Mining in Educational Data: Review and Future Directions

Abstract

One of the developing fields of the present times is educational data mining that pertains to developing methods that help in examining various kinds of data obtained from the educational field. A vital part is played by data mining in the education field, particularly when behavior is being assessed in an online learning setting. This is because data mining is capable of analyzing and identifying the hidden information regarding the data itself, which is very difficult and takes up a lot of time if performed manually. This review has the objective of examining the way data mining was handled by researchers in the past and the most recent trends on data mining in educational research, as well as to evaluate the likelihood of employing machine learning in the field of education. The various limitations inherent in the current research are examined and recommendations are made for future research.

Full text

Mining in Educational Data:
Review and Future Directions
Said A. Salloum
1,2(&)
, Muhammad Alshurideh
3,4
,
Ashraf Elnagar
1,5
, and Khaled Shaalan
2
1
Research Institute of Sciences and Engineering,
University of Sharjah, Sharjah, UAE
ssalloum@sharjah.ac.ae
2
Faculty of Engineering and IT, The British University in Dubai, Dubai, UAE
3
Faculty of Business, University of Jordan, Amman, Jordan
4
Management Department, University of Sharjah, Sharjah, UAE
5
Department of Computer Science, University of Sharjah, Sharjah, UAE
Abstract. One of the developing fields of the present times is educational data
mining that pertains to developing methods that help in examining various kinds
of data obtained from the educational field. A vital part is played by data mining
in the education field, particularly when behavior is being assessed in an online
learning setting. This is because data mining is capable of analyzing and
identifying the hidden information regarding the data itself, which is very dif-
ficult and takes up a lot of time if performed manually. This review has the
objective of examining the way data mining was handled by researchers in the
past and the most recent trends on data mining in educational research, as well
as to evaluate the likelihood of employing machine learning in the field of
education. The various limitations inherent in the current research are examined
and recommendations are made for future research.
Keywords: Educational data ming Online learning Machine learning
1 Introduction
The most advanced universities of present times frequently use data mining methods to
examine the data collected and to extract information and knowledge to facilitate
decision-making [1–6]. To offer significant understanding of the present research trends
of the Education Data Mining (EDM), several review studies were carried out [7–11].
However, further studies are still needed to examine this issue from different angles.
Previous research was found to disregard the examination of EDM in terms of electronic
learning (E-learning) studies [12–14] from a variety of perspectives [15–18]. The pur-
pose of Educational data mining is to develop techniques that analyze different kinds of
data obtained from the field of education [19]. In addition, the field of educational data
mining is evolving and is related to the development of techniques for examining the
different kinds of data obtained from the educational domain [20,21]. The purpose of
the this review paper is to determine ways of applying data mining techniques to higher
education system by providing the most widely used methods and the most relevant
studies carried out in this field to date. The remaining parts of the paper include the
©Springer Nature Switzerland AG 2020
A.-E. Hassanien et al. (Eds.): AICV 2020, AISC 1153, pp. 92–102, 2020.
https://doi.org/10.1007/978-3-030-44289-7_9

following: A summary of some relevant literature is presented in related work, as well as
the data mining techniques in educational systems are explained in Sect. 2.
2 Literature Review
Data mining is the most robust methodology used for assessing valuable information
from the data warehouse [22]. Data mining is used for predicting hidden information
through extraction method so that decision-making can be improved [23]. The use of
data mining for educational activities has been extended on the basis of performance of
students, staff and managerial decisions [19]. Another term that can be used for data
mining is knowledge discovery through data [24,25,26]. Data mining is a multidi-
mensional field comprising of different aspects, for example, learning, statistics,
information technology, artificial intelligence, retrieval and visualization of data [27].
The education system has become more balanced due to the improved mining appli-
cation [28]. The education field has seen a fast development of the idea of educational
data mining (EDM) with respect to the various kinds of educational institutes [29]. On
the other hand, academic analyst is specifically linked to institutional effectiveness and
problems related to student performance [12,13,30–37]. Those areas that directly
influence those studying at the institution are part of the EDM [38]. In this section, we
provide a review of the last ten studies applied to data mining techniques in educational
environment ranging from 2016 to 2019, as shown in Table 1.
