Skip to main content
SpringerLink
Log in

Effects of sustained attention and video lecture types on learning performances

  • Research Article
  • Published:
Educational Technology Research and Development Aims and scope Submit manuscript

Abstract

Video lectures are one of the primary learning resources embedded in e-learning environments. Many universities prepare video lectures and share them as open course materials with students. In order to make the learning process more efficient and effective, it is very important to design and personalize those learning resources for learners, taking into their needs and cognitive differences. The literature regarding how cognitively diverse users benefit from which media design is scarce. Therefore, the purpose of this research is to explore the effect of video lectures types (voice over type, picture-in-picture type, and screencast type) and learners’ sustained attention levels (low, and high) on their learning performance in an e-learning environment. The research was designed as a 2 × 3 factorial design. In addition, learners’ eye movements have been recorded during study sessions. Results indicate that main effect of learners’ sustained attention levels and video lecture types on learning performance were significant. Furthermore, it was observed that both for the students with high level of sustained attention and for the students with low level of sustained attention, the use of picture-in-picture types of video lectures led higher learning performance scores. Similarly, the eye tracking analyses also supported this finding.

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Altun, A. (2012). Ontologies for personalization: A new challenge for instructional designers. Procedia—Social and Behavioral Sciences, 64(9), 691–698. https://doi.org/10.1016/j.sbspro.2012.11.081.

    Article  Google Scholar 

  • Baddeley, A. D. (1999). Human memory. Boston, MA: Allyn & Bacon.

    Google Scholar 

  • Beege, M., Schneider, S., Nebel, S., & Rey, G. D. (2017). Look into my eyes! Exploring the effect of addressing in educational videos. Learning and Instruction, 49, 113–120. https://doi.org/10.1016/j.learninstruc.2017.01.004.

    Article  Google Scholar 

  • Boateng, R., et al. (2016). Videos in learning in higher education: Assessing perceptions and attitudes of students at the University of Ghana. Smart Learning Environments, 3(1), 1–13. https://doi.org/10.1186/s40561-016-0031-5.

    Article  Google Scholar 

  • Broadbent, D. (1958). Perception and communication. London: Pergamon Press.

    Book  Google Scholar 

  • Chen, C. M., & Wu, C. H. (2015). Effects of different video lecture types on sustained attention, emotion, cognitive load, and learning performance. Computers & Education, 80, 108–121. https://doi.org/10.1016/j.compedu.2014.08.015.

    Article  Google Scholar 

  • Chen, H. T. M., & Thomas, M. (2020). Effects of lecture video styles on engagement and learning. Educational Technology Research and Development. https://doi.org/10.1007/s11423-020-09757-6.

    Article  Google Scholar 

  • Chorianopoulos, K. (2018). A taxonomy of asynchronous instructional video styles. The International Review of Research in Open and Distributed Learning, 19(1), 2920.

    Article  Google Scholar 

  • Davies, R. (2006). Learner intent and online learning. In V. H. Wright, C. S. Sunal, & E. K. Wilson (Eds), Research on enhancing the interactivity of online learning: Information Age Publishing, Incorporated.

  • Day, J., Foley, J., & Catrambone, R. (2006). Investigating multimedia learning with web lectures. GVU Tech. Report GIT-GVU-06e25. GA: Georgia Institute of Technology.

  • de Koning, B. B., Tabbers, H. K., Rikers, R. M. J. P., & Paas, F. (2009). Towards a framework for attention cueing in instructional animations: Guidelines for research and design. Educational Psychology Review, 21(2), 113–140. https://doi.org/10.1007/s10648-009-9098-7.

    Article  Google Scholar 

  • Driver, J. (2001). A selective review of selective attention research from the past century. British Journal of Psychology, 92(1), 53–78. https://doi.org/10.1348/000712601162103.

    Article  Google Scholar 

  • Duchowski, A. (2007). Eye tracking methodology: Theory and practice. London: Springer Science & Business Media.

    Google Scholar 

  • Fee, A., & Budde-Sung, A. E. K. (2014). Using video effectively in diverse classes: What students want. Journal of Management Education, 38, 843–874. https://doi.org/10.1177/1052562913519082.

