Academia.eduAcademia.edu

Outline

Title
Abstract
Introduction
Literature Review
Research Methods
Data Collection
Data Analysis
Results
Discussion
Limitations and Future Direction
Conclusions
References
All Topics
Education
Educational Psychology

Effect of video styles on learner engagement in MOOCs

Profile image of Ruiqi DengRuiqi Deng

2023, Technology, Pedagogy and Education

https://doi.org/10.1080/1475939X.2023.2246981
visibility

…

description

22 pages

link

1 file

Abstract

Video lectures in massive open online courses (MOOCs) provide an opportunity to not only deliver instructional content but also engage learners. While there are many different styles of video lectures, it is not clear how video styles affect learner engagement. This study analysed and critiqued different typologies of video styles and classified MOOC video styles on a speaker-centric to media-centric spectrum. A total of 1372 survey responses were used for data analysis. The findings indicated that the ‘media-centric’ and ‘balanced’ video styles enhanced learner engagement to varying degrees in MOOCs of different study areas. In contrast, the ‘speaker-centric’ video style offered no advantages for promoting engagement in any MOOC study area. Effect sizes ranged from .03 to .07, indicating that video styles had a small to medium effect on engagement. These findings can provide new insights into the design of video lectures for different study areas in MOOCs.

Related papers

The Effects of Instructional Design on Student Engagement with Video Lectures at Cyber Universities
Jamie Costley

Journal of Information Technology Education: Research

Aim/Purpose: The number of students enrolled in online courses that use video lectures is on the rise. However, research shows that the number of students watching video lectures is low, and the number watching videos to completion is even lower. Background: This paper seeks to understand this problem by looking for correlations between instructional design and student engagement with video lectures. Methodology: Students at a cyber-university in South Korea (n=1801) were surveyed on their perception of the instructional design used in the courses they took and their engagement with online video lectures. Contribution: This paper contributes to the body of knowledge by demonstrating positive correlations between instructional design, watching, and finishing video lectures. Findings: While most other research has found low levels of online lecture viewership, this paper found significantly higher numbers watching and finishing videos. Other major findings of the paper are that five k...

View PDFchevron_right
An Exploratory Mixed Method Study on H5P Videos and Video-Related Activities in a MOOC Environment
Martin Ebner

International Journal of Technology-Enhanced Education, 2022

As the rampant pandemic witnessed significant growth in online learning, numerous studies focused on designing attractive videos to improve the quality of teaching. For an effective instructional video, whether a teacher should be present on the screen remained controversial. Therefore, the study conducted bibliometric analyses to review the previous evidence on the relationships between teacher presentation types and learning performance, attention distribution, and learning perceptions. The clustering results quantitatively proved that researchers paid more attention to the empirical studies on the effect of teacher presence. Both the positive and negative influence of teacher presence on learning outcomes and perceptions has also been presented. Additionally, eye-tracking results showed that teachers attracted relatively more attention in teacher-present videos. Future studies should resort to more objective methods to address the controversy and emphasize the value of the individual difference to avoid one-size-fits-all presentation types, thus enhancing the quality of videos.

View PDFchevron_right
Designing Video for Massive Open Online-Education: Conceptual Challenges from a Learner-Centered Perspective
Friedrich Hesse

One of the fundamental critiques of MOOCs is the character of student persistence and engagement in the courses for which students have registered. This study provides new evidence on this question based on unique student-level administrative data from several dozens of MOOCs offered by a major provider (i.e., Coursera). We enhance these administrative data by using IP addresses and GIS data to match enrolled students to the geographic and socioeconomic traits of their localities. We also complement the insights from this descriptive analysis with the evidence from a designed field experiment conducted in one of these MOOCs. Specifically, this experiment sought to leverage student interest in sustained persistence through a scheduling "nudge" that allowed students to pre-commit to watch the first lecture videos at a designated time of their choosing.

