Journal of Science Education and Technology

, Volume 23, Issue 5, pp 692–704 | Cite as

A Predictive Study of Learner Attitudes Toward Open Learning in a Robotics Class

  • Stanislav Avsec
  • David Rihtarsic
  • Slavko Kocijancic
Article

Abstract

Open learning (OL) strives to transform teaching and learning by applying learning science and emerging technologies to increase student success, improve learning productivity, and lower barriers to access. OL of robotics has a significant growth rate in secondary and/or high schools, but failures exist. Little is known about why many users stop their OL after their initial experience. Previous research done under different task environments has suggested a variety of factors affecting user satisfaction with different types of OL. In this study, we tested a regression model for student satisfaction involving students’ attitudes toward OL usage. A survey was conducted to investigate the critical factors affecting students’ achievements and satisfaction in OL of robotics with use of own developed direct manipulation learning environment as learning context. A multiple regression analyses were carried out to investigate how different facets of students’ expectations and experiences are related to perceived learning achievements and course satisfaction. Descriptive statistics and analysis of variance was performed to determine the effect of predictor variables to student satisfaction. The results demonstrate that students have significantly positive perceptions toward using OL of robotics as a learning-assisted tool. Furthermore, behavioral intention to use OL is influenced by perceived usefulness and self-efficacy. The following five major categories of satisfaction factors with OL course were revealed during analysis of the studies (effect sizes in parentheses): organization (0.69); implementation (0.61); professional content (0.53); interaction (0.43); self-efficacy (0.14). All these effect sizes were judged to be significant and large. The results also showed that learner–mentor/instructor interaction, learner–professional content interaction, and online and offline self-efficacy were good predictors of student satisfaction and course quality. Peer interactions and self-regulated learning have to be considered carefully. A learner–mentor/instructor and learner–professional content interaction are indicated as most significant interactions.

Keywords

Open learning Expectations in open learning Student satisfaction Direct manipulation environment of robotics Regression model 

