Skip to main content
SpringerLink
Log in

Investigating students’ use and adoption of with-video assignments: lessons learnt for video-based open educational resources

  • Published:
Journal of Computing in Higher Education Aims and scope Submit manuscript

Abstract

The use of video-based open educational resources is widespread, and includes multiple approaches to implementation. In this paper, the term “with-video assignments” is introduced to portray video learning resources enhanced with assignments. The goal of this study is to examine the factors that influence students’ intention to adopt with-video assignments. Extending the technology acceptance model by incorporating students’ emotions, we applied partial least squares structural equation modeling based on a sample of 73 students who systematically experienced with-video assignments in their studies. In addition, students’ activity was analyzed using aggregated time series visualizations based on video analytics. Learning analytics indicate that students make varying use of with-video assignments, depending on when they access them. Students are more likely to watch a greater proportion of the video when they use with-video assignments during the semester, as opposed to during the exams. Further, the findings highlight the important role of students’ emotions in adopting with-video assignments. In addition, perceived usefulness of with-video assignments increases their positive emotions and intention to adopt this medium, while perceived ease of use increases only their intentions. Together, these constructs explain 68% of the variance in students’ intention to adopt with-video assignments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Notes

  1. http://www.itslearning.net.

References

  • Agudo-Peregrina, Á. F., Hernández-García, Á., & Pascual-Miguel, F. J. (2014). Behavioral intention, use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning. Computers in Human Behavior, 34, 301–314.

    Article  Google Scholar 

  • Atif, A., Richards, D., Busch, P., & Bilgin, A. (2015). Assuring graduate competency: A technology acceptance model for course guide tools. Journal of Computing in Higher Education, 27(2), 94–113.

    Article  Google Scholar 

  • Barclay, L. J., & Kiefer, T. (2014). Approach or avoid? Exploring overall justice and the differential effects of positive and negative emotions. Journal of Management, 40(7), 1857–1898.

    Article  Google Scholar 

  • Beaudry, A., & Pinsonneault, A. (2010). The other side of acceptance: Studying the direct and indirect effects of emotions on information technology use. MIS Quarterly, 34(4), 689–710.

    Google Scholar 

  • Brotherton, J. A., & Abowd, G. D. (2004). Lessons learned from eClass: Assessing automated capture and access in the classroom. ACM Transactions on Computer-Human Interaction (TOCHI), 11(2), 121–155.

    Article  Google Scholar 

  • Buchanan, T., Sainter, P., & Saunders, G. (2013). Factors affecting faculty use of learning technologies: Implications for models of technology adoption. Journal of Computing in Higher Education, 25(1), 1–11.

    Article  Google Scholar 

  • Cohen, J. (1988). Statistical power analysis for the behavior science. Hillsdale: Lawrence Erlbaum Association.

    Google Scholar 

  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

    Article  Google Scholar 

  • Day, J. A., & Foley, J. D. (2006). Evaluating a web lecture intervention in a human–computer interaction course. IEEE Transactions on Education, 49(4), 420–431.

    Article  Google Scholar 

  • De Guinea, A. O., & Markus, M. L. (2009). Why break the habit of a lifetime? Rethinking the roles of intention, habit, and emotion in continuing information technology use. MIS Quarterly, 33(3), 433–444.

    Google Scholar 

  • Dhonau, S., & McAlpine, D. (2002). “Streaming” best practices: Using digital video-teaching segments in the fl/esl methods course. Foreign Language Annals, 35(6), 632–636.

    Article  Google Scholar 

  • Donkor, F. (2011). Assessment of learner acceptance and satisfaction with video-based instructional materials for teaching practical skills at a distance. The International Review of Research in Open and Distributed Learning, 12(5), 74–92.

    Article  Google Scholar 

  • Evans, C. (2008). The effectiveness of m-learning in the form of podcast revision lectures in higher education. Computers and Education, 50(2), 491–498.

    Article  Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382–388.

    Article  Google Scholar 

  • Fox, A. (2013). From moocs to spocs. Communications of the ACM, 56(12), 38–40.