View PDFchevron_right
Video-Based Education: Formats and Knowledge Processes in the Mooc Era
Natascia D'Amico

EDULEARN proceedings, 2017

The MOOC landscape has seen over time the absolute dominance of the video format. Regardless of the reasons for this, it's a fact that videos are the main medium in MOOC-although not new. When we talk about video format we should not make the mistake of thinking to a specific type of video. Indeed, this tool can be declined in very different ways. Simply, we could think about the possibility to have the teacher on the screen or not; to use the green screen technique or animations; to have a voiceover, to use the technique of storytelling or have pictures and words on the screen; and so on. The choice of each of these options will affect the production in terms of cost and time: without forgetting, last but not least, the impact of the format on the effectiveness of learning. Several studies move towards analysis of the MOOC aesthetic, trying to establish relationships and principles to finalize and optimize video design for learning. Our experience as MOOC producers at Federica Web Learning has given us the possibility to enjoy and observe more kind of video through different subjects and platforms. Our work can be summarized as a comparative analysis of different video formats (video-lectures as also videotrailers), essentially carried out through our experience on the ground. At the end, we'll try to make a map that could help teachers and institutions to choose the video formats most appropriate to the conditions and aims of each teaching situation online.

View PDFchevron_right
Designing video lectures for MOOC
Filomena Soares

2015

Educational videos differ from other teaching and learning technologies as they allow the benefit of using visual perception. Video lectures are not new to education, however with the use of innovative video technologies they can improve academic outcomes and extend the reach of education. They may offer extraordinary new experiences for higher education institutions (HEI). Through them lecturers can provide information and contents to students, and if used creatively, video lectures can become a powerful technological tool in education, inside and outside classrooms. Inside a classroom it can motivate students and improve topics' debate and outside it is a good support for students' selflearning. In some cases they can be used to work some subjects standing behind, but needed to support actual courses contents, that students do not remember (or were not even taught), opening an "in front to the past door" that backs students self-study. The student-educator dynamic is changing. Students are expecting exceptional instruction and educators are expecting students to be more and more well informed about subjects from online viewing.This article explores some of the potential benefits and challenges associated with the use of video lectures in the teaching and learning process at higher education. We will also discuss some thoughts and examples for the use of teaching materials to enhance student's learning and try to understand how video can act as powerful and innovative to enlighten teaching and learning (note that unfortunately, sometimes, the opposite is happening).

View PDFchevron_right
The impact of integrating MOOCs into campus courses on student engagement
fadiyah almutairi

2018

FACULTY OF PHYSICAL SCIENCES AND ENGINEERING Electronics and Computer Science Thesis for the degree of Doctor of Philosophy The Impact of Integrating MOOCs into Campus Courses on Student Engagement Fadiyah Muwaffaq Almutairi This thesis examines the impact of integrating elements of Massive Open Online Courses (MOOCs) into the on-campus learning of Saudi women’s higher educational institutions. It also explores and identifies patterns of student engagement in a blended MOOC design. MOOCs are courses which are delivered, usually by leading universities, with the promise of providing free, high-quality education to a limitless number of learners. In a ‘blended’ course design, instructors can integrate MOOC content with face-to-face educational activities and components. Thus enabling them to use lecture time to conduct meaningful discussions, to identify and clarify misconceptions, or to mentor students in group projects. The current study seeks to improve the general understanding of...

View PDFchevron_right
Moving Through MOOCS: Pedagogy, Learning Design and Patterns of Engagement
Amy Woodgate

Lecture Notes in Computer Science, 2015

Massive open online courses (MOOCs) are part of the lifelong learning experience of people worldwide. Many of these learners participate fully. However, the high levels of dropout on most of these courses are a cause for concern. Previous studies have suggested that there are patterns of engagement within MOOCs that vary according to the pedagogy employed. The current paper builds on this work and examines MOOCs from different providers that have been offered on the FutureLearn platform. A cluster analysis of these MOOCs shows that engagement patterns are related to pedagogy and course duration. Learners did not work through a three-week MOOC in the same ways that learners work through the first three weeks of an eight-week MOOC.

