Educational Data Mining: A Review of the State of the Art

Cited by (1,000)

Items 101–200 of 1,000

1. Predicting Drop-Out from Social Behaviour of Students.
   Jaroslav Bayer, Hana Bydžovská, Jan Géryk, Tomáš Obšívač, Lubomír Popelínský · 2012 · Educational Data Mining
2. Using Data Mining for Predicting Relationships between Online Question Theme and Final Grade
   M’hammed Abdous, Wu He, Cherng‐Jyh Yen · 2012 · ODU Digital Commons (Old Dominion University)
3. Student Academic Performance Prediction by using Decision Tree Algorithm
   Raza Hasan, Sellappan Palaniappan, Abdul Rafiez Abdul Raziff, Salman Mahmood, Kamal Uddin Sarker · 2018
4. Piecing the learning analytics puzzle: a consolidated model of a field of research and practice
   Dragan Gašević, Vitomir Kovanović, Srécko Joksimovíc · 2017 · Learning Research and Practice
5. Achieving big data privacy in education
   Joël R. Reidenberg, Florian Schaub · 2018 · Theory and Research in Education
6. Clustering Algorithms Applied in Educational Data Mining
   Ashish Dutt · 2014 · International Journal of Information and Electronics Engineering
7. Leveraging educational data mining for real-time performance assessment of scientific inquiry skills within microworlds.
   Gobert, Janice D., Sao Pedro, Michael A., Baker, Ryan S.J.d., Toto, Ermal, Montalvo, Orlando · 2012 · Zenodo
8. Penetrating the black box of time-on-task estimation
   Vitomir Kovanović, Dragan Gašević, Shane Dawson, Srécko Joksimovíc, Ryan S. Baker, Marek Hatala · 2015 · International Conference on Learning Analytics and Knowledge (LAK)
9. Quantitative approach to collaborative learning: performance prediction, individual assessment, and group composition
   Ling Cen, Dymitr Ruta, Leigh Powell, Benjamin Hirsch, Jason Ng · 2016 · International Journal of Computer-Supported Collaborative Learning
10. Machine learning approaches to predict learning outcomes in Massive open online courses
    Raghad Al-Shabandar, Abir Jaffar Hussain, Andy Laws, Robert Keight, Janet Lunn, Naeem Radi · 2017 · International Joint Conference on Neural Networks (IJCNN)
11. What public media reveals about MOOC s: A systematic analysis of news reports
    Vitomir Kovanović, Srécko Joksimovíc, Dragan Gašević, George Siemens, Marek Hatala · 2015 · British Journal of Educational Technology
12. Understanding smartphone usage in college classrooms: A long-term measurement study
    Inyeop Kim, Rihun Kim, Heepyung Kim, Duyeon Kim, Kyungsik Han, Paul Hong Lee, Gloria J. Mark, Uichin Lee · 2019 · Computers & Education
13. Data Mining in Education
    Abdulmohsen Algarni · 2016 · International Journal of Advanced Computer Science and Applications
14. Learning or Forgetting? A Dynamic Approach for Tracking the Knowledge Proficiency of Students
    Zhenya Huang, Qi Liu, Yuying Chen, Le Wu, Keli Xiao, Enhong Chen, Haiping Ma, Guoping Hu · 2020 · ACM Transactions on Information Systems
15. Practical early prediction of students’ performance using machine learning and eXplainable AI
    Jang, Yeonju, Choi, Seongyune, Jung, Heeseok, Kim, Hyeoncheol · 2022 · Education and Information Technologies
16. Predicting Student Academic Performance at Degree Level: A Case Study
    Raheela Asif, Agathe Merceron, Mahmood K. Pathan · 2014 · International Journal of Intelligent Systems and Applications
17. MCRS: A course recommendation system for MOOCs
    Hao Zhang, Tao Huang, Zhihan Lv, Sannyuya Liu, Zhili Zhou · 2017 · Multimedia Tools and Applications
18. Predicting student performance in a blended MOOC
    Rianne Conijn, Antoine van den Beemt, Pjl Pieter Cuijpers · 2018 · Journal of Computer Assisted Learning
19. Does Time-on-task Estimation Matter? Implications on Validity of Learning Analytics Findings
    Vitomir Kovanović, Dragan Gašević, Shane Dawson, Srécko Joksimovíc, Ryan S. Baker · 2016 · Journal of Learning Analytics
20. Teacher regulation of cognitive activities during student collaboration: Effects of learning analytics
    Anouschka van Leeuwen, Jeroen J. H. M. Janssen, Gijsbert Erkens, Mieke Brekelmans · 2015 · Computers & Education
