Anatomy of Student Models in Adaptive Learning Systems: A Systematic Literature Review of Individual Differences from 2001 to 2013

Abstract

This study brings an evidence-based review of user individual characteristics employed as sources of adaptation in recent adaptive learning systems. Twenty-two user individual characteristics were explored in a systematically designed search procedure, while 17 of them were identified as sources of adaptation in final selection. The content analysis of 98 selected publications that include evidence of adaptation efficiency is conducted. The quantitative representation of the findings shows current trends in the research of individual differences, as well as the tendencies of their further employment in student modeling. The article contributes to the body of knowledge on user individual differences and consequently to the research and development of adaptive learning systems. Additional contribution of the study is in-depth description of development and evaluation of the search strategy which makes the method easily replicable as well as suitable for modification and employment in systematic literature review in any research field.

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References

Acampora

Gaeta

, & Loia

(2010). Exploring e-learning knowledge through ontological memetic agents. IEEE Computational Intelligence Magazine, 5(2), 66–77.

Akbulut

, & Cardak

C. S.

(2012). Adaptive educational hypermedia accommodating learning styles: A content analysis of publications from 2000 to 2011. Computers & Education, 58(2), 835–842.

Alepis

, & Virvou

(2006). User modeling: An empirical study for affect perception through keyboard and speech in a bi-modal user interface. In Wade

Ashman

, & Smyth

(Eds.), Proceedings of AH 2006: Adaptive hypermedia and adaptive web-based systems. LNCS 4018, 338–341.

Benyon

, & Murray

(1993). Developing adaptive systems to fit individual aptitudes. Proceedings of the 1st international conference on intelligent user interfaces (pp. 115–121). Orlando, Florida.

Brown

(1978). Knowing when, where and how to remember: A problem of Metacognition. In Glaser

(Ed.), Advances in Instructional Psychology (pp. 77–165). Hillsdale, NJ: Lawrence Erlbaum Associates Publishers.

Brown

E. J.

Brailsford

T. J.

Fisher

, & Moore

(2009). Evaluating learning style personalization in adaptive systems: Quantitative methods and approaches. IEEE Transactions on Learning Technologies, 2(1), 10–22.

Browne

Norman

, & Rithes

(1990). Why build adaptive systems? In Browne

Totterdell

, & Norman

(Eds.), Adaptive user interfaces (pp. 15–59). London: Academic Press Inc.

Brusilovsky

(1996). Methods and techniques of adaptive hypermedia. User modeling and user-adapted interaction, 6, 87–129. (Reprinted in Adaptive Hypertext and Hypermedia, 1–43 (1998). Kluwer Academic Publishers.

Brusilovsky

(2001). Adaptive hypermedia. User Modeling and User-Adapted Interaction 11, 87–110.

10.

Brusilovsky

, & Milan

(2007). User models for adaptive hypermedia and adaptive educational systems. In Brusilovsky

Kobsa

, & Nejdl

(Eds.), The adaptive web. Methods and strategies of web personalization. LNCS 4321, (pp. 3–53). Berlin Heidelberg: Springer-Verlag.

11.

Brusilovsky

Sosnovsky

, & Yudelson

(2009). Addictive links: The motivational value of adaptive link annotation. New Review of Hypermedia and Multimedia, 15(1), 97–118.

12.

Carver

C.A.

Howard

R.A.

, & Lavelle

(1996). Enhancing student learning by incorporating learning styles into adaptive hypermedia. Proceedings of 1996 ED-MEDIA: World Conference on Educational Multimedia and Hypermedia (pp. 118–123). Boston, MA.

13.

Chang

Y. C.

Kao

W. Y.

Chu

C. P.

, & Chiu

C. H.

(2009). A learning style classification mechanism for e-learning. Computers & Education, 53(2), 273–285.

14.

Chen

Czerwinski

, & MacRedie

(2000). Individual differences in virtual environments—Introduction and overview. Journal of the American Society for Information Science, 51(6), 499–507.

15.

