing machine and perceptron Institutio
statistical learning method algorithm nal
using deep neural network Research
Database
;
[28] The aim is to discover the Sequential Random Deanship LMS
impact of online activity minimal Forest of E-
data and assessment grades optimization achieved Learning
in the LMS on student’s (SMO), logistic the highest and
performance regression, accuracy i.e Distance
multilayer 99.17% Education
perceptron (MLP), at King
decision tree (J48), Abdulaziz
random forest University
[29] Use of DM techniques to Decision tree, J48 achieve Umm Al- Tradition
predict students’ academic Naive Bayes the highest Qura al
performance and to help to accuracy University
advise students that is in Makkah
84.38%
[30] Developed “University decision tree Accuracy of university Tradition
Students Result Analysis algorithms: J48, J48 is student al
and Prediction System” database,
15
REPTree, and highest i.e from
Hoeffding Tree 85.64% students
through
Google
doc survey
[31] Proposed a Multi-task “Multi-task multi- The University MOOC
learning framework finding layer LSTM with proposed
out the performance of cross-entropy as model
students and “mastery of the loss function”, achieved F1-
knowledge points” in M-S-LSTM, M-F- score=93.59
MOOCs LSTM standard
using online behavior multi-layer
based on assignments. perceptron (MLP),
LSTM, standard
logistic regression
(LR), naïve Bayes
(NB).
[32] Proposed deep LSTM to find deep LSTM model, The OULA VLE
out students at-risk by SVM, Logistic proposed
converting the problem into Regression, ANN model
a sequential weekly format. achieved
90%
accuracy
[33] Aim to analyze various EDM Random Forest Random University Tradition
techniques for improving (RF), k-Nearest forest al
the accuracy of prediction Neighbour (k-NN), achieved
in a university course for Logistic the highest
student academic Regression Naïve accuracy i.e
performance. Bayes. 88%
[34] Applied ML methods to find Decision tree Accuracy is engineeri Tradition
out the final grades of algorithm 96.5% ng degree al
students using their at an
previous grades. Ecuadoria
n
university
[35] Behavioral data analyzed Naïve Bayes (NB), Logistic University Moodle
based on a learning Support Vector Regression of LMS
management system used machine (SVM), achieved Pernambu platform
for distance learning Logistic regression the highest co
courses in a public (LR), CART- accuracy Distance
University. Predictive Decision Tree that is 89.3% Learning
models have been Departme
developed, analyzed, and nt
compared. (NEAD/UP
E)
[36] Predicting student Decision tree (DT), Ensemble The LMS and
academic performance (ANN) artificial method University (SRS)Stud
using “multi-model neural network, the hybrid of the ent
heterogeneous ensemble” and (SVM) Support model West of record
approach Vector Machine, achieved Scotland system
the highest
16
an Ensemble accuracy question
method that is naire
hybrid model 77.69%
[37] Predict the performance of Decision Tree, 1- Naive Bayes Informatio Tradition
students before the Nearest achieved n al
completion of the course. Neighbour, Naive the highest Technolog
Analyzed the progress of Bayes, Neural accuracy y
the students throughout Networks, that is Engineeri
the course and combine Random Forest 83.6%, ng
them with prediction Trees University
results. , Pakistan.
Table 2: Advantages and Disadvantages of various techniques used in predicting student’s
performance
Predicting Techniques Advantages Disadvantages
k-NN [16] [38-42] Simple algorithm and easy to As it stores all training data it
understand, interpret & becomes a computationally
implement. expensive algorithm and
requires high memory storage.
As no assumption of data
therefore helpful for nonlinear When the size of N increases
data. the prediction becomes slow.
A versatile algorithm as it can k-NN fails if data points in the
be used for both regression & dataset are randomly spread.
classification both.
If the data point is far away
from the points in the dataset
then it is not sure for its class
label.
Not good for low latency
systems.
Naïve Bayes [17] Simple to understand and If conditional independence of
implement. features is False then Naïve
Bayes performance degrades.
If conditional independence of
features is true then Naïve Seldom is used for real-valued
Bayes performs very well. features.