    Article  Google Scholar 

  • Fiorella, L., & Mayer, R. E. (2016). Eight ways to promote generative learning. Educational Psychology Review, 28(4), 717–741. https://doi.org/10.1007/s10648-015-9348-9.

    Article  Google Scholar 

  • Guo, P. J., Kim, J., & Rubin, R. (2014). How video production affects student engagement: An empirical study of MOOC videos. In Proceedings of the first ACM conference on Learning @ scale conference (L@S ’14). ACM, New York, NY, USA, 41–50.

  • Haladjian, H. H., & Pylyshyn, Z. W. (2006). Implicit multiple object tracking without an explicit tracking task. Journal of Vision, 6(6), 773–773. https://doi.org/10.1167/6.6.773.

    Article  Google Scholar 

  • Hansch, A. et al. (2015). Video and online learning: Critical reflections and findings from the field. HIIG Discussion Paper Series No. 2015–02. SSRN: https://ssrn.com/abstract=2577882 or http://doi.org/https://doi.org/10.2139/ssrn.2577882

  • Harp, S. F., & Mayer, R. E. (1998). How seductive details do their damage: A theory of cognitive interest in science learning. Journal of Educational Psychology, 90, 414–434.

    Article  Google Scholar 

  • Helton, W. S., Kern, R. P., & Walker, D. R. (2009). Conscious thought and the sustained attention to response task. Consciousness and Cognition, 18(3), 600–607. https://doi.org/10.1016/j.concog.2009.06.002.

    Article  Google Scholar 

  • Henderson, J. M. (2007). Regarding scenes. Current Directions in Psychological Science, 16, 219–222. https://doi.org/10.1111/j.1467-8721.2007.00507.x.

    Article  Google Scholar 

  • Holmqvist, K., et al. (2011). Eye tracking: A comprehensive guide to methods and measures. Oxford: Oxford University Press.

    Google Scholar 

  • Hyönä, J. (2010). The use of eye movements in the study of multimedia learning. Learning and Instruction, 20, 172–176. https://doi.org/10.1016/j.learninstruc.2009.02.013.

    Article  Google Scholar 

  • Ilgaz, H., Altun, A., & Aşkar, P. (2014). The effect of sustained attention level and contextual cueing on implicit memory performance for e-learning environments. Computers in Human Behavior, 39, 1–7. https://doi.org/10.1016/j.chb.2014.06.008.

    Article  Google Scholar 

  • Ilioudi, C., Giannakos, M. N., & Chorianopoulos, K. (2013). Investigating differences among the commonly used video lecture styles. Paper presented at the WAVe 2013 the Workshop at LAK’13,April 8, 2013, Leuven, Belgium.

  • Irwin, D. E. (2004). Fixation location and fixation duration as indices of cognitive processing. In J. M. Henderson & F. Ferreira (Eds.), The interface of language, vision, and action: Eye movements and the visual world. New York: Psychology Press.

    Google Scholar 

  • Kelley, T. L. (1939). The selection of upper and lower groups for the validation of test items. Journal of Educational Psychology, 30, 17–24.

    Article  Google Scholar 

  • Kizilcec, R. F., Bailenson, J. N., & Gomez, C. J. (2015). The instructor’s face in video instruction: Evidence from two large-scale field studies. Journal of Educational Psychology, 107(3), 724–739.

    Article  Google Scholar 

  • Kokoç, M. (2019). Effects of using instructor presence in programming video lectures: An eye tracking analysis. Ege Journal of Educational Technologies, 3(2), 52–66.

    Google Scholar 

  • Kokoç, M., Ilgaz, H., & Altun, A. (2020). Individual cognitive differences and student engagement in video lectures and e-learning environments. In E. Alqurashi (Ed.), Handbook of research on fostering student engagement with instructional technology in higher education (pp. 78–93). Hershey: IGI Global.

    Chapter  Google Scholar 

  • Köster, J. (2018). Design of instructional videos. Video in the age of digital learning (pp. 49–55). Cham: Springer International Publishing.

    Chapter  Google Scholar 

  • Leong, V., et al. (2017). Speaker gaze increases information coupling between infant and adult brains. Proceedings of the National Academy of Sciences, 114(50), 13290–13295.

