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Education and Information Technologies

, Volume 24, Issue 6, pp 3329–3392 | Cite as

Adaptive hypermedia instructional system (AHIS): A model

  • Mohd Javed KhanEmail author
  • Khurram Mustafa
Article
  • 48 Downloads

Abstract

$$ AHIS\ Instruction=\frac{1}{a}\left(\frac{0.5\ast {Kcs}_{familiar}+{Kcs}_{new}}{Kcs_{known}+{Kcs}_{familar}+{Kcs}_{new}}+{\int}_{ilo=0}^6\left\{\sum \limits_{j=0}^n\frac{p_j.{m}_j.{e}_j.{n}_j}{c}-{d}_j\right\}\right) $$

AHIS equation describes a new model for instructional system design and develops a system based on Merrill’s Component Display Theory incorporating appropriate selection of Media, Ergonomics and Navigation Structures to produce learner engaging and effective learning outcome. A significant component of the proposed model is the integration of principles of Ergonomics having Graphic Aesthetic as one of the constituent. Graphic Aesthetic decides Unity, Proportion, Balance, Sequence, and Cohesion for interface design. Next component is selection of suitable Media as per the categorized learning content. Merrill Component Display Theory has been utilized to categorized learning content. Research shows that media effects are significant in teaching learning process. Third significant component of the model is selection of Navigation structures. Navigation structures decide learner concentration level (learner engagement), restrict them from getting disoriented in hyperspace and finally direct them to their learning objectives. Research in the field of Navigation structures reveals that it’s potential for accelerating learning, when employed with well designed interfaces. Hence, time demands development of instructional model which identifies categorized learning content(pj), principles of Ergonomics for Interface Design(ej), Media selection criteria (mj) and selection of appropriate Navigation structures(nj) and improved learner engagement by calculating learner’s prior knowledge level based on learner known concepts(kcsknown), familiar concepts (kcsfamiliar) and new concepts(kcsnew).

Keywords

Hypermedia Instruction Media Psychology Ergonomics Adaptive Learning content Cognitive Affective Psychomotor 