Table 1. List of studies focused on exploring educational data mining.
Authors Research problem Technique Approach
[39] Prediction of students
performance
Classification Decision tree
[40] Predicting academic
performance of students
Classification
and clustering
SVM, Naïve Bayes, Decision
tree and Neural Network
classifiers
[41] Predicting students’academic
procrastination
Classification
and clustering
k-means clustering, ZeroR,
OneR, ID3, J48, random
forest, decision stump, JRip,
PART, NBTree, and Prism
[42] How data mining can help
admission working process,
and how data mining can
predict the student’s jobs
Association
Rule Mining
and
Classification
ID 3 Decision Tree
[43] Predicting students’
performance
Clustering,
classification,
regression
Naive Bayes, Decision Tree,
and Artificial Neural Network
[44] Students’disposition analysis Clustering k-means
[45] Predicting students’
graduation
Classification Multi-Layer Perceptron
(MLP)
(continued)
Mining in Educational Data: Review and Future Directions 93

2.1 Data Mining Techniques in Educational Systems
One of the elements of data mining is educational data mining, the key focus of which
is on developing models for extracting hidden knowledge from the student’s data,
using which the academic performance of students may be enhanced. In the process of
Education Data mining, raw material may be converted from various educational
systems into valuable information that can be employed by teachers, students and their
parents, educational researchers and the developers of educational software system.
Educational data mining may also be considered as a new model that is part of the
prevailing education system, which is able to generate positive interaction with dif-
ferent parts of the system. This will enable it to eventually attain the objective of
enhancing teaching [51].
The definition of Educational Data Mining (EDM) is the application of techniques
of traditional data mining to educational data analysis, with the objective of obtaining
solutions to problems in the field of education [10]. There are certain EDM applications
that include the formulation of e-learning systems [10,52], clustering educational data
[53] as well as making student performance predictions [54]. Several kinds of tech-
niques are presently quite popular in educational data mining, which are part of the
following categories: sequential pattern, clustering, prediction, classification, machine
learning models and association rule analysis.
Table 1. (continued )
Authors Research problem Technique Approach
[46] Student placement prediction Clustering,
classification,
regression
J48, Naïve Bayes, Random
Forest, and Random Tree,
Multiple Linear Regression,
binomial logistic regression,
Recursive Partitioning and
Regression Tree (rpart),
conditional inference tree
(ctree) and Neural Network
(nnet) algorithms
[47] Students’performance
prediction- predicting final
grades of students
Multiple
regression
analysis
Recurrent Neural Network
(RNN)
[48] Students’performance
prediction
Classification NaiveBayes, Bayesian
Network, ID3, J48 and
Neural Network
[49] Predict dropout at the
universities
Multiple
regression
analysis
Neural Network - Multilayer
perceptron algorithms and
radial basis function
[50] Predicting students’academic
performance
Classification Decision tree classifiers and
neural networks
94 S. A. Salloum et al.

2.2 The Traditional Data Mining Techniques Applied in Educational
Settings
Clustering is the most extensively known techniques for data mining [39,55–62],
followed by classification [42,55,63–65] sequential pattern [18,58,66], prediction
[40,67], and finally, association rule analysis [42,55]. Going back to the year 1995 till
the year 2005, majority of the studies on educational data mining often used the
association rule analysis technique [11] because it involved a lesser degree of expertise
compared to other techniques [68]. Nonetheless, at the start of the year 2005, the trend
shifted as clustering and classification methods were often used for analysis by the
researchers [9]. There are frequently a large number of outputs obtained for association
rule, majority of which are not interesting and cannot be comprehended easily by those
who are not experienced in data mining [69]. To select the correct algorithms,
researchers should initially design the data and make it consistent with the required
output [9]. When their study is small scale in nature, they can use clustering approach
because data splitting that is required in classification approach is not needed in this
technique [9]. In addition, the researchers are always able to compare with various
algorithms using the same database that was used in [55]. This would help in deter-
mining if identical results would be attained when a distinct approach was used.
