You are here:

Adaptation criteria for the personalised delivery of learning materials: A multi-stage empirical investigation
ARTICLE

, University of Innsbruck School of Management Information Systems

Australasian Journal of Educational Technology Volume 30, Number 1, ISSN 0814-673X Publisher: Australasian Society for Computers in Learning in Tertiary Education

Full Text

Abstract

Personalised e-Learning represents a major step-change from the one-size-fits-all approach of traditional learning platforms to a more customised and interactive provision of learning materials. Adaptive learning can support the learning process by tailoring learning materials to individual needs. However, this requires the initial preparation of content upfront, which is a laborious task – and organizations have to target their limited resources effectively. In order to guide the process of creating adaptive learning materials, the criteria for adaptation – or adaptation needs – have to be known. The aim of this paper is to identify these adaptation criteria, applying a mixed method procedure. First, thirty adaptive systems selected from the literature are investigated using a qualitative content analysis. Then, the resulting set of adaptation criteria is validated by experts in the form of a series of two online questionnaires. As a result, a set of 13 adaptation criteria representing different adaptation needs emerge.

Citation

Thalmann, S. (2014). Adaptation criteria for the personalised delivery of learning materials: A multi-stage empirical investigation. Australasian Journal of Educational Technology, 30(1),. Australasian Society for Computers in Learning in Tertiary Education. Retrieved September 23, 2023 from .

