Predicting Academic Outcomes: A Survey from 2007 Till 2018

The tremendous growth of educational institutions’ electronic data provides the opportunity to extract information that can be used to predict students’ overall success, predict students’ dropout rate, evaluate the performance of teachers and instructors, improve the learning material according to students’ needs, and much more. This paper aims to review the latest trends in predicting students’ performance in higher education. We provide a comprehensive background for understanding Educational Data Mining (EDM). We also explain the measures of determining academic success and highlight the strengths and weaknesses of the most common data mining (DM) tools and methods used nowadays. Moreover, we provide a rich literature review of the EDM work that has been published during the past 12 years (2007–2018) with focus on the prediction of academic performance in higher education. We analyze the most commonly used features and methods in predicting academic achievement, and highlight the benefits of the mostly used DM tools in EDM. The results of this paper could assist researchers and educational planners who are attempting to carry out EDM solutions in the domain of higher education as we highlight the type of features that the previous researches found to have significant impact on the prediction, as well as the benefits and drawbacks of the DM methods and tools used for predicting academic outcomes.

[1]  J. L. Holland,et al.  Prediction of academic and extra-curricular achievement in college. , 1964 .

[2]  Edin Osmanbegović,et al.  DATA MINING APPROACH FOR PREDICTING STUDENT PERFORMANCE , 2012 .

[3]  Miltiadis D. Lytras,et al.  Predicting Student Performance using Advanced Learning Analytics , 2017, WWW.

[4]  Allan Tucker,et al.  The Prediction of Student Failure Using Classification Methods : A Case study , 2018 .

[5]  Zlatko J. Kovacic,et al.  Early Prediction of Student Success: Mining Students Enrolment Data , 2010 .

[6]  Divya Tomar,et al.  A survey on Data Mining approaches for Healthcare , 2013, BSBT 2013.

[7]  Johannes Berens,et al.  Early Detection of Students at Risk – Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods , 2018, SSRN Electronic Journal.

[8]  Pedro M. Domingos.  Rule Induction and Instance-Based Learning: A Unified Approach , 1995, IJCAI.

[9]  B. Umamaheswari,et al.  A Survey on Educational Data Mining in Field of Education , 2016 .

[10]  Yuzhuo Cai,et al.  The Role of Higher Education in Society and the Changing Institutionalized Features in Higher Education , 2015 .

[11]  Shaobo Huang,et al.  Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models , 2013, Comput. Educ..

[12]  Alejandro Peña-Ayala.  Review: Educational data mining: A survey and a data mining-based analysis of recent works , 2014 .

[13]  Muluken Alemu Yehuala.  Application Of Data Mining Techniques For Student Success And Failure Prediction (The Case Of Debre_Markos University) , 2015 .

[14]  Isma Farah Siddiqui,et al.  Predicting Students’ Academic Performance Through Supervised Machine Learning , 2020, 2020 International Conference on Information Science and Communication Technology (ICISCT).

[15]  John A. Johnson,et al.  The international personality item pool and the future of public-domain personality measures ☆ , 2006 .

[16]  Sadri Alija,et al.  How Attendance Affects the General Success of the Student , 2013 .

[17]  Fahad Munir,et al.  Factors Contributing to the Students Academic Performance: A Case Study of Islamia University Sub- Campus , 2013 .

[18]  Nawal Ali Yassein,et al.  Predicting Student Academic Performance in KSA using Data Mining Techniques , 2017 .

[19]  Kiri Wagstaff,et al.  Machine learning in space: extending our reach , 2011, Machine Learning.

[20]  D. Blazquez,et al.  Relationship between class attendance and student performance , 2016 .

[21]  F. Rafiei,et al.  Prediction of academic achievement based on learning strategies and outcome expectations among medical students , 2019, BMC Medical Education.

[22]  Mohamed El Zeweidy,et al.  A Comparative Analysis of Techniques for Predicting Academic Performance , 2013 .

[23]  Ying LU,et al.  Decision tree methods: applications for classification and prediction , 2015, Shanghai archives of psychiatry.

[24]  Afnan Algobail,et al.  Predicting Students’ Performance in University Courses: A Case Study and Tool in KSU Mathematics Department☆ , 2016 .

[25]  Gender Differences in Science Achievement, Science Self-concept, and Science Values , 2008 .

[26]  Alejandro Peña Ayala,et al.  Educational data mining: A survey and a data mining-based analysis of recent works , 2014, Expert Syst. Appl..

[27]  M. Hemalatha,et al.  Effectiveness Evaluation of Rule Based Classifiers for the Classification of Iris Data Set , 2012 .

[28]  A. Kaushal,et al.  Comparative Analysis to Highlight Pros and Cons of Data Mining Techniques-Clustering , Neural Network and Decision Tree , 2014 .

[29]  David Watkins,et al.  A longitudinal study of the approaches to learning of Australian tertiary students. , 1985 .