Notes

References

  1. Abbad, M. M., Morris, D., & De Nahlik, C. (2009). Looking under the bonnet: Factors affecting student Adoptation of E-Learnig Systems in Jordan. International Review of Research in Open and Distance Learning, 10(2).Google Scholar
  2. Agelight, L. (2001). Interface design guidelines for users of all ages. Retrieved from http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle: Interface+Design+Guidelines+for+Users+of+All+Ages#0%5Cn; http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle: Interface+design+guidelines+for+users+of+all+ages#0.
  3. Alberts, W. A., & van der Geest, T. M. (2011). A taxonomy of visuals in science communication. Journal of the Society for Technical Communication, 58(2).Google Scholar
  4. Altaboli, A., & Lin, Y. (2011). Investigating effects of screen layout elements on interface and screen design aesthetics. Advances in Human-Computer Interaction., 2011, 1–10.  https://doi.org/10.1155/2011/659758.CrossRefGoogle Scholar
  5. Andre, T. (1990). Type of inserted question and the study-posttest delay. The Journal of Experimental Education, 58(2), 77–86.MathSciNetCrossRefGoogle Scholar
  6. Atkinson, R. K. (2002). Optimizing learning from examples using animated pedagogical agents. Journal of Educational Psychology, 94(2), 416–427.  https://doi.org/10.1037//0022-0663.94.2.416.MathSciNetCrossRefGoogle Scholar
  7. Benabou, R., & Triole, J. (2003). Intrinsic and extrinsic motivation. Review of Economic Studies, 70(2), 489–520.  https://doi.org/10.1111/1467-937X.00253.MathSciNetCrossRefzbMATHGoogle Scholar
  8. Bernard, M. (2001). Developing schemas for the location of common web objects. Retrieved from http://psychology.wichita.edu/surl/usabilitynews/3W/web_object.htm, Developing Schemas for the Location of Common Web Objects
  9. Bernard, M., Mills, M., Peterson, M. and S. (2001). A Comparison of Popular Online Fonts: Which is Best and When? Retrieved from http://psychology.wichita.edu/surl/usabilitynews/3S/usability_news.html
  10. Bernard, M., Linda, B., Riley, S., Hackler, T., & Janzen, K. (2002a). A comparison of popular online fonts: Which size and type is best.Google Scholar
  11. Bernard, M., Marissa, F., & Hull, S. (2002b). The effects of line length on children and adult online Reading performance. Retrieved from http://usabilitynews.org/the-effects-of-line-length-on-children-and-adults-online-reading-performance/
  12. Bodemer, D., Ploetzner, R., Feuerlein, I., & Spada, H. (2004). The active integration of information during learning with dynamic and interactive visualisations. Learning and Instruction, 14, 325–341.  https://doi.org/10.1016/j.learninstruc.2004.06.006.CrossRefGoogle Scholar
  13. Byrne, M. D. (1993). Using icons to find documents: Simplicity is critical. In INTERACT’93 and CHI’93 conference on human factors in computing systems (pp. 446–453).Google Scholar
  14. Chen, Y.-L. (2014). A study on student self-efficacy and technology acceptance model within an online task-based learning environment. Journal of Computers, 9(1), 34–43.  https://doi.org/10.4304/jcp.9.1.34-43.CrossRefGoogle Scholar
  15. Chen, C., & Rada, R. (1996). Interacting with hypertext: A Meta analysis of experimental studies. Human Computer Interaction, 11(2), 125–156.CrossRefGoogle Scholar
  16. Cleveland, W. S. (1984). Graphs in scientific publications. The American Statistician, 38(4), 261–269.Google Scholar
  17. Corporation, M. (2012). Microsoft manual of style. In Redmond. Washington: Microsoft Press.Google Scholar
  18. Daft, R., Lenfel, R., & Trevino, L. (1986). The relationship among message equivocality, media selection, and manager performance: Implications for information support systems.Google Scholar
  19. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319.  https://doi.org/10.2307/249008.CrossRefGoogle Scholar
  20. Dias, P., Gomes, M. J., & Correia, A. P. (1999). Dissorientation in hypermedia Enviornments: Mechanism to support navigation. Journal of Educational Computing Research, 20(2), 93–117.CrossRefGoogle Scholar
  21. Driscoll, M. P. (2005). Psychology of learning for instruction. In Learning and instruction.Google Scholar
  22. Dyson, M. C. (2004). How physical text layout affects reading from screen. Behaviour & Information Technology, 23(6), 377–393.  https://doi.org/10.1080/01449290410001715714.CrossRefGoogle Scholar
  23. Edmonds, G. S., Branch, R. C., & Mukherjee, P. (1994). A conceptual framework for comparing instructional design models. Education Technology Reserach and Development, 42(4), 55–72.CrossRefGoogle Scholar
  24. Fitts, P. M. (1954). The information of the human motor system in controlling amplitude and movement. Journal of Exprimental Psychology, 47, 381–391.CrossRefGoogle Scholar
  25. Furnas, G. W., Landauer, T. K., Gomez, L. M., & Dumais, S. T. (1987). The vocabulary problem in human-system communication : An analysis and a solution. Communications of the ACM, 11, 964.CrossRefGoogle Scholar
  26. Grabinger, R. S. (1993). Computer screen designs: Viewer judgments. Educational Technology Research & Development, (35), 41.Google Scholar
