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Journal Article•DOI•

Literature Survey on Student’s Performance Prediction in Education using Data Mining Techniques

Mukesh Kumar, Amanpal Singh, Disha Handa
08 Nov 2017-International Journal of Education and Management Engineering-Vol. 7, Iss: 6, pp 40-49
TL;DR: The main objective of this article is to provide a great knowledge and understanding of different data mining techniques which have been used to predict the student progress and performance and hence how these prediction techniques help to find the most important student attribute for prediction.
Abstract: One of the most challenging tasks in the education sector in India is to predict student's academic performance due to a huge volume of student data. In the Indian context, we don't have any existing system by which analyzing and monitoring can be done to check the progress and performance of the student mostly in Higher education system. Every institution has their own criteria for analyzing the performance of the students. The reason for this happing is due to the lack of study on existing prediction techniques and hence to find the best prediction methodology for predicting the student academics progress and performance. Another important reason is the lack in investigating the suitable factors which affect the academic performance and achievement of the student in particular course. So to deeply understand the problem, a detail literature survey on predicting student’s performance using data mining techniques is proposed. The main objective of this article is to provide a great knowledge and understanding of different data mining techniques which have been used to predict the student progress and performance and hence how these prediction techniques help to find the most important student attribute for prediction. Actually, we want to improve the performance of the student in academic by using best data mining techniques. At last, it could also provide some benefits for faculties, students, educators and management of the institution.

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Citations
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Proceedings Article•DOI•
Predicting academic performance: a systematic literature review

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Arto Hellas1, Petri Ihantola1, Andrew Petersen2, Vangel V. Ajanovski, Mirela Gutica3, Timo Hynninen, Antti Knutas4, Juho Leinonen1, Chris Messom5, Soohyun Nam Liao6 •
University of Helsinki1, University of Toronto2, British Columbia Institute of Technology3, Lappeenranta University of Technology4, Monash University5, University of California, San Diego6
02 Jul 2018
TL;DR: In this paper, the authors present a systematic literature review of work in the area of predicting student performance, which shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used.
Abstract: The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.

172 citations

Journal Article•DOI•
Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review

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Abdallah Namoun, Abdullah Alshanqiti
29 Dec 2020-Applied Sciences
TL;DR: A decade of research work conducted between 2010 and November 2020 was surveyed to present a fundamental understanding of the intelligent techniques used for the prediction of student performance, where academic success is strictly measured using student learning outcomes as discussed by the authors.
Abstract: The prediction of student academic performance has drawn considerable attention in education. However, although the learning outcomes are believed to improve learning and teaching, prognosticating the attainment of student outcomes remains underexplored. A decade of research work conducted between 2010 and November 2020 was surveyed to present a fundamental understanding of the intelligent techniques used for the prediction of student performance, where academic success is strictly measured using student learning outcomes. The electronic bibliographic databases searched include ACM, IEEE Xplore, Google Scholar, Science Direct, Scopus, Springer, and Web of Science. Eventually, we synthesized and analyzed a total of 62 relevant papers with a focus on three perspectives, (1) the forms in which the learning outcomes are predicted, (2) the predictive analytics models developed to forecast student learning, and (3) the dominant factors impacting student outcomes. The best practices for conducting systematic literature reviews, e.g., PICO and PRISMA, were applied to synthesize and report the main results. The attainment of learning outcomes was measured mainly as performance class standings (i.e., ranks) and achievement scores (i.e., grades). Regression and supervised machine learning models were frequently employed to classify student performance. Finally, student online learning activities, term assessment grades, and student academic emotions were the most evident predictors of learning outcomes. We conclude the survey by highlighting some major research challenges and suggesting a summary of significant recommendations to motivate future works in this field.

116 citations

Cites methods from "Literature Survey on Student’s Perf..."

  • ...Performance prediction using data mining techniques [34]; Unindexed Journal Systematic review Six databases Prediction accuracy (%) Data mining techniques (2007—July 2016) − Did not survey student outcomes − Reported only five techniques − Did not discuss the limitations − Adopted a weak survey methodology + Discussed the factors predicting student performance...