Useful algorithm for high Easily overfit (means if data
dimensions for example text slightly changes model changes
classification, email spam. drastically) if you don’t use
Laplace smoothing.
Extensively used when we
have categorical features
Run time complexity, training
time complexity, run time-
space complexity are low.
Interpretability is good.
17
Logistic Regression [18] Perform well if classes are If classes are not almost
almost linearly separable. linearly separable then logistic
regression fails.
Model interpretability is easy
as we can determine feature If dimensionality is large then it
importance. is prone to overfit and has to
apply L1 regularize.
For small dimensionality, it
performs very well, Memory
efficient and it has less impact
on outliers because of a
sigmoid function.
Linear Regression [19] Simple to implement. The high impact of outliers.
Model Interpretability is easy. Multicollinearity must be
removed before applying LR.
Perform very well for a linearly
separable dataset. Prone to underfitting.
The impact of Overfitting can
be reduced by using
regularization.
Support Vector Machine [20] The real strength of SVM is the Not easy to find the right
kernel trick, with the right kernel/ appropriate kernel
kernel/ appropriate kernel function.
function SVM solves complex
Training time complexity is
problems.
high for a large dataset.
Very effective when the
Difficult to interpret and
dimensionality is high.
understand the model as we
Can do linearly inseparable cannot find feature importance
classification with global directly from the kernel.
optimal.
For RBF with small sigma,
outliers have a huge impact on
the model.
Decision Tree [21] High Interpretability In case of imbalanced data, we
have to balance the data and
Need not to perform feature
then apply DT.
standardization or
normalization. For large dimensionality time
complexity to train DT
Feature logical interaction is
increases dramatically.
inbuild in DT.
If a similarity matrix is given,
DT naturally extended to do
then DT does not work as DT
multiclass classification.
needs the features explicitly.
Feature importance is
As depth increases the
straightforward in DT.
possibility of overfitting
Space efficient. increases, interpretability
18
decreases, and the impact of
outliers can be significant.
Random Forest [22] Robust to outliers. Does not handle large
dimensionality very well.
Need not to perform feature
standardization or Does not handle categorical
normalization features with many categories
effectively.
Feature logical interaction is
inbuild in RF. Train time complexity is high.
RF naturally extended to do
multiclass classification.
Feature importance is
straightforward in RF.
Ensembled Methods [84-91] Captures linear and nonlinear Interpretability of the model
relationships in data. reduces due to increased
complexity.
Robust and stable model.
Train time is more.
It minimizes noise, bias, and
variance. Difficult to select a model to
ensemble.
Neural Network [23] [74-83] Non-linear program. The required large information
for training.
Operates with insufficient
data. Do not assist mixed variables.
Capable of updating and Black box nature.
reasoning.
4. Critical Analysis
The Comparative analysis shows that the techniques used to find out the student’s performance
are quite indecisive as different authors present different results.
It is also evident from the comparative analysis of the data that mostly the authors have used
supervised learning techniques whereas a few authors have chosen the unsupervised learning
techniques for predicting the performance of the students. So, there should be more emphasis
on the use of unsupervised learning techniques by the researchers.
It shows that the Decision tree is a mostly used technique by authors followed by neural network
and regression.
It is also evident from the comparative analysis that most authors predicted student’s
performance at the university level.
5. Conclusion
In this paper, we have reviewed EDM/LA techniques and their strengths and weaknesses for
predicting student performance. From the analysis of these papers, we can draw some conclusions.