[30]  Nick Cercone,et al.  Integrating Rule Induction and Case-Based Reasoning to Enhance Problem Solving , 1997, ICCBR.

[31]  Educational Data Mining with Focus on Dropout Rates , 2015 .

[32]  T. Eitle.  DO GENDER AND RACE MATTER? EXPLAINING THE RELATIONSHIP BETWEEN SPORTS PARTICIPATION AND ACHIEVEMENT , 2005 .

[33]  G. Franck.  Open access , 2012, Cell cycle.

[34]  Surjeet Kumar Yadav,et al.  Data Mining: A Prediction for Performance Improvement of Engineering Students using Classification , 2012, ArXiv.

[35]  Teknik Informatika,et al.  PREDICTION OF STUDENT ACADEMIC PERFORMANCE BY AN APPLICATION OF DATA MINING TECHNIQUES , 2011 .

[36]  Bhumika Gupta,et al.  Analysis of Various Decision Tree Algorithms for Classification in Data Mining , 2017 .

[37]  Syed Abbas Ali,et al.  Analyzing undergraduate students' performance using educational data mining , 2017, Comput. Educ..

[38]  M. A. Alekhina,et al.  The Reliability of Circuits in the Basis Anticonjunction with Constant Faults of Gates , 2014 .

[39]  Samuel DiGangi,et al.  A Data Mining Approach for Identifying Predictors of Student Retention from Sophomore to Junior Year , 2021, Journal of Data Science.

[40]  M. Geng.  A COMPARISON OF LOGISTIC REGRESSION TO RANDOM FORESTS FOR EXPLORING DIFFERENCES IN RISK FACTORS ASSOCIATED WITH STAGE ATDIAGNOSIS BETWEEN BLACK AND WHITE COLON CANCER PATIENTS , 2006 .

[41]  Michael C. Sturman,et al.  Searching for the Inverted U-Shaped Relationship Between Time and Performance: Meta-Analyses of the Experience/Performance, Tenure/Performance, and Age/Performance Relationships , 2003 .

[42]  Tanuja Sharma,et al.  Educational Data Mining-Students Performance Prediction , 2019, International Journal for Research in Applied Science and Engineering Technology.

[43]  Lorenz Kemper,et al.  Predicting student dropout: A machine learning approach , 2020, European Journal of Higher Education.

[44]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[45]  Tutut Herawan,et al.  A Systematic Review on Educational Data Mining , 2017, IEEE Access.

[46]  Mohammed Waziri Bularafa,et al.  Gender Difference in Students’ Academic Performance in Colleges of Education in Borno State, Nigeria: Implications for Counselling , 2015 .

[47]  Mukesh Kumar,et al.  Literature Survey on Student’s Performance Prediction in Education using Data Mining Techniques , 2017 .

[48]  Wu Zhang,et al.  Using machine learning to predict student difficulties from learning session data , 2018, Artificial Intelligence Review.

[49]  Ali Şimşek,et al.  Learning Strategies of Successful and Unsuccessful University Students , 2010 .

[50]  Dorina Kabakchieva,et al.  Predicting Student Performance by Using Data Mining Methods for Classification , 2013 .

[51]  Sebastián Ventura,et al.  Educational data mining: A survey from 1995 to 2005 , 2007, Expert Syst. Appl..

[52]  Odunayo Salau,et al.  The impact of engineering students' performance in the first three years on their graduation result using educational data mining , 2019, Heliyon.

[53]  Jevin D. West,et al.  Predicting Student Dropout in Higher Education , 2016, ArXiv.

[54]  Surjeet Kumar Yadav,et al.  Data Mining Applications: A comparative Study for Predicting Student's performance , 2012, ArXiv.

[55]  Jeena Thomas,et al.  Predicting College Students Dropout using EDM Techniques , 2015 .

[56]  Nguyen Thai Nghe,et al.  A comparative analysis of techniques for predicting academic performance , 2007, 2007 37th Annual Frontiers In Education Conference - Global Engineering: Knowledge Without Borders, Opportunities Without Passports.

[57]  Cynthia Demetriou,et al.  Integration , Motivation , Strengths and Optimism : Retention Theories Past , Present and Future , 2012 .

[58]  U. Kim.  Individualism and Collectivism : A Psychological, Cultural and Ecological Analysis , 1995 .

[59]  P. M. Arsad.  Students ’ English language proficiency and its impact on the overall student ’ s academic performance : An analysis and prediction using Neural Network Model , 2014 .

[60]  Ajay Kumar Pal.  Analysis and Mining of Educational Data for Predicting the Performance of Students , 2013 .

[61]  Robert A. Wooster,et al.  Marital Status and Academic Performance in College. , 1979 .

[62]  The Effect of Academic Load on Success for New College Students: Is Lighter Better? , 2002 .

[63]  Isaac W. Wait,et al.  Relationship Between TOEFL Score and Academic Success for International Engineering Students , 2009 .