References

  1. Aixia D, Wang D (2011) Factors influencing learner attitudes toward E-learning and development of E-learning environment based on the integrated E-learning platform. Int J e- Educ e-Bus e-Manage e-Learn 1(3):264–268Google Scholar
  2. Allen IE, Seaman J (2010) Class differences: online education in US. Retrieved from http://sloanconsortium.org/sites/default/files/class_differences.pdf
  3. Artino AR (2007) Online military training: using a social cognitive view of motivation and self-regulation to understand students’ satisfaction, perceived learning, and choice. Q Rev Distance Educ 8(3):191–202Google Scholar
  4. Bandura A (1977) Self-efficacy: toward a unifying theory of behavioural change. Psychol Rev 84(2):191–215CrossRefGoogle Scholar
  5. Bandura A (1988) Self-regulation of motivation and action through goal systems. In: Hamilton V, Bower GH, Frijda NH (eds) Cognitive perspectives on emotion and motivation. Kluwer Academy, Dordrecht, pp 37–61CrossRefGoogle Scholar
  6. Barak B, Zadok Y (2009) Robotics projects and learning concepts in science, technology and problem solving. Int J Technol Des Educ 19(3):289–307CrossRefGoogle Scholar
  7. Battalio J (2007) Interaction online: a reevaluation. Q Rev Distance Educ 8(4):339–352Google Scholar
  8. Biner PM, Bink ML, Huffman ML, Dean RS (1997) The impact of remote site group size on student satisfaction and relative performance in interactive telecourses. Am J Distance Educ 11(1):23–33CrossRefGoogle Scholar
  9. Cohen J (1992) A power primer. Psychol Bull 112:155–159CrossRefGoogle Scholar
  10. Dakers JR (2011) The rise of technological literacy in primary education. In: Benson C, Lunt J (eds) International handbook of primary technology education: reviewing the past twenty years. Sense Publishers, Rotterdam, pp 181–193CrossRefGoogle Scholar
  11. DeMiranda M (2004) The grounding of a discipline: cognition and instruction in technology education. Int J Technol Des Educ 14:61–77CrossRefGoogle Scholar
  12. Eisenkraft A (2010) Retrospective analysis of technological literacy of K-12 students in the USA. Int J Technol Des Educ 20:277–303CrossRefGoogle Scholar
  13. Gliner JA, Morgan GA (2000) Research methods in applied settings: an integrated approach to design and analysis. Erlbaum, MahwahGoogle Scholar
  14. Greenland S (2005) Investigating the drivers of student satisfaction: the application of regression analysis. Investig Univ Teach Learn 2(2):46–53Google Scholar
  15. Grigouridis E, Siskos Y (2010) Customer satisfaction evaluation, methods for measuring and implementing service quality. Springer, New YorkCrossRefGoogle Scholar
  16. Hamner E, Lauwers T, Bernstein D, Nourbakhsh I, Di Salvo C (2008) Robot diaries: broadening participation in the computer science pipeline through social technical exploration. Paper presented at the AAAI symposium on using AI to motivate greater participation in computer science. Retrieved from www.aaai.org/Papers/Symposia/Spring/2008/SS-08-08/SS08-08-008.pdf
  17. Handal B, MacNish J, Petocz P (2013) Adopting mobile learning in tertiary environments: instructional, curricular and organizational matters. Educ Sci 3:359–374CrossRefGoogle Scholar
  18. Hodges CB (2008) Self-efficacy in the context of online learning environments: a review of the literature and directions for research. Perform Improv Q 20(3–4):7–25CrossRefGoogle Scholar
  19. Hoepfl M (2007) Alternative classroom assessment tools and scoring mechanisms. In: Hoepfl M, Lindstrom M (eds) Assessment of technology education: council of technology teacher education 56th yearbook. McGraw-Hill, Peoria, pp 65–86Google Scholar
  20. Jonassen DH (2006) Modeling with technology, mindtools for conceptual change, 3rd edn. Pearson Prentice Hall, ColumbusGoogle Scholar
  21. Kaminski K, Switzer J, Gloeckner G (2009) Workforce readiness: a study of university students’ fluency with information technology. Comput Educ 53(2):228–233CrossRefGoogle Scholar
  22. Kelley TR (2008) Cognitive processes of students participating in engineering-focused design instruction. J Technol Educ 19:50–64Google Scholar
  23. Kuo YC, Walker AE, Belland BR, Schroder KEE (2013) A predictive study of student satisfaction in online education programs. Int Rev Res Open Distance Learn 14(1):16–39Google Scholar
  24. Linacre J (2008) The expected value of a point-biserial (or similar) correlation. Rasch Meas Trans 22(1):1154–1157Google Scholar
  25. Meyers LS, Gamst GC, Guarino AJ (2013) Performing data analysis using IBM SPSS. Willey, HobekenGoogle Scholar
  26. Montgomery DC, Peck EA, Vining GG (2001) Introduction to linear regression analysis, 3rd edn. Wiley, New YorkGoogle Scholar
  27. Moore MG (1989) Three types of interactions. Am J Distance Educ 3(2):1–6CrossRefGoogle Scholar
  28. Moore MG, Kearsley G (1996) Distance education: a systems view. Wadsworth, New YorkGoogle Scholar
  29. Mumtaz S (2000) Factors affecting teachers’ use of information and communications technology: a review of the literature. J Inform Technol Teach Educ 9(3):319–342CrossRefGoogle Scholar
  30. Noel-Levitz (2011) National online learners priorities report. Retrieved from https://www.noellevitz.com/upload/Papers_and_Research/2011/PSOL_report%202011.pdf
  31. Nykanen J, Lindh M (2012) Robotics and automation in primary teacher education—changing practices in the faculty of education at the University of Oulu, Finland. PATT- Pupils Attitudes toward Technology, 19–42Google Scholar
  32. Paechter M, Maier B, Macher D (2010) Students’ expectations of, and experiences in e-learning: their relation to learning achievements and course satisfaction. Comput Educ 54:222–229CrossRefGoogle Scholar
  33. Pawson C (2012) Comparative analysis of students ‘satisfaction with teaching on STEM versus non-STEM programmes. Psychol Teach Rev 18:16–21Google Scholar
  34. Petrina S, Feng F, Kim J (2007) Researching cognition and technology: how we learn across the lifespan. Int J Tech Design Educ 18:375–396CrossRefGoogle Scholar
  35. Pombo L, Smith M, Abelha M, Caixinha H, Costa N (2012) Evaluating an online e-module for Portuguese primary teachers: trainees’ perceptions. Technol Pedag Educ 21(1):21–36CrossRefGoogle Scholar
  36. Puzziferro M (2008) Online technologies self-efficacy and self-regulated learning as predictors of final grade and satisfaction in college-level online courses. Am J Distance Educ 22(2):72–89CrossRefGoogle Scholar
  37. Slangen L, Van Keulen H, Gravemeijer K (2011) What pupils can learn from working with robotic direct manipulation environments. Int J Technol Des Educ 21:449–469CrossRefGoogle Scholar
  38. Stiggins RJ, Chappuis J (2011) Introduction to student-involved assessment for learning. Pearson Education Inc, BostonGoogle Scholar
  39. Sullivan FR (2008) Robotics and science literacy: thinking skills, science process skills and systems understanding. J Res Sci Teach 45(3):373–394CrossRefGoogle Scholar
  40. Sun P, Tsai RJ, Finger G, Chen Y–Y, Yeh D (2007) What drives a successful e-learning? An empirical investigation of the critical factors influencing learner satisfaction. Comput Educ. doi:10.1016/j.compedu.2006.11.007 Google Scholar
  41. Taylor JS (2006) Student perceptions of selected technology student association activities. J Technol Educ 17(2):56–71Google Scholar
  42. Zimmerman BJ (1989) A social cognitive view of self-regulated academic learning. J Educ Psychol 81(3):329–339CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Stanislav Avsec
    • 1
  • David Rihtarsic
    • 1
  • Slavko Kocijancic
    • 1
  1. 1.Faculty of EducationUniversity of LjubljanaLjubljanaSlovenia

Personalised recommendations