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Learning content design and learner adaptation for adaptive e-learning environment: a survey

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Abstract

This paper presents a survey about learning content designs and various adaptation levels, in order to adapt the learners’ necessities in e-learning environment. Normally, learners have different learning styles, cognitive traits, learning goals and varying progress of their learning over period of time, which affects the learner’s performance while providing the same bundle of course to all learners. Hence, there is a need to create adaptive e-learning environment to offer appropriate learning content to all individuals. In general, the adaptation can be done based on learners’ characteristics. Here, we explore the adaptation that can be done, not only based on learner context parameters but also on the learning content (learning object) and the configuration of e-learning environment. In this paper, we provide a detail review about the various levels of adaptation, learning object design and process for learning content design, learner context parameters, and models/components of e-learning; moreover, we analyze and portray the associations among the components, necessary to achieve the well-defined adaptation in e-learning environment.

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References

  • Abowd GD, Dey AK, Brown PJ, Davies N, Smith M, Steggles P (1999) Towards a better understanding of context and context-awareness. In: Gellersen H-W (ed) Handheld and ubiquitous computing. Springer, Berlin, pp 304–307

  • Acampora G, Gaeta M, Loia V (2011) Hierarchical optimization of personalized experiences for e-Learning systems through evolutionary models. Neural Comput Appl 20(5):641–657

    Article  Google Scholar 

  • Albert D, Stefanutti L (2003) Ordering and combining distributed learning objects through skill maps and asset structures. In: Proceedings of the international conference on computers in education (ICCE 2003)

  • Anh NV, Ha NV, Dam HS (2008) Constructing a Bayesian belief network to generate learning path in adaptive hypermedia system. J Comput Sci Cybern 24(1):12–19

    Google Scholar 

  • Atif Y, Benlamri R, Berri J (2003) Learning objects based framework for self-adaptive learning. Educ Inf Technol 8(4):345–368

    Article  Google Scholar 

  • Bai SM, Chen SM (2008) Automatically constructing concept maps based on fuzzy rules for adapting learning systems. Expert syst Appl 35(1):41–49

    Article  MathSciNet  Google Scholar 

  • Bannan-Ritland B, Dabbagh N, Murphy K (2000) Learning object systems as constructivist learning environments: related assumptions, theories, and applications. In: Wiley DA (ed) The instructional use of learning objects. Association for Educational Communications and Technology, Bloomington. http://reusability.org/read/

  • Barker P, Campbell LM (2010) Metadata for learning materials: an overview of existing standards and current developments. Technol Instr Cognit Learn 7(3–4):225–243

    Google Scholar 

  • Bauer M, Maier R, Thalmann S (2010) Metadata generation for learning objects: an experimental comparison of automatic and collaborative solutions. In: Breitner MH, Lehner F, Staff J, Winand U (eds) E-Learning 2010. Physica-Verlag, Heidelberg, pp. 181–195

  • Beaumont IH (1994) User modelling in the interactive anatomy tutoring system ANATOM-TUTOR. User Model User Adapt Interact 4(1):21–45

    Article  MathSciNet  Google Scholar 

  • Berghel H (1997) Cyberspace 2000: dealing with information overload. Commun ACM 40(2):19–24

    Article  Google Scholar 

  • Bohl O, Scheuhase J, Sengler R, Winand U (2002) The sharable content object reference model (SCORM)-a critical review. In: Computers in education, 2002. Proceedings of the international conference on. IEEE, pp 950–951

  • Bousbia N, Labat JM, Rebai I, Balla A (2009) Indicators for deducting the learners’ learning styles: case of the navigation typology indicator. In: Advanced learning technologies, ICALT 2009. Ninth IEEE international conference on. IEEE, pp 385–389

  • Bousbia N, Rebaï I, Labat JM, Balla A (2010) Analysing the relationship between learning styles and navigation behaviour in web-based educational system. Knowl Manag E Learn Int J (KM&EL) 2(4):400–421