21. Data mining in educational technology classroom research: Can it make a contribution?
    Charoula Angeli, Sarah Katherine Howard, Jun Ma, Jie Yang, Paul A. Kirschner · 2017 · Computers & Education
22. Applications of Data Mining in Higher Education
    Monika Goyal, Rajan Vohra · 2012
23. Application of data mining in educational databases for predicting academic trends and patterns
    Suhem Parack, Zain Zahid, Fatima Merchant · 2012 · IEEE International Conference on Technology Enhanced Education (ICTEE)
24. Understanding online interaction in language MOOCs through learning analytics
    Elena Martín-Monje, María Dolores Castrillo, Jorge Mañana-Rodríguez · 2018 · Computer Assisted Language Learning
25. Implementing AutoML in Educational Data Mining for Prediction Tasks
    Maria Tsiakmaki, Georgios Kostopoulos, Sotiris B. Kotsiantis, O. Ragos · 2019 · Applied Sciences
26. Application of learning analytics using clustering data Mining for Students’ disposition analysis
    Sanyam Bharara, A. Sai Sabitha, Abhay Bansal · 2017 · Education and Information Technologies
27. Graph theory based model for learning path recommendation
    Guillaume Durand, Nabil Belacel, François LaPlante · 2013 · Information Sciences
28. Automatic Inference of Programming Performance and Experience from Typing Patterns
    Leinonen, Juho, Longi, Krista, Klami, Arto, Vihavainen, Arto · 2016 · Proceedings of the 47th ACM Technical Symposium on Computing Science Education
29. Learning Analytics: Principles and Constraints
    Mohammad Khalil, Martin Ebner · 2015
30. The theory of learning styles applied to distance learning
    Roberto Douglas da Costa, Gustavo Fontoura de Souza, Ricardo Alexsandro de Medeiros Valentim, Thales Barros de Castro · 2020 · Cognitive Systems Research
31. Techniques for sentiment analysis of Twitter data: A comprehensive survey
    Mitali Desai, Mayuri A. Mehta · 2016 · International Conference on Computing, Communication and Automation
32. Data mining techniques and applications — A decade review
    Hussain Ahmad Madni, Zahid Anwar, Munam Ali Shah · 2017 · International Conference on Automation and Computing (ICAC)
33. An Exploration of Factors Linked to Academic Performance in PISA 2018 Through Data Mining Techniques
    Adriana Gamazo, Fernando Martínez Abad · 2020 · Frontiers in Psychology
34. Increased Digital Resource Consumption in Higher Educational Institutions and the Artificial Intelligence Role in Informing Decisions Related to Student Performance
    Anjeela D. Jokhan, Aneesh A. Chand, Vineet Singh, Kabir A. Mamun · 2022 · Sustainability
35. Social network analysis of a gamified e-learning course: Small-world phenomenon and network metrics as predictors of academic performance
    Luis de‐Marcos, Eva García‐López, Antonio García‐Cabot, J. Medina, Adrián Domínguez‐Díaz, José-Javier Martínez-Herráiz, Teresa I. Díez-Folledo · 2016 · Computers in Human Behavior
36. Defining the Boundaries Between Artificial Intelligence in Education, Computer-Supported Collaborative Learning, Educational Data Mining, and Learning Analytics: A Need for Coherence
    Bart Rienties, Henrik Køhler Simonsen, Christothea Herodotou · 2020 · Frontiers in Education
37. An Overview of E-Learning in Cloud Computing
    Alberto Fernández, Daniel Peralta, Francisco Herrera, José M. Benítez · 2012 · Advances in intelligent systems and computing
38. A review of machine learning methods used for educational data
    Ersozlu, Zara, Taheri, Sona, Koch, Inge · 2024 · Education and Information Technologies
39. Data mining models for student careers
    Renza Campagni, Donatella Merlini, Renzo Sprugnoli, M. Cecilia Verri · 2015 · Expert Systems with Applications
40. Using institutional data to predict student course selections in higher education
    Ivana Ognjanović, Dragan Gašević, Shane Dawson · 2015 · The Internet and Higher Education
41. Predicting students' knowledge after playing a serious game based on learning analytics data: A case study
    Cristina Alonso‐Fernández, Iván Martínez‐Ortiz, Rafael Caballero, Manuel Freire, Baltasar Fernández‐Manjón · 2019 · Journal of Computer Assisted Learning
42. Predicting Students’ Academic Performance Through Supervised Machine Learning
    Engr. Sana Bhutto, Isma Farah Siddiqui, Qasim Ali Arain, Maleeha Anwar · 2020 · International Conference on Information Science and Communication Technology (ICISCT)