View PDFchevron_right
Looking at MOOCs Rapid Growth Through the Lens of Video-Based Learning Research
John Krogstie

International Journal of Emerging Technologies in Learning (iJET), 2014

Video learning is becoming an increasingly important part of contemporary education. In the decade there has been an increase of many and diverse forms of research efforts on video learning. This paper focuses on the video learning research of the last years based on 166 peerreviewed published academic papers. A categorization is then derived from these papers, delineating some basic characteristics of video learning. The taxonomy attempts to look at MOOCs rapid growth through the lens of videobased learning research. We also provide some directions for future research related to the use of video learning.

View PDFchevron_right
Learning with videos and quiz attempts: Explorative insights into behavior and patterns of MOOC participants
Martin Ebner

Learning and Collaboration Technologies, 2023

Many MOOCs use units with videos and quizzes, where a successful attempt after several tries is the basis for a MOOC certificate. A first in-depth analysis of quiz behavior within a MOOC at the Austrian MOOC platform iMooX.at had shown several quiz attempt patterns (Mair et al., 2022). As a next step, the researchers now collected details on video watching within a new edition of the same MOOC and therefore could combine data on quiz and video behavior. This analysis shows similar distribution of the quiz attempt patterns as in our first analysis. Additionally, the analysis indicates that learners who completed more quiz attempts than needed for full point results or passing have a higher average video watching time than learners who only made attempts until reaching a full score or passing.

View PDFchevron_right
Keeping Them Clicking: Promoting Student Engagement in MOOC Design
Dara Cassidy, Jemimah Bailey

The MOOC (Massive Open Online Course) is the latest buzzword in distance education, bringing together, for the first time, high quality 'Ivy League' providers, online education and a low-cost model.

View PDFchevron_right

References (80)