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Journal Article•DOI•
Student performance analysis and prediction in classroom learning: A review of educational data mining studies

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Anupam Khan1, Soumya K. Ghosh1•
Indian Institute of Technology Kharagpur1
01 Jan 2021-Education and Information Technologies
TL;DR: A systematic review of EDM studies on student performance in classroom learning focuses on identifying the predictors, methods used for such identification, time and aim of prediction, and is significantly the first systematic survey ofEDM studies that consider only classroom learning and focuses on the temporal aspect as well.
Abstract: Student performance modelling is one of the challenging and popular research topics in educational data mining (EDM). Multiple factors influence the performance in non-linear ways; thus making this field more attractive to the researchers. The widespread availability of e ducational datasets further catalyse this interestingness, especially in online learning. Although several EDM surveys are available in the literature, we could find only a few specific surveys on student performance analysis and prediction. These specific surveys are limited in nature and primarily focus on studies that try to identify possible predictor or model student performance. However, the previous works do not address the temporal aspect of prediction. Moreover, we could not find any such specific survey which focuses only on classroom-based education. In this paper, we present a systematic review of EDM studies on student performance in classroom learning. It focuses on identifying the predictors, methods used for such identification, time and aim of prediction. It is significantly the first systematic survey of EDM studies that consider only classroom learning and focuses on the temporal aspect as well. This paper presents a review of 140 studies in this area. The meta-analysis indicates that the researchers achieve significant prediction efficiency during the tenure of the course. However, performance prediction before course commencement needs special attention.

79 citations

Cites background from "Literature Survey on Student’s Perf..."

  • ...A few interesting EDM studies are available on text mining and social network analysis (Akçapinar 2015; Bayer et al. 2012; Chung and Kim 2016; Foley and Allan 2016; Montuschi et al. 2015; Pong-Inwong and Rungworawut 2012; Rani and Kumar 2017; Rekha et al. 2012; Romero et al. 2013)....

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  • ...In addition to formal measures, sentiment analysis of student feedback is an indirect assessment which facilitates the teachers to assess the students’ interest in class (Rani and Kumar 2017)....

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Book Chapter•DOI•
Predicting Pupil’s Successfulness Factors Using Machine Learning Algorithms and Mathematical Modelling Methods

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Solomia Fedushko1, Taras Ustyianovych1•
Lviv Polytechnic1
26 Jan 2019
TL;DR: An algorithm of actions on machine learning using, determining the students success level and analyzing the obtained data is described, showing that performance metrics assessment is an integral part of modern education process that is slightly crucial for its improvement and pupil’s trends in education exploration.
Abstract: Taking into account the challenges and problems that are faced by the modern educational process, it is considered to use modern intelligent systems and algorithms to improve the education and teaching levels in educational institutions. The article describes an algorithm of actions on machine learning using, determining the students success level and analyzing the obtained data. This research can be efficiently used to find out and detect the modern educational problems, and individual and collective pupils sample features, implement the classification process and regression analysis of the data set. Results obtained from the algorithms usage, data analysis are described and demonstrated. The main features, knowledge and insights obtaining methods from the dataset are determined. The applied method is quite efficient and is capable of assessing pupil’s performance metrics. Predicting student’s and pupil’s characteristics will help to segment and divide them into different classes so that it will allow pupils to develop communication, leadership, and self-management skills while studying at school or university. The results show that performance metrics assessment is an integral part of modern education process that is slightly crucial for its improvement and pupil’s trends in education exploration.

44 citations

Journal Article•DOI•
Toward Predicting Student’s Academic Performance Using Artificial Neural Networks (ANNs)

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Yahia Baashar, Gamal Alkawsi, Abdulsalam Salihu Mustafa, Ammar Ahmed Alkahtani, Yazan A. Alsariera, Abdulrazzaq Qasem Ali, Wahidah Hashim, Sieh Kiong Tiong 
26 Jan 2022-Applied Sciences
TL;DR: In this article , the authors examined and surveyed the current literature regarding the ANN methods used in predicting students' academic performance and attempted to capture a pattern of the most used ANN techniques and algorithms.
Abstract: Student performance is related to complex and correlated factors. The implementation of a new advancement of technologies in educational displacement has unlimited potentials. One of these advances is the use of analytics and data mining to predict student academic accomplishment and performance. Given the existing literature, machine learning (ML) approaches such as Artificial Neural Networks (ANNs) can continuously be improved. This work examines and surveys the current literature regarding the ANN methods used in predicting students’ academic performance. This study also attempts to capture a pattern of the most used ANN techniques and algorithms. Of note, the articles reviewed mainly focused on higher education. The results indicated that ANN is always used in combination with data analysis and data mining methodologies, allowing studies to assess the effectiveness of their findings in evaluating academic achievement. No pattern was detected regarding selecting the input variables as they are mainly based on the context of the study and the availability of data. Moreover, the very limited tangible findings referred to the use of techniques in the actual context and target objective of improving student outcomes, performance, and achievement. An important recommendation of this work is to overcome the identified gap related to the only theoretical and limited application of the ANN in a real-life situation to help achieve the educational goals.

