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ORIGINAL RESEARCH
published: 07 December 2021
doi: 10.3389/fpsyg.2021.698490
Frontiers in Psychology | www.frontiersin.org 1December 2021 | Volume 12 | Article 698490
Edited by:
Jochen Kuhn,
University of Kaiserslautern, Germany
Reviewed by:
Jónathan Heras,
University of La Rioja, Spain
Sotiris Kotsiantis,
University of Patras, Greece
*Correspondence:
Xunqun Shang
shang@nwpu.edu.cn
Specialty section:
This article was submitted to
Educational Psychology,
a section of the journal
Frontiers in Psychology
Received: 21 April 2021
Accepted: 02 November 2021
Published: 07 December 2021
Citation:
Zhang Y, Yun Y, An R, Cui J, Dai H and
Shang X (2021) Educational Data
Mining Techniques for Student
Performance Prediction: Method
Review and Comparison Analysis.
Front. Psychol. 12:698490.
doi: 10.3389/fpsyg.2021.698490
Educational Data Mining Techniques
for Student Performance Prediction:
Method Review and Comparison
Analysis
Yupei Zhang 1,2 , Yue Yun 1,2 , Rui An 1,2 , Jiaqi Cui 1,2 , Huan Dai 1,2 and Xunqun Shang 1,2
*
1School of Computer Science, Northwestern Polytechnical University, Xi’an, China, 2Key Laboratory of Big Data Storage and
Management, Ministry of Industry and Information Technology, Xi’an, China
Student performance prediction (SPP) aims to evaluate the grade that a student will
reach before enrolling in a course or taking an exam. This prediction problem is a kernel
task toward personalized education and has attracted increasing attention in the field of
artificial intelligence and educational data mining (EDM). This paper provides a systematic
review of the SPP study from the perspective of machine learning and data mining.
This review partitions SPP into five stages, i.e., data collection, problem formalization,
model, prediction, and application. To have an intuition on these involved methods,
we conducted experiments on a data set from our institute and a public data set.
Our educational dataset composed of 1,325 students, and 832 courses was collected
from the information system, which represents a typical higher education in China. With
the experimental results, discussions on current shortcomings and interesting future
works are finally summarized from data collections to practices. This work provides
developments and challenges in the study task of SPP and facilitates the progress of
personalized education.
Keywords: personalized education, review and discussion, educational data mining (EDM), student performance
prediction, pattern recognition
1. INTRODUCTION
Educational data mining (EDM), a very young research field, focuses on learning latent patterns in
various educational situations, including student’s knowledge analysis (Yeung and Yeung, 2018),
student’s learning behavior analysis (Juhaˇ
nák et al., 2019), teacher’s curriculum planning (Reeves,
2018), course time arrangement (Zhang et al., 2018a). All involved studies have the final goal that is
to improve the student learning performance (Liu et al., 2018, 2019; Anand, 2019; Wang et al.,
2020), as well as other additional goals like reducing educational costs (Gronberg et al., 2004).
As a result, in the past decades, various researches were concentrated on student performance
prediction, referred to as SPP in this paper, (Sweeney et al., 2015; Polyzou and Karypis, 2016;
Thanh-Nhan et al., 2016; Cakmak, 2017; Hu et al., 2017; Morsy and Karypis, 2017) or were
evaluated by the student’s final grades (Al-Radaideh et al., 2006; Shovon et al., 2012; Ahmed and
Elaraby, 2014; Meier et al., 2015; Al-Barrak and Al-Razgan, 2016). While several review papers
have summarized previous EDM research studies (Shahiri and Husain, 2015; Saa, 2016), this paper
provides a more completed survey on the problem of SPP from the perspective of machine learning
and data mining.
Zhang et al. Student Performance Prediction Survey
Student academic performance has various definitions varying
from difficult points of view, but the quantified evaluation plays
an important role in current educational institutions. SPP makes
great sense to aid all stakeholders in the educational process.
For students, SPP could help them choose suitable courses or
exercises and make their plans for academic periods (Ibrahim
and Rusli, 2007). For instructors, SPP can help adjust learning
materials and teaching programs based on the student’s ability
and find the at-risk students (Bayer et al., 2012; Kloft et al.,
2014). For educational managers, SPP could help to check the
curriculum program and to optimize the course system (Reeves,
2018). Overall, stakeholders in the educational progress could
have better plans to improve the education performance. Besides,
the data-driven SPP study provides an objective reference for the
education system.
Student performance prediction can be formulated into
different problems in various situations. In this paper, we define
the SPP problem in the general machine learning formulation,
shown as follows:
Problem 1 (SPP). Denote by D= {(s1,c1,y1,1), ..., (sn,cm,
yn,m)}the educational data, where sipresents the student-wise
features, cjpresents the course-wise features, and yi,jis the i-th
student’s grade on the j-th course. The goal of SPP is to seek a
mapping Msuch that M(si,cj)=yi,j.
In Problem 1, student-wise features include student
demographics that affect the course grade, while course-
wise features include course descriptions that affect the course
grade. In general, the grade is produced from the event that a
student enrolls in a course, where all educational information is
usually divided into student type and course type.
Based on the above problem definition, there are five general
steps to solve Problem 1, i.e., data pre-processing and feature
selection, problem reformulation, model learning, performance
prediction, and result analysis. More specifically, the five steps
could be shown as follows:
(1) The first step is to collect data from the special SPP situation.
As is shown in Problem 1, the data could consist of the
triple {student, course, grade} to describe the scoring event.
Student-wise features include age, sexual, healthy, economy,
education level, etc. In contrast, course-wise features include
frequent, duration, scale, open season, etc. (Elbadrawy et al.,
2014; Kennedy et al., 2015; De Barba et al., 2016). The
extended features for students could be parent’s features,
classmate-group’s features, learning-records’ features, etc and
for courses could be instructor’s features, prerequisite courses’
features, assistants’ features, etc. For the grade, there are three
broadly used models: passed-failed model, grade model, and
score model. Note that it is called grade for clarity in this
paper. In addition, the learning situation could be divided
into offline classrooms, online classrooms, and blending
classrooms (Rovai and Jordan, 2004).
(2) After the data is prepared, the second step aims to
reformulate Problem 1. In general, Problem 1 is reformulated
into clustering, classification, and regression. The clustering
formulation is to group the X= {si,cj}n,m
i=1,j=1into multi-
clusters, where each cluster contains the instances with high
similarities. Many studies partition Xinto different clusters
based on students and/or courses in SPP (Cakmak, 2017). The
classification formulation aims to predict the discrete grade
using a machine-learning classifier, such as logic regression
(Elbadrawy et al., 2014) and support vector machine (SVM)
(Xu and Yang, 2016). The regression formulation is to predict
the continued grade by using a regression model, such as
linear regression (LR) (Alario-Hoyos et al., 2016) and neural
networks (Oladokun et al., 2008). Besides, many studies
transfer continuous scores into discrete grades (Shahiri and
Husain, 2015).
(3) In the third step, the chosen machine-learning model is
developed to build the mappings Mfor the reformulated
problem. Many studies employed the traditional machine-
learning methods, such as decision trees (DTs) (Al-Radaideh
et al., 2006; Koprinska et al., 2015), neighborhood method
(Meier et al., 2015), LR (Anozie and Junker, 2006), neural
networks (Andrews et al., 1995; Sorour et al., 2014), and
kernel-based method (Boser et al., 1992). The new feature-
learning techniques have been investigated in SPP, such as
Lasso regression (Sorour et al., 2014; Zhang et al., 2018b;
Zhang and Liu, 2020), matrix factorization (MF) (Slim et al.,
2014), tensor factorization (TF) (Thai-Nghe et al., 2011a),
and deep neural networks (Kim et al., 2018). In these
methods, MF and deep learning have attracted increasing
attention for SPP. However, simple methods can show more
meaningful interpretations than a complex learning model
(Van Merrienboer and Sweller, 2005).