  1. Alario-Hoyos, C., Muñoz-Merino, P. J., Pérez-Sanagustín, M., Delgado Kloos, C., & Parada, G. A. H. (2016). Who are the top contributors in a MOOC? Relating participants' performance and contributions. Journal of Computer Assisted Learning, 32(3), 232-243. https://doi.org/10.1111/jcal.12127
  2. Alemayehu, L., & Chen, H.-L. (2021). Learner and instructor-related challenges for learners' engagement in MOOCs: A review of 2014-2020 publications in selected SSCI indexed journals. Interactive Learning Environments, 31(5), 3172-3194. Advance online publication. https://doi.org/10.1080/10494820.2021.1920430
  3. Alemdag, E. (2022). Effects of instructor-present videos on learning, cognitive load, motivation, and social presence: A meta-analysis. Education and Information Technologies, 27(9), 12713-12742. https://doi.org/10.1007/s10639-022- 11154-w Appleton, J. J., Christenson, S. L., Kim, D., & Reschly, A. L. (2006). Measuring cognitive and psychological engagement: Validation of the student engagement instrument. Journal of School Psychology, 44(5), 427-445. https://doi.org/10. 1016/j.jsp.2006.04.002
  4. Baker, R., Xu, D., Park, J., Yu, R., Li, Q., Cung, B., Fischer, C., Rodriguez, F., Warschauer, M., & Smyth, P. (2020). The benefits and caveats of using clickstream data to understand student self-regulatory behaviors: Opening the black box of learning processes. International Journal of Educational Technology in Higher Education, 17(1), 13. https://doi.org/10. 1186/s41239-020-00187-1
  5. Carmichael, M., Reid, A.-K., & Karpicke, J. (2018). Assessing the impact of educational video on student engagement, critical thinking and learning: The current state of play. SAGE.
  6. Chen, Y., Gao, Q., Yuan, Q., & Tang, Y. (2019). Facilitating students' interaction in MOOCs through timeline-anchored discussion. International Journal of Human-Computer Interaction, 35(19), 1781-1799. https://doi.org/10.1080/ 10447318.2019.1574056
  7. Chen, H.-T. M., & Thomas, M. (2020). Effects of lecture video styles on engagement and learning. Educational Technology Research & Development, 68(5), 2147-2164. https://doi.org/10.1007/s11423-020-09757-6
  8. Chen, C. J., Wong, V. S., Teh, C. S., & Chuah, K. M. (2017). MOOC videos-derived emotions. Journal of Telecommunication, Electronic and Computer Engineering, 9(2-9), 137-140. https://jtec.utem.edu.my/jtec/article/view/2688
  9. Chorianopoulos, K. (2018). A taxonomy of asynchronous instructional video styles. International Review of Research in Open & Distributed Learning, 19(1), 295-311. https://doi.org/10.19173/irrodl.v19i1.2920
  10. Cobos, R., & Ruiz-Garcia, J. C. (2021). Improving learner engagement in MOOCs using a learning intervention system: A research study in engineering education. Computer Applications in Engineering Education, 29(4), 733-749. https:// doi.org/10.1002/cae.22316
  11. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
  12. Comer, D. K., Baker, R., & Wang, Y. (2015). Negativity in massive online open courses: Impacts on learning and teaching and how instructional teams may be able to address it. InSight: A Journal of Scholarly Teaching, 10, 92-113. https:// doi.org/10.46504/10201508co
  13. Costley, J., & Lange, C. H. (2017). Video lectures in e-learning. Interactive Technology & Smart Education, 14(1), 14-30. https://doi.org/10.1108/ITSE-08-2016-0025
  14. Daniels, L. M., Adams, C., & McCaffrey, A. (2016). Emotional and social engagement in a massive open online course: An examination of Dino 101. In M. P. McCreery (Ed.), Emotions, technology, and learning (pp. 25-41). Academic Press.
  15. Dart, S. (2020, December 6-9). Khan-style video engagement in undergraduate engineering: Influence of video duration, content type and course. Proceedings of the 31st Annual Conference of the Australasian Association for Engineering Education, Sydney, Australia.
  16. Dart, S., & Gregg, A. (2021, December). Know your stuff, show enthusiasm, keep it on message: Factors influencing video engagement in two mechanical engineering courses. Proceedings of the Research in Engineering Education Symposium and Australasian Association for Engineering Education Annual Conference, Australia.
  17. Deng, R., & Benckendorff, P. (2021). What are the key themes associated with the positive learning experience in MOOCs? An empirical investigation of learners' ratings and reviews. International Journal of Educational Technology in Higher Education, 18(1), 9. https://doi.org/10.1186/s41239-021-00244-3