22 citations

References
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Posted Content•
Data Mining Approach for Predicting Student Performance

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Edin Osmanbegovic, Mirza Suljic
01 Jan 2012-Econometric Reviews
TL;DR: In this article, different methods and techniques of data mining were compared during the prediction of students' success, applying the data collected from the surveys conducted during the summer semester at the University of Tuzla, the Faculty of Economics, academic year 2010-2011, among first year students and the data taken during the enrollment.
Abstract: Although data mining has been successfully implemented in the business world for some time now, its use in higher education is still relatively new, i.e. its use is intended for identification and extraction of new and potentially valuable knowledge from the data. Using data mining the aim was to develop a model which can derive the conclusion on students' academic success. Different methods and techniques of data mining were compared during the prediction of students' success, applying the data collected from the surveys conducted during the summer semester at the University of Tuzla, the Faculty of Economics, academic year 2010-2011, among first year students and the data taken during the enrollment. The success was evaluated with the passing grade at the exam. The impact of students' socio-demographic variables, achieved results from high school and from the entrance exam, and attitudes towards studying which can have an affect on success, were all investigated. In future investigations, with identifying and evaluating variables associated with process of studying, and with the sample increase, it would be possible to produce a model which would stand as a foundation for the development of decision support system in higher education.

243 citations

"Literature Survey on Student’s Perf..." refers background or methods in this paper

  • ...The family attributes like parent’s qualification, parent’s occupation, family income, family status, Family Support for study are also taken as important for the academics prediction [7, 9, 15, 19, 24]....

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  • ...The most important personal attributes of the student like gender, age, interested in the study, admission type, Study Behaviour are taken into consideration [7, 8, 9, 11, 12, 13, 18, 19, 24]....

    [...]

  • ...Whereas for academic attributes like high school grade, students’ previous semester marks, class test grade, seminar performance, assignment performance, attendance in class and lab work, previous schools marks are taken into consideration [5, 6, 7, 8, 9, 10, 15, 16, 18, 19, 24] and for institutional attributes most the researcher are taken medium of teaching, accommodation type, infrastructure, water and toilet facilities, teaching methodology, transportation facilities into consideration[4, 7, 9, 12, 16, 18, 24]....

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  • ...They further used the different attribute for the prediction like Gender, Family, Distance, High School, GPA, Entrance exam, Scholarships, Time, Materials, the Internet, Grade importance, Earnings [9]....

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Journal Article•DOI•
Classification and Prediction Based Data Mining Algorithms to Predict Slow Learners in Education Sector

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Parneet Kaur1, Manpreet Singh2, Gurpreet Singh Josan3•
Punjab Technical University1, Guru Nanak Dev Engineering College, Ludhiana2, Punjabi University3
01 Jan 2015-Procedia Computer Science
TL;DR: This paper focus on identifying the slow learners among students and displaying it by a predictive data mining model using classification based algorithms and a knowledge flow model is also shown among all five classifiers.

212 citations

Journal Article•DOI•
Data Mining: A prediction for Student's Performance Using Classification Method

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Abeer Badr El Din Ahmed, Ibrahim Sayed Elaraby
01 Jan 2014
TL;DR: The classification task is used to predict the final grade of students and as there are many approaches that are used for data classification, the decision tree (ID3) method is used here.
Abstract: Currently the amount huge of data stored in educational database these database contain the useful information for predict of students performance. The most useful data mining techniques in educational database is classification. In this paper, the classification task is used to predict the final grade of students and as there are many approaches that are used for data classification, the decision tree (ID3) method is used here.