(4) With the learned model, the fourth step could predict the
grade for a new student on a new course. That is, the new
instance {sp,cq}is fed into Mto achieve the yp,q. In this step,
the current studies often have different strategies. The works
(Al-Radaideh et al., 2006; Shovon et al., 2012; Ahmed and
Elaraby, 2014; Meier et al., 2015; Al-Barrak and Al-Razgan,
2016) predicted a course grade of the student involved in
training data, while the work (Ren et al., 2018) predicted the
next-term grade based on the grade records. Besides, several
studies predicted the course grade following the progress of a
whole education period (Xu et al., 2017). However, few studies
are focused on the pattern learning from Problem 1, ignoring
the specific student or course.
(5) When the model delivers results, it is hoped that the result
could show some explainable patterns to help the stakeholders
improve their respective tasks in education. In general, SPP
provides explanations to the issues of students in route
teaching and learning, e.g., the students at risk of dropout
(Quadri and Kalyankar, 2010), the students of different
knowledge statuses (Meier et al., 2015), the key factors to
learning (Mayilvaganan and Kalpanadevi, 2014), and the
course associations (Zhang et al., 2021a). A grading system
that could predict the student grade in education progress
might be a good tool to improve outcomes.
The remainder of this paper is organized as follows. Section
2 reviews the studies, including data collection, problem
formulation, the used method, performance prediction, and
practical application. Section 3 shows two evaluations using
Frontiers in Psychology | www.frontiersin.org 2December 2021 | Volume 12 | Article 698490
Zhang et al. Student Performance Prediction Survey
traditional machine learning methods on two data sets. Section
4 discusses current works and future problems, and Section 5
concludes this paper.
2. A REVIEW ON SPP PROCESS
In this section, we summarizes the existing literature by a
systematic review of SPP. As mentioned above, the existing
studies in the five stages include (1) Data collection, literature
pays attention to the tasks that are mainly dependent on
the data information in hand. (2) Problem formulation, as
mentioned in the literature, the research mainly consists of three
formulations from their faced problems. (3) The used methods,
various machine learning methods 2.2 are employed toward
solving individual situations. (4) Performance prediction, the
different evaluations are resulted from the used situations, e.g.,
the next-term prediction and the GPA prediction. (5) Practical
application, the SPP models could be used under the complex
real-world situations to aid students and teachers.
2.1. Data Collection
In the past decades, the study on SPP was mainly focused on
the traditional classroom, where small datasets were collected
in offline education. Now, online courses are being accepted by
students and educational institutions, e.g., Coursera and edX,
thus causing many kinds of research on the massive educational
data from online education. Besides, the blending classroom that
integrates both offline and online strategies provides a new path
toward personalized education. Related literature is shown in
Table 1, where many research focused on the online classroom
due to MOOCs and data enrichment.
2.1.1. Offline Classroom
In traditional education, students usually finish academic courses
in offline classrooms, where the research data could be obtained.
The data from the offline classroom is usually composed of
student learning records, courses, and teachers. On the obtained
data, various grade prediction methods are employed to conduct
data analysis, which can be grouped into statistical methods and
pattern recognition methods, such as (Al-Radaideh et al., 2006;
Dekker et al., 2009; Shovon et al., 2012; Ahmed and Elaraby,
2014; Meier et al., 2015; Sweeney et al., 2015; Al-Barrak and Al-
Razgan, 2016; Polyzou and Karypis, 2016; Thanh-Nhan et al.,
2016; Morsy and Karypis, 2017). To pursuit a better performance
on SPP, the societal background information is considered in
terms of various metrics (Nghe et al., 2007; Elbadrawy et al., 2014;
Mayilvaganan and Kalpanadevi, 2014; Koprinska et al., 2015;
Sweeney et al., 2016; Thanh-Nhan et al., 2016; Hu et al., 2017;
Ren et al., 2018). The background data usually contains student
demographics (parents’ education, family income, household
registration), curriculum plans, teachers’ quality and style, and
student performance evaluation. Specially, the work of Hu et al.
showed that background features significantly improved the
prediction model performance (Hu et al., 2017).
In addition to these above attributions, behavior features
were also considered, e.g., the social dependence relationship
obtained from emails and social networks (Bayer et al., 2012).
This study combined social behavior features with background
attributions to train the prediction model and finally achieved
an improvement the prediction accuracy by 10%. The comments
from students after each lesson were considered by Sorour et al.
(2014) and Luo et al. (2015). These comments show student
learning attitude, subject understanding, course difficulty, and
activity in a classroom. Especially, Koprinska et al. (2015)
explored multiple data sources, including demographics, social
behaviors, and academic data. Their experiments analyzed the
most important features and then discussed how to use them to
improve teaching and learning.
2.1.2. Online Classroom
Recently, with the development of online learning platforms,
e.g., MOOCs (Massive Open Online Courses), students choose
to learn the online courses or as supplements to a traditional
classroom. More researchers paid an amount of attention to
the online classrooms for their widely used range and massive
educational records. The data could be easily obtained in an
online classroom, as each educational activity is recorded with
log files, e.g., click stream of the mouse, texts from discussions,
learning-time length, etc.
Historical performance data and background information.
Many studies on SPP in online classroom used historical
performance data (Meier et al., 2015; Lorenzen et al., 2017),
students’ background information, course’s descriptors, and
teachers’ background (Elbadrawy et al., 2014; Kennedy et al.,
2015; De Barba et al., 2016) to train their prediction
models. Kennedy et al. analyzed the grade information, course
background information, and an event log of interactions
from 6,635 learners (Kennedy et al., 2015). Then, Kennedy
found that prior knowledge is the most significant predictor
of MOOC success, followed by students’ ability to revisit their
previous work.
Data from the log file. The log file is an important
characteristic that distinguishes online classrooms from
traditional offline classrooms, where the log file could easily
record the online-learning-process data. Many researchers
explored these processing data to predict student grades, e.g.,
(Thai-Nghe et al., 2010b; Toscher and Jahrer, 2010; Elbadrawy
et al., 2014). For instance, Su et al. used both exercise records
and the question texts to model the student exercising process
(Su et al., 2018); researchers made attempts to understand the
performance of individual students deeply by analyzing the
comments from students. Student’s comments can reflect their
learning attitudes to the lesson, understanding of subjects,
difficulties to learn, and learning activities, which potentially
associate to the grade (Dietz-Uhler and Hurn, 2013; Goda et al.,
2013; Sorour et al., 2014). In these data sets, the educational
data set from the Knowledge Discovery and Data Mining Cup is
widely used for validation, i.e., the process records from learning
the course of "Algebra" and learning the course of "Bridge To
Algebra" (Tabandeh and Sami, 2010; Thai-Nghe et al., 2010a,
2012; Toscher and Jahrer, 2010; Yu et al., 2010; Hwang and Su,
2015; Thai-Nghe and Schmidt-Thieme, 2015). The two data
sets take the form of interaction records between students and
computer-aided-tutoring systems. Students solve problems in
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Zhang et al. Student Performance Prediction Survey
TABLE 1 | The works studied in different situations.