  27. Guerra, J., Schunn, C. D., Bull, S., Barria-pineda, J., & Brusilovsky, P. (2018). Navigatio support in complex open learner models: Assessing visual design alternatives. New Review of Hypermedia and Multimedia, 24(3), 160–192.  https://doi.org/10.1080/13614568.2018.1482375.CrossRefGoogle Scholar
  28. Gustafson, K. L., & Branch, R. M. (1997). Survey of Instructional Development Models. Syracuse, NY: Information Resource Publications, Syracuse University.Google Scholar
  29. Harris, R. L. (2000). Information graphics: A comprehensive illustrated reference. Oxford University Press.Google Scholar
  30. Hartely, J. (1996). Text design. In Handbook of research for eduactional comunications and technology (pp. 795–820).Google Scholar
  31. Hartley, J. (1987). Designing electronic text : The role of print-based research. ECTJ, 35(1), 3–17.Google Scholar
  32. Hartley, J. (2004). Designing instructional and informational text. In Handbook of research on educational communications and technology (pp. 917–947).Google Scholar
  33. Hasan, E. H. R. (2001). Instructional design and media selection. University of Twente. Retrieved from www.tup.utwente.nl/uk/catalogue/educational/media-selection
  34. Hoffler, T. N., & Leutner, D. (2007). Instructional animation versus static pictures : A meta-analysis. Learning and Instruction, 17, 722–738.  https://doi.org/10.1016/j.learninstruc.2007.09.013.CrossRefGoogle Scholar
  35. Human N Health. (2013). Effect of different colors on human mind and body. Retrieved December 18, 2014, from https://humanhealth.com/effect-of-different-colors-on-human-mind-and-body/243/.
  36. Jared Spool. (2014). Evolution trumps usability. Retrieved from www.uie.com/Articles/evolution_trumps_usability.htm
  37. Jonassen, D. (1988). Designing structured hypertext and structuring access to hypertext. Journal of Educational Technology. Google Scholar
  38. Keller, J. M. (1979). Motivation and instructional design: A theoretical perspective. Journal of Instructional Development, 2(4), 26–34.CrossRefGoogle Scholar
  39. Khan, M. J., & Mustafa, K. (2018). Modelling adaptive hypermedia instructional system: A framework. Multimedia Tools and Applications, 67(3).  https://doi.org/10.1007/s11042-018-6819-2.CrossRefGoogle Scholar
  40. Kozma, R. B. (1991). Learning with media. Review of Educational Research, 61(2), 179–211.CrossRefGoogle Scholar
  41. Krathwohl, D. R. (2002). A revision of Bloom’s taxonomy: An overview. Theory Into Practice, 41(1), 212–225.  https://doi.org/10.1207/s15430421tip4104.CrossRefGoogle Scholar
  42. Krathwohl, D. R., Bloom, B. S., & Masia, B. B. (1964). Taxnomy of educational objectives: The classification of Eucational goals. Handbook II: Affective domain. In International semantic web for elearning workshop at adaptive hypermedia.Google Scholar
  43. Kruk, R. S., & Muter, P. (1984). Reading of continuous text on video screens. Human Factors, 3(26), 339–345.CrossRefGoogle Scholar
  44. Kurosu, M., & Kashimura, K. (1995). Apparent usability vs. inherent usability. In CHI 95 mosaic of creativity (pp. 1–2). USA: Denver, Colorado.Google Scholar
  45. Lachner, A., Backfisch, I., & Nuckles, M. (2018). Does the accuracy matter? Accurate concept map feedback helps students improve the cohesion of their explanations. Educational Technology Research and Development, 66, 2018–1067.  https://doi.org/10.1007/s11423-018-9571-4.CrossRefGoogle Scholar
  46. Lash, J. (2002). Persuasive navigation. Retrieved from http://digital-web.com/articles/persuasive_navigation. Accessed 6 Dec 2018
  47. Lee, D., Huh, Y., Lin, C.-Y., & Reigeluth, C. M. (2018). Technology functions for personalized learning in learner- centered schools. Educational Technology Research and Development, 66(5), 1269–1302.  https://doi.org/10.1007/s11423-018-9615-9.CrossRefGoogle Scholar
  48. Liu, S., Liao, H., & Pratt, J. A. (2009). Impact of media richness and flow on e-learning technology acceptance. Computers & Education, 52(3), 599–607.  https://doi.org/10.1016/j.compedu.2008.11.002.CrossRefGoogle Scholar
  49. Macdonald-ross, M. (1977). How numbers are shown a review of Reserach on the presentation of Quantative data in texts. AV Communication Review, 25(4), 359–409.Google Scholar
  50. Mayer, R. E. (2003). The promise of multimedia learning: Using the same instructional design methods across different media. Learning and Instruction, 13(2), 125–139.  https://doi.org/10.1016/S0959-47520200016-6.CrossRefGoogle Scholar
  51. Mayer, R. E. (2005). Cognitive theory of multimedia learning. In The Cambridge handbook of multimedia learning (Vol. 41, pp. 31–48).  https://doi.org/10.1207/s15326985ep4102_2.CrossRefGoogle Scholar
  52. Mayer, R. E., & Moreno, R. (2002). Animation as an aid to multimedia learning. Educational Psychology Review, 14(1), 87–99.  https://doi.org/10.1023/A:1013184611077.CrossRefGoogle Scholar
  53. Mayer, R. E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology, 93(1), 187–198.  https://doi.org/10.1037/0022-0663.93.1.187.CrossRefGoogle Scholar