    Article  Google Scholar 

  • Gefen, D., Straub, D., & Boudreau, M.-C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the association for information systems, 4(1), 7.

    Google Scholar 

  • Giannakos, M. N. (2013). Exploring the video-based learning research: A review of the literature. British Journal of Educational Technology, 44(6), E191–E195.

    Article  Google Scholar 

  • Giannakos, M. N., Krogstie, J., & Aalberg, T. (2016a). Video-based learning ecosystem to support active learning: Application to an introductory computer science course. Smart Learning Environments, 3, 11.

    Article  Google Scholar 

  • Giannakos, M. N., Sampson, D. G., Kidziński, Ł., & Pardo, A. (2016b). Enhancing video-based learning experience through smart environments and analytics. In Paper presented at the workshop on smart environments and analytics in video-based learning (SE@ VBL).

  • Giannakos, M. N., & Vlamos, P. (2013). Educational webcasts’ acceptance: Empirical examination and the role of experience. British Journal of Educational Technology, 44(1), 125–143.

    Article  Google Scholar 

  • Guo, P. J., Kim, J., & Rubin, R. (2014). How video production affects student engagement: An empirical study of mooc videos. In Paper presented at the proceedings of the first ACM conference on learning@ scale conference.

  • Hannafin, M. J. (1984). Guidelines for using locus of instructional control in the design of computer-assisted instruction. Journal of instructional development, 7(3), 6–10.

    Article  Google Scholar 

  • Harley, D., Henke, J., Lawrence, S., McMartin, F., Maher, M., Gawlik, M., et al. (2003). Costs, culture, and complexity: An analysis of technology enhancements in a large lecture course at UC Berkeley. Center for Studies in Higher Education. http://escholarship.org/uc/item/68d9t1rm. Accessed 14 Jan 2017.

  • Heilesen, S. B. (2010). What is the academic efficacy of podcasting? Computers and Education, 55(3), 1063–1068.

    Article  Google Scholar 

  • Hibbeln, M. T., Jenkins, J. L., Schneider, C., Valacich, J., & Weinmann, M. (2016). Inferring negative emotion from mouse cursor movements. MIS Quarterly. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2708108. Accessed 14 Jan 2017.

  • Hox, J. J., & Bechger, T. M. (1998). An introduction to structural equation modeling. Family Science Review, 11, 354–373.

    Google Scholar 

  • Jadin, T., Gruber, A., & Batinic, B. (2009). Learning with E-lectures: The meaning of learning strategies. Educational Technology and Society, 12(3), 282–288.

    Google Scholar 

  • Jarvis, C., & Dickie, J. (2009). Acknowledging the ‘forgotten’and the ‘unknown’: The role of video podcasts for supporting field-based learning. Planet, 22(1), 61–63.

    Article  Google Scholar 

  • Kay, R. H., & Loverock, S. (2008). Assessing emotions related to learning new software: The computer emotion scale. Computers in Human Behavior, 24(4), 1605–1623.

    Article  Google Scholar 

  • Kazlauskas, A., & Robinson, K. (2012). Podcasts are not for everyone. British Journal of Educational Technology, 43(2), 321–330.

    Article  Google Scholar 

  • Khalil, M., Kastl, C., & Ebner, M. (2016). Portraying MOOCs Learners: a Clustering Experience Using Learning Analytics. Research Track, 265.

  • Kim, J., Guo, P. J., Cai, C. J., Li, S.-W. D., Gajos, K. Z., & Miller, R. C. (2014). Data-driven interaction techniques for improving navigation of educational videos. In Paper presented at the proceedings of the 27th annual ACM symposium on user interface software and technology.

  • Kleftodimos, A., & Evangelidis, G. (2016). An interactive video-based learning environment supporting learning analytics: Insights obtained from analyzing learner activity data. In Y. Li, M. Chang, M. Kravcik, E. Popescu, R. Huang, Kinshuk, et al. (Eds.), State-of-the-art and future directions of smart learning. Lecture notes in educational technology (pp. 471–481). Singapore: Springer.

    Chapter  Google Scholar 

  • Lee, M.-C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Computers and Education, 54(2), 506–516.