Data source Sub-source Reference Count
Offline classroom Al-Radaideh et al., 2006; Nghe et al., 2007; Dekker et al., 2009; Bayer et al.,
2012; Shovon et al., 2012; Ahmed and Elaraby, 2014; Elbadrawy et al., 2014;
Mayilvaganan and Kalpanadevi, 2014; Sorour et al., 2014; Koprinska et al.,
2015; Luo et al., 2015; Meier et al., 2015; Sweeney et al., 2015, 2016; Al-Barrak
and Al-Razgan, 2016; Polyzou and Karypis, 2016; Thanh-Nhan et al., 2016; Hu
et al., 2017; Morsy and Karypis, 2017; Ren et al., 2018
20
Historical grade data & background information Elbadrawy et al., 2014; Kennedy et al., 2015; Meier et al., 2015; De Barba et al.,
2016; Lorenzen et al., 2017
5
Online classroom Historical grade data & background information Tabandeh and Sami, 2010; Thai-Nghe et al., 2010a,b, 2012; Toscher and
Jahrer, 2010; Yu et al., 2010; Dietz-Uhler and Hurn, 2013; Goda et al., 2013;
Elbadrawy et al., 2014, 2016; Kloft et al., 2014; Sorour et al., 2014; Hwang and
Su, 2015; Thai-Nghe and Schmidt-Thieme, 2015; Xu and Yang, 2016; Adejo
and Connolly, 2017; Yang et al., 2017; Su et al., 2018
18
Historical grade data & background information Kloft et al., 2014; Wen et al., 2014a,b; Arguello and Shaffer, 2015; Koprinska
et al., 2015; Wang et al., 2015; Wong et al., 2015; Lu et al., 2017; Gitinabard
et al., 2018
9
Blending classroom Elbadrawy et al., 2014; Sorour et al., 2014; Koprinska et al., 2015; Meier et al.,
2015; Zacharis, 2016
7
the tutor system, and each interaction between student and
system was logged as a transaction. Four key terms form the
building blocks of our data, i.e., problem, step, knowledge
component, and opportunity and step start time, first transaction
time, correct transaction time, and so on. Especially, before
training the prediction models, (Thai-Nghe et al., 2012) selected
the features that are more related to student performance.
In addition, the click-stream was recorded in the log file. The
click-stream data includes thousands of weblog records which
can be generally classified into two types (Kloft et al., 2014; Xu
and Yang, 2016; Yang et al., 2017): (1) the page view log, including
the number of requests, the number of active days, the number
of page views, the number of homework page views, and so
on. (2) lecture video log, including the number of requests, the
number of video views, the number of start-stop during video
plays, the number of re-listening during video views, and so on.
Many researchers attempted to apply these data of click-stream to
model the state of learning of students and training the prediction
model (Elbadrawy et al., 2014, 2016; Xu and Yang, 2016; Adejo
and Connolly, 2017).
Data from the discussion forum. The discussion forum
is another important characteristic that distinguishes online
classrooms from the traditional offline classroom. All the
students could discuss the course or the problems with each other
(Kloft et al., 2014; Wen et al., 2014a; Arguello and Shaffer, 2015;
Wong et al., 2015). In this way, researchers obtained massive
behavior records to train the prediction models. The forum
data is broadly obtained from the student’s posts. Participants
usually create a thread by making a root post and reply to
existing threads by adding comments at the end. Several papers
predicted student performance using the student’s active forum
data including submissions, numbers of forum posts, length of
the forum thread, and so on (Wen et al., 2014b; Koprinska
et al., 2015; Wang et al., 2015; Lu et al., 2017; Gitinabard
et al., 2018). Furthermore, click stream and forum data are also
integrated to enhance the grade predication (Koprinska et al.,
2015; Lu et al., 2017; Gitinabard et al., 2018). (Gitinabard et al.,
2018) applied a combination of modeling and feature-selection
methods to identify the important features in both dropout and
certification prediction. The author analyzed the discussion texts
to obtain the social relationships of a learner, e.g., two posts that
shared the same root thread. In this research, the author also
considered the forum features, including forum activities and the
submission counts.
2.1.3. Blending Classroom
In the blending context of the offline and online classrooms,
researchers obtain more attributions for student performance
from multiple data sources and obtain better prediction
performance (Rovai and Jordan, 2004; Elbadrawy et al., 2014;
Sorour et al., 2014; Koprinska et al., 2015; Meier et al., 2015;
Zacharis, 2016). Nick et al. used student data stored in MOOCs.
They predicted student success based on four learning activities:
communication via emails, collaborative content creation with
wiki, content interaction measured by files viewed, and self-
evaluation through online quizzes. Next, a model based on the
Multi-Layer Perceptron Neural Network was trained to predict
student performance in a blended learning course environment.
The model predicted the performance of students with a correct
classification rate of 98.3% (Zacharis, 2016).
2.2. Problem Formulation
In EDM methods, predicting student learning performance is
a problem that maps student information to his/her grades.
Usually, this problem could be formalized into machine learning
problems, i.e., clustering, classification, and regression. Here, we
generally give the formulations of prediction models in the study
of SPP. Denote the training dataset by D= {(si,cj,yi,j)|i=
1...n,j=1...m}, where yi,jis the grade of i-th student that
Frontiers in Psychology | www.frontiersin.org 4December 2021 | Volume 12 | Article 698490
Zhang et al. Student Performance Prediction Survey
obtained on j-th course, nand mare the number of students and
courses, respectively.
1. Clustering. The works of Oyelade et al. (2010) and Hwang and
Su (2015) formalized SPP into a cluster problem, where the
students are grouped into multi-clusters G= {g1...gk}, where
kis the number of clusters and then the objective student’s
performance is predicted in the specific cluster. The problem
could be defined as follows:
Problem 2 (SPP-Clustering). The goal of SPP-Clustering
is to seek a mapping of clustering M1such that M1(S,C)=
G. To predict an object student that lies in the cluster gk,
a new mapping M2is built such that M2(si,cj)=yi,jfor
(si,cj)∈gk.
Usually, M2is created by computing the average value of the
performance of all students of gkon course cj(Cakmak, 2017).
2. Classification. When researchers consider the SPP task as a
classification task, the prediction output is the discrete grades
for a student, e.g., GPAs (Veloski et al., 2000), pass/fail (Thai-
Nghe et al., 2009; Bayer et al., 2012; Abu-Oda and El-Halees,
2015), or others (Elbadrawy et al., 2014; Hu et al., 2017; Morsy
and Karypis, 2017). Let Y= {yi,j}be the label set of different
classifications, and the value of yi,jis one element of the label
set, ℓ1,..., ℓk.
Problem 3 (SPP-Classification). The goal of SPP-
Classification is to seek a mapping M3, such that
M3(si,cj,A)=max{p1, ..., pk} = yi,j, where pkis the
possibility of (si,cj) belonging to ℓk.
Here, the student’s grades are generally divided into several
categories, like A, B, C, and D (Xu et al., 2017), according to
their scores.
3. Regression. The regression model is a function that represents
the mapping between input variables and output variables.
The regression problem is equivalent to function fitting:
selecting a function curve to fit the known data well and
predict the unknown data well. In SPP, regression techniques
are often used to predict the continuous scores of students in
specific courses (Polyzou and Karypis, 2016; Hu et al., 2017;
Morsy and Karypis, 2017).
Problem 4 (SPP-Regression). The goal of SPP-Regression
is to seek a mapping M4, such that M4(si,cj,A)=yi,j, where
yi,jis usually continuous scores.
2.3. Current Methods
As mentioned above, there are mainly three problem
formulations in SPP. Here, we make a systematic review of
the methods used in SPP, as follows. Table 2 shows the statistic
of those related researches.
2.3.1. Decision Trees
Decision trees (DTs) are a non-parametric supervised learning
method used for classification and regression. It learns the
splitting rule to divide the data according to their features
and obtains the labels by voting at leaf nodes (Safavian and
Landgrebe, 1991).
Decision trees could deliver interpretable results and thus
obtain much attention for SPP (Al-Radaideh et al., 2006; Nghe
et al., 2007; Dekker et al., 2009; Thai-Nghe et al., 2009; Bunkar
et al., 2012; Shovon et al., 2012; Koprinska et al., 2015; Al-
Barrak and Al-Razgan, 2016; Saa, 2016). The tree model can be
transformed into a set of "if-then" rules that are intuitive and
easy to understand by human beings. Al-Barrak et al. studied and
evaluated the "if-then" rules to improve prediction accuracy in
the higher education system (Al-Barrak and Al-Razgan, 2016).