  18. Deng, R., Benckendorff, P., & Gannaway, D. (2019). Progress and new directions for teaching and learning in MOOCs. Computers & Education, 129, 48-60. https://doi.org/10.1016/j.compedu.2018.10.019
  19. Deng, R., Benckendorff, P., & Gannaway, D. (2020). Learner engagement in MOOCs: Scale development and validation. British Journal of Educational Technology, 51(1), 245-262. https://doi.org/10.1111/bjet.12810
  20. Deng, R., & Gao, Y. (2023a). Effects of embedded questions in pre-class videos on learner perceptions, video engage- ment, and learning performance in flipped classrooms. Active Learning in Higher Education, Advance online publica- tion. https://doi.org/10.1177/14697874231167098
  21. R. DENG
  22. Deng, R., & Gao, Y. (2023b). A review of eye tracking research on video-based learning. Education and Information Technologies, 28(6), 7671-7702. https://doi.org/10.1007/s10639-022-11486-7
  23. Deng, R., & Gao, Y. (2023c). Using learner reviews to inform instructional video design in MOOCs. Behavioral Sciences, 13 (4), 330. https://doi.org/10.3390/bs13040330
  24. Endres, T., Weyreter, S., Renkl, A., & Eitel, A. (2020). When and why does emotional design foster learning? Evidence for situational interest as a mediator of increased persistence. Journal of Computer Assisted Learning, 36(4), 514-525. https://doi.org/10.1111/jcal.12418
  25. Gallego-Romero, J. M., Alario-Hoyos, C., Estévez-Ayres, I., & Delgado Kloos, C. (2020). Analyzing learners' engagement and behavior in MOOCs on programming with the Codeboard IDE. Educational Technology Research & Development, 68(5), 2505-2528. https://doi.org/10.1007/s11423-020-09773-6
  26. Greene, J. A., Oswald, C. A., & Pomerantz, J. (2015). Predictors of retention and achievement in a massive open online course. American Educational Research Journal, 52(5), 925-955. https://doi.org/10.3102/0002831215584621
  27. Guo, P. J., Kim, J., & Rubin, R. (2014, March). How video production affects student engagement: An empirical study of MOOC videos. The first ACM conference on learning at scale, Atlanta, GA.
  28. Guzmán-Valenzuela, C., Gómez-González, C., Rojas-Murphy Tagle, A., & Lorca-Vyhmeister, A. (2021). Learning analytics in higher education: A preponderance of analytics but very little learning? International Journal of Educational Technology in Higher Education, 18(1), 23. https://doi.org/10.1186/s41239-021-00258-x
  29. Haensel, J. X., Smith, T. J., & Senju, A. (2022). Cultural differences in mutual gaze during face-to-face interactions: A dual head-mounted eye-tracking study. Visual Cognition, 30(1-2), 100-115. https://doi.org/10.1080/13506285.2021.1928354
  30. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis: Pearson new international edition. Pearson.
  31. Hansch, A., McConachie, K., Schmidt, P., Hillers, L., Newman, C., & Schildhauer, T. (2015). The role of video in online learning: Findings from the field and critical reflections. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2577882
  32. Hoi, V. N., & Le Hang, H. (2021). The structure of student engagement in online learning: A bi-factor exploratory structural equation modelling approach. Journal of Computer Assisted Learning, 37(4), 1141-1153. https://doi.org/10.1111/jcal.12551
  33. Hong, J., Pi, Z., & Yang, J. (2018). Learning declarative and procedural knowledge via video lectures: Cognitive load and learning effectiveness. Innovations in Education and Teaching International, 55(1), 74-81. https://doi.org/10.1080/ 14703297.2016.1237371
  34. Hughes, C., Costley, J., & Lange, C. (2019). The effects of multimedia video lectures on extraneous load. Distance Education, 40(1), 54-75. https://doi.org/10.1080/01587919.2018.1553559
  35. Hu, H., Zhang, G., Gao, W., & Wang, M. (2020). Big data analytics for MOOC video watching behavior based on Spark. Neural Computing and Applications, 32(11), 6481-6489. https://doi.org/10.1007/s00521-018-03983-z
  36. Jiang, Y., & Peng, J.-E. (2023). Exploring the relationships between learners' engagement, autonomy, and academic performance in an English language MOOC. Computer Assisted Language Learning, 1-26. Advance online publication. https://doi.org/10.1080/09588221.2022.2164777