2.3 Machine Learning Applied to Learning Analytics and Educational
Data Mining
It would be appropriate to consider Machine Learning (ML) as a component of artificial
intelligence (AI). Machine learning is essentially the process through which a machine
or model is provided access to data and is able to learn on its own. It was conceived by
Arthur Samuel in 1959 that we should not have to teach computers; instead, we should
allow them to learn themselves. To explain his theory, he came up with the term
‘Machine Learning’, which is now the standard term used to explain the computers’
ability to learn on their own [70]. Machine Learning (ML) refers to the programming of
computers to enhance a performance standard through example data or experience [71].
When a machine learning algorithm is implemented, it indicates that a model is
implemented that outputs appropriate information, considering that input data has been
given [72]. Another extensively used method in the field of Data Mining is Artificial
Neural Network (ANN) [73,74]. This method has been used in clustering [59,75],
regression [47,76], classification [47,76–80], time series forecasting [47,79] and
visualization. Though ANN has been used quite extensively in data mining, a signif-
icant aspect to point out is that there is a section of the data mining community that
somewhat criticizes the fact that the models formulated using this paradigm cannot be
interpreted [81]. These models do offer accurate predictions from the data; however, it
is not easy to obtain human-interpretable rules that encapsulate their predictions [74].
At present, researchers and scientists are intrigued by the use of machine learning in the
field of education. A few of the areas of interest include the following:
Mining in Educational Data: Review and Future Directions 95

2.3.1 Predict Student Performance
Predicting student performance is an important use of machine learning. The machine
learning model is used for “learning”about every student, which enables it to identify
their shortcomings and determine ways through which they can improve, for example
by attending more lectures or reviewing further literature [47,54,78,80,82–84]. Using
these models many kinds of knowledge can be discovered such as clustering, associ-
ation rules, and classifications [85]. The discovered knowledge can be used for pre-
diction regarding enrolment of students [86] in a particular course, alienation of
traditional classroom teaching model [87], detection of unfair means used in online
examination, detection of abnormal values in the result sheets of the students, pre-
diction about students performance and so on [85].
2.3.2 Use Unbiased Methods for Testing and Grading Students
It becomes possible to generate computerized adaptive assessments through machine
learning [88,89]. Teachers and students receive consistent feedback from the machine
learning-based assessment regarding the way the student learns, the support that they
require and the developments they are experience in terms of their learning objectives
[72,76,90].
2.3.3 Enhance Retention
Retention rates are also enhanced through machine learning, for example learning
analytics. When students “at risk”are determined, they can be approached by schools
and universities to assist them in achieving success [91]. Prediction models that include
all personal, social, psychological and other environmental variables are necessitated
for the effective prediction of the retention rate of the students. The retention of stu-
dents with high accuracy is beneficial for identify the students with low academic
achievements initially. It is required that the identified students can be assisted more by
the teacher so that their performance is improved in future [86].
2.3.4 Provide Support to Teachers and Institution Stuff
Using algorithms based on machine learning, it becomes easier to perform classifica-
tion of the written assessment tests of students [92]. Intelligent analysis of assessment
data assists in achieving a better understanding of student performance, the quality of
the test and individual question [93].
3 Conclusion and Future Work
In this paper, the state of the art is evaluated in terms of EDM and surveys of the most
pertinent studies till now in this field. Once all the published bibliography in the area of
EDM has been gathered and reviewed, we chose the most significant studies of every
author. After this, every study was categorized, not only on the basis of the kind of data
and DM techniques employed, but also on the basis of the kind of educational activity
they tackle. The introduction of EDM is considered as a forthcoming research domain
that is relevant to various well-established research domains, such as classical data
mining techniques, association, classification, regression, clustering and prediction.