    Article  Google Scholar 

  • Lee, M. K., Cheung, C. M., & Chen, Z. (2005). Acceptance of internet-based learning medium: The role of extrinsic and intrinsic motivation. Information and Management, 42(8), 1095–1104.

    Article  Google Scholar 

  • Lee, B.-C., Yoon, J.-O., & Lee, I. (2009). Learners’ acceptance of e-learning in South Korea: Theories and results. Computers and Education, 53(4), 1320–1329.

    Article  Google Scholar 

  • Leijen, Ä., Lam, I., Wildschut, L., Simons, P. R.-J., & Admiraal, W. (2009). Streaming video to enhance students’ reflection in dance education. Computers and Education, 52(1), 169–176.

    Article  Google Scholar 

  • Liu, C., & Forsythe, S. (2011). Examining drivers of online purchase intensity: Moderating role of adoption duration in sustaining post-adoption online shopping. Journal of retailing and consumer services, 18(1), 101–109.

    Article  Google Scholar 

  • Ljubojevic, M., Vaskovic, V., Stankovic, S., & Vaskovic, J. (2014). Using supplementary video in multimedia instruction as a teaching tool to increase efficiency of learning and quality of experience. The International Review of Research in Open and Distributed Learning, 15(3), 275–291.

    Article  Google Scholar 

  • Maag, M. (2006). Podcasting and MP3 players: Emerging education technologies. Computers Informatics Nursing, 24(1), 9–13.

    Article  Google Scholar 

  • McCombs, S., & Liu, Y. (2007). The efficacy of podcasting technology in instructional delivery. International Journal of Technology in Teaching and Learning, 3(2), 123–134.

    Google Scholar 

  • McGreal, R., Sampson, D. G., Chen, N.-S., Krishnan, M. S., & Huang, R. (2012). The open educational resources (OER) movement: Free learning for all students. In Paper presented at the 2012 IEEE 12th international conference on advanced learning technologies.

  • Mikalef, P., Pappas, I. O., & Giannakos, M. (2016). An integrative adoption model of video-based learning. The International Journal of Information and Learning Technology, 33(4), 219–235.

    Article  Google Scholar 

  • Ngai, E. W., Poon, J., & Chan, Y. (2007). Empirical examination of the adoption of WebCT using TAM. Computers and Education, 48(2), 250–267.

    Article  Google Scholar 

  • O’brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality and Quantity, 41(5), 673–690.

    Article  Google Scholar 

  • O’brien, A., & Hegelheimer, V. (2007). Integrating CALL into the classroom: The role of podcasting in an ESL listening strategies course. ReCALL, 19(02), 162–180.

    Article  Google Scholar 

  • Pappas, I. O., Giannakos, M. N., & Sampson, D. G. (2016a). Making sense of learning analytics with a configurational approach. In Paper presented at the proceedings of the workshop on smart environments and analytics in video-based learning (SE@ VBL), LAK2016.

  • Pappas, I. O., Kourouthanassis, P. E., Giannakos, M. N., & Chrissikopoulos, V. (2014). Shiny happy people buying: The role of emotions on personalized e-shopping. Electronic Markets, 24(3), 193–206.

    Article  Google Scholar 

  • Pappas, I. O., Kourouthanassis, P. E., Giannakos, M. N., & Chrissikopoulos, V. (2016b). Explaining online shopping behavior with fsQCA: The role of cognitive and affective perceptions. Journal of Business Research, 69(2), 794–803.

    Article  Google Scholar 

  • Pappas, I. O., Mikalef, P., & Giannakos, M. N. (2016c). Video-Based Learning Adoption: A typology of learners. Paper presented at the Proceedings of the workshop on Smart Environments and Analytics in Video-Based Learning (SE@VBL), LAK2016.

  • Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879.

    Article  Google Scholar 

  • Rienties, B., & Rivers, B. A. (2014). Measuring and understanding learner emotions: Evidence and prospects. Learning Analytics Review, 1, 1–28.

    Google Scholar 

  • Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3. Bönningstedt: SmartPLS.

    Google Scholar 

  • Roca, J. C., & Gagné, M. (2008). Understanding e-learning continuance intention in the workplace: A self-determination theory perspective. Computers in Human Behavior, 24(4), 1585–1604.