Chen

C. M.

Lee

H. M.

, & Chen

Y. H.

(2005). Personalized e-learning system using item response theory. Computers & Education, 44(3), 237–255.

16.

Chen

, & MacRedie

(2002). Cognitive styles and hypermedia navigation: Development of a learning model. Journal of the American Society for Information Science and Technology, 53(1), 3–15.

17.

Chen

C. M.

, & Sun

Y. C.

(2012). Assessing the effects of different multimedia materials on emotions and learning performance for visual and verbal style learners. Computers & Education, 59(4), 1273–1285.

18.

Chi

, & VanLehn

(2010). Meta-cognitive strategy instruction in intelligent tutoring systems: How, when, and why. Educational Technology & Society, 13(1), 25–39.

19.

Chin

D. N.

(2001). Empirical evaluation of user models and user-adapted systems. User Modeling and User Adapted Interaction, 11, 181–194.

20.

Cho

H. C.

Gay

Davidson

, & Ingraffea

(2007). Social networks, communication styles, and learning performance in a CSCL community. Computers & Education, 49(2), 309–329.

21.

Chrysafiadi

, & Virvou

(2013). Student modeling approaches: A literature review for the last decade. Expert Systems with Applications, 40(11), 4715–4729.

22.

Chu

H. C.

Hwang

G. J.

Tsai

C. C.

, & Tseng

J. C. R.

(2010). A two-tier test approach to developing location-aware mobile learning systems for natural science courses. Computers & Education, 55(4), 1618–1627.

23.

Cook

D. A.

(2005). Learning and cognitive styles in web-based learning: Theory, evidence, and application. Academic Medicine, 80(3), 266–278.

24.

De Bra

, & Calvi

(1998). AHA! An open Adaptive Hypermedia Architecture. The New Review of Hypermedia and Multimedia, 115–139.

25.

Desmarais

M. C.

, & de Baker

R. S. J.

(2012) A review of recent advances in learner and skill modeling in intelligent learning environments. User Modeling and User Adapted Interaction, 22(1–2), 9–38.

26.

Dillon

, & Watson

(1996). User analysis in HCI—The historical lessons from individual differences research. International Journal on Human-Computer Studies, 45, 619–637.

27.

Egan

(1988). Individual differences in human-computer interaction. In Helander

(Ed.), Handbook of human-computer interaction (pp. 543–568). North-Holland: Elsevier Science B. V. Publishers.

28.

Ekstrom

French

Harman

, & Dermen

(1976). Manual for kit of factor referenced cognitive tests. Princeton, NJ: Educational Testing Service.

29.

Eysenck

H. J.

(1992). Four ways five factors are not basic. Personality and Individual Differences, 13, 667–673.

30.

Felder

R. M.

, & Silverman

L. K.

(1988). Learning and teaching styles in engineering education. Engineering Education, 78 (7), 674–681.

31.

Ford

, & Chen

S. Y.

(2000). Individual differences, hypermedia navigation and learning: An Empirical Study. Journal of Educational Multimedia and Hypermedia, 9(4), 281–311.

32.

Gama

(2004). Metacognition in interactive learning environments: The reflection assistant model. In Lester

J. C.

Vicario

R. M.

, & Paraguaçu

(Eds.), Proceedings of 7th international conference on intelligent tutoring systems (pp. 668–677). Berlin: Springer.

33.

Garcia

Amandi

Schiaffino

, & Campo

(2006). Evaluating Bayesian networks' precision for detecting students' learning styles. Computers & Education, 49(3), 794–808.

34.

Gena

, & Weibelzahl

(2007). Usability engineering for the adaptive web. In Brusilovsky

Kobsa

, & Nejdl

(Eds.), The adaptive web. Methods and strategies of web personalization. LNCS 4321, 720–762. Berlin Heidelberg, Springer-Verlag.

35.

Germanakos

Tsianos

Lekkas

Mourlas

, & Samaras

(2009). Realizing comprehensive user profile as the core element of adaptive and personalized communication environments and systems. Computer Journal, 52(7), 749–770.