96 S. A. Salloum et al.

In this study, new methods are examined and the latest studies in the field of Learning
Analytics and Educational Data Mining that have used Deep Learning techniques are
determined. It is believed that EDM is an imminent event that is going to alter the
overall field of education. In the scope of future section, the different areas that require
improvement are stated, for example: obtaining bigger datasets, including adaptable
datasets, hybridizing the techniques used, improving the credibility of EDM and per-
forming comparisons between the various methods. An idea of the way the findings of
the studies performed on the suggested lines will enhance the EDM research is pre-
sented in future scope. In addition, other DM fields will benefit from the latest algo-
rithms created. The study will also enhance the confidence of the EDM authorities to
incorporate the recommendations put forward into the practical world systems. The
system will be influenced by the solutions put forward in a way that the students,
teachers and the administration are able to achieve the ideal outcomes from the system.
References
1. Saa, A.A., Al-Emran, M., Shaalan, K.: Factors affecting students’performance in higher
education: a systematic review of predictive data mining techniques. Technol. Knowl. Learn.
24, 567–598 (2019)
2. Salloum, S.A., AlHamad, A.Q., Al-Emran, M., Shaalan, K.: A survey of Arabic text mining,
vol. 740 (2018)
3. Mhamdi, C., Al-Emran, M., Salloum, S.A.: Text mining and analytics: a case study from
news channels posts on Facebook, vol. 740 (2018)
4. Hassanien, A.E., Darwish, A., El-Askary, H.: Machine Learning and Data Mining in
Aerospace Technology. Springer, Cham (2020)
5. Hassanien, A.E.: Machine Learning Paradigms: Theory and Application. Springer, Cham
(2019)
6. Ismail, F.H., Hassanien, A.E.: Extracting valuable associations among textural features of
medical images. In: 2018 13th International Conference on Computer Engineering and
Systems (ICCES), pp. 605–608 (2018)
7. Ahuja, R., Jha, A., Maurya, R., Srivastava, R.: Analysis of educational data mining. In:
Harmony Search and Nature Inspired Optimization Algorithms, pp. 897–907. Springer
(2019)
8. Sarra, A., Fontanella, L., Di Zio, S.: Identifying students at risk of academic failure within
the educational data mining framework. Soc. Indic. Res. 146(1–2), 41–60 (2019)
9. Mohamad, S.K., Tasir, Z.: Educational data mining: a review. Procedia-Soc. Behav. Sci. 97,
320–324 (2013)
10. Baker, R.S.J.D., Yacef, K.: The state of educational data mining in 2009: a review and future
visions. JEDM: J. Educ. Data Min. 1(1), 3–17 (2009)
11. Romero, C., Ventura, S.: Educational data mining: a survey from 1995 to 2005. Expert Syst.
Appl. 33(1), 135–146 (2007)
12. Salloum, S.A., Alhamad, A.Q.M., Al-Emran, M., Monem, A.A., Shaalan, K.: Exploring
students’acceptance of E-learning through the development of a comprehensive technology
acceptance model. IEEE Access 7, 128445–128462 (2019)
13. Alshurideh, M., Salloum, S.A., Al Kurdi, B., Al-Emran, M.: Factors affecting the social
networks acceptance: an empirical study using PLS-SEM approach. In: 8th International
Conference on Software and Computer Applications (2019)
Mining in Educational Data: Review and Future Directions 97

14. Alshurideh, M.T., Salloum, S.A., Al Kurdi, B., Monem, A.A., Shaalan, K.: Understanding
the quality determinants that influence the intention to use the mobile learning platforms: a
practical study. Int. J. Interact. Mob. Technol. 13(11), 157–183 (2019)
15. Mitrofanova, Y.S., Sherstobitova, A.A., Filippova, O.A.: Modeling smart learning processes
based on educational data mining tools. In: Smart Education and e-Learning 2019, pp. 561–
571. Springer (2019)
16. Menaka, M.S., Kesavaraj, G.: A study on e-learning system to analyse student performance
using data mining (2019)
17. Cerezo, R., Bogarín, A., Esteban, M., Romero, C.: Process mining for self-regulated learning
assessment in e-learning. J. Comput. High. Educ. 32,74–88 (2020)
18. Keskin, S., Şahin, M., Yurdugül, H.: Online learners’navigational patterns based on data
mining in terms of learning achievement. In: Learning Technologies for Transforming
Large-Scale Teaching, Learning, and Assessment, pp. 105–121. Springer (2019)
19. Fernandes, E., Holanda, M., Victorino, M., Borges, V., Carvalho, R., Van Erven, G.:
Educational data mining: predictive analysis of academic performance of public school
students in the capital of Brazil. J. Bus. Res. (2018)
20. Salloum, S.A., Al-Emran, M., Monem, A.A., Shaalan, K.: Using text mining techniques for
extracting information from research articles. In: Studies in Computational Intelligence, vol.
