CoChat
Sign Up
Cited by 2,251Online Learning and AnalyticsIntelligent Tutoring Systems and Adaptive LearningEducational Technology and Pedagogy

The DOI matches its Crossref record; 271 of the 279 references it cites were found in a registry or an index.

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

Romero, Cristóbal, Ventura, Sebastián·2010·IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)
DOI 10.1109/tsmcc.2010.2053532
Open this paper in CoChat — free account

Abstract

Educational data mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyze educational data in order to study educational questions. This paper surveys the most relevant studies carried out in this field to date. First, it introduces EDM and describes the different groups of user, types of educational environments, and the data they provide. It then goes on to list the most typical/common tasks in the educational environment that have been resolved through data-mining techniques, and finally, some of the most promising future lines of research are discussed.

Overview

Integrity signals

Registry

Crossref · 10.1109/tsmcc.2010.2053532

Cites

271 of 271 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 279 references

Research works printed but not linked: 53

  1. mazza, 2003, the design of a course data visualizator: an empirical study, Proc Int Conf New Educ Environ, 215
  2. mcdonald, 2004, predicting student success, J Math Teaching Learning, 1, 1
  3. ben-naim, 2008, visualization and analysis of student interaction in an adaptive exploratory learning environment, Proc Int Workshop Intell Support Exploratory Environ Eur Conf Technol Enhanced Learn, 1
  4. beck, 2005, 20th Nat Conf Artif Intell (AAAI) Workshop Educ Data Mining
  5. beck, 2004, 7th Int Conf Workshop Analyzing Student-tutor Interaction Logs Improve Educ Outcomes
  6. beck, 2000, 5th Int Conf Intell Tutoring Syst (ITS) Workshop Applying Mach Learning to ITS Design/Construction
  7. beck, 2007, 7th IEEE Int Conf Adv Learn Technol Workshop Educ Data Mining
  8. beck, 2006, 21st Nat Conf Artif Intell (AAAI) Workshop Educ Data Mining
  9. yudelson, 2006, mining student learning data to develop high level pedagogic strategy in a medical its, Proceedings of AAAI2005 workshop on Educational Data Mining, 1
  10. yang, 2002, an agent-based recommender system for lesson plan sequencing, Proc Int Conf Adv Learning Technol, 14
  11. winters, 2005, topic extraction from item-level grades, Proceedings of AAAI2005 workshop on Educational Data Mining, 7
  12. mostow, 2005, An educational data mining tool to browse tutor-student interactions: Time will tell!, Proc Workshop Educational Data Mining, 15
  13. muehlenbrok, 2005, automatic action analysis in an interactive learning environment, Proc 12th Int Conf Artif Intell Educ Workshop Usage Anal Learn Syst, 73
  14. barnes, 2009, 2nd Int Conf Educ Data Mining
  15. baker, 2010, 3rd Int Conf Educ Data Mining
  16. chen, 2007, mining key formative assessment rules based on learner portfiles for web-based learning systems, Proc IEEE Int Conf Adv Learn Technol, 1
  17. zafra, 2009, predicting student grades in learning management systems with multiple instance programming, Proc Int Conf Educ Data Mining, 307
  18. kristofic, 2005, recommender system for adaptive hypermedia applications, Proc Stud Res Conf Informat Inf Technol, 229
  19. yu, 2001, on learning behavior analysis of web based interactive environment, Proc Int Conf Elect Electron Comp Eng, 1
  20. burr, 2004, pattern of user behavior in university online forums, Int'l J Instructional Technology and Distance Learning, 1, 11
  21. markellou, 2005, using semantic web mining technologies for personalized e-learning experiences, Web-based education, 461
  22. castro, 2007, Evolution of Teaching and Learning Paradigms in Intelligent Environment (Studies in Computational Intelligence) vol 62, 183
  23. choquet, 2005, Artif Intell Educ Conf (AIED) Workshop Usage Analysis Learning Syst
  24. zukhri, 2007, solving new student allocation problem with genetic algorithms: a hard problem for partition based approach, J Zhejiang Univ, 2, 6
  25. delgado, 2006, Predicting students' marks from. Moodle logs using neural network models, Proc Int Conf Current Dev Technol -Assist Educ, 586
  26. gibbs, 2003, evaluating student behavior on instructional web sites using web server logs, Proc Ninth Sloan-C Int Conf Online Learn, 1