The comparative analysis indicates ambivalent results on techniques that can best predict student’s
performance. Asif et al., [37] showed that for predicting student’s performance Naïve Bayes achieved
the highest classification accuracy at 83.6%. However, Rodrigues et al., [35] noted that logistic
19
regression outperformed the decision tree (CART), support vector machine, Naïve Bayes with 89.3%
prediction accuracy. Moreover, Adejo et al., [36] indicated that the ensembled hybrid model achieved
the highest prediction accuracy at 77.69% as compared to DT, ANN, SVM. According to Ramaswami
et al., [33] Random Forest outperformed NB, LR, K-NN with 88% prediction accuracy. Baneres et al.,
[25] noted that SVM achieved the highest prediction accuracy with 92.41% as compared to however it
is SV, KNN, CART, NB. Hung et al., [24] noted that deep NN achieved 95.53% prediction accuracy
and outperformed RF, SVM. However, it is indecisive which technique predicts the student’s
performance more accurately as different authors present different results. It is evident from the
reviewed papers that DT (22%) is a mostly used technique by the authors for predicting student’s
performance followed by neural network and regression. In addition to Random Forest, SVM, NB,
Ensemble methods have also been used. Moreover, it is evident from the data collected for this paper
that most authors used supervised learning techniques whereas only a few authors (2%) used
unsupervised learning techniques for the prediction of student’s performance. It is an opportunity for
the researchers to conduct further research in unsupervised learning techniques. Also, 52% of the papers
reviewed have predicted student’s performance at the university level. It would be encouraging for the
researcher to apply the same working line of predictive techniques on Blended, VLE, LMS, MOODLE,
MOOC environments.
6. References
[1] Chae, B. K. (2019). A general framework for studying the evolution of the digital innovation
ecosystem: The case of big data. International Journal of Information Management, 45, 83–94.
[2] Dhankhar A., Solanki K. (2019). A Comprehensive Review of Tools & Techniques for Big Data
Analytics. International Journal of Emerging Trends in Engineering Research, vol 7, No.11, pp:
556-562.
[3] Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., & Uthurusamy, R. (Eds.). (1996, February).
Advances in knowledge discovery and data mining. American Association for Artificial
Intelligence.
[4] Romero, C., & Ventura, S. (2010). Educational data mining: a review of the state of the art. IEEE
Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6), 601-
618.
[5] Dhankhar A., Solanki K., Dalal S., Omdev (2021) Predicting Students Performance Using
Educational Data Mining and Learning Analytics: A Systematic Literature Review. In: Raj J.S.,
Iliyasu A.M., Bestak R., Baig Z.A. (eds) Innovative Data Communication Technologies and
Application. Lecture Notes on Data Engineering and Communications Technologies, vol 59.
Springer, Singapore. https://doi.org/10.1007/978-981-15-9651-3_11
[6] Petersen, K.; Vakkalanka, S.; Kuzniarz, L. Guidelines for conducting systematic mapping studies
in software engineering: An update. Inf. Softw. Technol. 2015, 64, 1–18.
[7] Moher, D.; Liberati, A.; Tetzlaff, J.; Altman, D.G.; Prisma Group. Preferred reporting items for
systematic reviews and metaanalyses: The PRISMA statement. BMJ 2009, 6, 1–8.
[8] Chui, K. T., Fung, D. C. L., Lytras, M. D., & Lam, T. M.: Predicting at-risk university students in
a virtual learning environment via a machine learning algorithm. Computers in Human
Behavior, 107, 105584 (2020).
[9] Waheed, H., Hassan, S. U., Aljohani, N. R., Hardman, J., Alelyani, S., & Nawaz, R.: Predicting
academic performance of students from VLE big data using deep learning models. Computers in
Human Behavior, 104, 106189 (2020).
[10] Xing, W., Chen, X., Stein, J., & Marcinkowski, M.: Temporal predication of dropouts in MOOCs:
Reaching the low hanging fruit through stacking generalization. Computers in human
behavior, 58, 119-129 (2016).
[11] Burgos, C., Campanario, M. L., de la Peña, D., Lara, J. A., Lizcano, D., & Martínez, M. A.: Data
mining for modeling students’ performance: A tutoring action plan to prevent academic
dropout. Computers & Electrical Engineering, 66, 541-556 (2018).
[12] Qiu, L., Liu, Y., & Liu, Y.: An integrated framework with feature selection for dropout prediction
in massive open online courses. IEEE Access, 6, 71474-71484 (2018).
20
[13] Qu, S., Li, K., Zhang, S., & Wang, Y.: Predicting achievement of students in smart campus. IEEE
Access, 6, 60264-60273 (2018).
[14] Olive, D. M., Huynh, D. Q., Reynolds, M., Dougiamas, M., & Wiese, D.: A quest for a one-size-
fits-all neural network: Early prediction of students at risk in online courses. IEEE Transactions
on Learning Technologies, 12(2), 171-183 (2019).
