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Technology-Enhanced Learning: Correlates of Acceptance of Assistive Technology in Collaborative Working Setting

  • Wiktoria WilkowskaEmail author
  • Thiemo Leonhardt
  • Matthias Ehlenz
  • Martina Ziefle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11590)

Abstract

Considering stagnant interest in science, technology, engineering and math (STEM), on the one side, and an increasing dropout rates in computer science at different levels of the university education due to difficult and complex learning contents, on the other side, appropriate technology solutions which support learners are very promising. In this study we examine a digital learning assistant in the form of tangible objects interacting with a multi-touch tabletop. This learning tool is meant to support students in the way they learn complex material. In an experimental setting, participants working in groups had the task to acquire novel learning content (i.e., regular expressions) and, using the assistive technology, to assign the correct expressions to their predefined terms. Results revealed that the users’ psychological factors and performance factors which result from the interaction with the learning tool are significantly connected with the correlates of acceptance and affect, therefore, the later adoption of such technology. The positive overall assessments show a high attachment for, and willingness to, use such technology. However, user diversity has to be considered in the design and further development. Knowledge gained discloses expedient hints for technology-enhanced learning that can support and accompany the education at different levels, i.e., in schools, vocational training, and university education.

Keywords

Tangible user interfaces Learning assisting technology Technology acceptance User diversity 

Notes

Acknowledgements

We would like to thank Christian Cherek and the company Elector for providing the tangible prototypes, which were used in the study. Furthermore, we thank all participants for their engagement and interest in contributing their ideas and thoughts to novel developments in digitally assisted education. The work was funded by the German Federal Ministry of Research and Education [Project TABULA, reference number 16SV7574K].