  27. nesbit, 2008, sequential pattern analysis software for educational event data, Proc Int Conf Methods Tech Behav Res
  28. girones, 2006, ariadne, a guiding thread in the learning process's labyrinth, Proc Int Conf Current Dev Technol -Assist Educ, 287
  29. feng, 2006, informing teachers live about student learning: reporting in the assistment system, Technol Instruction Cognition Learn J, 3, 1
  30. freedman, 2007, Statistics
  31. ramli, 2005, web usage mining using a priori algorithm: uum learning care portal case, Proc Int Conf Inf Knowl Manage, 1
  32. rallo, 2005, using data mining and social networks to analyze the structure and content of educative online communities, Proc Int Conf Multimedia ICTs Educ, 1
  33. romero, 2007, Proc Int Workshop Applying Data Mining E-Learning (ADML) 2nd Eur Conf Technol Enhanced Learn (EC-TEL07)
  34. ibrahim, 2007, predicting students' academic performance: comparing artificial neural network, decision tree and linear regression, Proc Annu SAS Malaysia Forum, 1
  35. rosta, 2006, social navigation support in a course recommendation system, Proc Int Conf Adaptive Hypermedia Adaptive Web-Based Syst, 1
  36. kay, 2006, mining patterns of events in students' teamwork data, Proc Workshop Educational Data Mining, 1
  37. kim, 2006, mining and assessing discussions on the web through speech act analysis, Proc AAAI Workshop Web Content Mining Human Lang Technol, 1
  38. kotsiantis, 2003, preventing student dropout in distance learning systems using machine learning techniques, Proc 3rd Int Conf Knowl -Based Intell Inf Eng Syst, 3
  39. spacco, 2006, inferring use cases from unit testing, Proc Workshop Educational Data Mining, 1
  40. simko, 2009, automatic concept telationships discovery for an adaptive e-course, Proc Int Conf Educ Data Mining, 171
  41. heiner, 2006, 8th Int Conf Intell Tutoring Syst (ITS) Workshop Educ Data Mining
  42. heiner, 2007, 13th Int Conf Artif Intell Educ Workshop Educ Data Mining
  43. talavera, 2004, mining student data to characterize similar behavior groups in unstructured collaboration spaces, Proc Workshop Artif Intell CSCL, 17
  44. tang, 2005, smart recommendation for an evolving e-learning system, Int J E-Learning, 4, 105
  45. avouris, 2005, why logging of fingertip actions is not enough for analysis of learning activities, Proc AIED Conf Workshop Usage Anal Learn Syst, 1
  46. ayers, 2006, Do skills combine additively to predict task difficulty in eighth grade mathematics?, Proceedings of AAAI2005 workshop on Educational Data Mining, 14
  47. baker, 2007, track on educational data mining, 11th Int Conf User Model Workshop Data Mining User Model
  48. hien, 2007, a decision support system for evaluating international student applications, Proc Frontiers Educ Conf, 1
  49. baker, 2008, 1st Int Conf Educ Data Mining
  50. hbscher, 2007, Domain specific interactive data mining, Proc 11th Int Conf User Model Workshop Data Mining User Model, 81
  51. ueno, 2002, Learning log database and data mining system for e-learning—on line statistical outlier detection of irregular learning processes, Proc Int Conf Adv Learning Technol, 436
  52. vee, 2006, understanding novice errors and error paths in object-oriented programming through log analysis, Proc Workshop Educational Data Mining, 13
  53. vellido, 2007, Evolution of Teaching and Learning Paradigms in Intelligent Environment Studies in Computational Intelligence (SCI) (Advanced Information and Knowledge Processing) vol 62, 223

Cited by

Sorted by citations

Items 501–600 of 1,000

Related

See all

Metadata

Authors
Romero, C., & Ventura, S.
Affiliations
University of Córdoba (ES)
ORCID
Venue
IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2010-11
Volume
40
Issue
6
Pages
601-618
ISSN
  • 1094-6977 (ISSN-L)
  • 1558-2442
DOI
10.1109/tsmcc.2010.2053532
OpenAlex
W2006444123
Cited by
2,251
References
279
Citations by year
2026: 204 · 2025: 141 · 2024: 117 · 2023: 118 · 2022: 142 · 2021: 144 · 2020: 204 · 2019: 196 · 2018: 185 · 2017: 167 · 2016: 155 · 2015: 145 · 2014: 140 · 2013: 90 · 2012: 70
FWCI
147.61
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations
IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
  • Social Sciences › Education › Educational Technology and Pedagogy
Keywords
educational data mining, educational data analysis, learning analytics, educational environment, future research directions

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review