43. A Framework for Using Hypothesis-Driven Approaches to Support Data-Driven Learning Analytics in Measuring Computational Thinking in Block-Based Programming Environments
    Grover, Shuchi, Basu, Satabdi, Bienkowski, Marie, Eagle, Michael, Diana, Nicholas, Stamper, John · 2017 · ACM Transactions on Computing Education
44. Leveraging machine-learned detectors of systematic inquiry behavior to estimate and predict transfer of inquiry skill
    Michael São Pedro, Ryan S. Baker, Janice D. Gobert, Orlando Montalvo, Adam Nakama · 2011 · User Modeling and User-Adapted Interaction
45. Student course grade prediction using the random forest algorithm: Analysis of predictors' importance
    Mirna Nachouki, Elfadil Abdalla Mohamed, Riyadh A. K. Mehdi, Mahmoud Abou Naaj · 2023 · Trends in Neuroscience and Education
46. A Model to Predict Low Academic Performance at a Specific Enrollment Using Data Mining
    Camilo Ernesto López Guarín, Elizabeth León Guzmán, Fabio Augusto Gonzalez · 2015 · IEEE Revista Iberoamericana de Tecnologias del Aprendizaje
47. Recommender system for big data in education
    Surabhi Dwivedi, V. S. Kumari Roshni · 2017 · National Conference on E-Learning & E-Learning Technologies (ELELTECH)
48. Predicting Secondary School Students' Performance Utilizing a Semi-supervised Learning Approach
    Ioannis E. Livieris, Konstantina Drakopoulou, Vassilis Tampakas, Tassos A. Mikropoulos, Panagiotis Pintelas · 2018 · Journal of Educational Computing Research
49. Improving early prediction of academic failure using sentiment analysis on self‐evaluated comments
    Liang-Chih Yu, C.W. Lee, H.I. Pan, Chih‐Yueh Chou, Po-Yao Chao, Zihan Chen, Shu Fen Tseng, Chien‐Lung Chan, Kuo-Hua Robert Lai · 2018 · Journal of Computer Assisted Learning
50. Educational Big Data: Predictions, Applications and Challenges
    Xiaomei Bai, Fuli Zhang, Jinzhou Li, Teng Guo, Abdul Aziz, Aijing Jin, Feng Xia · 2021 · Big Data Research
51. Monitoring MOOCs
    K Stephens, Marti A. Hearst, Armando Fox · 2014 · ACM Conference on Learning @ Scale (L@S)
52. Virtual learning environment to predict withdrawal by leveraging deep learning
    Saeed‐Ul Hassan, Hajra Waheed, Naif Radi Aljohani, Mohsen Ali, Sebastián Ventura, Francisco Herrera · 2019 · International Journal of Intelligent Systems
53. Integrating Data Mining in Program Evaluation of K-12 Online Education
    Jui-Long Hung, Yu‐Chang Hsu, Kerry Rice · 2012 · ScholarWorks (Boise State University)
54. On the Use of Soft Computing Methods in Educational Data Mining and Learning Analytics Research: a Review of Years 2010–2018
    Charitopoulos, Angelos, Rangoussi, Maria, Koulouriotis, Dimitrios · 2020 · International Journal of Artificial Intelligence in Education
55. A Survey on Educational Data Mining and Research Trends
    Rajni Jindal, Malaya Dutta Borah · 2013 · International Journal of Database Management Systems
56. MLaaS: A Cloud-Based System for Delivering Adaptive Micro Learning in Mobile MOOC Learning
    Geng Sun, Tingru Cui, Jianming Yong, Jun Shen, Shiping Chen · 2015 · IEEE Transactions on Services Computing
57. Using supervised machine learning on large-scale online forums to classify course-related Facebook messages in predicting learning achievement within the personal learning environment
    Jiun‐Yu Wu, Yi-Cheng Hsiao, Mei-Wen Nian · 2018 · Interactive Learning Environments
58. Online engagement and performance on formative assessments mediate the relationship between attendance and course performance
    Chang Lu, Maria Cutumisu · 2022 · International Journal of Educational Technology in Higher Education
59. A bibliometric perspective of learning analytics research landscape
    Hajra Waheed, Saeed‐Ul Hassan, Naif Radi Aljohani, Muhammad Wasif · 2018 · Behaviour and Information Technology
60. Blurring Boundaries in Education: Context and Impact of MOOCs
    Loeckx, Johan · 2016 · The International Review of Research in Open and Distributed Learning
61. VisMOOC: Visualizing video clickstream data from Massive Open Online Courses
    Conglei Shi, Siwei Fu, Qing Chen, Huamin Qu · 2015 · IEEE Pacific Visualization Symposium
62. Investigating temporal access in a flipped classroom: procrastination persists
    AlJarrah, Abeer, Thomas, Michael K., Shehab, Mohamed · 2018 · International Journal of Educational Technology in Higher Education
63. Identifying Students at Risk of Academic Failure Within the Educational Data Mining Framework
    Annalina Sarra, Lara Fontanella, Simone Di Zio · 2018 · Social Indicators Research