    Article  Google Scholar 

  • Lusk, M. M., & Atkinson, R. (2007). Animated pedagogical agents: Does their degree of embodiment impact learning from static or animated worked examples? Applied Cognitive Psychology, 21(6), 747–764. https://doi.org/10.1002/acp.1347.

    Article  Google Scholar 

  • Mayer, R. E. (2014a). Multimedia instruction. In J. M. Spector, et al. (Eds.), Handbook of research on educational communications and technology (pp. 385–399). New York: Springer.

    Chapter  Google Scholar 

  • Mayer, R. E. (2014b). Principles of multimedia learning based on social cues: Personalization, voice, image, and embodiment principles. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 345–368). New York: Cambridge University Press.

    Chapter  Google Scholar 

  • Mayer, R. E. (2009). Multimedia learning (2nd ed.). New York: Cambridge University Press.

    Book  Google Scholar 

  • Mayer, R. E., Fiorella, L., & Stull, A. (2020). Five ways to increase the effectiveness of instructional video. Educational Technology Research and Development. https://doi.org/10.1007/s11423-020-09749-6.

    Article  Google Scholar 

  • Mutlu Bayraktar, D., & Bayram, S. (2017). Evaluation of multimedia learning environment designed according to different attention types via eye tracking method. Erzincan University Journal of Education Faculty, 19(2), 119–138.

    Google Scholar 

  • Paivio, A. (1971). Imagery and verbal processes. New York: Holt, Rinehart & Winston.

    Google Scholar 

  • Pashler, H. et al. (2007). Organizing instruction and study to improve student learning (NCER 2007–2004). Washington, DC: National Center for Educational Research, Institute of Educational Sciences, U.S. Department of Education.

  • Pi, Z., Hong, J., & Yang, J. (2017). Effects of the instructor’s pointing gestures on learning performance in video lectures. British Journal of Educational Technology, 48, 1020–1029. https://doi.org/10.1111/bjet.12471.

    Article  Google Scholar 

  • Preradovic, N. M., Lauc, T., & Panev, I. (2020). Investigating interactivity in instructional video tutorials for an undergraduate informatics course. Issues in Educational Research, 30(1), 203.

    Google Scholar 

  • Raney, G. E., Campbell, S. J., & Bovee, J. C. (2014). Using eye movements to evaluate the cognitive processes involved in text comprehension. Journal of Visualized Experiments, 83, 1–7.

    Google Scholar 

  • Roda, C. (2011). Human attention in digital environments. New York: Cambridge University Press.

    Book  Google Scholar 

  • Rovai, A. P., Baker, J. D., & Ponton, M. K. (2013). Social science research design and statistics: A practitioner’s guide to research methods and IBM SPSS. Chesapeake, VA: Watertree Press LLC.

    Google Scholar 

  • Sadik, A. (2016). Students’ preferences for types of video lectures: lecture capture vs. screencasting recordings. Journal of Educational Multimedia and Hypermedia, 25(2), 189–208. Waynesville, NC USA: Association for the Advancement of Computing in Education (AACE). Retrieved April 10, 2020 from https://www.learntechlib.org/primary/p/151643/.

  • Scagnoli, N. I., Choo, J., & Tian, J. (2019). Students’ insights on the use of video lectures in online classes. British Journal of Educational Technology, 50(1), 399–414. https://doi.org/10.1111/bjet.12572.

    Article  Google Scholar 

  • Schweizer, K. (2010). The relationship of attention and ıntelligence. In A. Gruszka, G. Matthews, & B. Szymura (Eds.), Handbook of individual differences in cognition: Attention, memory, and executive control (pp. 247–262). New York: Springer.

    Chapter  Google Scholar 

  • Schweizer, K., Zimmermann, P., & Koch, W. (2000). Sustained attention, intelligence, and the crucial role of perceptual processes. Learning and Individual Differences, 12(3), 271–286. https://doi.org/10.1016/S1041-6080(01)00040-1.

    Article  Google Scholar 

  • Steinmayr, R., Ziegler, M., & Träuble, B. (2010). Do intelligence and sustained attention interact in predicting academic achievement?”. Learning and Individual Differences, 20(1), 14–18. https://doi.org/10.1016/j.lindif.2009.10.009.