  37. Jung, E., Kim, D., Yoon, M., Park, S., & Oakley, B. (2019). The influence of instructional design on learner control, sense of achievement, and perceived effectiveness in a supersize MOOC course. Computers & Education, 128, 377-388. https:// doi.org/10.1016/j.compedu.2018.10.001
  38. Jung, Y., & Lee, J. (2018). Learning engagement and persistence in Massive Open Online Courses (MOOCS). Computers & Education, 122, 9-22. https://doi.org/10.1016/j.compedu.2018.02.013
  39. Kay, R. H. (2012). Exploring the use of video podcasts in education: A comprehensive review of the literature. Computers in Human Behavior, 28(3), 820-831. https://doi.org/10.1016/j.chb.2012.01.011
  40. Kennedy, G., Coffrin, C., de Barba, P., & Corrin, L. (2015, March). Predicting success: How learners' prior knowledge, skills and activities predict MOOC performance. 5th International Conference on Learning Analytics and Knowledge, New York.
  41. Kim, J., Kwon, Y., & Cho, D. (2011). Investigating factors that influence social presence and learning outcomes in distance higher education. Computers & Education, 57(2), 1512-1520. https://doi.org/10.1016/j.compedu.2011.02.005
  42. Kuo, T. M., Tsai, C.-C., & Wang, J.-C. (2021). Linking web-based learning self-efficacy and learning engagement in MOOCs: The role of online academic hardiness. The Internet and Higher Education, 51, 100819. https://doi.org/10.1016/j. iheduc.2021.100819
  43. Lackmann, S., Léger, P.-M., Charland, P., Aubé, C., & Talbot, J. (2021). The influence of video format on engagement and performance in online learning. Brain Sciences, 11(2), 128. https://doi.org/10.3390/brainsci11020128
  44. Lallé, S., & , and Conati, C. (2020). A data-driven student model to provide adaptive support during video watching across MOOCs. In I. I. Bittencourt, M. Cukurova, K. Muldner, R. Luckin, & E. Millán, Artificial intelligence in education (pp. 282-295). Springer. https://doi.org/10.1007/978-3-030-52237-7_23
  45. Lange, C., & Costley, J. (2020). Improving online video lectures: Learning challenges created by media. International Journal of Educational Technology in Higher Education, 17(1), 16. https://doi.org/10.1186/s41239-020-00190-6
  46. Lan, M., & Hew, K. F. (2020). Examining learning engagement in MOOCs: A self-determination theoretical perspective using mixed method. International Journal of Educational Technology in Higher Education, 17(1), 7. https://doi.org/10. 1186/s41239-020-0179-5
  47. Lee, Y. (2018). Effect of uninterrupted time-on-task on students' success in Massive Open Online Courses (MOOCs). Computers in Human Behavior, 86, 174-180. https://doi.org/10.1016/j.chb.2018.04.043
  48. Lemay, D. J., & Doleck, T. (2022). Predicting completion of Massive Open Online Course (MOOC) assignments from video viewing behavior. Interactive Learning Environments, 30(10), 1782-1793. https://doi.org/10.1080/10494820.2020. 1746673
  49. Li, K. (2019). MOOC learners' demographics, self-regulated learning strategy, perceived learning and satisfaction: A structural equation modeling approach. Computers & Education, 132, 16-30. https://doi.org/10.1016/j.compedu. 2019.01.003
  50. Li, Q., & Baker, R. (2018). The different relationships between engagement and outcomes across participant subgroups in Massive Open Online Courses. Computers & Education, 127, 41-65. https://doi.org/10.1016/j.compedu.2018.08.005
  51. Majid, S., Khine, W. K. K., Oo, M. Z. C., & Lwin, Z. M. (2012 , November). An analysis of YouTube videos for teaching information literacy skills. Proceedings of the International Conference on Computers and Advanced Technology in Education (ICCAT 2001). Beijing, China.
  52. Manuel, F., Maur, A., Weiser, C., & Winkel, K. (2021). Pre-class video watching fosters achievement and knowledge retention in a flipped classroom. Computers & Education, 179, 104399. https://doi.org/10.1016/j.compedu.2021. 104399
  53. Maroco, J., Maroco, A. L., Campos, J. A. D. B., & Fredricks, J. A. (2016). University student's engagement: Development of the University Student Engagement Inventory (USEI). Psicologia: Reflexão e Crítica, 29(1), 1-12. https://doi.org/10. 1186/s41155-016-0042-8
  54. Mayer, R. E., & Moreno, R. (2002). Animation as an aid to multimedia learning. Educational Psychology Review, 14(1), 87-99. https://doi.org/10.1023/A:1013184611077