References

  1. 1.
    Harasim, L.: Shift happens: online education as a new paradigm in learning. Internet High. Educ. 3(1), 41–61 (2000)CrossRefGoogle Scholar
  2. 2.
    Vergel, J., Quintero, G.A., Isaza-Restrepo, A., Ortiz-Fonseca, M., Latorre-Santos, C., Pardo-Oviedo, J.M.: The influence of different curriculum designs on students’ dropout rate: a case study. Med. Educ. Online 23(1) (2018).  https://doi.org/10.1080/10872981.2018.1432963
  3. 3.
    Chen, X.: STEM attrition: college students’ paths into and out of STEM fields. Statistical Analysis Report, National Center for Education Statistics, Washington (2013)Google Scholar
  4. 4.
    Schäfer, A., Holz, J., Leonhardt, T., Schroeder, U., Brauner, P., Ziefle, M.: From boring to scoring–a collaborative serious game for learning and practicing mathematical logic for computer science education. Comput. Sci. Educ. 23(2), 87–111 (2013)CrossRefGoogle Scholar
  5. 5.
    Cuban, L.: Teachers and Machines: The Classroom Use of Technology Since 1920. Teachers College Press, New York (1986)Google Scholar
  6. 6.
    Saito, T., Kim, S.: A meta-analysis on e-learning effectiveness in higher education. Jpn. J. Educ. Technol. 32(4), 339–350 (2009).  https://doi.org/10.15077/jjet.kj00005353782CrossRefGoogle Scholar
  7. 7.
    Shakibaei, Z., Khalkhali, A., Andesh, M.: Meta-analysis of studies on educational technology in Iran. Procedia – Soc. Behav. Sci. 28, 923–927 (2011).  https://doi.org/10.1016/j.sbspro.2011.11.170CrossRefGoogle Scholar
  8. 8.
    Karich, A.C., Burns, M.K., Maki, K.E.: Updated meta-analysis of learner control within educational technology. Rev. Educ. Res. 84(3), 392–410 (2014).  https://doi.org/10.3102/0034654314526064CrossRefGoogle Scholar
  9. 9.
    Fan, Z., Cheng, W., Chen, G., Huang, R.: Meta-analysis in educational technology research: a content analysis. In: 16th International Conference on Advanced Learning Technologies (ICALT), Austin, TX, USA, pp. 460–62 (2016).  https://doi.org/10.1109/icalt.2016.94
  10. 10.
    Chauhan, S.: A meta-analysis of the impact of technology on learning effectiveness of elementary students. Comput. Educ. 105, 14–30 (2017).  https://doi.org/10.1016/j.compedu.2016.11.005CrossRefGoogle Scholar
  11. 11.
    Rahman, M.N.A., Zamri, S.N.A.S., Eu, L.K.: A meta-analysis study of satisfaction and continuance intention to use educational technology. Int. J. Acad. Res. Bus. Soc. Sci. 7(4), 1059–1072 (2017).  https://doi.org/10.6007/ijarbss/v7-i4/2915CrossRefGoogle Scholar
  12. 12.
    Bruner, J.S.: The Process of Education. Harvard University Press, Cambridge (1977)Google Scholar
  13. 13.
    Meyer, H.: Leitfaden Unterrichtsvorbereitung, 9th edn. Cornelsen, Berlin (2018)Google Scholar
  14. 14.
    McCombs, B.L., Whisler, J.S.: The Learner-Centered Classroom and School: Strategies for Increasing Student Motivation and Achievement, 1st edn. Jossey-Bass, San Francisco (1997)Google Scholar
  15. 15.
    Mayer, R.E.: Multimedia Learning, 2nd edn. Cambridge University Press, Cambridge (2001)CrossRefGoogle Scholar
  16. 16.
    Bloom, B.S.: Taxonomy of Educational Objectives: The Classification of Educational Goals Handbook I. Longmans, Green and Company, New York (1956)Google Scholar
  17. 17.
    Krathwohl, D.R.: A revision of Bloom’s taxonomy: an overview. Theory Pract. 41(4), 212–218 (2002).  https://doi.org/10.1207/s15430421tip4104_2CrossRefGoogle Scholar
  18. 18.
    Gould, J.D., Boies, S.J., Lewis, C.: Making usable, useful, productivity-enhancing computer applications. Commun. ACM 34(1), 74–85 (1991)CrossRefGoogle Scholar
  19. 19.
    Davis, F.D.: User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. Int. J. Man Mach. Stud. 38(3), 475–487 (1993)CrossRefGoogle Scholar
  20. 20.
    Rogers, E.M.: Diffusion of innovations, 3rd edn. The Free Press, New York (1983)Google Scholar
  21. 21.
    Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3), 319–340 (1989)CrossRefGoogle Scholar
  22. 22.
    Venkatesh, V., Davis, F.D.: A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag. Sci. 46(2), 186–204 (2000)CrossRefGoogle Scholar
  23. 23.
    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)CrossRefGoogle Scholar
  24. 24.
    Agarwal, R., Prasad, J.: A conceptual and operational definition of personal innovativeness in the domain of information technology. Inf. Syst. Res. 9(2), 204–215 (1998)CrossRefGoogle Scholar
  25. 25.
    Carroll, J.M., Thomas, J.C., Malhotra, A.: Presentation and representation in design problem-solving. Br. J. Psychol. 71(1), 143–153 (1980).  https://doi.org/10.1111/j.2044-8295.1980.tb02740.xCrossRefGoogle Scholar
  26. 26.
    Han, I., Black, J.B.: Incorporating haptic feedback in simulation for learning physics. Comput. Educ. 57(4), 2281–2290 (2011).  https://doi.org/10.1016/j.compedu.2011.06.012CrossRefGoogle Scholar
  27. 27.
    Oswald, W.D., Roth, E.: Der Zahlen-Verbindungs-Test (ZVT) [The Number Connection Test (NCT)]. Hogrefe, Göttingen (1987)Google Scholar
  28. 28.
    Abras, C., Maloney-Krichmar, D., Preece, J.: User-centered design. In: Bainbridge, W. (ed.) Encyclopedia of Human-Computer Interaction, vol. 37, no. 4, pp. 445–456. Sage Publications, Thousand Oaks (2004)Google Scholar
  29. 29.
    Mao, J.Y., Vredenburg, K., Smith, P.W., Carey, T.: The state of user-centered design practice. Commun. ACM 48(3), 105–109 (2005)CrossRefGoogle Scholar
  30. 30.
    Beier, G.: Kontrollüberzeugungen im Umgang mit Technik [Locus of control while interacting with technology]. Rep. Psychol. 24(9), 684–693 (1999)Google Scholar
  31. 31.
    Osgood, C.E.: Semantic differential technique in the comparative study of cultures. Am. Anthropol. 66(3), 171–200 (1964)CrossRefGoogle Scholar
  32. 32.
    Connolly, T.M., Boyle, E.A., MacArthur, E., Hainey, T., Boyle, J.M.: A systematic literature review of empirical evidence on computer games and serious games. Comput. Educ. 59(2), 661–686 (2012)CrossRefGoogle Scholar
  33. 33.
    Wouters, P., Van Nimwegen, C., Van Oostendorp, H., Van Der Spek, E.D.: A meta-analysis of the cognitive and motivational effects of serious games. J. Educ. Psychol. 105(2), 249–265 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Wiktoria Wilkowska
    • 1
    Email author
  • Thiemo Leonhardt
    • 2
  • Matthias Ehlenz
    • 3
  • Martina Ziefle
    • 1
  1. 1.Chair of Communication ScienceRWTH Aachen UniversityAachenGermany
  2. 2.Didactics of Computer ScienceTU DresdenDresdenGermany
  3. 3.Learning TechnologiesRWTH Aachen UniversityAachenGermany

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