    Article  Google Scholar 

  • Roehl, A., Reddy, S. L., & Shannon, G. J. (2013). The flipped classroom: An opportunity to engage millennial students through active learning. Journal of Family and Consumer Sciences, 105(2), 44.

    Article  Google Scholar 

  • Saadé, R., & Bahli, B. (2005). The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: An extension of the technology acceptance model. Information and Management, 42(2), 317–327.

    Article  Google Scholar 

  • Scherer, K. R. (2005). What are emotions? And how can they be measured? Social Science Information, 44(4), 695–729.

    Article  Google Scholar 

  • Traphagan, T., Kucsera, J. V., & Kishi, K. (2010). Impact of class lecture webcasting on attendance and learning. Educational Technology Research and Development, 58(1), 19–37.

    Article  Google Scholar 

  • Ullrich, C., Shen, R., & Xie, W. (2013). Analyzing student viewing patterns in lecture videos. In Paper presented at the 2013 IEEE 13th international conference on advanced learning technologies (ICALT).

  • Van Zanten, R., Somogyi, S., & Curro, G. (2012). Purpose and preference in educational podcasting. British Journal of Educational Technology, 43(1), 130–138.

    Article  Google Scholar 

  • Wachtler, J., & Ebner, M. (2015). Impacts of interactions in learning-videos: A subjective and objective analysis. In Paper presented at the EdMedia: World conference on educational media and technology.

  • Wachtler, J., Hubmann, M., Zöhrer, H., & Ebner, M. (2016). An analysis of the use and effect of questions in interactive learning-videos. Smart Learning Environments, 3(1), 13.

    Article  Google Scholar 

Download references

Acknowledgements

This work was carried out during the tenure of an ERCIM “Alain Bensoussan” Fellowship Programme. This study was funded by The Research Council of Norway, project FUTURE LEARNING (Grant Number 255129/H20). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No 704110.

Author information

Authors and Affiliations

  1. Department of Computer and Information Science, Norwegian University of Science and Technology (NTNU), Sem Sælands vei 7-9, 7491, Trondheim, Norway

    Ilias O. Pappas, Michail N. Giannakos & Patrick Mikalef

Authors
  1. Ilias O. Pappas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michail N. Giannakos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Patrick Mikalef
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michail N. Giannakos.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Appendix

Appendix

Construct and scale items

Mean

SD

Loading

What do you think about the video assignments?

 Perceived ease of use (CA = 0.709)

  It was easy*

5.71

1.12

 

  I found it flexible

5.60

1.17

0.786

  The process was clear and understandable

6.25

1.02

0.74

  It was easy for me to attain skills in the assignment

5.66

1.12

0.849

 Perceived usefulness (CA = 0.942)

  I found this assignment useful*

5.75

1.2

0.537

  Completing similar assignments will improve my performance in the course

5.58

1.17

0.944

  Completing similar assignments will enhance my effectiveness in the course

5.66

1.13

0.948

  Completing this kind of assignment increased my capabilities in the course

5.53

1.22

0.948

 Emotions (CA = 0.825)

  Using this kind of assignment is enjoyable

5.68

1.28

0.921

  Using this kind of assignment is exciting

4.90

1.49

0.924

  Using this kind of assignment makes me feel good*

3.65

1.12

0.572

 Intention to adopt (CA = 0.955)

  I intend to use similar types of assignments in the future

6.04

1.05

0.945

  My general intention to use similar types of assignments in the future is very high

5.93

1.13

0.973

  I will regularly use similar types of assignments in the future

5.73

1.19

0.938

  I will think about using similar types of assignments

5.93

1.1

0.922

  1. CA Cronbach’s alpha
  2. * Deleted due to low loading

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pappas, I.O., Giannakos, M.N. & Mikalef, P. Investigating students’ use and adoption of with-video assignments: lessons learnt for video-based open educational resources. J Comput High Educ 29, 160–177 (2017). https://doi.org/10.1007/s12528-017-9132-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12528-017-9132-6

Keywords

Search

Navigation