The IPTEACES E-Learning Framework: Success Indicators, the Impact on Student Social Demographic Characteristics and the Assessment of Effectiveness



This paper proposes and describes a new instructional design framework, primarily inspired through a pedagogical benchmark, designated as IPTEACES (Involvement, Preparation, Transmission, Exemplification, Application, Connection, Evaluation and Simulation), conceived to facilitate e-learning by reducing diversity in e-Learning programmes facing a non-homogeneous audience. More specifically, this paper describes the outcome of a case study on the application of IPTEACES framework to the insurance intermediaries’ certification course in Portugal (n = 3726) from 16 different corporations connected with the insurance and banking industry. This paper presents an overview of the IPTEACES framework, a brief description of the universe of students who attended the courses as well as the learning results. The results achieved by this certification course will be subject to a detail analysis of the success indicators (score) through the use of a regression tree via exhaustive CHAID (Chi-squared Automatic Interaction Detector) in order to better comprehend the impact that this framework had among the socio demographic different characteristics. Also, results will be presented in the application of a benchmark methodology proposed by Levy (Assessing the value of e-learning systems. Hershey: Information Science Publishing, 2006) and (Int J Inform Syst Serv Sector 1(1):93–118, 2009) for the assessment of effectiveness of IPTEACES e-Learning framework. This e-Learning project achieved the category of “High effectiveness” (score = 0.757) based on the assessment from 1,317 students on satisfaction and importance of 41 e-Learning system characteristics.


Regression Tree Banking Industry Insurance Industry Success Indicator High Average Rating 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Biggs, D., DeVille, B. & Suen, E. (1991). A Method of Choosing Multiway Partitions for Classification and Decision Trees. Journal of Applied Statistics18(1):49–62.CrossRefGoogle Scholar
  2. Boyd, D. (2004). The characteristics of successful online students. New Horizons in Adult Education, 18(2), 31–39.Google Scholar
  3. Clark, R., Nguyen, F., & Sweller, J. (2006). Efficiency in Learning: Evidence-Based Guidelines to Manage Cognitive Load. San Francisco: Pfeiffer.Google Scholar
  4. Doll, W. J., & Torkzadeh, G. (1991). The measurement of end-user computing satisfaction: Theoretical and methodological issues. MIS Quarterly, 15(1), 5–9.Google Scholar
  5. Gagne, R. M. (1985). The Conditions of Learning and Theory of Instruction, 4th ed. New York: Holt, Rinehart and Winston.Google Scholar
  6. Gagne, R., Briggs, L. & Wager, W. (1992). Principles of instructional design (4th ed.). Englewood Cliffs, NJ: Prentice-Hall. Instructional message design: principles from the behavioral and cognitive sciences (Englewood).Google Scholar
  7. Keller, J. (2008). ‘First principles of motivation to learn and e3-learning’ Distance Education Vol. 29, No. 2, pp. 175–185.CrossRefGoogle Scholar
  8. Levy, Y., & Murphy, K. (2002). Toward a value framework for online learning system. In Proceedings for the Hawaii International Conference on System Sciences (HICSS – 35), 1–9.Google Scholar
  9. Levy, Y. (2006). Assessing the value of e-learning systems. Hershey, PA: Information Science Publishing.Google Scholar
  10. Levy, Y. (2009). Murph, K. & Zanakisy, S.,A Value-Satisfaction Taxonomy of IS Effectiveness (VSTISE): A Case Study of User Satisfaction with IS and User-Perceived Value of IS, International Journal of Information Systems in the Service Sector, 1(1), 93–118.CrossRefGoogle Scholar
  11. Merrill, D. (2002). First principles of instruction. Educ. Technol. Res. Dev., 50(3), 43–59.CrossRefGoogle Scholar
  12. Merrill, D. (2007). First principles of instruction: a synthesis. In R. A. Reiser & J. V. Dempsey (Eds.), Trends and Issues in Instructional Design and Technology, 2nd Edition (Vol. 2, pp. 62–71). Upper Saddle River, NJ: Merrill/Prentice Hall.Google Scholar
  13. Nisbet, R., Elder, J. & Miner, G. (2009). Handbook of statistical analysis and data mining applications. London: Academic Press.Google Scholar
  14. Rokeach, M. (1969). Beliefs, attitudes, and values. San Francisco, CA: Jossey-Bass Inc. Publishers.Google Scholar
  15. Schrum, L., & Hong, S. (2002a). Dimensions and strategies for online success : Voices from experienced educators. Journal of Asynchronous Learning Networks, 6(1).Google Scholar
  16. Schrum, L., & Hong, S. (2002b). From the Field: Characteristics of Successful Tertiary Online Students and Strategies of Experienced Online Educators. Education and Information Technologies, 7(1), 5–16.CrossRefGoogle Scholar
  17. van Merriënboer, J. G., & Kirschner, P. (2007). Ten Steps to Complex Learning. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  18. Webster, J. & Hackley, P., “Teaching effectiveness in technology-mediated distance learning.” Academy of Management Journal, 1997, Issue 6, Vol. 40, pp. 1282–1309.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Universidade Aberta (Portuguese Open University)LisbonPortugal

Personalised recommendations