References

View References & Citations Map
  1. Akbulut, Y., & 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.
  2. Baker, T., Dekkers, M., Heery, R., Patel, M., & Salokhe, G. (2001). What terms does your metadata use? Application profiles as machine-understandable narratives. Paper presented at the Int’L. Conf. On Dublin Core and Metadata Applications, Tokyo. Retrieved from http://dcpapers.dublincore.org/pubs/article/view/654
  3. Boll, S. (2003). MM4U-A framework for creating personalized multimedia content. Paper presented at the Ninth International Conference on Distributed Multimedia Systems, Miami, USA. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=3C9B70A902331ED42F3DF0E0D25606DE ?doi=10.1.1.59.6880&rep=rep1&type=pdf
  4. Boyle, T. (2003). Design principles for authoring dynamic, reusable learning objects. Australian Journal of Educational Technology, 19(1), 46-58.
  5. Brooks, C., & McCalla, G. (2006). Towards flexible learning object metadata. International Journal of Continuing Engineering Education and Lifelong Learning, 16(1/2), 50-63.
  6. Brusilovsky, P. (1996). Methods and techniques of adaptive hypermedia. User Modeling and User Adapted Interaction (Special issue on adaptive hypertext and hypermedia), 6(2-3), 87-129.
  7. Brusilovsky, P. (2001). Adaptive hypermedia. User Modeling and User Adapted Interaction, 11(1/2), 87110.
  8. Brusilovsky, P. (2003). Adaptive navigation support in educational hypermedia: The role of student knowledge level and the case for meta-adaptation. British Journal of Educational Technology, 34(4), 487-497.
  9. Bunt, A., Carenini, G., & Conati, C. (2007). Adaptive content presentation for the web. In P. Brusilovsky, A. Kobsa & W. Nejdl (Eds.), The Adaptive Web (pp. 409 – 432). Berlin/Heidelberg: Springer LNCS
  10. 4321.Chen, C., Lee, H., & Chen, Y. (2005). Personalized e-learning system using item response theory Computers& Education, 44(3), 237-255.
  11. Chen, I.J., Chang, C.C., & Yen, J.C. (2012). Effects of presentation mode on mobile language learning: A performance efficiency perspective. Australasian Journal of Educational Technology, 28(1), 122137.
  12. Coffield, F., Moseley, D., Hall, E., & Ecclestone, K. (2004). Learning styles and pedagogy in post-16 learning: A systematic and critical review. London: Learning and Skills Research Centre. Retrieved from http://www.hull.ac.uk/php/edskas/learning%20styles.pdf
  13. Conclan, O., Dagger, D., & Wade, V. (2002). Towards a standards-based approach to e-learning personalization using reusable learning objects. Paper presented at the World Conference on ELearning in Corporations., Dublin, Ireland. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1.6916
  14. Doty, D.H., & Glick, W.H. (1994). Typologies as a unique form of theory building: Toward improved understanding and modeling. The Academy of Management Review, 19(2), 230-251.
  15. Duval, E., Hodgens, W.H., Sutton, S., & Weibel, S.L. (2002). Metadata principles and practicalities. DLib Magazine, 8(4).
  16. Foss, J.G.K., Cristea, A.I., & Hendrix, M. (2010). Continuous use of authoring for adaptive educational hypermedia: A long-term case study. Paper presented at the 10th IEEE International Conference on Advanced Learning Technologies, ICALT 2010.Retrieved from http://dl.acm.org/citation.cfm?id=1846121
  17. Gkatzidou, S., & Pearson, E. (2009). The potential for adaptable accessible learning objects: A case study inaccessible vodcasting. Australasian Journal of Educational Technology, 25(2), 292-307.
  18. Grant, S., Aitchison, T., Henderson, E., Christie, J., Zare, S., McMurray, J., & Dargie, H. (1999). A comparison of the reproducibility and the sensitivity to change of visual analogue scales, Borg scales, and Likert scales in normal subjects during submaximal exercise. CHEST, 116(5), 1208-1217.
  19. Gu, X., Zha, C., Li, S., & Laffey, J.M. (2011). Design, sharing and co-construction of learning resources: A case of lifelong learning communities in Shanghai. Australasian Journal of Educational Technology, 27(2), 204-220.
  20. Hanson, W.E., Creswell, J.D., Creswell, J.D., Plano Clark, V.L., & Petska, K.S. (2005). Mixed methods research designs in counseling psychology. Journal of Counseling Psychology, 52(3), 224235.
  21. Karampiperis, P., & Sampson, D. (2006). Adaptive learning objects sequencing for competence-based learning. Paper presented at the 6th IEEE International Conference on Advanced Learning Technologies, Kerkrade, The Nederlands. Retrieved from http://dspace.learningnetworks.org/bitstream/1820/682/1/310karsam.pdf
  22. Kay, J. (2000). User modeling for adaptation. In C. Stephanidis (Ed.), User Interfaces for all (pp. 271294).
  23. Klemke, R., Ternier, S., Kalz, M., & Specht, M. (2010). Implementing infrastructures for managing learning objects. British Journal of Educational Technology, 41(6), 873–882.
  24. Lee, A.S. (1991). Integrating positivist and interpretive approaches to organizational research. Organization Science, 2(4), 342-365.
  25. Maier, P., Armstrong, R., Hall, W., & Ng, M.H. (2005). JointZone: users’ views of an adaptive online learning resource for rheumatology. Learning, Media and Technology, 30(3), 281-297.
  26. Maier, R., & Thalmann, S. (2007). Describing learning objects for situation-oriented knowledge management applications. Paper presented at the 4th Conference of Professional Knowledge Management (WM07), Potsdam.
  27. Mingers, J. (2000). Multimethodology: The theory and practice of combining management science methodologies.West Sussex, England: John Wiley& Sons.
  28. Mueller, D., & Strohmeier, S. (2011). Design characteristics of virtual learning environments: State of research. Computers& Education, 57(4), 2505–2516.
  29. O’Neil, A.F. (2008). The current status of instructional design theories in relation to today’s authoring systems. British Journal of Educational Technology, 39(2), 251–267.
  30. Ochoa, X., Cardinaels, K., Meire, M., & Duval, E. (2005). Frameworks for the automatic indexation of learning management systems content into learning object repositories. Paper presented at the World Conference on Educational Multimedia, Hypermedia and Telecommunications, Montreal, Canada. Retrieved from http://ariadne.cti.espol.edu.ec/xavier/papers/Ochoa-EDMedia2005.pdf
  31. Oppermann, R. (1994). Adaptive user support-Ergonomic design of manually and automatically adaptable software (Vol. 40). Hillsdale, NJ: Lawrence Erlbaum Associates.
  32. Osborne, J.W., & Overbay, A. (2004). The power of outliers (and why researchers should always check for them). Practical assessment Research 9(6), 1-8.
  33. Paramythis, A., & Stephanidis, C. (2005). A generic adaptation framework for web-based hypermedia systems. In S.Y. Chen& G.D. Magoulas (Eds.), Adaptable and Adaptive Hypermedia Systems (pp. 80-102). Hershey: IRM Press.
  34. Patton, M.Q. (2002). Qualitative research& Evaluation methods (3rd ed.). Thousand Oaks, CA: Sage.
  35. Pawlowski, J.M., & Bick, M. (2006). Managing& Re-using didactical expertise: The didactical object model. Educational Technology& Society, 9(1), 84-96.
  36. Pazzani, M.J., & Billsus, D. (2007). Content-based recommendation systems. In P. Brusilovsky, A. Kobsa & W. Nejdl (Eds.), The Adaptive Web (pp. 325–341). Berlin/Heidelberg: Springer-LNCS4321.
  37. Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y., & Podsakoff, N.P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903.
  38. Schoonenboom, J. (2012). Four scenarios for determining the size and reusability of learning objects. Australasian Journal of Educational Technology, 28(2), 249-265.
  39. Silverman, D. (1993). Interpreting qualitative data: Methods for analyzing talk, text, and interaction. London: Sage.
  40. Teddlie, C., & Tashakkori, A. (2009). Foundations of the mixed method research. Thousand Oaks, CA: Sage.
  41. Triantafillou, E., Pomportsis, A., Demetriadis, S., & Georgiadou, E. (2004). The value of adaptivity based on cognitive style: An empirical study. British Journal of Educational Technology, 35(1), 95–106.
  42. Wee, H.L., Fong, K.Y., Tse, C., Machin, D., Cheung, Y.B., Luo, N., & Thumboo, J. (2008). Optimizing the design of visual analogue scales for assessing quality of life: A semi-qualitative study among Chinese-speaking Singaporeans. Journal of Evaluation in Clinical Practice, 14(1), 121-126.
  43. Yalcinalp, S., & Gulbahar, Y. (2010). Ontology and taxonomy design and development for Personalised web-based learning systems. British Journal of Educational Technology, 41(6), 883–896.
  44. Zouaq, A., Nkambou, R., & Frasson, C. (2007). An integrated approach for automatic aggregation of learning knowledge objects. Interdisciplinary Journal of Knowledge and Learning Objects, 3(1), 135162.

These references have been extracted automatically and may have some errors. Signed in users can suggest corrections to these mistakes.

Suggest Corrections to References

Cited By

View References & Citations Map

These links are based on references which have been extracted automatically and may have some errors. If you see a mistake, please contact info@learntechlib.org.

Links