[15] Ramesh, A., Goldwasser, D., Huang, B., Daume, H., & Getoor, L.: Interpretable Engagement
Models for MOOCs using Hinge-loss Markov Random Fields. IEEE Transactions on Learning
Technologies (2018).
[16] https://towardsdatascience.com/machine-learning-basics-with-the-k-nearest-neighbors-
algorithm-6a6e71d01761.
[17] https://towardsdatascience.com/naive-bayes-classifier-81d512f50a7c
[18] https://machinelearningmastery.com/logistic-regression-for-machine-learning/
[19] https://towardsdatascience.com/linear-regression-detailed-view-ea73175f6e86
[20] https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-
algorithms-934a444fca47
[21] https://towardsdatascience.com/decision-tree-in-machine-learning-e380942a4c96
[22] https://towardsdatascience.com/understanding-random-forest-58381e0602d2
[23] https://towardsdatascience.com/understanding-neural-networks-19020b758230
[24] Hung, J. L., Shelton, B. E., Yang, J., & Du, X.: Improving predictive modeling for at-risk student
identification: A multistage approach. IEEE Transactions on Learning Technologies, 12(2), 148-
157 (2019).
[25] Baneres, D., Rodríguez-Gonzalez, M. E., & Serra, M.: An early feedback prediction system for
learners at-risk within a first-year higher education course. IEEE Transactions on Learning
Technologies, 12(2), 249-263 (2019).
[26] Al-Shabandar, R., Hussain, A. J., Liatsis, P., & Keight, R.: Detecting At-Risk Students With Early
Interventions Using Machine Learning Techniques. IEEE Access, 7, 149464-149478 (2019).
[27] Tsai, S. C., Chen, C. H., Shiao, Y. T., Ciou, J. S., & Wu, T. N.: Precision education with statistical
learning and deep learning: a case study in Taiwan. International Journal of Educational
Technology in Higher Education, 17, 1-13 (2020).
[28] Alhassan, A., Zafar, B., & Mueen, A.: Predict Students Academic Performance based on their
Assessment Grades and Online Activity Data. International Journal of Advances Computer
Science and Applications, 11(4) (2020).
[29] Alhakami, H., Alsubait, T., & Aliarallah, A.: Data Mining for Student Advising. International
Journal of Advanced Computer Science and Applications, 11(3) (2020).
[30] Hoque, M. I., kalam Azad, A., Tuhin, M. A. H., & Salehin, Z. U.: University Students Result
Analysis and Prediction System by Decision Tree Algorithm. Advances in Science, Technology
and Engineering Systems Journal Vol. 5, No. 3, 115-122 (2020).
[31] Qu, S., Li, K., Wu, B., Zhang, X., & Zhu, K.: Predicting Student Performance and Deficiency in
Mastering Knowledge Points in MOOCs Using Multi-Task Learning. Entropy, 21(12), 1216
(2019).
[32] Aljohani, N. R., Fayoumi, A., & Hassan, S. U. (2019). Predicting at-risk students using clickstream
data in the virtual learning environment. Sustainability, 11(24), 7238, (2019).
[33] Ramaswami, G., Susnjak, T., Mathrani, A., Lim, J., & Garcia, P. (2019). Using educational data
mining techniques to increase the prediction accuracy of student academic
performance. Information and Learning Sciences.
[34] Buenaño-Fernández, D., Gil, D., & Luján-Mora, S.: Application of machine learning in predicting
performance for computer engineering students: A case study. Sustainability, 11(10), 2833 (2019).
[35] Rodrigues, R. L., Ramos, J. L. C., Silva, J. C. S., Dourado, R. A., & Gomes, A. S.: Forecasting
Students' Performance Through Self-Regulated Learning Behavioral Analysis. International
Journal of Distance Education Technologies (IJDET), 17(3), 52-74 (2019).
[36] Adejo, O. W., & Connolly, T.: Predicting student academic performance using multi-model
heterogeneous ensemble approach. Journal of Applied Research in Higher Education (2018.)
[37] Asif, R., Merceron, A., Ali, S. A., & Haider, N. G.: Analyzing undergraduate students' performance
using educational data mining. Computers & Education, 113, 177-194 (2017).