Based on different feature selection methods and pruning rules,
the DT model has three main algorithms, i.e., ID3, CART, and
C4.5. Bunkar et al. compared the three DT algorithms. They
carried out experiments to seek the best one (Bunkar et al.,
2012). Among these DT algorithms and other machine learning
algorithms, the DT showed a higher precision on their used data
set. Nghe et al. investigated the decision tree and the Bayesian
Network to predict the academic performance of undergraduates
and postgraduates from two academic institutions. In their
experiment, the accuracy of the DT is always 3–12% higher than
the Bayesian Network (Nghe et al., 2007).
Many studies used the ensemble algorithm to combine the DT
with other models (Dekker et al., 2009; Thai-Nghe et al., 2009;
Bunkar et al., 2012; Shovon et al., 2012). For example, when
predicting pass or fail in an exam, the main problem is that the
number of "passed" students is much higher than the number of
"failed" students. The prediction results are dropped down due
to this imbalance issue. Thai-Nghe et al. proposed to address the
problem of class imbalance through over-sampling techniques
and used the cost-sensitive learning (CSL) method to improve the
prediction (Thai-Nghe et al., 2009). The authors first re-balanced
data sets then used the DT on the balanced data. Compared
with the original data set, the results were significantly improved.
There is also an imbalance problem in predicting student dropout
(Dekker et al., 2009). The DT and CSL are combined in the
experiments to predict student dropout, where the decision tree
gives an accepted accuracy of about 80%.
In online classrooms, researchers also integrated multiple
data sources to improve the performance of the DT. Koprinska
et al. considered multiple data sources, including click-streams,
submission steps of an academic task and outcomes in an
automatic marking system, assessment marks in a semester,
and student engagement with discussion forums, to build an
improved DT classifier (Koprinska et al., 2015). The results
showed that multiple data sources could improve the prediction
accuracy compared to the single data source.
2.3.2. Linear Regression
In statistics, LR is a linear approach to modeling the relationship
between a scale response and one or more explanatory variables.
The case of one explanatory variable is referred to as one variable
LR, while for more than one explanatory variable, it is referred to
as multi-variable LR.
A way to model the problem of grade prediction is to take into
account the academic degree program. Degree program always
requires students to take a set of courses in order, due to the
knowledge provided by the previous courses being essential for
subsequent courses (Tabandeh and Sami, 2010; Wang et al., 2015;
Alario-Hoyos et al., 2016; Ren et al., 2016; Morsy and Karypis,
2017). With this idea, Polyzou et al. developed course-specific
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Zhang et al. Student Performance Prediction Survey
TABLE 2 | The related works with different machine learning models.
Proposed methods Problem formulation Reference Count
Decision trees Classification Safavian and Landgrebe, 1991; Al-Radaideh et al., 2006; Nghe et al.,
2007; Dekker et al., 2009; Thai-Nghe et al., 2009; Bunkar et al., 2012;
Shovon et al., 2012; Koprinska et al., 2015; Al-Barrak and Al-Razgan,
2016; Saa, 2016
10
Linear regression Regression Tabandeh and Sami, 2010; Elbadrawy et al., 2014; Kennedy et al.,
2015; Meier et al., 2015; Wang et al., 2015; Alario-Hoyos et al., 2016;
De Barba et al., 2016; Polyzou and Karypis, 2016; Ren et al., 2016; Hu
et al., 2017; Lorenzen et al., 2017; Morsy and Karypis, 2017
13
Support vector machines Classification Kentli and Sahin, 2011; Bydžovská, 2016; Xu and Yang, 2016 4
Matrix factorization Regression / Clustering Lee and Seung, 2001; Thai-Nghe et al., 2010a, 2011a, 2012; Toscher
and Jahrer, 2010; Bokde et al., 2015; Hwang and Su, 2015; Sweeney
et al., 2015; Thai-Nghe and Schmidt-Thieme, 2015; Elbadrawy et al.,
2016; Polyzou and Karypis, 2016; Hu et al., 2017; Lorenzen et al.,
2017; Ren et al., 2018; Zhang et al., 2020c
15
Collaborative filtering Classification / Clustering
Sheena et al., 2000; Schafer et al., 2007; Li and Zaman, 2014;
Bydžovská, 2015; Meier et al., 2015; Cakmak, 2017; Jyoti and Walia,
2017
7
Artificial neural network Classification / Clustering / Regression Andrews et al., 1995; Oladokun et al., 2008; Sorour et al., 2014; Luo
et al., 2015; Shahiri and Husain, 2015; Młynarska et al., 2016;
Zacharis, 2016; Yang et al., 2017; Su et al., 2018
9
Deep learning Classification / Clustering / Regression Guo et al., 2015; Yang et al., 2017; Kim et al., 2018; Hu and Rangwala,
2019a,b
5
Other methods Regression / Clustering Slim et al., 2014; Li et al., 2016 Iqbal et al., 2017 3
regression (CSR) (Polyzou and Karypis, 2016), and predict
student grades in a course using a sparse linear combination
(Zhang and Liu, 2020). Following this way, there are many
improved works (Polyzou and Karypis, 2016; Hu et al., 2017;
Morsy and Karypis, 2017). While the CSR model fails to consider
the side-factors for student performance, Hu et al. proposed to
combine content features with CSR models (Hu et al., 2017).
They extracted features related to students and courses and
incorporated these features into the prediction model. However,
the LR model suffers from the sparsity problem when there are
many elective courses. To address this limitation, Polyzou et al.
developed a sparse LR method for student-specific regression
(SSR), using the student-course-specific grade matrix. Elbadrawy
et al. (2014) proposed to enhance the student-specific grade
models in the LR model to predict student performance. Thus,
each student was predicted by a specific LR model. Its advantage
is that the models could exploit all student’s historical grades
and thus mitigate the data sparsity issue. On their used data,
the multiple LR models achieve an RMSE of 0.147, while
the traditional single LR model obtained an RMSE of 0.177,
benefiting from the consideration of individual information.
2.3.3. Support Vector Machines
In machine learning, SVMs are very effective supervised learning
models that could be used for both classification and regression.
SVM splits the data by seeking the maximized margin between
two classes (Cortes and Vapnik, 1995). Due to SVM’s powerful
capability of classification, it has been investigated many times
for SPP studies or used as a baseline method.
According to psychology, the behaviors potentially affect the
student evaluation. Xu et al. divided students into three categories
based on the detailed records of learning activities on MOOCs
platforms, i.e., certification earning, video watching, and course
sampling (Xu and Yang, 2016). Then, the authors built a predictor
based on SVM to predict certification obtaining (Cortes and
Vapnik, 1995). Fulya et al. (Kentli and Sahin, 2011) employed
SVM on 504 data records from the classroom to predict the GPA.
Hana et al. (Bydžovská, 2016) compared the traditional machine
learning algorithms for SPP, including SVM, LR, Random Forest
et al., where SVM is the best on both study-related data and social
behavior data. However, SVM suffers from computation cost in
big data due to its optimization limitation.
2.3.4. Matrix Factorization
Matrix Factorization aims to decompose a matrix into two
matrices, finding latent features between the two matrices (Bokde
et al., 2015). For SPP, each element could be generalized by the
product of a student representation and a course representation,
where both representations are yielded in the latent feature space
(Zhang et al., 2020c). That is, letting the student vector be the
row of the raw matrix and the course vector be the column of the
raw matrix, matrix competition aims to seek two latent feature
metrics for student and course to approximate the original matrix
student performance matrix (Thai-Nghe et al., 2010a, 2012;
Toscher and Jahrer, 2010; Hwang and Su, 2015; Thai-Nghe and
Schmidt-Thieme, 2015; Elbadrawy et al., 2016). However, except
for the historical grades, there are many additional factors that
influence student performance, such as the course difficulty, the
quality and teaching style of the instructor, the academic level
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Zhang et al. Student Performance Prediction Survey
of students. Hu et al. proposed a hybrid LR-MF model that
considered those features of students, courses, and instructors,
to improve the performance of the curriculum-specific model
(Hu et al., 2017). Ren et al. (2018) proposed additive latent effect
models by incorporating the above factors to predict the student’s
next-term grades. The experimental results demonstrated that
their methods significantly outperformed the baselines for SPP.