740. Springer (2018)
21. Salloum, S.A., Al-Emran, M., Abdallah, S., Shaalan, K.: Analyzing the Arab gulf
newspapers using text mining techniques. In: International Conference on Advanced
Intelligent Systems and Informatics, pp. 396–405 (2017)
22. Salloum, S.A., Al-Emran, M., Shaalan, K.: Mining social media text: extracting knowledge
from Facebook. Int. J. Comput. Digit. Syst. 6(2), 73–81 (2017)
23. Salloum, S.A., Mhamdi, C., Al-Emran, M., Shaalan, K.: Analysis and classification of
Arabic newspapers’Facebook pages using text mining techniques. Int. J. Inf. Technol. Lang.
Stud. 1(2), 8–17 (2017)
24. Cummins, M.R.: Nonhypothesis-driven research: data mining and knowledge discovery. In:
Clinical Research Informatics, pp. 341–356. Springer (2019)
25. Salloum, S.A., Al-Emran, M., Monem, A.A., Shaalan, K.: A survey of text mining in social
media: Facebook and Twitter perspectives. Adv. Sci. Technol. Eng. Syst. J 2(1), 127–133
(2017)
26. Alomari, K.M., AlHamad, A.Q., Salloum, S.: Prediction of the digital game rating systems
based on the ESRB (2019)
27. Arunachalam, A.S., Velmurugan, T.: Analyzing student performance using evolutionary
artificial neural network algorithm. Int. J. Eng. Technol. 7(2.26), 67–73 (2018)
28. Romero, C., Ventura, S., García, E.: Data mining in course management systems: moodle
case study and tutorial. Comput. Educ. 51(1), 368–384 (2008)
29. Sachin, R.B., Vijay, M.S.: A survey and future vision of data mining in educational field. In:
2012 Second International Conference on Advanced Computing & Communication
Technologies, pp. 96–100 (2012)
30. Salloum, S.A., Shaalan, K.: Factors affecting students’acceptance of e-learning system in
higher education using UTAUT and structural equation modeling approaches. In:
International Conference on Advanced Intelligent Systems and Informatics, pp. 469–480
(2018)
31. Salloum, S.A., Al-Emran, M., Habes, M., Alghizzawi, M., Ghani, M.A., Shaalan, K.:
Understanding the impact of social media practices on e-learning systems acceptance. In:
International Conference on Advanced Intelligent Systems and Informatics, pp. 360–369
(2019)
98 S. A. Salloum et al.

32. Salloum, S.A., Mhamdi, C., Al Kurdi, B., Shaalan, K.: Factors affecting the adoption and
meaningful use of social media: a structural equation modeling approach. Int. J. Inf. Technol.
Lang. Stud. 2(3), 96–109 (2018)
33. Salloum, S.A., Maqableh, W., Mhamdi, C., Al Kurdi, B., Shaalan, K.: Studying the social
media adoption by university students in the United Arab Emirates. Int. J. Inf. Technol.
Lang. Stud. 2(3), 83–95 (2018)
34. Salloum, S.A.S., Shaalan, K.: Investigating students’acceptance of e-learning system in
higher educational environments in the UAE: applying the extended technology acceptance
model (TAM). The British University in Dubai (2018)
35. Habes, M., Alghizzawi, M., Khalaf, R., Salloum, S.A., Ghani, M.A.: The relationship
between social media and academic performance: Facebook perspective. Int. J. Inf. Technol.
