When Teachers Support Students in Technology Mediated Learning

  • Leonardo Caporarello
  • Massimo Magni
  • Ferdinando Pennarola
Conference paper
Part of the Lecture Notes in Information Systems and Organisation book series (LNISO, volume 13)


This paper focuses on information technology adoption and use within the education sector. We have analyzed the impact on learning effectiveness of technology mediated learning environments, namely characterized by the adoption of tablet based technologies, as a revolutionary complement to traditional teaching/learning techniques. Our research analyzes the effect of “Support Activities” on grades. “Support Activities” are defined in this paper as the set of constructs like “Teachers’ Encouragement”, “Classmates’ Encouragement” and “Technical Support Availability”. Grades are used as a measure of learning effectiveness. A sample of 370 students participated in our study, being attendants of experimental classes using tablets as ordinary working tool to access to digital resources. Our mainstream theory reference was built on the theoretical foundations of Technology Acceptance Model, by comparing the perceived effect of those constructs between grade ranges. Finally, the experimental sample was compared to classes where the same teachers used traditional learning resources. The aim of this work is to give a practical understanding of support factors influencing tablet-mediated learning effectiveness. In particular, our findings show the differences between scientific and humanistic subjects. Our research confirms that technology alone does not revolutionize teaching and learning; nonetheless, it contributes to an improved experience if support initiatives are deployed.