36.

Giovannella

, & Carcone

(2011). A new application to detect “emotional perception and styles” of primary school children, and their evolution with age. Proceedings of ICALT 2011: 11th IEEE International Conference on Advanced Learning Technologies, pp. 53–55.

37.

Gogoulou

Gouli

Grigoriadou

Samarakou

, & Chinou

(2007). A web-based educational setting supporting individualized learning, collaborative learning and assessment. Educational Technology & Society, 10(4), 242–256.

38.

Graf

, & Kinshuk (2007). Providing adaptive courses in learning management systems with respect to learning styles. Proceedings of Elearn 2007: The World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 2576–2583). Chesapeake, VA: AACE.

39.

Graf

Lin

, & Kinshuk (2008). The relationship between learning styles and cognitive traits—Getting additional information for improving student modeling. Computers in Human Behavior, 24, 122–137.

40.

Graff

M. G.

(2005). Individual differences in hypertext browsing strategies. Behaviour and Information Technology, 24(2), 93–100.

41.

Granić

(2002). Foundation of adaptive interfaces for computerized educational systems. Ph.D. thesis. University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, Croatia (in Croatian).

42.

Granić

, & Nakiç

(2010). Enhancing the learning experience: Preliminary framework for user individual differences. HCI in work and learning, life and leisure. LNCS 6389, 384–399.

43.

Grimley

, & Riding

(2009). Individual Differences and Web-Based Learning. In Mourlas

Tsianos

, & Germanakos

(Eds.), Cognitive and Emotional Processes in Web-Based Education: Integrating Human Factors and Personalization. (pp. 209–228). Hershey, PA: IGI Global.

44.

Harrigan

Kravčík

Steiner

, & Wade

(2009). What do academic users really want from an adaptive learning system? Proceedings of UMAP'09: The 17th International Conference on User Modeling, Adaptation, and Personalization. LNCS 5535 (pp. 454–460).

45.

Honey

, & Mumford

(1992). The Manual of Learning Styles (3rd ed.). Maidenhead: Peter Honey.

46.

Hook

(2000). Steps to take before intelligent user interfaces become real. Journal of Interaction with Computers, 12(4), 409–426.

47.

Huang

E. Y.

Lin

S. W.

, & Huang

T. K.

(2012). What type of learning style leads to online participation in the mixed-mode e-learning environment? A study of software usage instruction. Computers & Education, 58(1), 338–349.

48.

Huang

E. Y.

Lin

S. W.

, & Huang

T. K.

(2012). What type of learning style leads to online participation in the mixed-mode e-learning environment? A study of software usage instruction. Computers & Education, 58(1), 338–349.

49.

Huang

S. L.

, & Yang

C. W.

(2009). Designing a semantic bliki system to support different types of knowledge and adaptive learning. Computers & Education, 53(3), 701–712.

50.

Hurley

, & Weibelzahl

(2007). Using MotSaRT to support on-line teachers in student motivation. In Duval

Klamma

, & Wolpers

. Creating new learning experiences on a global scale. Proceedings of EC-TEL 2007: Second European Conference on Technology Enhanced Learning, LNCS 4753, 101–111.

51.

Hsu

C. K.

Hwang

G. J.

, & Chang

C. K.

(2013). A personalized recommendation-based mobile learning approach to improving the reading performance of EFL students. Computers & Education, 63, 327–336.

52.

ISO FDIS 9241–210 (2009) Human-centered design process for interactive systems. ISO.

53.

Johnson

R. D.

(2005). An empirical investigation of sources of application-specific computer-self-efficacy and mediators of the efficacy—Performance relationship. International Journal of Human-Computer Studies, 62(6), 737–758.

54.

Jonassen

D. H.

, & Grabowski

B. L.

(1993). Handbook of Individual Differences, Learning, and Instruction. Hillsdale, NJ: Lawrence Erlbaum Associates.

55.