64. A Survey of Educational Data-Mining Research.
    Richard A. Huebner · 2013 · Research in higher education journal
65. Data Analytics and Decision- Making in Education: Towards the Educational Data Scientist as a Key Actor in Schools and Higher Education Institutions.
    Agasisti, Tommaso, Bowers, Alex J. · 2017 · Handbook of Contemporary Education Economics
66. Early Prediction of student’s Performance in Higher Education: A Case Study
    Mónica V. Martins, Daniel Tolledo, Jorge Machado, Luís Baptista, Valentim Realinho · 2021 · Advances in intelligent systems and computing
67. Activity sequence modelling and dynamic clustering for personalized e-learning
    Mirjam Köck, Alexandros Paramythis · 2011 · User Modeling and User-Adapted Interaction
68. Interpretable Multiview Early Warning System Adapted to Underrepresented Student Populations
    Alberto Cano, John D. Leonard · 2019 · IEEE Transactions on Learning Technologies
69. A survey on educational data mining methods used for predicting students' performance
    Wen Jun Xiao, Ping Ji, Juan Hu · 2021 · Engineering Reports
70. Online At-Risk Student Identification using RNN-GRU Joint Neural Networks
    Yanbai He, Rui Chen, Xinya Li, Chuanyan Hao, Sijiang Liu, Gangyao Zhang, Bo Jiang · 2020 · Information
71. Using data mining on student behavior and cognitive style data for improving e-learning systems: a case study
    Miloš Jovanović, Milan Vukičević, Miloš Milovanović, Miroslav Minović · 2012 · International Journal of Computational Intelligence Systems
72. Impact of artificial intelligence on assessment methods in primary and secondary education: Systematic literature review
    Miguel Martínez-Comesaña, Xurxo Rigueira, Ana Larrañaga, Javier Martínez Martínez, Iago Ocarranza-Prado, Denis Kreibel · 2023 · Revista de Psicodidáctica (English ed )
73. Learning Analytics to identify dropout factors of Computer Science studies through Bayesian networks
    Carmen Lacave, Ana Isabel Molina, José A. Cruz-Lemus · 2018 · Behaviour and Information Technology
74. Predictive Models for Imbalanced Data: A School Dropout Perspective
    Thiago Medeiros Barros, Plácido A. Souza Neto, Ivanovitch Medeiros Dantas da Silva, Luiz Affonso Guedes · 2019 · Education Sciences
75. PeakVizor: Visual Analytics of Peaks in Video Clickstreams from Massive Open Online Courses
    Qing Chen, Yuanzhe Chen, Dongyu Liu, Conglei Shi, Yingcai Wu, Huamin Qu · 2015 · IEEE Transactions on Visualization and Computer Graphics
76. Application of Computational Intelligence to Improve Education in Smart Cities
    Everton Gomede, Fernando Henrique Gaffo, Gabriel Ulian Briganó, Rodolfo Miranda de Barros, Leonardo de Souza Mendes · 2018 · Sensors
77. Discovering learning processes using Inductive Miner: A case study with Learning Management Systems (LMSs)
    Alejandro Bogarín, Rebeca Cerezo, Cristóbal Romero · 2018 · Psicothema
78. Comparison of Predicting Student‘s Performance using Machine Learning Algorithms
    V. Vijayalakshmi, K. Venkatachalapathy · 2019 · International Journal of Intelligent Systems and Applications
79. Impacto de la inteligencia artificial en los métodos de evaluación en la educación primaria y secundaria: revisión sistemática de la literatura
    Miguel Martínez-Comesaña, Xurxo Rigueira, Ana Larrañaga, Javier Martínez Martínez, Iago Ocarranza-Prado, Denis Kreibel · 2023 · Revista de Psicodidáctica
80. Advanced Technologies for Personalized Learning, Instruction, and Performance
    Mieke Vandewaetere, Geraldine Clarebout · 2013 · Springer eBooks
81. Applying Educational Data Mining to Explore Students’ Learning Patterns in the Flipped Learning Approach for Coding Education
    Hui-Chun Hung, I-Fan Liu, Che-Tien Liang, Yu-Sheng Su · 2020 · Symmetry
82. Application of machine learning and data mining in predicting the performance of intermediate and secondary education level student