In the context of predicting the score of an exercise, MF was
employed to implicitly encode "slip rate" (the probability that the
student knows how to solve a question but makes a mistake) and
the "guess rate" (the probability that the student does not know
how to solve a question but guesses correctly) of the student in
an examination, resulting in an excellent performance on the
educational data set of Knowledge Discovery and Data Mining
Cup 2010 (Thai-Nghe et al., 2010a). In (Lee and Seung, 2001;
Hwang and Su, 2015), Non-negative MF (NMF) was used to
integrate the non-negativity of student grades. TF was exploited
to take temporal effects into account in the MF model (Thai-Nghe
et al., 2011a), resulting from the improvements of the student’s
ability. Since grade matrix is an implicitly low rank, low-rank MF
(LRMF) was investigated in the data sets from the online learning
platform in the work of Lorenzen et al. (2017).
The MF-based model assumes a low-dimensional latent
feature space that could represent both students and courses.
However, the set of courses is usually an incomplete subset
because courses are usually selected for various requirements. To
address this problem, Polyzou et al. developed a course-specific
MF (CSMF) method that estimates an MF model for each course,
where a dense subset of the data could be available (Polyzou and
Karypis, 2016). The dense course-specific matrix could make a
more reliable estimation. In addition, the cold start is a necessary
problem.... needed to be considered in SPP. Sweeney et al.
proposed to combine Factorization Machines (FM) and Random
Forests (RF) to create a hybrid model, taking advantage of both
models to solve the cold-start problem (Sweeney et al., 2015).
2.3.5. Collaborative Filtering
Collaborative filtering predicates a user’s interests by seeking
similar preferences from other users (Schafer et al., 2007). The
underlying assumption is that if person A has the same opinions
as person B on an issue, A is more likely to have B’s opinion on
another issue than a randomly chosen person. For instance, a CF
recommendation system could predict which television show a
user would like, given a partial subset of those user’s tastes (likes
or dislikes) (Sheena et al., 2000; Li and Zaman, 2014).
When applied to SPP, CF finds the most similar students
with target students based on grade records. Sirikayon et al.
performed various methods to calculate student similarity,
including Pearson correlation, cosine similarity, and Euclidean
distance. (Bydžovská, 2015). Their experiments showed that
Pearson correlation achieves the lowest prediction error, where
a prior course clustering could enhance predictability. Cakmak
et al.’s work enhanced the standard CF by integrating automated
outlier eliminations and GPA-based similarity filtering (Cakmak,
2017). Their methods estimated student course grades with an
average error rate of 0.26, with an error improvement of 16%,
compared with other methods.
In CF, the k-nearest neighbor algorithm (k-NN) is a non-
parametric method used for classification, where prediction is
based on the k nearest neighbors in given data (Jyoti and Walia,
2017). To predict the normalized score or grade of student sin
performance kof year y, researchers often define the similarity
or employ a traditional similarity metric. The performance of a
student is then predicted on his/her k nearest neighbors. (Meier
et al., 2015) derived a confident estimate on grade prediction and
demonstrated the performance of the proposed algorithm on a
real data set composed of 700 undergraduate students enrolled in
the course of digital signal processing at UCLA in the past 7 years.
2.3.6. Artificial Neural Network
Neural networks are a series of algorithms that endeavor to
recognize underlying relationships in a set of data by mimicking
the information process of the human brain. In this sense, neural
networks refer to systems of neurons, either organic or artificial.
ANN is a model composed of multiple neural layers and is trained
in iterative optimization. ANN has a wide range of applications
due to its power in modeling the approximation from inputs to
outputs (Andrews et al., 1995). Hence, many studies used ANN
to predict student performance (Oladokun et al., 2008; Sorour
et al., 2014; Luo et al., 2015; Shahiri and Husain, 2015; Młynarska
et al., 2016; Zacharis, 2016; Yang et al., 2017; Su et al., 2018). For
instance, Oladokun et al. employed the multi-layer perception on
the pre-admission data of five different university graduates for
SPP, achieving an accuracy of about 74% (Oladokun et al., 2008).
Many researchers collected these features from student’ self
assesses by using ANN models to predict student’s performance.
Researchers asked for student’ comments per lesson to reflect
their learning attitude and understanding degree of course
content and learning difficulty. With this data, Sorour et al.
conducted experiments with the Latent semantic analysis (LSA)
technique and ANN model (Sorour et al., 2014), achieving an
average prediction accuracy of about 82.6%. Luo et al. employed
Word2Vec and ANN to predict student grades in each lesson
based on their comments (Luo et al., 2015). The experiment
results showed that the prediction rate reached 80% on the 6
consecutive lessons, and a final prediction rate reached 94%
from all 15 lessons. Tsung-Yen et al. trained a time series neural
network based on both previous performance and click-stream
data (Yang et al., 2017). The prediction model outperformed the
method of using average past grades by more than 60%, and
the lasso regression by more than 15%. To take all advantage of
both students exercise records and the texts of exercises, Su et al.
developed a novel Exercise-Enhanced Recurrent Neural Network
(EERNN), where authors adopted a bidirectional LSTM to learn
exercise representation from texts and then proposed the EERNN
to trace student states in their sequential exercising process (Su
et al., 2018).
2.3.7. Deep Learning
The deep learning-based model, one of the powerful mapping-
based methods, aims to learn deep nonlinear features from
original features for sequent tasks and has become benchmarks in
a wide range of applications in recent years (LeCun et al., 2015).
To predict student performance, Guo et al. trained a student
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Zhang et al. Student Performance Prediction Survey
performance prediction network of six fully connected layers
on the high-school data composed of background data, school-
life data, past-study data, and personal descriptions (Guo et al.,
2015). Yang et al. used a time series deep neural network to
predict the evolution of a student’s grade in massive open online
courses (MOOCs) based on the data on student behaviors (Yang
et al., 2017). Kim et al. (2018) recast the student performance
prediction as a sequential event prediction and proposed a deep
model, GritNet, for this problem by integrating the bidirectional
long short-term memory (LSTM). To capture the sequential
features of students grades in prior courses, Hu et al. modeled
the learning behavior and performance using RNNs with LSTM
for the next-course grade prediction (Hu and Rangwala, 2019b).
Waheed et al. showed that deep neural networks achieved a
higher prediction accuracy than logistic regression and SVMs
on the clickstream data (Yang et al., 2017). Hu et al. proposed
attention-based graph convolutional networks to predict next-
term course grades based on past grades (Hu and Rangwala,
2019a). Yupei et al. proposed a sparse attention convolutional
neural networks (SACNN) to predict undergraduate grades in
Chinese higher education, where they not only achieve a good
prediction accuracy but also gave the explanation for the question
"why a student is predicted to pass/fail based on the course’s
association?" (Zhang et al., 2021a).
2.3.8. Other Methods
Restricted Boltzmann Machine (RBM), an unsupervised machine
learning technique, creates a bipartite graph composed of two
network layers. The first layer is called the visible layer, which is
used to receive data features. These nodes are connected to the
second layer, called the hidden layer containing symmetrically
weighted connections. Iqbal et al. investigated CF, MF, and
RBM methods to predict student academic performance in the
Information Technology University (ITU) (Iqbal et al., 2017).
RBM technology was better than other techniques among all the
mentioned methods on their data sets from the results.
Markov Network was also developed to predict the next-
time course in a sequence (Slim et al., 2014). Slim et al. used
MN to represent the curriculum graphs of a particular degree
course. Based on GPA in a given semester, the MN model could
predict GPA in the next semesters. They analyzed 400 students
from the University of New Mexico (UNM) who have completed
their degree programs. The mean square error (MSE) is used to
measure the performance of the framework. The results showed
that as the number of semester grades increases, MSE gradually
declines (Slim et al., 2014).