181 citations

Journal Article•DOI•
The Prediction of Students' Academic Performance Using Classification Data Mining Techniques

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Fadhilah Ahmad, Nur Hafieza Ismail, Azwa Abdul Aziz
01 Jan 2015-Applied mathematical sciences
TL;DR: This paper proposes a framework for predicting students’ academic performance of first year bachelor students in Computer Science course and shows the Rule Based is a best model among the other techniques by receiving the highest accuracy value.
Abstract: Data Mining provides powerful techniques for various fields including education. The research in the educational field is rapidly increasing due to the massive amount of students’ data which can be used to discover valuable pattern pertaining students’ learning behaviour. This paper proposes a framework for predicting students’ academic performance of first year bachelor students in Computer Science course. The data were collected from 8 year period intakes from July 2006/2007 until July 2013/2014 that contains the students’ demographics, previous academic records, and family background information. Decision Tree, Naive Bayes, and Rule Based classification techniques are applied to the students’ data in order to produce the best students’ academic performance prediction model. The experiment result shows the Rule Based is a best model among the other techniques by receiving the highest accuracy value of 71.3%. The extracted knowledge from prediction model will be used to identify and profile the student to determine the students’ level of success in the first semester.

112 citations

"Literature Survey on Student’s Perf..." refers background or methods in this paper

  • ...The family attributes like parent’s qualification, parent’s occupation, family income, family status, Family Support for study are also taken as important for the academics prediction [7, 9, 15, 19, 24]....

    [...]

  • ...The most important personal attributes of the student like gender, age, interested in the study, admission type, Study Behaviour are taken into consideration [7, 8, 9, 11, 12, 13, 18, 19, 24]....

    [...]

  • ...Whereas for academic attributes like high school grade, students’ previous semester marks, class test grade, seminar performance, assignment performance, attendance in class and lab work, previous schools marks are taken into consideration [5, 6, 7, 8, 9, 10, 15, 16, 18, 19, 24] and for institutional attributes most the researcher are taken medium of teaching, accommodation type, infrastructure, water and toilet facilities, teaching methodology, transportation facilities into consideration[4, 7, 9, 12, 16, 18, 24]....

    [...]

  • ...They further used nine different parameters like gender, race and hometown, GPA, family income, university entry mode, grades Malay Language, English, and Mathematics [7]....

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Journal Article•DOI•
Predicting Student Academic Performance at Degree Level: A Case Study

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Raheela Asif, Agathe Merceron, Mahmood K. Pathan
08 Dec 2014-International Journal of Intelligent Systems and Applications
TL;DR: A case study on predicting performance of students at the end of a university degree at an early stage of the degree program, in order to help universities not only to focus more on bright students but also to initially identify students with low academic achievement and find ways to support them.
Abstract: Universities gather large volumes of data with reference to their students in electronic form. The advances in the data mining field make it possible to mine these educational data and find information that allow for innovative ways of supporting both teachers and students. This paper presents a case study on predicting performance of students at the end of a university degree at an early stage of the degree program, in order to help universities not only to focus more on bright students but also to initially identify students with low academic achievement and find ways to support them. The data of four academic cohorts comprising 347 undergraduate students have been mined with different classifiers. The results show that it is possible to predict the graduation performance in 4th year at university using only pre-university marks and marks of 1st and 2nd year courses, no socio-economic or demographic features, with a reasonable accuracy. Furthermore courses that are indicators of particularly good or poor performance have been identified.

91 citations

"Literature Survey on Student’s Perf..." refers background or methods in this paper

  • ...They used HSC marks, marks in MPC, Maths marks in HSC, marks in various subject studied in the regular course of a programming language, CSA, Logic design, OOP, DBMS, ALP, FAM, SAD, Data Structure etc for their analysis [10]....

    [...]

  • ...Whereas for academic attributes like high school grade, students’ previous semester marks, class test grade, seminar performance, assignment performance, attendance in class and lab work, previous schools marks are taken into consideration [5, 6, 7, 8, 9, 10, 15, 16, 18, 19, 24] and for institutional attributes most the researcher are taken medium of teaching, accommodation type, infrastructure, water and toilet facilities, teaching methodology, transportation facilities into consideration[4, 7, 9, 12, 16, 18, 24]....

    [...]

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