CoChat
Sign Up
Cited by 21scientometrics and bibliometrics researchOnline Learning and AnalyticsIntelligent Tutoring Systems and Adaptive Learning

Bibliometric Literature Review of Adaptive Learning Systems

Open access
Koutsantonis, Dionisios, Koutsantonis, Konstantinos, Bakas, Nikolaos P., Plevris, Vagelis, Langousis, Andreas, Chatzichristofis, Savvas A.·2022·Sustainability
DOI 10.3390/su141912684
Open this paper in CoChat — free accountOpen PDF

Abstract

In this review paper, we computationally analyze a vast volume of published articles in the field of Adaptive Learning, as obtained by the Scopus Database. Particularly, we use a query with search terms targeting the area of Adaptive Learning Systems by utilizing a combination of specific keywords. Accordingly, we apply a multidimensional scaling algorithm to construct bibliometric maps for keywords, authors, and references. Subsequently, we present the computational results for the studied dataset, reveal significant patterns appearing in the field of adaptive learning and the inter-item associations, and interpret the findings based on the current state-of-the-art literature in the area. Furthermore, we demonstrate the time-series of the evolution of the research terms, their trends over time, as well as their prevalent statistical associations.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/su141912684

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

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?

  • More recent systems increasingly employ artificial intelligence techniques to automate learner modeling, including inferring learning styles, cognitive states, or knowledge levels directly from interaction data.

    “More recent systems increasingly employ artificial intelligence techniques to automate learner modeling, including inferring learning styles, cognitive states, or knowledge levels directly from interaction data [14].”

    From AI for All: Adaptive, Accessible, and Inclusive Learning Experiences in the Age of Intelligent LMSs (2026) · cites this paper as [14]

    The Discussion (page not supplied) says recent adaptive-learning literature favors neural networks and that AI enables automated analysis of student-behavior data. Section 5.1 (page not supplied) describes learner models involving knowledge level and learning style, while the paper also reports a system that detects mind-wandering from gaze data. It does not establish the broader claim that recent systems increasingly infer all these learner characteristics directly from interaction data. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

No citation lists for this paper

No citation lists for this paper yet — checked again daily.