In Li et al. (2016), the fuzzy-clustering model and multi-
variable regression were combined into an framework to predict
student academic performance. The authors considered both the
historical scores and the attributes that are related to normal
study behavior. In this study, students were clustered by using the
fuzzy C-means model based on their existing academic records
to discover the relationship between the required grade and the
previous grades. By considering student behaviors, the similarity
between the objective student and other students with similar
academic records was calculated to generate an offset value.
Finally, based on the cluster membership, the similarity, and
the offset value, the objective grade was predicted in terms of a
predefined linear system.
2.4. Performance Evaluation
With different goals, these studies of SPP focus on three-time
spans of courses, i.e., single course grade prediction, the next-
term performance prediction, and the whole learning period
prediction. In this section, we reviewed the existing literature on
SPP from their periods in SPP. The summary of these research
studies is shown in Table 3.
2.4.1. Single Course Grade Prediction
Many researchers focused on the single target course. They
analyzed the score that the student would reach in the final
exam or mid-term test (Tabandeh and Sami, 2010; Thai-Nghe
et al., 2010a; Toscher and Jahrer, 2010; Yu et al., 2010). In these
studies, most of the researchers are interested in the knowledge
level of students on the target course and focused on one specific
examination. The authors predicted the student’s knowledge
level and the student’s grade using test questions (Thai-Nghe
et al., 2012; Hwang and Su, 2015; Thai-Nghe and Schmidt-
Thieme, 2015) and the process features of interest (Xu and
Yang, 2016; Yang et al., 2017). Chein-Shung et al. developed a
novel regularization framework that imposes locality preserving
constraints into the weighted regularized nonnegative MF for
SPP (Xu and Yang, 2016). The author predicted the performance
on Algebra and Bridge by using exam question texts, solution
steps, and skills. Tsung-Yen et al. incorporated richer data from
the learning process of video watching, to train a time-series
neural network, followed by predicting CFA scores of students.
2.4.2. Next-Term Performance Prediction
Many researchers were focused on predicting student next-
term performance to adjust the teaching plan (Sweeney et al.,
2015; Elbadrawy et al., 2016; Morsy and Karypis, 2017; Ren
et al., 2018). This research aims to estimate student learning
performance on courses that are expected to engage in the next
term. Students can use estimated grades to select courses for
which they will perform well, thereby allowing them to make
progress toward graduation. The estimated grade could also
provide suggestions for the difficulty rating for courses, which
helps students prioritize their studies and manage time schedules.
Besides, course instructors and departments could also benefit
from knowing student’ registration on all courses. This enables
them to make adjustments, such as holding additional office
hours and allocating teaching assistants. Zhiyun et al. proposed
additive latent effect (ALE) models that incorporate additive
effects associated with students and courses to solve the next-
term performance prediction. Especially, authors were able to
highlight the improved prediction performance of ALE with the
use of latent factors of course instructors, student academic levels,
and student global latent effects (Ren et al., 2018).
2.4.3. Performance Prediction in Entire Learning
Period
The entire learning-period prediction predicts the indicators
of the entire learning process, such as the GPA. This study
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Zhang et al. Student Performance Prediction Survey
TABLE 3 | The list of references for performance evaluation.
Performance evaluation Reference Count
Single course grade prediction Tabandeh and Sami, 2010; Thai-Nghe et al., 2010a, 2012; Toscher and Jahrer,
2010; Yu et al., 2010; Hwang and Su, 2015; Thai-Nghe and Schmidt-Thieme,
2015; Xu and Yang, 2016; Yang et al., 2017
9
The next-term performance prediction Sweeney et al., 2015; Elbadrawy et al., 2016; Morsy and Karypis, 2017; Ren
et al., 2018
4
Whole learning period’s performance prediction Oladokun et al., 2008; Vituli ´
c and Zupan ˇ
ci ˇ
c, 2013; Meier et al., 2015; Al-Barrak
and Al-Razgan, 2016; Hunt et al., 2017; Xu et al., 2017; Tampakas et al., 2018
7
aims to improve student’s final GPA and graduation ratio
(Oladokun et al., 2008; Meier et al., 2015). In these studies,
researchers are mostly focused on the final GPAs (Vituli´
c and
Zupanˇ
ciˇ
c, 2013; Al-Barrak and Al-Razgan, 2016) to trace student
performance over the academic semesters (Hunt et al., 2017; Xu
et al., 2017; Tampakas et al., 2018). Especially, they collected
the performance of the student in each term of the whole
learning period and other side information, e.g., background
information of students/courses. Michael and Muna employed
the J48 algorithm to predict the student’s final GPA based on their
transcripts and their course grades (Al-Barrak and Al-Razgan,
2016). Jie et al. proposed an ensemble method to predict students’
future performance in degree programs, using the data of their
current and past grades. A latent factor model-based course
clustering method was developed to discover relevant courses
as base predictors. An ensemble-based progressive prediction
architecture was also developed to incorporate students’ ability
improvements into the prediction model. Additionally, this work
could provide good suggestions on curriculum designs in degree
programs (Xu et al., 2017).
2.5. Practical Application
As mentioned above, researchers studied the task of SPP with
different goals and used the prediction results in different
situations. Here, we listed some practical applications, shown in
Table 4.
2.5.1. Recommendation System
Many researchers combined SPP tasks with recommendation
systems to enhance education outcomes by making a
personalized educational plan. To offer personalized exercise
recommendations, Nguyen et al. proposed to use context-aware
models for SPP by utilizing all interactions of the given student-
task pairs. This approach could be applied in a personalized
learning environment, such as recommending exercises to
students and predicting student performance (Thai-Nghe et al.,
2011b). Yu et al. proposed an EERNN framework for SPP by
taking both students’ exercise records to exercise texts into
account. In EERNN, authors first designed a bidirectional
LSTM to learn exercise representations from texts and then
proposed a new network architecture to trace student states
(i.e., knowledge states) in their sequential exercising process
with the combination of exercise representations. To make
final predictions, the authors designed two strategies under
EERNN, i.e., EERNNM with Markov property and EERNNA
with an Attention mechanism (Su et al., 2018). For library book
recommendations, Defu et. al. proposed a supervised content-
aware MF for mutual reinforcement of academic performance
prediction based on library data (Lian et al., 2016). For course
recommendation (Ray and Sharma, 2011; Denley, 2013;
Elbadrawy and Karypis, 2016), Asmaa et. al. investigated how
students and course academic features influence the enrollment
patterns and then applied these key features to define student
and course groups at various levels of granularity. Finally, the
authors combined these groups with existing grade predictions
and top-n course ranking models, e.g., neighborhood-based
user collaborative filtering, MF, and popularity-based ranking
approaches (Elbadrawy and Karypis, 2016).
2.5.2. Early Warning System
The early warning system is a key application based on the study
of SPP. In the context of traditional offline education, instructors
wish to know the students who are at risk of dropping out or the
students who possibly fail in the examination (Blanchfield, 1971;
Dekker et al., 2009; Quadri and Kalyankar, 2010; Bayer et al.,
2012; Abu-Oda and El-Halees, 2015). The online classroom is the
same, where instructors wish to know the students under the risk
or have a lower motivation to finish his/her tasks (Yang et al.,
2013; Kloft et al., 2014; Lu et al., 2017; Gitinabard et al., 2018).
As mentioned above, many researchers study the common
problem of dropping out. Bayer et al. studied the structured
data of students’ social behaviors, e.g., e-mail and discussion
board conversations. They introduced learning a classifier for
student failure prediction using a CSL method (Bayer et al.,
2012). Especially, the authors described extraction features from
both student data and behavior graph data. Niki et al. conducted
a survival analysis to identify dropouts by a combination of
modeling and feature selection methods. The author evaluated
three different models under different definitions of dropout.