Jovanovic

Vukicevic

Milovanovic

, & Minovic

(2012). Using data mining on student behavior and cognitive style data for improving e-learning systems: A case study. International Journal of Computational Intelligence Systems, 5(3), 597–610.

56.

Juvina

, & van Oostendorp

(2006). Individual Differences and Behavioral Metrics Involved in Modeling web Navigation. Universal Access in the Information Society, 4(3), 258–269.

57.

Kabassi

, & Virvou

(2004). Personalised adult e-training on computer use based on multiple attribute decision making. Interacting with Computers, 16(1), 115–132.

58.

Kallinen

, & Ravaja

(2005). Effects of the rate of computer-mediated speech on emotion-related subjective and physiological responses. Behavior & Information Technology, 24(5), 365–373.

59.

Kalyuga

, & Sweller

(2005). Rapid dynamic assessment of expertise to improve the efficiency of adaptive e-learning. ETR&D-Educational Technology Research and Development, 53(3), 83–93.

60.

Kelly

, & Tangney

(2006). Adapting to Intelligence Profile in an Adaptive Educational System. Journal of Interacting With Computers, 18(3), 385–409.

61.

Kitchenham

Brereton

O. P.

Budgen

Turner

Bailey

, & Linkman

(2009). Systematic literature reviews in software engineering — A systematic literature review. Information and Software Technology, 51(1), 7–15.

62.

Kitchenham

, & Charters

(2007). Guidelines for performing Systematic Literature reviews in Software Engineering Version 2.3. Keele University and University of Durham, Technical report EBSE-2007-01.

63.

Klasnja-Milicevic

Vesin

Ivanovic

, & Budimac

(2011). E-Learning personalization based on hybrid recommendation strategy and learning style identification. Computers & Education, 56(3), 885–899.

64.

Kobsa

(1995). Supporting User Interfaces for All through User Modeling. Proceedings of HCI International 1995: 6th International Conference on Human-Computer Interaction, pp. 155–157. Yokohama, Japan.

65.

Lekkas

Germanakos

Tsianos

Mourlas

, & Samaras

(2013). Personality and Emotion as Determinants of the Learning Experience: How Affective Behavior Interacts with Various Components of the Learning Process. Human-Computer Interaction. Applications and Services. LNCS 8005, 418–427.

66.

Leslie

S. J.

Hartswood

Meurig

McKee

S. P.

Slack

Procter

(2006). Clinical decision support software for management of chronic heart failure: Development and evaluation. Computers in Biology and Medicine, 36(5), 495–506.

67.

Limongelli

Sciarrone

Temperini

, & Vaste

(2009). Adaptive Learning with the LS-Plan System: A Field Evaluation. IEEE Transactions on Learning Technologies, 2(3), 203–215.

68.

J. J.

Chan

Y. C.

, & Yeh

S. W.

(2012). Designing an adaptive web-based learning system based on students' cognitive styles identified online. Computers & Education, 58(1), 209–222.

69.

Loboda

T. D.

, & Brusilovsky

(2010). User-adaptive explanatory program visualization: Evaluation and insights from eye movements. User Modeling and User-Adapted Interaction, 20(3), 191–226.

70.

Magoulas

G. D.

Chen

S. Y.

, & Papanikolaou

K. A.

(2003). Integrating layered and heuristic evaluation for adaptive learning environments. In Weibelzahl

, & Paramythis

(Eds.), Proceedings of UM2003: Second Workshop on Empirical Evaluation of Adaptive Systems, pp. 5–14.

71.

McNulty

J. A.

Sonntag

, & Sinacore

J. M.

(2009). Evaluation of Computer-Aided Instruction in a Gross Anatomy Course: A Six-year Study. Anatomical Sciences Education, 2(1), 2–8.

72.

Medina-Medina

Molina-Ortiz

, & Garcia-Cabrera

(2011). Adaptation and user modeling in hypermedia learning environments using the SEM-HP model and the JSEM-HP tool. Knowledge and Information Systems, 29(3), 629–656.

73.