  54. McFarlane, T., Green, K., & Hoffman, E. (1997). Teachers’ Attitudes Toward Technology: Psychometric Evaluation of the Technology Attitude Survey. Annual Meeting of the American Educational Research Association Annual Meeting of the American Educational Research Association (Vol. 1997, No. 1)., 3–13. Retrieved from http://eric.ed.gov/?id=ED411279. Accessed 3 Mar 2018
  55. Merrill, M. D. (1983). Component Diaply theory. In Instructional design theories and models: An overview of their current status (pp. 282–333).Google Scholar
  56. Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222.  https://doi.org/10.1287/isre.2.3.192.CrossRefGoogle Scholar
  57. Morville, P. (1998). Information Architecture for the World Wide Web (first). O’Reilly.Google Scholar
  58. Moshagen, M., Musch, J., & Göritz, A. S. (2009). A blessing , not a curse : Experimental evidence for beneficial effects of visual aesthetics on performance. Ergonomics, 52(10), 1311–1311.  https://doi.org/10.1080/00140130903061717, 1320CrossRefGoogle Scholar
  59. Ngo, D. C. L., Teo, L. S., & Byrne, J. G. (2000). Formalising guidelines for the design of screen layouts. Displays, 21, 3–15.CrossRefGoogle Scholar
  60. Norma, S. P., & Maria, G. W. (1990). The use of color in computer interfaces: Preliminary research. In CMU-ITC. Pennsylvania: Pittsburgh.Google Scholar
  61. Novak, E., Daday, J., & McDaniel, K. (2018). Using a mathematical model of motivation, volition, and performance to examine students e-text learning experiences. Educational Technology Research and Development, 66(5), 1189–1209.  https://doi.org/10.1007/s11423-018-9599-5.CrossRefGoogle Scholar
  62. Patil, D. (2012). Dynamically-created landing webpage. India: United States patent application publication.Google Scholar
  63. Quade, A. M. (1993). An assessment of the effectiveness of a hypertext instructional delivery system when compared to a traditional CAI tutorial. New Orleans, Louisiana.Google Scholar
  64. Revilla, M. A., Saris, W. E., & Krosnick, J. A. (2014). Choosing the number of categories in agree – Disagree scales. Sociological Methods & Research, 43(1), 73–97.  https://doi.org/10.1177/0049124113509605.MathSciNetCrossRefGoogle Scholar
  65. Ritzhaupt, A. D., Pastore, R., Wang, J., & Davis, R. O. (2018). Effects of organizatiinal picture and modality as feedback startegy on learner startegy comprehension and satisfaction. Educational Technology Research and Development, 66, 11423.  https://doi.org/10.1007/s11423-018-9575-0, 1069, 1086CrossRefGoogle Scholar
  66. Rogers, B. L., & Chaparro, B. (2003). Breadcrumb navigation : Further investigation of usage. Retrieved from https://usability.bcs.org Google Scholar
  67. Ross, S. M., & Morrison, G. R. (2004). EXPERIMENTAL RESEARCH METHODS. Handbook of Research on Educational Communications and Technology (Vol. 2, pp. 1021–1043).Google Scholar
  68. Shapiro, A., & Niederhauser, D. (2004). Learning from hypertext : Research issues and findings. Handbook of Research on Educational Communications and Technology (Vol. 2, pp. 605–620).Google Scholar
  69. Szabo, M., & Kanuka, H. (1998). Effects of violating screen design principles of balance , Unity , and focus on recall learning , study time , and completion rate. Journal of Educational Multimedia and Hypermedia, 8(1), 23–42.Google Scholar
  70. Termens, M., Ribera, M., Porras, M., Boldú, M., Sulé, A., & Paris, P. (2009). Web Content Accessibility Guidelines : from 1 . 0 to 2 . 0. In 18th International Conference on World Wide Web (p. 2009). Spain.  https://doi.org/10.1145/1526709.1526912
  71. Tolhurst, D. (1992). A checklist for evaluating content based hypertext computer software. Journal of Educational Technology. Google Scholar
  72. Trant, J., & Wyman, B. (2006). Investigating social tagging and folksonomy in art museums with steve.museum. In Collaborative web tagging workshop at WWW2006 (pp. 1–6). Edinburgh, Scotland.Google Scholar
  73. University of Twente. (2016). Expectancy value theory.Pdf. Retrieved September 20, 2016, from https://www.utwente.nl/cw/theorieenoverzicht. Accessed 20 Sept 2016
  74. Walz, J. (2001). Reading hypertext: Lower-level process. Candian Modern Language Review, 57(3), 475–494.  https://doi.org/10.3138/cmlr.57.3.475.CrossRefGoogle Scholar
  75. Williams, R. (2004). Non-Designer’s design book. Peachpit press. Berkeley, California, USA. Retrieved from http://www.amazon.com/dp/0321534042. Accessed 23 Mar 2016
  76. Yen, J., Tsai, C., & Chen, I. (2010). Exploring the effects of game-based instructional design on 3D animation : A perspective of technology acceptance. WSEAS Transactions on Information Science and Applications, 7(7), 955–964.Google Scholar
  77. Zain, J. M., Tey, M., & Goh, Y. (2007). Does aesthetics of web page Interface matters to mandarin learning ? Journal of Computer Science and Netwrok Security, 7(8), 43–51.Google Scholar

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Authors and Affiliations

  1. 1.Department of Computer ScienceJamia Millia IslamiaNew DelhiIndia

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