Tablet technologies Technology mediated learning Learning effectiveness 


  1. 1.
    Avvisati, F., Hennessy, S., Kozma, R.B., Vincent-Lancrin, S.: Review of the Italian Strategy for Digital Schools, OECD Education Working Papers, 90 OECD Publishing. (2013)
  2. 2.
    Dejaeger, K., Goethals, F., Giangreco, A., Mola, L., Baesens, B.: Gaining insight into student satisfaction using comprehensible data mining techniques. Eur. J. Oper. Res. 218(2), 548–562 (2012)CrossRefGoogle Scholar
  3. 3.
    North-Samardzic, A., Braccini, A.M., Spagnoletti, P., Za, S.: Applying media synchronicity theory to distance learning in virtual worlds : a design science approach. Int. J. Innov. Learn. 15(3), 328–346 (2014)CrossRefGoogle Scholar
  4. 4.
    Spagnoletti, P., Resca, A.: A design theory for IT supporting online communities. In: Proceedings of the 45th Hawaii International Conference on System Sciences, pp. 4082–4091 (2012)Google Scholar
  5. 5.
    Sorrentino, M., De Marco, M.: Implementing e-government in hard times: when the past is wildly at variance with the future. Inf. Polity 18(4), 331–342 (2013)Google Scholar
  6. 6.
    Mosconi, E.M., Silvestri, C., Poponi, S., Braccini, A.M.: Public policy innovation in distance and on-line learning: reflections on the italian case. In: Spagnoletti, P. (ed.) Organizational Change And Information Systems—Working and Living Together in New Ways, pp. 381–389. Springer, Berlin (2013)CrossRefGoogle Scholar
  7. 7.
    Ruggieri, A., Mosconi, E.M., Poponi, S., Braccini, A.M.: Strategies and policies to avoid digital divide: the italian case in the european landscape. In: Mola, L., Pennarola, F., Za, S. (eds.) From Information to Smart Society—Environment. Springer, Politics and Economics (2014)Google Scholar
  8. 8.
    Brynjolfsson, E.: The productivity paradox of information technology. Commun. ACM 36(12), 66–77 (1993)CrossRefGoogle Scholar
  9. 9.
    Devaraj, S., Kohli, R.: Performance impacts of information technology: Is actual usage the missing link? Manage. Sci. 49(3), 273–289 (2003)CrossRefGoogle Scholar
  10. 10.
    Venkatesh, V., Goyal, S.: Expectation disconfirmation and technology adoption—polynomial modeling and response surface analysis. MIS Q. 34(2), 281–303 (2010)Google Scholar
  11. 11.
    Venkatesh, V., Davis, F.D.: A theoretical extension of the technology acceptance model—4 longitudinal field studies. Manage. Sci., Informs. 46(2), 186–204 (2000)Google Scholar
  12. 12.
    Venkatesh, V.: Determinants of perceived ease of use, Integrating Control, Intrinsic Motivation and Emotion into the TAM. Inf. Syst. Res. Informs. 11(4), 342–365 (2000)CrossRefGoogle Scholar
  13. 13.
    Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User acceptance of information technology—toward a unified view. MIS Q. 27(3), 425–478 (2003)Google Scholar
  14. 14.
    Venkatesh, V., Bala, H.: Technology acceptance model 3 and a research agenda on interventions. Decis. Sci. 39(2), (2008)Google Scholar
  15. 15.
    Venkatesh, V., Thong, J.Y.L., Xu, X.: Consumer acceptance and use of information technology—extending the unified theory of acceptance and use of technology. MIS Q. 36(1), 157–178 (2012)Google Scholar
  16. 16.
    Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3), 319–340 (1989)CrossRefGoogle Scholar
  17. 17.
    Adams, D.A., Nelson, R.R., Todd, P.A.: Perceived usefulness, ease of use, and usage of information technology: a replication. MIS Q. 16, 227–247 (1992)CrossRefGoogle Scholar
  18. 18.
    Hendrickson, A.R., Massey, P.D., Cronan, T.P.: On the test-retest reliability of perceived usefulness and perceived ease of use scales. MIS Q. 17, 227–230 (1993)CrossRefGoogle Scholar
  19. 19.
    Segars, A.H., Grover, V.: Re-examining perceived ease of use and usefulness: a confirmatory factor analysis. MIS Q. 17, 517–525 (1993)CrossRefGoogle Scholar
  20. 20.
    Subramanian, G.H.: A replication of perceived usefulness and perceived ease of use measurement. Decis. Sci. 25(5/6), 863–873 (1994)CrossRefGoogle Scholar
  21. 21.
    Szajna, B.: Software evaluation and choice: predictive evaluation of the Technology Acceptance Instrument. MIS Q. 18(3), 319–324 (1994)CrossRefGoogle Scholar
  22. 22.
    Brown, S.A., Venkatesh, V.: Model of adoption of technology in households: a baseline model test and extension incorporating household life cycle. MIS Q. 29(3), 399–426 (2005)Google Scholar
  23. 23.
    Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: Extrinsic and intrinsic motivation to use computers in the workplace. J. Appl. Soc. Psychol. 22, 1111–1132 (1992)CrossRefGoogle Scholar
  24. 24.
    Venkatesh, V.: Creation of favorable user perceptions- exploring the role of intrinsic motivation. MIS Q. 23(2), (1999)Google Scholar
  25. 25.
    Nicholson, J., Nicholson, D., Valacich, J.S.: Examining the effects of technology attributes on learning—A contingency perspective. J. Inf. Technol. Educ. 7 (2008)Google Scholar
  26. 26.
    Hu, P.J., Hui, W.: Examining the role of learning engagement in technology-mediated learning and its effects on learning effectiveness and satisfaction. Decis. Support Syst. 53, 782–792 (2012)CrossRefGoogle Scholar
  27. 27.
    Bretz, R.D., Judge, T.A.: Realistic job previews: a test of the adverse self-selection hypothesis. J. Appl. Psychol. 83, 330–337 (1998)CrossRefGoogle Scholar
  28. 28.
    Taylor, S., Todd, P.A.: Understanding information technology usage: a test of competing models. Inf. Syst. Res. 6(2), 144–176 (1995)CrossRefGoogle Scholar
  29. 29.
    Bergeron, F., Rivard, S., Serre, L.: Investigating the support role of the information center. MIS Q. 14(3), 247–260 (1990)CrossRefGoogle Scholar
  30. 30.
    Cragg, P., King, M.: Small-firm computing: motivators and inhibitors. MIS Q. 17(1), 47–60 (1993)CrossRefGoogle Scholar
  31. 31.
    Harrison, D.A., Mykytyn, P.P., Riemenschneider, C.K.: Executive decisions about adoption of information technology in small business: theory and empirical tests. Inf. Syst. Res. 8(2), 171–195 (1997)CrossRefGoogle Scholar
  32. 32.
    Venkatesh, V., Davis, F.D.: A model of the antecedents of perceived ease of use: development and test. Decis. Sci. 27, 451–481 (1996)CrossRefGoogle Scholar
  33. 33.
    Karahanna, E., Straub, D.W., Chervany, N.L.: Information technology adoption across time: a cross-sectional comparison of pre-adoption and post- adoption beliefs. MIS Q. 23, 183–213 (1999)CrossRefGoogle Scholar
  34. 34.
    Bharracherjee, A., Premkumar, G.: Understanding changes in belief and attitude toward information technology usage: a theoretical model and longitudinal test. MIS Q. 28, 229–254 (2004)Google Scholar
  35. 35.
    Bhattacherjee, A.: Understanding information systems continuance: an expectation-confirmation model. MIS Q. 25, 351–370 (2001)CrossRefGoogle Scholar
  36. 36.
    Rai, A., Lang, S., Welker, R.: Assessing the validity of IS success models: an empirical test and theoretical analysis. Inf. Syst. Res. 13, 50–69 (2002)CrossRefGoogle Scholar
  37. 37.
    Delone, W.H., McLean, E.R.: The DeLone and McLean model of information systems success: a ten year update. J. Manage. Inf. Syst. 19(4), 60–95 (2003)Google Scholar
  38. 38.
    Alavi, M., Leidner, D.E.: Research commentary: technology-mediated learning—a call for greater depth and breadth of research. Inf. Syst. Res. 12(1), 1–10 (2001)CrossRefGoogle Scholar
  39. 39.
    Casalino, N., Buonocore, F., Rossignoli, C., Ricciardi, F.: Transparency, openness and knowledge sharing for rebuilding and strengthening government institutions. In: IASTED Multiconferences-Proceedings of the IASTED International Conference on Web-Based Education, WBE 2013, pp. 866–871 (2013)Google Scholar
  40. 40.
    Casalino, N., Buonocore, F., Rossignoli, C., Ricciardi, F.: Transparency, openness and knowledge sharing for rebuilding and strengthening government institutions. In: IASTED Multiconferences-Proceedings of the IASTED International Conference on Web-Based Education, WBE 2013, pp. 866–871 (2013)Google Scholar
  41. 41.
    Zardini, A., Mola, L., vom Brocke, J., Rossignoli, C.: The shadow of ECM: the hidden side of decision processes. In: Respício, A., Adam, F., Phillips-Wren, G., Teixeira, C., Telhada, J. (eds.) Bridging the Socio-technical Gap in Decision Support Systems, 212, pp. 3–12, IOS Press, Amsterdam, Holland (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Leonardo Caporarello
    • 1
  • Massimo Magni
    • 1
  • Ferdinando Pennarola
    • 1
  1. 1.Department of Management and TechnologyBocconi UniversityMilanItaly

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