Lang. Stud. 2(1), 12–18 (2018)
36. Salloum, S.A., Al-Emran, M., Shaalan, K., Tarhini, A.: Factors affecting the E-learning
acceptance: a case study from UAE. Educ. Inf. Technol. 24, 509–530 (2019)
37. Al-Emran, M., Salloum, S.A.: Students’attitudes towards the use of mobile technologies in
e-evaluation. Int. J. Interact. Mob. Technol. 11(5), 195–202 (2017)
38. Kabakchieva, D.: Predicting student performance by using data mining methods for
classification. Cybern. Inf. Technol. 13(1), 61–72 (2013)
39. Durairaj, M., Vijitha, C.: Educational data mining for prediction of student performance
using clustering algorithms. Int. J. Comput. Sci. Inf. Technol. 5(4), 5987–5991 (2014)
40. Francis, B.K., Babu, S.S.: Predicting academic performance of students using a hybrid data
mining approach. J. Med. Syst. 43(6), 162 (2019)
41. Akram, A., et al.: Predicting students’academic procrastination in blended learning course
using homework submission data. IEEE Access 7, 102487–102498 (2019)
42. Rojanavasu, P.: Educational data analytics using association rule mining and classification.
In: 2019 Joint International Conference on Digital Arts, Media and Technology with ECTI
Northern Section Conference on Electrical, Electronics, Computer and Telecommunications
Engineering (ECTI DAMT-NCON), pp. 142–145 (2019)
43. Sana, B., Siddiqui, I.F., Arain, Q.A.: Analyzing students’academic performance through
educational data mining. 3c Tecnol. glosas innovación Apl. a la pyme 8(29), 402–421 (2019)
44. Bharara, S., Sabitha, S., Bansal, A.: Application of learning analytics using clustering data
Mining for Students’disposition analysis. Educ. Inf. Technol. 23(2), 957–984 (2018)
45. Nurhayati, O.D., Bachri, O.S., Supriyanto, A., Hasbullah, M.: Graduation prediction system
using artificial neural network. Int. J. Mech. Eng. Technol. 9(7), 1051–1057 (2018)
46. Rao, K.S., Swapna, N., Kumar, P.P.: Educational data mining for student placement
prediction using machine learning algorithms. Int. J. Eng. Technol. Sci. 7(1.2), 43–46 (2018)
47. Okubo, F., Yamashita, T., Shimada, A., Ogata, H.: A neural network approach for students’
performance prediction. In: LAK 2017, pp. 598–599 (2017)
48. Almarabeh, H.: Analysis of students’performance by using different data mining classifiers.
Int. J. Mod. Educ. Comput. Sci. 9(8), 9 (2017)
49. Alban, M., Mauricio, D.: Neural networks to predict dropout at the universities. Int. J. Mach.
Learn. Comput. 9(2), 149–153 (2019)
50. Feng, J.: Predicting students’academic performance with decision tree and neural network
(2019)
51. Jie, W., Hai-yan, L., Biao, C., Yuan, Z.: Application of educational data mining on analysis
of students’online learning behavior. In: 2017 2nd International Conference on Image,
Vision and Computing (ICIVC), pp. 1011–1015 (2017)
Mining in Educational Data: Review and Future Directions 99

52. Lara, J.A., Lizcano, D., Martínez, M.A., Pazos, J., Riera, T.: A system for knowledge
discovery in e-learning environments within the European Higher Education Area-
Application to student data from Open University of Madrid, UDIMA. Comput. Educ.