References 62 · Cited by 21

Research works printed but not linked: 55

  1. Koutsantonis, D.; Panayiotopoulos, J.C. Expert system personalized knowledge retrieval. Oper. Res. 2011, 11, 215-227. [CrossRef] · DOI
  2. Mastorodimos, D.; Chatzichristofis, S.A. Studying Affective Tutoring Systems for Mathematical Concepts. J. Educ. Technol. Syst. 2019, 48, 14-50. [CrossRef] · DOI
  3. Dima, A.; Bugheanu, A.M.; Dinulescu, R.; Potcovaru, A.M.; Stefanescu, C.A.; Marin, I. Exploring the Research Regarding Frugal Innovation and Business Sustainability through Bibliometric Analysis. Sustainability 2022, 14, 1326. [CrossRef] · DOI
  4. Popescu, D.V.; Dima, A.; Radu, E.; Dobrotȃ, E.M.; Dumitrache, V.M. Bibliometric Analysis of the Green Deal Policies in the Food Chain. Amfiteatru Econ. 2022, 24, 410-428. [CrossRef] · DOI
  5. Plevris, V.; Solorzano, G.; Bakas, N. Literature review of historical masonry structures with machine learning. In Proceedings of the 7th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, Crete, Greece, 24-26 June 2019; ECCOMAS: Crete, Greece, 2019; pp. 1547-1562.
  6. Plevris, V.; Bakas, N.; Markeset, G.; Bellos, J. Literature review of masonry structures under earthquake excitation utilizing machine learning algorithms. In Proceedings of the 6th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, Rhodes Island, Greece, 15-17 June 2017; ECCOMAS: Rhodes Island, Greece, 2017; pp. 2685-2694.
  7. Papadaki, M.; Bakas, N.; Ochieng, E.; Karamitsos, I.; Kirkham, R. Big data from social media and scientific literature databases reveals relationships among risk management, project management and project success. PM World J. 2019, 8. [CrossRef] · DOI
  8. Dimopoulos, T.; Bakas, N. An artificial intelligence algorithm analyzing 30 years of research in mass appraisals. RELAND Int. J. Real Estate Land Plan. 2019, 2, 10-27.
  9. Kruskal, J.B. Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 1964, 29, 1-27. [CrossRef] · DOI
  10. Moré, J.J. The Levenberg-Marquardt algorithm: Implementation and theory. In Numerical Analysis; Springer: Berlin, Germany, 1978; pp. 105-116.
  11. Shepard, R.N. The analysis of proximities: Multidimensional scaling with an unknown distance function. I. Psychometrika 1962, 27, 125-140. [CrossRef] · DOI
  12. van Eck, N.; Waltman, L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 2009, 84, 523-538. [CrossRef] · DOI · Paper page
  13. Srivastava, N.; Hinton, G.; Krizhevsky, A.; Sutskever, I.; Salakhutdinov, R. Dropout: A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 2014, 15, 1929-1958.
  14. Conn, A.R.; Gould, N.I.; Toint, P. A globally convergent augmented Lagrangian algorithm for optimization with general constraints and simple bounds. SIAM J. Numer. Anal. 1991, 28, 545-572. [CrossRef] · DOI
  15. Audet, C.; Dennis, J.E., Jr. Analysis of generalized pattern searches. SIAM J. Optim. 2002, 13, 889-903. [CrossRef] · DOI
  16. Shi, Y.; Eberhart, R. A modified particle swarm optimizer. In Proceedings of the 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence, Anchorage, AK, USA, 4-9 May 1998; IEEE: Piscataway, NJ, USA, 1998; pp. 69-73.
  17. Plevris, V. Innovative Computational Techniques for the Optimum Structural Design Considering Uncertainties. Ph.D. Thesis, National Technical University of Athens, Athens, Greece, 2009.
  18. Moayyeri, N.; Gharehbaghi, S.; Plevris, V. Cost-Based Optimum Design of Reinforced Concrete Retaining Walls Considering Different Methods of Bearing Capacity Computation. Mathematics 2019, 7, 1232. [CrossRef] · DOI
  19. Byrd, R.H.; Gilbert, J.C.; Nocedal, J. A trust region method based on interior point techniques for nonlinear programming. Math. Program. 2000, 89, 149-185. [CrossRef] · DOI
  20. Ugray, Z.; Lasdon, L.; Plummer, J.; Glover, F.; Kelly, J.; Martí, R. Scatter search and local NLP solvers: A multistart framework for global optimization. INFORMS J. Comput. 2007, 19, 328-340. [CrossRef] · DOI
  21. Levenberg, K. A method for the solution of certain non-linear problems in least squares. Q. Appl. Math. 1944, 2, 164-168. [CrossRef] · DOI