    Bashir Khan Yousafzai, Maqsood Hayat, Sher Afzal · 2020 · Education and Information Technologies
83. Educational data mining: a systematic review of research and emerging trends
    Xu Du, Juan Yang, Jui-Long Hung, Brett E. Shelton · 2020 · Information Discovery and Delivery
84. Applications of Educational Data Mining and Learning Analytics Tools in Handling Big Data in Higher Education
    Santosh Kumar Ray, Mohammed Saeed · 2018 · Springer eBooks
85. A Decision Tree Approach for Predicting Students Academic Performance
    Kolo David Kolo, Solomon Adelowo Adepoju, John Kolo Alhassan · 2015 · International Journal of Education and Management Engineering
86. Analyzing Process Data from Problem-Solving Items with N-Grams
    Qiwei He, Matthias von Davier · 2015 · Advances in higher education and professional development book series
87. Generating descriptive model for student dropout: a review of clustering approach
    Natthakan Iam-On, Tossapon Boongoen · 2017 · Human-centric Computing and Information Sciences
88. Generalized mixed‐effects random forest: A flexible approach to predict university student dropout
    Massimo Pellagatti, Chiara Masci, Francesca Ieva, Anna Maria Paganoni · 2021 · Statistical Analysis and Data Mining The ASA Data Science Journal
89. Educational Data Mining versus Learning Analytics: A Review of Publications From 2015 to 2019
    Clare Baek, Tenzin Doleck · 2021 · Interactive Learning Environments
90. Data Mining in Higher Education : University Student Dropout Case Study
    Ghadeer S. Abu-Oda, Alaa M. El-Halees · 2015 · International Journal of Data Mining & Knowledge Management Process
91. Predicting Students Success in Blended Learning—Evaluating Different Interactions Inside Learning Management Systems
    Luiz Antonio Buschetto Macarini, Cristian Cechinel, Matheus Francisco Batista Machado, Vinícius Faria Culmant Ramos, Roberto Muñoz · 2019 · Applied Sciences
92. Data mining techniques applied in educational environments: Literature Review
    Andrés Villanueva Manjarres, Luis Gabriel Moreno Sandoval, Martha Janneth Salinas Suárez · 2018 · Digital Education Review
93. Empowering Teachers with AI: Co-Designing a Learning Analytics Tool for Personalized Instruction in the Science Classroom
    Tanya Nazaretsky, Carmel Bar, Michal Walter, Giora Alexandron · 2022 · International Conference on Learning Analytics and Knowledge (LAK)
94. An affective and Web 3.0-based learning environment for a programming language
    Ramón Zataraín Cabada, María Lucía Barrón Estrada, Francisco González-Hernández, Raúl Oramas Bustillos, Carlos Alberto Reyes-Garcia · 2017 · Telematics and Informatics
95. Predicting University Dropout trough Data Mining: A systematic Literature
    Mayra Albán, David Mauricio · 2019 · Indian Journal of Science and Technology
96. Using machine learning to predict physics course outcomes
    Cabot Zabriskie, Jie Yang, Seth DeVore, John C. Stewart · 2019 · Physical Review Physics Education Research
97. The use of an online learning and teaching system for monitoring computer aided design student participation and predicting student success
    S. Akhtar, Steven Warburton, Wei Xu · 2015 · International Journal of Technology and Design Education
98. Toward Fully Automated Person-Independent Detection of Mind Wandering
    R. Bixler, Sidney K. D'Mello · 2014 · User Modeling, Adaptation, and Personalization
99. Using educational data mining to assess students’ skills at designing and conducting experiments within a complex systems microworld
    Janice D. Gobert, Yoon Jeon Kim, Michael São Pedro, Michael Kennedy, Cameron G. Betts · 2015 · Thinking Skills and Creativity
100. Presenting the Regression Tree Method and its application in a large-scale educational dataset
     Cristiano Mauro Assis Gomes, Enio Galinkin Jelihovschi · 2019 · International Journal of Research & Method in Education