Melis

Haywood

, & Smith

T. J.

(2006). LeActiveMath. In Nejdl

, & Tochtermann

(Eds.), Proceedings of Innovative Approaches for Learning and Knowledge Sharing. LNCS 4227, 660–666.

74.

Moridis

C. N.

, & Economides

A. A.

(2009). Mood recognition during online self-assessment test. IEEE Transactions on Learning Technologies, 2(1), 50–61.

75.

Munoz-Organero

Munoz-Merino

P. J.

, & Kloos

C. D.

(2011). Adapting the speed of reproduction of audio content and using text reinforcement for maximizing the learning outcome though mobile phones. IEEE Transactions on Learning Technologies, 4(3), 233–238.

76.

Nakić

, & Granić

(2009). User individual differences in intelligent interaction: Do they matter? Universal Access in Human-Computer Interaction. Intelligent and Ubiquitous Interaction Environments. LNCS 5615, 694–703.

77.

Norcio

, & Stanley

(1989). Adaptive human-computer interfaces: A literature survey and perspective. IEEE Transactions on System, Man and Cybernetics, 19(2), 399–408.

78.

Ozpolat

, & Akar

G. B.

(2009). Automatic detection of learning styles for an e-learning system. Computers & Education, 53(2), 355–367.

79.

Papanikolaou

K. A.

Grigoriadou

Kornilakis

, & Magoulas

G. D.

(2003). Personalizing the interaction in a Web-based educational hypermedia system: The case of INSPIRE. User Modeling and User-Adapted Interaction, 13(3), 213–267.

80.

Paramythis

Weibelzahl

, & Masthoff

(2010). Layered evaluation of interactive adaptive systems: Framework and formative methods. User Modeling and User-Adapted Interaction, 20(5), 383–453.

81.

Pashler

McDaniel

Rohrer

, & Bjork

, (2008). Learning styles: Concepts and evidence. A Journal of the Association for Psychological Science, 9(3), 105–119.

82.

Pask

(1976). Styles and strategies of learning. British Journal of Educational Psychology, 46, 128–148.

83.

Preece

Rogers

Sharp

Benyon

Holland

, & Carey

(1994). Humansomputer interaction. Addison-Wesley, Harlow, England.

84.

Riding

R. J.

, & Buckle

C. F.

(1990). Learning styles and training performance. Sheffield: Training Agency.

85.

Rothrock

Koubek

Fuchs

Haas

, & Salvendy

(2002). Review and reappraisal of adaptive interfaces: Toward biologically-inspired paradigms. Theoretical Issues in Ergonomic Science, 3(1), 47–84.

86.

Sadler-Smith

, & Riding

(1999). Cognitive style and instructional preferences. Instructional Science, 27, 355–371.

87.

Salehi

, & Kamalabadi

I. N.

(2013). Hybrid recommendation approach for learning material based on sequential pattern of the accessed material and the learner's preference tree. Knowledge-Based Systems, 48, 57–69.

88.

Sampayo-Vargas

Cope

C. J.

, & Graeme

J. B.

(2013). The effectiveness of adaptive difficulty adjustments on students' motivation and learning in an educational computer game. Computers & Education, 69, 452–462.

89.

Schiaffino

Garcia

, & Amandi

(2008). Eteacher: Providing personalized assistance to e-learning students. Computers & Education, 51(4), 1744–1754.

90.

Shih

H. P.

(2008). Using a cognition-motivation-control view to assess the adoption intention for Web-based learning. Computers & Education, 50(1), 327–337.

91.

Solimeno

Mebane

M. E.

Tomai

, & Francescato

(2008). The influence of students and teachers characteristics on the efficacy of face-to-face and computer supported collaborative learning. Computers & Education, 51(1), 109–128.

92.

Stanney

, & Salvendy

(1995). Information visualization: Assisting low spatial individuals with information access tasks through the use of visual mediators. Ergonomics, 38(6), 1184–1198.

93.

Stash

, & De Bra

(2004). Incorporating cognitive styles in AHA! (The adaptive hypermedia architecture). Proceedings of the IASTED International Conference Web-Based Education, pp. 378–383.