72,23–36 (2014)
53. Chakraborty, B., Chakma, K., Mukherjee, A.: A density-based clustering algorithm and
experiments on student dataset with noises using Rough set theory. In: 2016 IEEE
International Conference on Engineering and Technology (ICETECH), pp. 431–436 (2016)
54. Chauhan, N., Shah, K., Karn, D., Dalal, J.: Prediction of student’s performance using
machine learning (2019). SSRN 3370802
55. Pechenizkiy, M., Calders, T., Vasilyeva, E., De Bra, P.: Mining the student assessment data:
lessons drawn from a small scale case study. In: Educational Data Mining 2008 (2008)
56. Shih, Y.-C., Huang, P.-R., Hsu, Y.-C., Chen, S.Y.: A complete understanding of
disorientation problems in Web-based learning. Turkish Online J. Educ. Technol. 11(3),
1–13 (2012)
57. Talavera, L., Gaudioso, E.: Mining student data to characterize similar behavior groups in
unstructured collaboration spaces. In: Workshop on Artificial Intelligence in CSCL. 16th
European Conference on Artificial Intelligence, pp. 17–23 (2004)
58. Perera, D., Kay, J., Koprinska, I., Yacef, K., Zaïane, O.R.: Clustering and sequential pattern
mining of online collaborative learning data. IEEE Trans. Knowl. Data Eng. 21(6), 759–772
(2008)
59. Dutt, A., Aghabozrgi, S., Ismail, M.A.B., Mahroeian, H.: Clustering algorithms applied in
educational data mining. Int. J. Inf. Electron. Eng. 5(2), 112 (2015)
60. Bogarín, A., Romero, C., Cerezo, R., Sánchez-Santillán, M.: Clustering for improving
educational process mining. In: Proceedings of the Fourth International Conference on
Learning Analytics and Knowledge, pp. 11–15 (2014)
61. Fernandes, E., Holanda, M., Victorino, M., Borges, V., Carvalho, R., Van Erven, G.:
Educational data mining: predictive analysis of academic performance of public school
students in the capital of Brazil. J. Bus. Res. 94, 335–343 (2019)
62. Palomo-Duarte, M., Berns, A., Yañez Escolano, A., Dodero, J.-M.: Clustering analysis of
game-based learning: worth it for all students? J. Gaming Virtual Worlds 11(1), 45–66
(2019)
63. Ahmed, A.B.E.D., Elaraby, I.S.: Data mining: a prediction for student’s performance using
classification method. World J. Comput. Appl. Technol. 2(2), 43–47 (2014)
64. Anjewierden, A., Kolloffel, B., Hulshof, C.: Towards educational data mining: using data
mining methods for automated chat analysis to understand and support inquiry learning
processes (2007)
65. Adebayo, A.O., Chaubey, M.S.: Data mining classification techniques on the analysis of
student’s performance. GSJ 7(4), 45–52 (2019)
66. Kay, J., Maisonneuve, N., Yacef, K., Zaïane, O.: Mining patterns of events in students’
teamwork data. In: Proceedings of the Workshop on Educational Data Mining at the 8th
International Conference on Intelligent Tutoring Systems (ITS 2006), pp. 45–52 (2006)
67. Tiwari, A.K., Ramakrishna, G., Sharma, L.K., Kashyap, S.K.: Academic performance
prediction algorithm based on fuzzy data mining. Int. J. Artif. Intelegence 8(1), 26–32 (2019)
68. Merceron, A., Yacef, K.: Revisiting interestingness of strong symmetric association rules in
educational data. In: Proceedings of the International Workshop on Applying Data Mining in
e-Learning, Creete, Greece, pp. 3–12 (2007)
69. García, E., Romero, C., Ventura, S., Calders, T.: Drawbacks and solutions of applying
association rule mining in learning management systems. In: Proceedings of the International
Workshop on Applying Data Mining in e-Learning (ADML 2007), Crete, Greece, pp. 13–22
(2007)
100 S. A. Salloum et al.

70. Samuel, A.L.: Some studies in machine learning using the game of checkers. II—recent
progress. IBM J. Res. Dev. 11(6), 601–617 (1967)
71. Alpaydin, E.: Introduction to Machine Learning. MIT Press, Cambridge (2009)
72. Kučak, D., Juričić, V., Đambić, G.: Machine learning in education-a survey of current
research trends. In: Annals of DAAAM and Proceedings, vol. 29 (2018)