21
[38] Rubiano, S. M. M., & Garcia, J. A. D.: Analysis of data mining techniques for constructing a
predictive model for academic performance. IEEE Latin America Transactions, 14(6), 2783-2788
(2016).
[39] Wakelam, E., Jefferies, A., Davey, N., & Sun, Y.: The potential for student performance prediction
in small cohorts with minimal available attributes. British Journal of Educational
Technology, 51(2), 347-370 (2020).
[40] Guerrero-Higueras, Á. M., Fernández Llamas, C., Sánchez González, L., Gutierrez Fernández, A.,
Esteban Costales, G., & González, M. Á. C.: Academic Success Assessment through Version
Control Systems. Applied Sciences, 10(4), 1492 (2020).
[41] Al-Sudani, S., & Palaniappan, R.: Predicting students’ final degree classification using an extended
profile. Education and Information Technologies, 24(4), 2357-2369, 2019.
[42] Zhou, Q., Quan, W., Zhong, Y., Xiao, W., Mou, C., & Wang, Y.: Predicting high-risk students
using Internet access logs. Knowledge and Information Systems, 55(2), 393-413.
[43] Injadat, M., Moubayed, A., Nassif, A. B., & Shami, A.: Systematic ensemble model selection
approach for educational data mining. Knowledge-Based Systems, 105992 (2020).
[44] Ashraf, M., Zaman, M., & Ahmed, M.: An Intelligent Prediction System for Educational Data
Mining Based on Ensemble and Filtering approaches. Procedia Computer Science, 167, 1471-
1483 (2020).
[45] Huang, A. Y., Lu, O. H., Huang, J. C., Yin, C. J., & Yang, S. J.: Predicting students’ academic
performance by using educational big data and learning analytics: evaluation of classification
methods and learning logs. Interactive Learning Environments, 28(2), 206-230 (2020).
[46] Francis, B. K., & Babu, S. S.: Predicting academic performance of students using a hybrid data
mining approach. Journal of medical systems, 43(6), 162 (2019).
[47] Adekitan, A. I., & Noma-Osaghae, E.: Data mining approach to predicting the performance of first
year student in a university using the admission requirements. Education and Information
Technologies, 24(2), 1527-1543, 2019.
[48] Livieris, I. E., Tampakas, V., Karacapilidis, N., & Pintelas, P.: A semi-supervised self-trained two-
level algorithm for forecasting students’ graduation time. Intelligent Decision Technologies, 13(3),
367-378 (2019).
[49] Gershenfeld, S., Ward Hood, D., & Zhan, M.: The role of first-semester GPA in predicting
graduation rates of underrepresented students. Journal of College Student Retention: Research,
Theory & Practice, 17(4), 469-488, 2016.
[50] Strang, K. D.: Predicting student satisfaction and outcomes in online courses using learning
activity indicators. International Journal of Web-Based Learning and Teaching Technologies
(IJWLTT), 12(1), 32-50 (2017).
[51] Ellis, R. A., Han, F., & Pardo, A.: Improving learning analytics–combining observational and self-
report data on student learning. Journal of Educational Technology & Society, 20(3), 158-169
(2017).
[52] Christensen, B. C., Bemman, B., Knoche, H., & Gade, R.: Pass or Fail? Prediction of Students?
Exam Outcomes from Self-reported Measures and Study Activities. IxD&A, 39, 44-60 (2018).
[53] Yang, S. J., Lu, O. H., Huang, A. Y., Huang, J. C., Ogata, H., & Lin, A. J.: Predicting students'
academic performance using multiple linear regression and principal component analysis. Journal
of Information Processing, 26, 170-176 (2018).
[54] B. Raveendran Pillai, Gautham. J.: Deep regressor: Cross
subject academic performance prediction system for university level students “International
Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075,
Volume-8, Issue-11S (2019).
[55] Sothan, S. (2019). The determinants of academic performance: evidence from a Cambodian
University. Studies in Higher Education, 44(11), 2096-2111.