    Google Scholar 

  • Boyle C, Encarnacion AO (1998) MetaDoc: an adaptive hypertext reading system. In: Brusilovsky P, Kobsa A, Vassileva J (eds) Adaptive hypertext and hypermedia. Springer, Netherlands, pp 71–89

  • Boytcheva S, Kovatcheva E (2005) Development of adaptive e-learning system based on learning objects. In: Proceedings, International Conference on E-learning. Berlin

  • Brusilovsky P (1996) Methods and techniques of adaptive hypermedia. User Model User Adapt interact 6(2–3):87–129

    Article  MATH  Google Scholar 

  • Brusilovsky P (2001) Adaptive hypermedia. User Model User Adapt interact 11(1–2):87–110

    Article  MATH  Google Scholar 

  • Brusilovsky P (2003) Adaptive navigation support in educational hypermedia: the role of student knowledge level and the case for meta-adaptation. Br J Educ Technol 34(4):487–497

    Article  Google Scholar 

  • Carver CA Jr, Howard RA, Lane WD (1999) Enhancing student learning through hypermedia courseware and incorporation of student learning styles. Educ IEEE Trans 42(1):33–38

    Article  Google Scholar 

  • Canales A, Peña A, Peredo R, Sossa H, Gutiérrez A (2007) Adaptive and intelligent web based education system: towards an integral architecture and framework. Expert Syst Appl 33(4):1076–1089

    Article  Google Scholar 

  • Canter D, Rivers R, Storrs G (1985) Characterizing user navigation through complex data structures. Behav Inf Technol 4(2):93–102

    Article  Google Scholar 

  • Carbó JM, Mor E, Minguillón J (2005) User navigational behavior in e-learning virtual environments. In: Web intelligence, 2005. Proceedings. The 2005 IEEE/WIC/ACM international conference on. IEEE, pp 243–249

  • Chen JN, Huang YM, Chu WCC (2005) Applying dynamic fuzzy petri net to web learning system. Interact Learn Environ 13(3):159–178

    Article  Google Scholar 

  • Chen CM (2008) Intelligent web-based learning system with personalized learning path guidance. Comput Educ 51(2):787–814

    Article  Google Scholar 

  • Chu CP, Chang YC, Tsai CC (2011) PC2PSO: personalized e-course composition based on particle swarm optimization. Appl Intell 34(1):141–154

    Article  Google Scholar 

  • Currier S, Campbell LM (2005) Evaluating 5/99 content for reusability as learning objects. VINE 35(1/2):85–96

    Article  Google Scholar 

  • da Silva Lopes R, Fernandes MA (2009) Adaptative instructional planning using workflow and genetic algorithms. In: Computer and information science, ICIS 2009. Eighth IEEE/ACIS international conference on. IEEE, pp 87–92

  • Dagger D, Wade V, Conlan O (2005) Personalisation for all: making adaptive course composition easy. Educ Technol Soc 8(3):9–25

    Google Scholar 

  • Das M, Bhaskar M, Chithralekha T, Sivasathya S (2010) Context aware e-learning system with dynamically composable learning objects. Int J Comput Sci Eng 2(4):1245–1253

    Google Scholar 

  • De Bra P, Houben GJ, Wu H (1999, February) AHAM: a Dexter-based reference model for adaptive hypermedia. In: Proceedings of the tenth ACM conference on hypertext and hypermedia: returning to our diverse roots: returning to our diverse roots. ACM, pp 147–156

  • De Bra P, Aroyo L, Cristea A (2004) Adaptive web-based educational hypermedia. In: Web dynamics. Springer, Berlin, pp 387–410

  • Deborah LJ, Baskaran R, Kannan A (2014) Learning styles assessment and theoretical origin in an E-learning scenario: a survey. Artif Intell Rev 42(4):801–819

  • de Marcos L, Martinez JJ, Gutierrez JA (2007) Competency-based learning object sequencing using particle swarms. In: Tools with artificial intelligence, ICTAI 2007. 19th IEEE international conference on IEEE, vol2, 111–116