94.

Tarpin-Bernard

Marfisi-Schottman

, & Habieb-Mammar

(2009). AnAmeter: The first steps to evaluating adaptation. Proceedings of UMAP2009: 6th Workshop on User-Centred Design and Evaluation of Adaptive Systems, pp. 11–20. Trento, Italy: CEUR.

95.

Thalmann

(2008). Adaptation criteria for preparing learning material for adaptive usage: Structured content analysis of existing systems. In Holzinger

(Ed.), HCI and usability for education and work. LNCS 5298, 411–418.

96.

Tobar

C. M.

(2003). Yet another evaluation framework. In Weibelzahl

& Paramythis

(Eds.), Proceedings of UM2003: Second Workshop on Empirical Evaluation of Adaptive Systems, pp. 15–24.

97.

Triantafillou

Pomportsis

, & Demetriadis

(2003). The design and the formative evaluation of an adaptive educational system based on cognitive styles. Computers & Education, 41(1), 87–103.

98.

Tseng

J. C. R.

Chu

H. C.

Hwang

G. J.

, & Tsai

C. C.

(2008). Development of an adaptive learning system with two sources of personalization information. Computers & Education, 51(2), 776–786.

99.

Tsianos

Germanakos

Lekkas

Mourlas

Belk

Christodoulou

(2008). Enhancing e-learning environments with users' cognitive factors: The case of EKPAIDEION. Proceedings of ECEL 2008: 7th European Conference on e-Learning. Agia Napa, Cyprus.

100.

Tsianos

Lekkas

Germanakos

Mourlas

, & Samaras

(2009). An Experimental Assessment of the Use of Cognitive and Affective Factors in Adaptive Educational Hypermedia. IEEE Transactions on Learning Technologies, 2(3), 249–258.

101.

Vandewaetere

Desmet

, & Clarebout

(2011). The contribution of learner characteristics in the development of computer-based adaptive learning environments. Computers in Human Behavior, 27(1), 118–130.

102.

van Seters

J. R.

Ossevoort

M. A.

Tramper

, & Goedhart

M. J.

(2012). The influence of student characteristics on the use of adaptive e-learning material. Computers & Education, 58(3), 942–952.

103.

van Velsen

van der Geest

Klaassen

, & Steehouder

, (2008). User-centered evaluation of adaptive and adaptable system. Engineering Review, 23(3), 261–281.

104.

Wang

H. C.

T. Y.

, & Chang

C. Y.

(2006). A web-based tutoring system with styles-matching strategy for spatial geometric transformation. Interacting with Computers, 18(3), 331–355.

105.

Weber

, & Brusilovsky

(2001). ELM-ART: An adaptive versatile system for webbased instruction. International Journal of Artificial Intelligence in Education, 12, 351–384.

106.

Webster

, & Watson

R. T.

(2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly 26(2), xiii–xxiii.

107.

Weibelzahl

(2001). Evaluation of adaptive systems. In Bauer

Gmytrasiewicz

P. J.

, & Vassileva

(Eds.), Proceedings of UM2001: Eighth International Conference on User Modeling. LNCS 2109, 292–294.

108.

Weibelzahl

(2005). Problems and pitfalls in the evaluation of adaptive systems. In Chen

& Magoulas

(Eds.), Adaptable and Adaptive Hypermedia Systems (pp. 285–299). IRM Press, Hershey.

109.

Weibelzahl

, & Kelly

(2005). Adaptation to motivational states in educational systems. Proceedings of LWA2005: The workshop week Lernen—Wissensentdeckung—Adaptivität, pp. 80–84.

110.

Witkin

Moore

Gooddenough

, & Cox

(1977). Field-dependent and field-independent cognitive styles and their educational implications. Review of Educational Research, 47, 1–64.

111.

Zhang

, & Salvendy

(2001). The implication of visualization ability and structure preview design for web information search tasks. International Journal of Human-Computer Interaction, 13(1), 75–95.