[56] Moreno-Marcos, P. M., Pong, T. C., Muñoz-Merino, P. J., & Kloos, C. D.: Analysis of the factors
influencing learners’ performance prediction with learning analytics. IEEE Access, 8, 5264-5282
(2020).
[57] Zhang, X., Sun, G., Pan, Y., Sun, H., He, Y., & Tan, J.: Students performance modeling based on
behavior pattern. Journal of Ambient Intelligence and Humanized Computing, 9(5), 1659-1670
(2018).
22
[58] Gitinabard, N., Xu, Y., Heckman, S., Barnes, T., & Lynch, C. F.: How widely can prediction
models be generalized? performance prediction in blended courses. IEEE Transactions on
Learning Technologies, 12(2), 184-197 (2019).
[59] Moreno-Marcos, P. M., Pong, T. C., Muñoz-Merino, P. J., & Kloos, C. D.: Analysis of the factors
influencing learners’ performance prediction with learning analytics. IEEE Access, 8, 5264-5282
(2020).
[60] Dhankhar, A., Solanki, K., Rathee, A., & Ashish.: Predicting Student’s Performance by using
Classification Methods. International Journal of Advanced Trends in Computer Science and
engineering. 8(4), 1532-1536, 2019.
[61] Evale, D.: Learning management system with prediction model and course-content
recommendation module. Journal of Information Technology Education: Research, 16(1), 437-
457 (2016).
[62] Tran, T. O., Dang, H. T., Dinh, V. T., & Phan, X. H.: Performance prediction for students: a multi-
strategy approach. Cybernetics and Information Technologies, 17(2), 164-182 (2017).
[63] Seidel, E., & Kutieleh, S.: Using predictive analytics to target and improve first year student
attrition. Australian Journal of Education, 61(2), 200-218 (2017).
[64] Kostopoulos, G., Kotsiantis, S., Pierrakeas, C., Koutsonikos, G., & Gravvanis, G. A.: Forecasting
students' success in an open university. International Journal of Learning Technology, 13(1), 26-
43, (2018).
[65] Jastini Mohd. Jamil, Nurul Farahin Mohd Pauzi, Izwan Nizal Mohd. Shahara Nee.: An Analysis
on Student Academic Performance by Using Decision Tree Models, The Journal of Social Sciences
Research ISSN(e): 2411-9458, ISSN(p): 2413-6670 Special Issue. 6, pp: 615-620 (2018)
[66] Bucos, M., & Drăgulescu, B.: Predicting student success using data generated in traditional
educational environments. TEM Journal, 7(3), 617 (2018).
[67] Helal, S., Li, J., Liu, L., Ebrahimie, E., Dawson, S., Murray, D. J., & Long, Q.: Predicting academic
performance by considering student heterogeneity. Knowledge-Based Systems, 161, 134-146
(2018).
[68] Yaacob, W. F. W., Nasir, S. A. M., Yaacob, W. F. W., & Sobri, N. M.: Supervised data mining
approach for predicting student performance. Indones. J. Electr. Eng. Comput. Sci, 16, 1584-1592,
(2019).
[69] Mimis, M., El Hajji, M., Es-Saady, Y., Guejdi, A. O., Douzi, H., & Mammass, D.: A framework
for smart academic guidance using educational data mining. Education and Information
Technologies, 24(2), 1379-1393, 2019.
[70] Huang, A. Y., Lu, O. H., Huang, J. C., Yin, C. J., & Yang, S. J.: Predicting students’ academic
performance by using educational big data and learning analytics: evaluation of classification
methods and learning logs. Interactive Learning Environments, 28(2), 206-230 (2020).
[71] Gutiérrez, L., Flores, V., Keith, B., & Quelopana, A.: Using the Belbin method and models for
predicting the academic performance of engineering students. Computer Applications in
Engineering Education, 27(2), 500-509 (2019).
[72] Crivei, L. M., Ionescu, V. S., & Czibula, G.: An analysis of supervised learning methods for
predicting students’ performance in academic environments. ICIC Express Lett, 13, 181-190
(2019).
[73] Sadiq, H.M., & Ahmed, S.N.: Classifying and Predicting Students’ Performance using Improved
Decision Tree C4.5 in Higher Education Institutes, Journal of Computer Science, 15(9), 1291-
1306.