  • de Marcos L, Martínez JJ, Gutierrez JA (2008, July) Swarm intelligence in e-learning: a learning object sequencing agent based on competencies. In: Proceedings of the 10th annual conference on genetic and evolutionary computation. ACM, pp 17–24

  • Dharani B, Geetha TV (2013) Adaptive learning path generation using colored petri nets based on behavioral aspects. In: International conference on recent trends in information technology (ICRTIT). Manuscript accepted for publication

  • Edmonds EA (1981) Adaptive man-computer interfaces. In: Computing skills and the user interface. Academic Press, New York, pp 389–426

  • El Bachari E, Abelwahed EH, El Adnani M (2011) E-Learning personalization based on dynamic learners’ preference. Int J Comput Sci Inf Technol 3(3):200–216

    Google Scholar 

  • Esichaikul V, Lamnoi S, Bechter C (2011) Student Modelling in Adaptive E-Learning Systems. Knowl Manag E Learn Int J (KM&EL) 3(3):342–355

    Google Scholar 

  • Felder RM, Silverman LK (1988) Learning and teaching styles in engineering education. Eng Educ 78(7):674–681

    Google Scholar 

  • Fouad KM (2012) Semantic retrieval and recommendation in adaptive e-learning system. ICCIT 2012:609–614

    Google Scholar 

  • Froschl C (2005) User modeling and user profiling in adaptive e-learning systems. Master Thesis, Graz

  • Gao S, Zhang Z, Hawryszkiewycz I (2005) Supporting adaptive learning in hypertext environment: a high level timed Petri net based approach. In: Advanced learning technologies, ICALT 2005. Fifth IEEE international conference on. IEEE, pp 735–739

  • Ghirardini B (2011) E-learning methodologies a guide for designing and developing e-learning courses. Food and Agriculture Organization of the United Nations, Rome. ISBN 978-92-5-1070970

  • Gibbons AS, Nelson J, Richards R (2001) The nature and origin of instructional objects. In: Wiley DA (ed) The instructional use of learning objects. Association for Educational Communications and Technology, Bloomington

    Google Scholar 

  • Goyal M, Yadav D, Choubey A (2012) E-learning: current state of art and future prospects. Int J Comput Sci 9(3:2):490–499

    Google Scholar 

  • Graf S, Kinshuk KB, Khan FA, Maguire P, Mahmoud A, Rambharose T, Zhang Q (2012) Adaptivity and personalization in learning systems based on students’ characteristics and context. In: The 1st international symposium on smart learning environment, pp 33–36

  • Graf S (2013) Dynamic student modelling of learning styles for advanced adaptivity in learning management systems. Int J Inf Syst Soc Change (IJISSC) 4(1):85–100

    Article  Google Scholar 

  • Gregorc AF, Ward HB (1977) Implications for learning and teaching: a new definition for individual. NASSP Bull 61(406):20–26

    Article  Google Scholar 

  • Guo Q, Zhang M (2009) Implement web learning environment based on data mining. Knowl Based Syst 22(6):439–442

    Article  Google Scholar 

  • Hammami S, Qassem S, Muhaideb SA (2012) Adaptive e-learning using the semantic web: a comparative survey. Int J Inf Commun Technol Res 2(4):366–372

    Google Scholar 

  • Hench T, Whitelock D (2010) Towards a model for evaluating student learning via e-assessment. In: Computer based learning in science, conference proceedings 2010, Poland, pp 15–21

  • Hodgins W, Duval E (2002) Draft standard for learning object metadata. IEEE 1484:1–2002

    Google Scholar 

  • Honey P, Mumford A (1992) The manual of learning styles, 3rd edn. Peter Honey Publications Limited, Maidenhead, Berkshire

  • Huang MJ, Huang HS, Chen MY (2007) Constructing a personalized e-learning system based on genetic algorithm and case-based reasoning approach. Expert Syst Appl 33(3):551–564