73. Stahl, F., Jordanov, I.: An overview of the use of neural networks for data mining tasks.
Wiley Interdiscip Rev. Data Min. Knowl. Discov. 2(3), 193–208 (2012)
74. Coelho, O.B., Silveira, I.: Deep learning applied to learning analytics and educational data
mining: a systematic literature review. In: Brazilian Symposium on Computers in Education
(Simpósio Brasileiro de Informática na Educação-SBIE), vol. 28, no. 1, p. 143 (2017)
75. Vellido, A., Castro, F., Nebot, A.: Clustering educational data. In: Handbook of Educational
Data Mining, pp. 75–92 (2010)
76. Li, J., Wong, Y., Kankanhalli, M.S.: Multi-stream deep learning framework for automated
presentation assessment. In: 2016 IEEE International Symposium on Multimedia (ISM),
pp. 222–225 (2016)
77. Gross, E., Wshah, S., Simmons, I., Skinner, G.: A handwriting recognition system for the
classroom. In: Proceedings of the Fifth International Conference on Learning Analytics and
Knowledge, pp. 218–222 (2015)
78. Guo, B., Zhang, R., Xu, G., Shi, C., Yang, L.: Predicting students performance in
educational data mining. In: 2015 International Symposium on Educational Technology
(ISET), pp. 125–128 (2015)
79. Tang, S., Peterson, J.C., Pardos, Z.A.: Deep neural networks and how they apply to
sequential education data. In: Proceedings of the Third (2016) ACM Conference on Learning
@ Scale, pp. 321–324 (2016)
80. Wang, L., Sy, A., Liu, L., Piech, C.: Deep knowledge tracing on programming exercises. In:
Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale, pp. 201–204
(2017)
81. Craven, M.W., Shavlik, J.W.: Using neural networks for data mining. Futur. Gener. Comput.
Syst. 13(2–3), 211–229 (1997)
82. Anozie, N., Junker, B.W.: Predicting end-of-year accountability assessment scores from
monthly student records in an online tutoring system (2006)
83. Khan, I., Al Sadiri, A., Ahmad, A.R., Jabeur, N.: Tracking student performance in
introductory programming by means of machine learning. In: 2019 4th MEC International
Conference on Big Data and Smart City (ICBDSC), pp. 1–6 (2019)
84. Livieris, I.E., Drakopoulou, K., Tampakas, V.T., Mikropoulos, T.A., Pintelas, P.: Predicting
secondary school students’performance utilizing a semi-supervised learning approach.
J. Educ. Comput. Res. 57(2), 448–470 (2019)
85. Yadav, S.K., Pal, S.: Data mining: a prediction for performance improvement of engineering
students using classification, arXiv Prepr. arXiv:1203.3832 (2012)
86. Yadav, S.K., Bharadwaj, B., Pal, S.: Mining education data to predict student’s retention: a
comparative study, arXiv Prepr. arXiv:1203.2987 (2012)
87. Akinola, O.S., Akinkunmi, B.O., Alo, T.S.: A data mining model for predicting computer
programming proficiency of computer science undergraduate students (2012)
88. Luckin, R., Holmes, W., Griffiths, M., Forcier, L.B.: Intelligence unleashed: an argument for
AI in education (2016)
89. Meseguer-Brocal, G., Cohen-Hadria, A., Peeters, G.: DALI: a large dataset of synchronized
audio, lyrics and notes, automatically created using teacher-student machine learning
paradigm, arXiv Prepr. arXiv:1906.10606 (2019)
90. El-Alfy, E.-S.M., Abdel-Aal, R.E.: Construction and analysis of educational tests using
abductive machine learning. Comput. Educ. 51(1), 1–16 (2008)
Mining in Educational Data: Review and Future Directions 101

91. Đambić, G., Krajcar, M., Bele, D.: Machine learning model for early detection of higher
education students that need additional attention in introductory programming courses. Int.
J. Digit. Technol. Econ. 1(1), 1–11 (2016)
92. Celar, S., Stojkic, Z., Seremet, Z., Marusic, Z., Zelenika, D.: Classification of test documents
based on handwritten student ID’s characteristics. Procedia Eng. 100, 782–790 (2015)
93. Pechenizkiy, M., Trcka, N., Vasilyeva, E., Van der Aalst, W., De Bra, P.: Process mining
online assessment data. In: International Working Group on Educational Data Mining (2009)
102 S. A. Salloum et al.