[74] Jorda, E. R., & Raqueno, A. R.: Predictive Model for the Academic Performance of the
Engineering Students Using CHAID and C 5.0 Algorithm. International Journal of Engineering
Research and Technology. ISSN 0974-3154, Volume 12, Number 6, pp. 917-928 (2019).
[75] Vora, D. R., & Rajamani, K.: A hybrid classification model for prediction of academic
performance of students: a big data application. Evolutionary Intelligence, 1-14, (2019).
[76] Kokoç, M., & Altun, A.: Effects of learner interaction with learning dashboards on academic
performance in an e-learning environment. Behaviour & Information Technology, 1-15 (2019).
[77] Ramanathan, L., Parthasarathy, G., Vijayakumar, K., Lakshmanan, L., & Ramani, S.: Cluster-
based distributed architecture for prediction of student’s performance in higher education. Cluster
Computing, 22(1), 1329-1344 (2019).
23
[78] Adekitan, A. I., & Noma-Osaghae, E.: Data mining approach to predicting the performance of first
year student in a university using the admission requirements. Education and Information
Technologies, 24(2), 1527-1543, 2019.
[79] Pal, V. K., & Bhatt, V. K. K.: Performance Prediction for Post Graduate Students using Artificial
Neural Network. International Journal of Innovative Technology and Exploring Engineering
(IJITEE) ISSN, 2278-3075 (2019).
[80] Guerrero-Higueras, Á. M., Fernández Llamas, C., Sánchez González, L., Gutierrez Fernández, A.,
Esteban Costales, G., & González, M. Á. C.: Academic Success Assessment through Version
Control Systems. Applied Sciences, 10(4), 1492 (2020).
[81] Yang, T. Y., Brinton, C. G., Joe-Wong, C., & Chiang, M.: Behavior-based grade prediction for
MOOCs via time series neural networks. IEEE Journal of Selected Topics in Signal
Processing, 11(5), 716-728 (2017).
[82] Waheed, H., Hassan, S. U., Aljohani, N. R., Hardman, J., Alelyani, S., & Nawaz, R.: Predicting
academic performance of students from VLE big data using deep learning models. Computers in
Human Behavior, 104, 106189 (2020).
[83] Coussement, K., Phan, M., De Caigny, A., Benoit, D. F., & Raes, A.: Predicting student dropout
in subscription-based online learning environments: The beneficial impact of the logit leaf
model. Decision Support Systems, 113325 (2020).
[84] Wan, H., Liu, K., Yu, Q., & Gao, X.: Pedagogical Intervention Practices: Improving Learning
Engagement Based on Early Prediction. IEEE Transactions on Learning Technologies, 12(2), 278-
289 (2019).
[85] Xu, J., Moon, K. H., & Van Der Schaar, M.: A machine learning approach for tracking and
predicting student performance in degree programs. IEEE Journal of Selected Topics in Signal
Processing, 11(5), 742-753 (2017).
[86] Bhagavan, K. S., Thangakumar, J., & Subramanian, D. V.: Predictive analysis of student academic
performance and employability chances using HLVQ algorithm. Journal of Ambient Intelligence
and Humanized Computing, 1-9 (2020).
[87] Kamal, P., & Ahuja, S.: An ensemble-based model for prediction of academic performance of
students in undergrad professional course. Journal of Engineering, Design and Technology (2019).
[88] Adekitan, A. I., & Noma-Osaghae, E.: Data mining approach to predicting the performance of first
year student in a university using the admission requirements. Education and Information
Technologies, 24(2), 1527-1543, 2019.
[89] Shanthini, A., Vinodhini, G., & Chandrasekaran, R. M.: Predicting Students' Academic
Performance in the University Using Meta Decision Tree Classifiers. J. Comput. Sci., 14(5), 654-
662 (2018).
[90] https://towardsdatascience.com/ensemble-methods-in-machine-learning-what-are-they-and-why-
use-them-68ec3f9fef5f
[91] Dhankhar, A., & Solanki, K.: State of the art of learning analytics in higher education.
International journal of emerging trends in engineering research, 8(3), 868-877 (2020).
24