    Article  Google Scholar 

  • Huang YM, Chen JN, Huang TC, Jeng YL, Kuo YH (2008a) Standardized course generation process using dynamic fuzzy petri nets. Expert Syst Appl 34(1):72–86

    Article  Google Scholar 

  • Huang YM, Chen JN, Kuo YH, Jeng YL (2008b) An intelligent human-expert forum system based on fuzzy information retrieval technique. Expert Syst Appl 34(1):446–458

    Article  Google Scholar 

  • Kamceva E, Mitrevski P, (2012) On the general paradigms for implementing adaptive e-learning systems. In: ICT Innovations, 2012 Web Proceedings. Ohrid, Macedonia, pp 281–289

  • Karampiperis P, Sampson D (2005) Adaptive learning resources sequencing in educational hypermedia systems. Educ Techno Soc 8(4):128–147

    Google Scholar 

  • Karampiperis P, Sampson D (2006) Automatic learning object selection and sequencing in web-based intelligent learning systems. In: Ma Z (ed) Web-based intelligent e-learning systems: technologies and applications Information Science Publishing, pp 56–71

  • Keefe JW (1987) Learning style theory and practice. National Association of Secondary School Principals publisher, Reston

    Google Scholar 

  • Keefe JW (1991) Learning style: cognitive and thinking skills. National Association of Secondary School Principals, Reston

    Google Scholar 

  • Kinshuk TL (2004) Application of learning styles adaptivity in mobile learning environments. Third Pan Commonwealth Forum on Open Learning, New Zealand, pp 4–8. http://www.col.org/pcf3/Papers/PDFs/KinshukLin1.pdf

  • Kolb DA (1999) Learning style inventory. Version 3: Technical specifications. Availble from TRG Hey/McBer, Training Resources Group, 116 Huntington Avenue, Boston, MA 02116. trg_mcber@haygroup.com

  • Kolb DA, Boyatzis RE, Mainemelis C (2001) Experiential learning theory: Previous research and new directions. Perspect Think Learn Cognit Styles 1:227–247

    Google Scholar 

  • Koohang A (2004) Creating learning objects in collaborative e-learning settings. Issues Inf Sys 4(2):584–590

    Google Scholar 

  • Kritikou Y, Demestichas P, Adamopoulou E, Demestichas K, Theologou M, Paradia M (2008) User profile modeling in the context of web-based learning management systems. J Netw Comput Appl 31(4):603–627

    Article  Google Scholar 

  • Kwasnicka H, Szul D, Markowska-Kaczmar U, Myszkowski PB (2008, June) Learning Assistant-Personalizing Learning Paths in e-Learning Environments. In: Computer Information Systems and Industrial Management Applications, 2008. CISIM’08. 7th. IEEE. pp 308–314

  • Lehman R (2007) Learning object repositories. New Dir Adult Continuing Educ 2007(113):57–66

    Article  Google Scholar 

  • Li Y, Huang R (2006) Dynamic composition of curriculum for personalized e-learning. Front Artif Intell Appl 151:569

    Google Scholar 

  • Lin HW, Shih LK, Chang WC, Yang CH, Wang CC (2004) A Petri nets-based approach to modeling SCORM sequence. In: multimedia and expo, ICME’04. 2004 IEEE international conference on. IEEE, Vol. 2, pp 1247–1250

  • Lin JC, Wu KC (2007) Finding a fitting learning path in e-learning for juvenile. In: Advanced learning technologies, ICALT 2007. Seventh IEEE international conference on. IEEE, pp 449–453

  • Liu JH, Huang BS, Chao M (2005) The design of learning object authoring tool based on SCORM. In: Advanced learning technologies, 2005. ICALT 2005. Fifth IEEE international conference on. IEEE, pp 778–782

  • Manouselis N, Sampson D (2003) Agent-based e-learning course recommendation: matching learner characteristics with content attributes. Int J Comput Appl 25(1):50–64