  22. Marquardt, D.W. An algorithm for least-squares estimation of nonlinear parameters. J. Soc. Ind. Appl. Math. 1963, 11, 431-441. [CrossRef] · DOI · Paper page
  23. Bakas, N.P.; Plevris, V.; Langousis, A.; Chatzichristofis, S.A. ITSO: A novel inverse transform sampling-based optimization algorithm for stochastic search. Stoch. Environ. Res. Risk Assess. 2022, 36, 67-76. [CrossRef] · DOI
  24. Plevris, V.; Bakas, N.P.; Solorzano, G. Pure Random Orthogonal Search (PROS): A Plain and Elegant Parameterless Algorithm for Global Optimization. Appl. Sci. 2021, 11, 5053. [CrossRef] · DOI
  25. Liu, W.H.; Sherman, A.H. Comparative analysis of the Cuthill-McKee and the reverse Cuthill-McKee ordering algorithms for sparse matrices. SIAM J. Numer. Anal. 1976, 13, 198-213. [CrossRef] · DOI
  26. Anderson, J.R.; Corbett, A.T.; Koedinger, K.R.; Pelletier, R. Cognitive tutors: Lessons learned. J. Learn. Sci. 1995, 4, 167-207. [CrossRef] · DOI · Paper page
  27. Felder, R.M.; Silverman, L.K. Learning and teaching styles in engineering education. Eng. Educ. 1988, 78, 674-681.
  28. Murray, T. Authoring intelligent tutoring systems: An analysis of the state of the art. Int. J. Artif. Intell. Educ. (IJAIED) 1999, 10, 98-129.
  29. Koedinger, K.R.; Anderson, J.R.; Hadley, W.H.; Mark, M.A. Intelligent tutoring goes to school in the big city. Int. J. Artif. Intell. Educ. 1997, 8, 30-43.
  30. VanLehn, K. The behavior of tutoring systems. Int. J. Artif. Intell. Educ. 2006, 16, 227-265.
  31. Corbett, A.T.; Anderson, J.R. Knowledge tracing: Modeling the acquisition of procedural knowledge. User Model. -User-Adapt. Interact. 1994, 4, 253-278. [CrossRef] · DOI · Paper page
  32. Brusilovsky, P. Methods and Techniques of Adaptive Hypermedia. In Learner Modeling and Learner-Adapted Interaction; Springer, Dordrecht, The Netherlands, 1996; Volume 6.
  33. Aleven, V.; Mclaren, B.M.; Sewall, J.; Koedinger, K.R. A new paradigm for intelligent tutoring systems: Example-tracing tutors. Int. J. Artif. Intell. Educ. 2009, 19, 105-154.
  34. Conati, C.; Maclaren, H. Empirically building and evaluating a probabilistic model of user affect. User Model. -User-Adapt. Interact. 2009, 19, 267-303. [CrossRef] · DOI · Paper page
  35. Piech, C.; Bassen, J.; Huang, J.; Ganguli, S.; Sahami, M.; Guibas, L.J.; Sohl-Dickstein, J. Deep knowledge tracing. In Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canads, 7-12 December 2015; Volume 28.
  36. Strayer, J.F. How learning in an inverted classroom influences cooperation, innovation and task orientation. Learn. Environ. Res. 2012, 15, 171-193. [CrossRef] · DOI
  37. Baker, R.S.; D'Mello, S.K.; Rodrigo, M.M.T.; Graesser, A.C. Better to be frustrated than bored: The incidence, persistence, and impact of learners' cognitive-affective states during interactions with three different computer-based learning environments. Int. J. Hum.-Comput. Stud. 2010, 68, 223-241. [CrossRef] · DOI · Paper page
  38. VanLehn, K.; Lynch, C.; Schulze, K.; Shapiro, J.A.; Shelby, R.; Taylor, L.; Treacy, D.; Weinstein, A.; Wintersgill, M. The Andes physics tutoring system: Lessons learned. Int. J. Artif. Intell. Educ. 2005, 15, 147-204.
  39. Schulze, K.G.; Shelby, R.N.; Treacy, D.J.; Wintersgill, M.C.; Vanlehn, K.; Gertner, A. Andes: An intelligent tutor for classical physics. J. Electron. Publ. 2000, 6. [CrossRef] · DOI
  40. Conati, C.; Gertner, A.; Vanlehn, K. Using Bayesian networks to manage uncertainty in student modeling. User Model. User-Adapt. Interact. 2002, 12, 371-417. [CrossRef] · DOI · Paper page
  41. Chen, C.M.; Lee, H.M.; Chen, Y.H. Personalized e-learning system using item response theory. Comput. Educ. 2005, 44, 237-255. [CrossRef] · DOI
  42. Papanikolaou, K.A.; Grigoriadou, M.; Kornilakis, H.; Magoulas, G.D. Personalizing the Interaction in a Web-based Educational Hypermedia System: The case of INSPIRE. User Model. User-Adapt. Interact. 2003, 13, 213-267. [CrossRef] · DOI