    Article  Google Scholar 

  • Stull, A. T., Fiorella, L., & Mayer, R. E. (2018). An eye-tracking analysis of instructor presence in video lectures. Computers in Human Behavior, 88, 263–272. https://doi.org/10.1016/j.chb.2018.07.019.

    Article  Google Scholar 

  • Sun, P.-C., et al. (2008). What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50(4), 1183–1202. https://doi.org/10.1016/j.compedu.2006.11.007.

    Article  Google Scholar 

  • Swarts, J. (2012). New modes of help: Best practices for instructional video. Technical Communication, 59(3), 195–206.

    Google Scholar 

  • Sweller, J. (1999). Instructional design in technical areas. Camberwell, Australia: ACER.

    Google Scholar 

  • van der Meij, H., & van der Meij, J. (2013). Eight guidelines for the design of instructional videos for software training. Technical Communication, 60(3), 205–228.

    Google Scholar 

  • van Gog, T., Verveer, I., & Verveer, L. (2014). Learning from video modeling examples: Effects of seeing the human model’s face. Computers & Education, 72, 323–327. https://doi.org/10.1016/j.compedu.2013.12.004.

    Article  Google Scholar 

  • van Merriënboer, J. J. G., & Ayres, P. (2005). Research on cognitive load theory and its design implications for e-learning. Educational Technology Research and Development, 53(3), 5–13. https://doi.org/10.1007/BF02504793.

    Article  Google Scholar 

  • Wang, J., & Antonenko, P. D. (2017). Instructor presence in instructional video: Effects on visual attention, recall, and perceived learning. Computers in Human Behavior, 71, 79–89. https://doi.org/10.1016/j.chb.2017.01.049.

    Article  Google Scholar 

  • Wang, J., Antonenko, P., & Dawson, K. (2020). Does visual attention to the instructor in online video affect learning and learner perceptions? An eye-tracking analysis. Computers & Education, 146, 103779. https://doi.org/10.1016/j.compedu.2019.103779.

    Article  Google Scholar 

  • Wittrock, M. C. (1989). Generative processes of comprehension. Educational Psychologist, 24, 345–376.

    Article  Google Scholar 

  • Wu, J.-H., Tennyson, R. D., & Hsia, T.-L. (2010). A study of student satisfaction in a blended e-learning system environment. Computers & Education, 55(1), 155–164. https://doi.org/10.1016/j.compedu.2009.12.012.

    Article  Google Scholar 

  • Zhang, D., Zhou, L., Briggs, R. O., & Nunamaker, J. F. (2006). Instructional video in e-learning: Assessing the impact of interactive video on learning effectiveness. Information & Management, 43(1), 15–27. https://doi.org/10.1016/j.im.2005.01.004.

    Article  Google Scholar 

  • Zimmermann, L., et al. (2015). Do semantic contextual cues facilitate transfer learning from video in toddlers? Frontiers in Psychology, 6, 561. https://doi.org/10.3389/fpsyg.2015.00561.

    Article  Google Scholar 

Download references

Funding

This research was supported by a project funded by the Scientific and Technological Research Council of Turkey (TUBITAK, Grant ID: 117K663).

Author information

Authors and Affiliations

  1. Department of Computer Education and Instructional Technologies, Fatih Faculty of Education, Trabzon University, Akçaabat, 61335, Trabzon, Turkey

    Mehmet Kokoç

  2. Open and Distance Education Faculty, Ankara University, Dogol Caddesi, 06100, Ankara, Turkey

    Hale IIgaz

  3. Faculty of Education, Department of Computer Education and Instructional Technologies, Hacettepe University, Beytepe, 06800, Ankara, Turkey

    Arif Altun

Authors
  1. Mehmet Kokoç
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hale IIgaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Arif Altun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mehmet Kokoç.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kokoç, M., IIgaz, H. & Altun, A. Effects of sustained attention and video lecture types on learning performances. Education Tech Research Dev 68, 3015–3039 (2020). https://doi.org/10.1007/s11423-020-09829-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11423-020-09829-7

Keywords

Search

Navigation