    Google Scholar 

  • Markovic S, Jovanovic N (2012) Learning style as a factor which affects the quality of e-learning. Artif Intell Rev 38(4):303–312

    Article  Google Scholar 

  • Marquez JM, Ortega JA, Gonzalez-Abril L, Velasco F (2008) Creating adaptive learning paths using ant colony optimization and bayesian networks. In: Neural Networks, IJCNN 2008. (IEEE world congress on computational intelligence). IEEE international joint conference on. IEEE, pp 3834–3839

  • Melis E, Andres E, Budenbender J, Frischauf A, Goduadze G, Libbrecht P, Martin P, Ullrich C (2001) ActiveMath: a generic and adaptive web-based learning environment. Int J Artif Intell Educ (IJAIED) 12:385–407

    Google Scholar 

  • Menendez VH, Prieto ME (2008) A Learning Object Composition Model. In: Kaschek R, Kop C, Steinberger C, Fliedl G (eds) Information Systems and e-Business Technologies. Springer, Berlin Heidelberg, pp 469–474

  • Menendez VH, Zapata A, Prieto-Mendez ME, Romero C, Serrano-Guerrero J (2011) A similarity-based approach to enhance learning objects management systems. In Intelligent systems design and applications (ISDA), 2011 11th international conference on IEEE, pp 996–1001

  • Messick S (1970) The criterion problem in the evaluation of instruction: assessing possible, not just probable, intended outcomes. In: Wittrock MC, Wiley DE (eds) The evaluation of instruction: issues and problems. Holt Rinehart and Winston, New York, pp 183–220

    Google Scholar 

  • Najjar LJ (1996) Multimedia information and learning. J Educ Multimed Hypermedia 5:129–150

    Google Scholar 

  • Noor SFM, Yusof N, Hashim SZM (2007) Determining important metadata for accessibility and reusability of learning object. 1st Int Malays Educ Technol Conv 2:760–765

    Google Scholar 

  • Noor SFM, Yusof N, Hashim SZM (2011) Creating granular learning object towards reusability of learning object in e-learning context. In: Electrical engineering and informatics (ICEEI), 2011 international conference on. IEEE, pp 1–5

  • Oton S, Ortiz A, de-Marcos L, de Dios SM, García A, García E, Hilera JR, Barchino R (2012) Developing distributed repositories of learning objects. In: Elvis Pontes (ed)Methodologies, tools and new developments for e-learning. INTECH Open Access Publisher, Croatia

  • Paramythis A, Loidl-Reisinger S (2003) Adaptive learning environments and e-learning standards. In: Second european conference on e-learning, pp 369–379

  • Phobun P, Vicheanpanya J (2010) Adaptive intelligent tutoring systems for e-learning systems. Proced Soc Behav Sci 2(2):4064–4069

    Article  Google Scholar 

  • Pushpa M (2012) ACO in e-learning: towards an adaptive learning path. Int J Comput Sci Eng 4(3):458–462

    Google Scholar 

  • Romero C, De Bra P, Ventura S, de Castro C (2002) Using knowledge levels with AHA! For discovering interesting relationships. In: Proceedings of the AACE ELearn’2002 Conference, pp 2721–2722

  • Roy D, Sarkar S, Ghose S (2010) A comparative study of learning object metadata, learning material repositories, metadata annotation & an automatic metadata annotation tool. Adv Semant Comput 2:103–126

    Google Scholar 

  • Seki K, Matsui T, Okamoto T (2005) An adaptive sequencing method of the learning objects for the e-learning environment. Electron Commun Jpn (Part III Fundam Electron Sci) 88(3):54–71

    Article  Google Scholar 

  • Semet Y, Yamont Y, Biojout R, Luton E, Collet P (2003) Artificial ant colonies and e-learning: an optimization of pedagogical paths. In: 10th International conference on human-computer interaction, pp 1031–1035

  • Sengupta S, Sahu S, Dasgupta R (2011) Construction of learning path using ant colony optimization from a frequent pattern graph. Int J Comput Sci Issues (8) 6(1):314–321