Besides, the author assessed models over time by evaluating
whether models learned on week 1 could predict dropouts in
week 2 (Gitinabard et al., 2018).
2.5.3. Other Applications
There are many other applications of SPP due to lots of
studies using SPP as an evaluation. David et al. explored
the relationships between teacher expectations and student
achievements in physical education classes. Student achievement
may confirm teacher expectations because these expectations
create self-fulfilling prophecies, perceptual biases, and accurate
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Zhang et al. Student Performance Prediction Survey
TABLE 4 | The list of references of the practical application of SPP.
Practical application Reference Count
The recommendation system Ray and Sharma, 2011; Thai-Nghe et al., 2011b; Denley, 2013; Elbadrawy and
Karypis, 2016; Lian et al., 2016; Su et al., 2018
6
Early warning system Blanchfield, 1971; Dekker et al., 2009; Quadri and Kalyankar, 2010; Bayer et al.,
2012; Yang et al., 2013; Kloft et al., 2014; Abu-Oda and El-Halees, 2015; Lu
et al., 2017; Gitinabard et al., 2018
9
Other applications Jussim, 1989; Trouilloud et al., 2002; Hu and Huang, 2018; Lam et al., 2018;
Juha ˇ
nák et al., 2019; Kushwaha et al., 2019; Supianto et al., 2019
7
predictions (Jussim, 1989). Another purpose was to examine the
mediating role played by students’ perceived ability in the teacher
expectancy process (Trouilloud et al., 2002). Libor et al. studied
the task of SPP by exploring students’ behavior and interaction
patterns in different types of online quiz-based activities within
learning management systems (LMS) (Juhaˇ
nák et al., 2019).
Especially, many studies of SPP were applied to discover a better
learning pattern and thereby improve the educational output
(Hu and Huang, 2018; Lam et al., 2018; Kushwaha et al., 2019;
Supianto et al., 2019).
3. COMPARISON EXPERIMENTS
This section adopted traditional machine learning algorithms to
predict student performance on two data sets, i.e., a private data
set from our institution and a public data set.
3.1. Data Description
One of the two data sets was collected during the 2005 and 2006
academic years from two Portuguese schools (Dataset 1). Data
features include student grades (the grades of the three semesters
are labeled as G1, G2, and G3), demographic features, social
features, and school-related features. The data set provided two
distinct courses, Mathematics and Portuguese Language. This data
set can be obtained from https://archive.ics.uci.edu/ml/datasets/
Student+Performance.
The other data set was collected from the Computer Science
department at our institution on 694 undergraduate students
of 2014, 2015, and 2016 (referred to as Dataset 2). The data
contains student background features (e.g., Gender, class, age,
nationality, political status), course credits, course hours of one
week, and grades on 39 courses. The 39 courses were taken in
different semesters.
3.2. Problem Reformulation
We recast this student grade prediction into classification
and regression.
•Classification formulation: Dataset 1 was classified based on
the Erasmus grade conversion system, where there were
5 Levels. While Dataset 2 was classified based on student
evaluation with 3 Level grades. The level details of the two data
sets are shown in Table 5.
TABLE 5 | The details of the two datasets used in experiments.
Dataset 1 0-9 10-11 12-13 14-15 16-20
fail sufficient satisfactory Good Excellent
Dataset 2 grade <60 60 ≤grade ≤80 80 <grade
Warning Good Very Good
•Regression formulation: Dataset 1 has numeric outputs
ranging from 0 to 20, and Dataset 2 has numeric outputs
varying from 0 to 100.
3.3. The Used Methods
In this study, the aim is to summarize current works and have
comparisons between the used methods. Hence, we employed
those methods that had been adopted in related references,
as follows:
Naive Bayes: Naive Bayes is based on Bayes’ theorem with
feature condition independent hypothesis. For the training
set, the joint probability distribution of input and output is
first studied based on the independent hypothesis of feature
conditions. Then based on this model, Bayes theorem is used to
calculate the ywith the maximum posterior probability for the
given input x.
k-nearest neighbor is a basic classification and regression
method in machine learning. KNN was used by Cover and Haut
for SPP (Cover and Hart, 1967). KNN first determines the on the
K training data set for a test data point, and then use the majority
of the classes of the ktraining data points to predict the classes of
the test point.
Decision tree: A decision tree is a commonly used
classification and regression method in SPP. The algorithm
consists of three parts, i.e., feature selection, tree generation,
and pruning. The main implementation includes ID3 and C4.5
proposed by Quinlan (1986), and CART proposed by Breiman
et al. (Loh, 2011). In this experiment, we used the C4.5 algorithm.
Support vector machine: Vapnik originally proposed SVM,
and Chervonenkis (Vapnik and Chervonenkis, 1964; Cortes and
Vapnik, 1995). Boser et al. proposed a non-linear SVM by using
kernel methods and soft margin maximization (Boser et al.,
1992). Weston et al. extended it to multi-classification (Weston
and Watkins, 1999). We here used the sequential minimal
optimization (SMO) algorithm proposed by Platt (1998).
Bagging: Breiman proposed bagging (Breiman, 1996).
Bagging is a technique that reduces generalization errors by
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Zhang et al. Student Performance Prediction Survey
combining several models. The main idea is to train several
different models separately and then let all models vote for the
output. In this experiment, we adopted the C4.5 algorithm as the
base classifier.
Random Forest: Random Forest is an ensemble learning
method proposed by Breiman (2001). It uses voting mechanisms
from multiple DT to improve the shortcomings of DT. In this
experiment, we chose the C4.5 algorithm as the base classifier,
a.k.a. weak classifier.
3.4. Model Training and Parameter
Selection
We implemented all methods by using Weka3.8 1software.
All experiments were evaluated with 10-folds cross-validation
(García et al., 2010). That is, we partitioned the dataset into 10-
folds and performed the evaluation ten times. In each evaluation,
1-fold was used as the test set, and other folds were used
as the training set. After ten runs, the average metrics were
calculated as the final evaluation results. In our classification
experiment, models were evaluated using the prediction accuracy
(ACC), while the root mean squared error (RMSE) in the
regression experiment. Two-sample t-test was adapted to verify
the statistical significance of the difference between the two
methods (Sheskin, 2003).
Hyperparameter selection is an important task to extract
more accurate results. Grid Search is generally used for hyper
parameter optimization. In grid search, different models having
different parameter values are trained and then evaluated
using cross-validation. The 10-folds cross-validation could be
performed on each model, and the hyper parameters with
optimum results are then selected. For Naive Bayes, KNN, DT,
SVM, Bagging and Random Forest, one could used grid search
10-folds cross-validation for hyper parameters selection for each
model, and then perform model training and comparison with
the selected parameters. The used hyperparameters are: K=5
for KNN; c=2 for SVM; the number of features=6 for Random
Forest; n=100 for bagging and boosting. The other parameters
that are not mentioned here are set to default values in Weka.
3.5. Experiments
We conducted experiments on two education data sets to predict
student grades on specific courses to compare these mentioned
methods. We additionally investigated feature effects in SPP.
3.5.1. Effects of Previous Courses
As many researchers mentioned, a student’s grades are closely
related to his previous grades. In the prediction experiments of
Portuguese and Mathematics grades in Dataset 1, the features G1
and G2 are thought to have great influences on G3. Therefore,
each DM model has three input configurations:
A: Use background features (demographic features, social
features, and school features) to predict G3.
B: Add G2 based on A.
C: Add G1 based on B.
1https://www.cs.waikato.ac.nz/ml/weka
The experimental results are shown in Figures 1,2. In these
figures, the solid line is the classification method, ACC is
marked on the left primary axis as the evaluation metric,
and the dotted line is the regression method, and RMSE is
marked on the right secondary axis as the evaluation metric.