  43. Brusilovsky, P.; Schwarz, E.; Weber, G. ELM-ART: An intelligent tutoring system on World Wide Web. In Proceedings of the International Conference on Intelligent Tutoring Systems, Montréal, Canada, 12-14 June 1996, Springer: Berlin, Germany, 1996; pp. 261-269.
  44. Chen, C.M.; Chung, C.J. Personalized mobile English vocabulary learning system based on item response theory and learning memory cycle. Comput. Educ. 2008, 51, 624-645. [CrossRef] · DOI
  45. Vrugt, J.A.; Robinson, B.A.; Hyman, J.M. Self-adaptive multimethod search for global optimization in real-parameter spaces. IEEE Trans. Evol. Comput. 2008, 13, 243-259. [CrossRef] · DOI
  46. Castro, J.R.; Castillo, O.; Melin, P.; Rodríguez-Díaz, A. A hybrid learning algorithm for a class of interval type-2 fuzzy neural networks. Inf. Sci. 2009, 179, 2175-2193. [CrossRef] · DOI
  47. Morze, N.; Varchenko-Trotsenko, L.; Terletska, T.; Smyrnova-Trybulska, E. Implementation of adaptive learning at higher education institutions by means of Moodle LMS. J. Physics Conf. Ser. 2021, 1840-1 , 012062. [CrossRef] · DOI
  48. Ullah, N.; Mugahed Al-Rahmi, W.; Alzahrani, A.I.; Alfarraj, O.; Alblehai, F.M. Blockchain technology adoption in smart learning environments. Sustainability 2021, 13, 1801. [CrossRef] · DOI
  49. Molenaar, I.; Horvers, A.; Baker, R.S. What can moment-by-moment learning curves tell about students' self-regulated learning? Learn. Instr. 2021, 72, 101206. [CrossRef] · DOI
  50. Han, J.; Kim, K.H.; Rhee, W.; Cho, Y.H. Learning analytics dashboards for adaptive support in face-to-face collaborative argumentation. Comput. Educ. 2021, 163, 104041. [CrossRef] · DOI
  51. Alamri, H.A.; Watson, S.; Watson, W. Learning technology models that support personalization within blended learning environments in higher education. TechTrends 2021, 65, 62-78. [CrossRef] · DOI
  52. Schiff, D. Out of the laboratory and into the classroom: The future of artificial intelligence in education. AI Soc. 2021, 36, 331-348. [CrossRef] · DOI · Paper page
  53. Hutt, S.; Krasich, K.R.; Brockmole, J.; D'Mello, S.K. Breaking out of the lab: Mitigating mind wandering with gaze-based attention-aware technology in classrooms. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, Yokohama, Japan, 8-13 May 2021; pp. 1-14.
  54. Tabuenca, B.; Serrano-Iglesias, S.; Carruana-Martin, A.; Villa-Torrano, C.; Dimitriadis, Y.A.; Asensio-Perez, J.I.; Alario-Hoyos, C.; Gomez-Sanchez, E.; Bote-Lorenzo, M.L.; Martinez-Mones, A.; et al. Affordances and core functions of smart learning environments: A systematic literature review. IEEE Trans. Learn. Technol. 2021, 14, 129-145. [CrossRef] · DOI
  55. Christopoulos, A.; Sprangers, P. Integration of educational technology during the Covid-19 pandemic: An analysis of teacher and student receptions. Cogent Educ. 2021, 8, 1964690. [CrossRef] · DOI

Authors
Koutsantonis, D., Koutsantonis, K., Bakas, N. P., Plevris, V., Langousis, A., & Chatzichristofis, S. A.
Affiliations
  • Research and Development Department, RDC Informatics, 10442 Athens, Greece
  • Qatar University (QA)
  • University of Patras (GR)
  • Neapolis University Pafos (CY)
Corresponding author
  • Bakas, Nikolaos P.
  • Chatzichristofis, Savvas A.
ORCID
Venue
Sustainability
Publisher
MDPI AG
Published
2022-10-05
Volume
14
Issue
19
Pages
12684
ISSN
2071-1050 (ISSN-L)
DOI
10.3390/su141912684
OpenAlex
W4304172175
Cited by
21
References
62
Citations by year
2026: 4 · 2025: 7 · 2024: 4 · 2023: 5 · 2022: 1
FWCI
2.57
Type
journal article
Language
en
Open access
Licence
Locations
  • Sustainability (publishedVersion, cc-by)
  • Qatar University QSpace (Qatar University) (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › scientometrics and bibliometrics research
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
Keywords
adaptive learning systems, adaptive learning, research trends, multidimensional scaling, keyword co-occurrence, research evolution, educational technology

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