    Google Scholar 

  • Shute V, Towle B (2003) Adaptive e-learning. Educ Psychol 38(2):105–114

    Article  Google Scholar 

  • Sieber S, Henrich A (2010) Metadata for learning objects-a cure for information overflow? IADIS Int Conf E Learn 2010:251–255

    Google Scholar 

  • Souhaib A, Mohamed K, Eddine E, Ahmed I (2010) Adaptive hypermedia systems for e-learning. Int J Emerg Technol Learn (iJET) 5(3):47–51

    Google Scholar 

  • Stangl W (2002) Der HALB-Test [The HALB test]. http://arbeitsblaetter.stangl-taller.at/TEST/HALB/

  • Stewart C, Cristea A, Brailsford T, Ashman H (2005) ‘Authoring once, delivering many’: Creating reusable adaptive courseware. In: International Conference on Web-Based Education - WBE 05, Switzerland, pp 1–6

  • Tan XH, Shen RM, Wang Y (2012) Personalized course generation and evolution based on genetic algorithms. J Zhejiang Univ Sci C 13(12):909–917

    Article  Google Scholar 

  • Tseng JC, Chu HC, Hwang GJ, Tsai CC (2008) Development of an adaptive learning system with two sources of personalization information. Comput Educ 51(2):776–786

    Article  Google Scholar 

  • Tzouveli P, Mylonas P, Kollias S (2008) An intelligent e-learning system based on learner profiling and learning resources adaptation. Comput Educ 51(1):224–238

    Article  Google Scholar 

  • Van Merrienboer JJ, Ayres P (2005) Research on cognitive load theory and its design implications for e-learning. Educ Tech Res Dev 53(3):5–13

    Article  Google Scholar 

  • Vassileva J, Deters R (1998) Dynamic courseware generation on the WWW. Br J Educ Technol 29(1):5–14

    Article  Google Scholar 

  • Wang TI, Wang KT, Huang YM (2008) Using a style-based ant colony system for adaptive learning. Expert Syst Appl 34(4):2449–2464

    Article  Google Scholar 

  • Wagner D (2002) The new frontier of learning object design. E-Learn Dev J 1–8

  • Watson C, Li FW, Lau RW (2010) A pedagogical interface for authoring adaptive e-learning courses. In: Proceedings of the second ACM international workshop on Multimedia technologies for distance learning. ACM, pp 13–18

  • Weber G (1999) Adaptive learning systems in the World Wide Web. Courses Lect Int Cent Mech Sci 407:371–378

  • Wenger E (1987) Artificial intelligence and tutoring system: computational and cognitive approaches to the communication of knowledge, vol 1. Morgan Kaufmann Publishers, Burlington, pp 19–21

    Google Scholar 

  • Wiley D (1999) Learning objects and the new CAI: So what do I do with a learning object. Retrieved December 2013: http://opencontent.org/docs/instruct-arch.pdf

  • Wiley DA (2000) Learning object design and sequencing theory. Ph.D. Thesis, Brigham Young University

  • Wiley DA. II. (2001) Connecting learning objects to instructional design theory: a definition, a metaphor, and a taxonomy. Utah State University, In: Wiley DA (ed) The instructional use of learning objects: online version. http://reusability.org/read/chapters/wiley.doc

  • Wu H, De Bra P (2001) Sufficient conditions for well-behaved adaptive hypermedia systems. In: Zhong N, Yao Y, Liu J, Ohsuga S (eds) Web intelligence: research and development lecture notes in computer science 2198, Springer, Berlin, pp 148–152

  • Zhu F, Cao J (2008) Learning activity sequencing in personalized education system. Wuhan Univ J Nat Sci 13(4):461–465

    Article  Google Scholar 

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Premlatha, K.R., Geetha, T.V. Learning content design and learner adaptation for adaptive e-learning environment: a survey. Artif Intell Rev 44, 443–465 (2015). https://doi.org/10.1007/s10462-015-9432-z

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