As shown in Figure 2, when only background features are
used to predict G3, poor prediction results are delivered. The
ACC of DM models is about 0.3, and RMSE is about 0.75,
indicating that background features have little influence on
G3. However, when G2 and G1 were considered, the accuracy
was increased significantly with a p-value <0.05, and RMSE
decreased significantly, indicating that the performance of
the DM model could be improved by adding the grades of
prerequisite courses. In particular, with G2, Random Forest
reaches 0.75 on classification accuracy, and SVM for regression
reaches about 0.25 on RSME. The similar trend could be observed
in Figure 2 as well.
3.5.2. Grade Prediction on Specific Courses
In Dataset 2, we selected a course grade in the sixth
semester (Operating System) and a course grade in the fifth
semester (Computer Composition and System Structure)
to survey these mentioned methods. All models were
set to different input configurations corresponding to
the different number of semesters and the different
number of prerequisite courses. Here, we took the sixth
semester as an example (The fifth semester is similar to the
sixth semester):
A: Using background features and other course features from the
same period of the sixth semester.
B: Adding the course features of the fifth semester to settings A.
C: Adding the course features of the fourth semester to
settings B.
D: Adding the course features of the third semester to settings C.
E: Adding the course features of the second semester to
settings D.
F: Adding the course features of the first semester to settings E.
The experimental results are shown in Figures 3A,C. It can
be seen that with the increase of the number of prerequisite
courses, ACC increases and RMSE decreases. The performance
of all models could be improved by increasing the grades of
prerequisite courses. However, although the ACC is increased,
it is not monotonously increasing. However, RMSE is decreased,
but not monotonically decreasing. More noise may be introduced
as the number of semesters increases. Thus, we used the
Lasso algorithm for feature selection. The Lasso algorithm
gives the weights of these features in the prediction process
by compressing the weight of the uncorrelated and redundant
features to zero. Figures 3B,D, respectively, show the weights
of relevant courses. As can be seen, the last semester and the
same semester of course account for greater weight. The weights
of background features and other semester course features are
zeros. Therefore, in Dataset 2, background features have less
influence on the prediction of grades, while some courses in the
last semester have greater influences on the predicted grades.
Frontiers in Psychology | www.frontiersin.org 11 December 2021 | Volume 12 | Article 698490
Zhang et al. Student Performance Prediction Survey
FIGURE 1 | Mathematics.
3.6. Result Analysis and Conclusion
From the experiment results above, the most accurate classifier
is Random forest. Random forest is an ensemble learning
algorithm by combining multiple weak classifiers and the final
results are obtained by voting or averaging those multiple weak
results, resulting in high accuracy and better generalization
performance. Wherein, the prerequisite course grades play the
most important role in the random forest classifier, which
shows that the records might uncover the characters of a
student on learning. In addition, the performance of all
models is significantly improved with integrating the features
from the prerequisite courses (p-value <0.01). On the other
hand, the background features have a small influence on the
predictions. Overall, if we could access more information on
the prerequisite courses, the performance of all models would
be better.
Specifically, after feature extraction on Dataset 2, the courses
of last semester and the courses in the same term make a great
influence on the grade prediction. In contrast, other features
have small influences, while these redundant features might
introduce noise. In the task of SPP, it is thus necessary to select
informative features and remove redundant features to reach
a better performance. By the way, these study results could
convince the conclusions in the work of Debopam et al. (Sanyal
et al., 2020).
4. ETHICAL CONSIDERATIONS ON SPP
ALGORITHMS
There are lots of successful algorithms to predict the student
grade mentioned above. While some institutions use the SPP
algorithm to guide the students on learning contexts and/or
learning pathways, the ethic should be considered for the real-
world use of a computer algorithm due to the agnostic of the
impacts on education.
4.1. Data Access and Collection
This study motivates us to continuously collect more educational
data from real-world education, while privacy issues arose to be
taken into account (Ekowo and Palmer, 2017). The educational
data consists of many important personal information which
could be harmful if the data is leaked.
Morozov et. al. proposed three provocative positions in
this shift toward algorithms (Morozov and Morozov, 2013).
That is, (1) personal privacy needs to be politicized in data
intensive problem solving under scrutiny; (2) the value of the
personal data needs to be considered as a shift thinking; (3) data
sharing needs to be carefully conducted in the development of
provocative digital services. Princloo et al. extended into five
theses to make sure the secure use of data (Prinsloo et al.,
2015), which were focused on data access, the proposition of an
Frontiers in Psychology | www.frontiersin.org 12 December 2021 | Volume 12 | Article 698490
Zhang et al. Student Performance Prediction Survey
FIGURE 2 | Portuguese language.
integrated data system, the skills and capability to manage data,
and systematically mapping the data elements for reporting and
analytics, respectively.
These suggestions were concluded into three points by Angelo
et al. (Fynn, 2016), i.e., data access, the value of personal
data, and the data origins. For data access, he advocated a
data analytics framework should be set up to consider the
institutional assumption, practices, and ideology underpinning
the data mining technology. For the value of personal data, it
should be clearly claimed that student remains the option to save
or remove data and the predictive analysis has positive effects
on students at high risk. For data origins, student success is a
complex phenomenon from different predictor variables across
institution types, material resources, heterogeneous student,
socio-economic status, disciplinary contexts, and so on.
4.2. Algorithm Threat and Advantage
Researchers could access a huge amount of educational data, and
they considered the artificial intelligence algorithms to enhance
the performance of SPP tasks, i.e., KNNs, SVMs, MFs, etc.
Students and teachers enjoy the advantage of algorithms, while
suffering from their threats (Ekowo and Palmer, 2016; Fynn,
2016).
These algorithms reviewed above improved the performance
of SPP, finding students at-risk, and further bring out targeted
student advising. These advantages enhances the educational
quality, reduce the drop-out ratios, and help students be better.
On the other hand, the education might suffer from these
algorithms, leading to agnosticism. The threat lies in 2-folds:
the invisible control of our behaviors to meet an undisclosed
ideological agenda and the unknown decision making from
an agnostic algorithm (Fynn, 2016). Most models focus on
the improvement of their used metrics, while less consider
the interpretability. The use of these algorithms put us in an
unsafe situation.
Several types of research start to pay attention to the reason
hiden in algorithms. Zhang et al. proposed one robust MF model
to solve the SPP task, while integrating a graph to improve
the interpretability (Zhang et al., 2020c). In the recent works
(Zhang et al., 2021a), the authors attempt to probe the reason
why the target students will fail on a course by using the course
relationship. The predicted grade without any interpretation will
not be convinced and could not apply in a real educational
environment. All in all, we should be reasonable about the
benefits and disadvantages and develop a more useful educational
analysis model with strong interpretability.
4.3. Analytic Bias
The study on students often is effected by race, gender, age,
finance, and so on, leading to model bias and unfairness in
practices. As mentioned in the study of Jiang et al. (Ekowo
and Palmer, 2016), education fairness and algorithm fairness
Frontiers in Psychology | www.frontiersin.org 13 December 2021 | Volume 12 | Article 698490
Zhang et al. Student Performance Prediction Survey
FIGURE 3 | In (A,C): The solid lines are classification methods, and ACCs are marked on the left primary axis as the evaluation metric. The dotted lines are regression
methods, and RMSEs are marked on the right secondary axis as the evaluation metric. In (B,D): The x-axis is the course whose weights are not zero after the feature
selection by Lasso, and (*) represents the semester of the course. The y-axis is the weight of the course.
are important to the SPP task. Jiang et al. hold the point that
analytic bias consists of educational data bias and algorithm bias.
In the research (Ekowo and Palmer, 2016), they introduced the
SPP task into three stages: data construction, model training,
and inference. Then, Jiang designed some strategies for the three
stages, i.e., weight loss by sample strategy for data constracution,
adversarial learning for model training, and removing features
for prediction in the last stage. Mostly, the scores could not truly
reflect students’ ability level, where two students with similar
reviews may obtain different score on open-ended assignments
(Cleary, 1968). Bes