  55. Noetel, M., Griffith, S., Delaney, O., Sanders, T., Parker, P., Del Pozo Cruz, B., & Lonsdale, C. (2021). Video improves learning in higher education: A systematic review. Review of Educational Research, 91(2), 204-236. https://doi.org/10.3102/ 0034654321990713
  56. Perna, L. W., Ruby, A., Boruch, R. F., Wang, N., Scull, J., Ahmad, S., & Evans, C. (2014). Moving through MOOCs: Understanding the progression of users in massive open online courses. Educational Researcher, 43(9), 421-432. https://doi.org/10.3102/0013189x14562423
  57. Pi, Z., Chen, M., Zhu, F., Yang, J., & Hu, W. (2022). Modulation of instructor's eye gaze by facial expression in video lectures. Innovations in Education and Teaching International, 59(1), 15-23. https://doi.org/10.1080/14703297.2020. 1788410
  58. Pi, Z., Xu, K., Liu, C., & Yang, J. (2020). Instructor presence in video lectures: Eye gaze matters, but not body orientation. Computers & Education, 144, 103713. https://doi.org/10.1016/j.compedu.2019.103713
  59. Plass, J. L., & Kaplan, U. (2016). Emotional design in digital media for learning. In S. Tettegah & M. Gartmeier (Eds.), Emotions, technology, design, and learning (pp. 131-162). Academic Press.
  60. Pursel, B. K., Zhang, L., Jablokow, K. W., Choi, G. W., & Velegol, D. (2016). Understanding MOOC students: Motivations and behaviours indicative of MOOC completion. Journal of Computer Assisted Learning, 32, 202-217. https://doi.org/10.1111/ jcal.12131
  61. Romero-Rodríguez, L. M., Ramírez-Montoya, M. S., & González, J. R. V. (2019). Gamification in MOOCs: Engagement application test in energy sustainability courses. IEEE Access, 7, 32093-32101. https://doi.org/10.1109/access.2019. 2903230
  62. Ruipérez-Valiente, J. A., Staubitz, T., Jenner, M., Halawa, S., Zhang, J., Despujol, I., Maldonado-Mahauad, J., Montoro, G., Peffer, M., Rohloff, T., Lane, J., Turro, C., Li, X., Pérez-Sanagustín, M., & Reich, J. (2022). Large scale analytics of global and regional MOOC providers: Differences in learners' demographics, preferences, and perceptions. Computers & Education, 180, 104426. https://doi.org/10.1016/j.compedu.2021.104426
  63. Santos Espino, J. M., Afonso Suárez, M. D., & González-Henríquez, J. J. (2020). Video for teaching: Classroom use, instructor self-production and teachers' preferences in presentation format. Technology, Pedagogy & Education, 29 (2), 147-162. https://doi.org/10.1080/1475939x.2020.1726805
  64. Santos Espino, J. M., Afonso Suárez, M. D., & Guerra Artal, C. (2016). Speakers and boards: A survey of instructional video styles in MOOCs. Technical Communication, 63(2), 101-115. https://www.ingentaconnect.com/content/stc/tc/2016/ 00000063/00000002/art00004
  65. Sanz-Martínez, L., Er, E., Martínez-Monés, A., Dimitriadis, Y., & Bote-Lorenzo, M. L. (2019). Creating collaborative groups in a MOOC: A homogeneous engagement grouping approach. Behaviour & Information Technology, 38(11), 1107-1121. https://doi.org/10.1080/0144929x.2019.1571109
  66. Semenova, T. (2022). The role of learners' motivation in MOOC completion. Open Learning: The Journal of Open, Distance & E-Learning, 37(3), 273-287. https://doi.org/10.1080/02680513.2020.1766434
  67. Seo, K., Dodson, S., Harandi, N. M., Roberson, N., Fels, S., & Roll, I. (2021). Active learning with online video: The impact of learning context on engagement. Computers & Education, 165. https://doi.org/10.1016/j.compedu.2021.104132
  68. Shah, D. (2022). By the numbers: MOOCs in 2021. https://www.classcentral.com/report/mooc-stats-2021/ Smith, K. B. (2002). Typologies, taxonomies, and the benefits of policy classification. Policy Studies Journal, 30(3), 379-395. https://doi.org/10.1111/j.1541-0072.2002.tb02153.x
  69. Stöhr, C., Stathakarou, N., Mueller, F., Nifakos, S., & McGrath, C. (2019). Videos as learning objects in MOOCs: A study of specialist and non-specialist participants' video activity in MOOCs. British Journal of Educational Technology, 50(1), 166-176.
  70. Sun, Y., Guo, Y., & Zhao, Y. (2020). Understanding the determinants of learner engagement in MOOCs: An adaptive structuration perspective. Computers & Education, 157, 103963. https://doi.org/10.1016/j.compedu.2020.103963
  71. Tawfik, A. A., Reeves, T. D., Stich, A. E., Gill, A., Hong, C., McDade, J., Pillutla, V. S., Zhou, X., & Giabbanelli, P. J. (2017). The nature and level of learner-learner interaction in a chemistry Massive Open Online Course (MOOC). Journal of Computing in Higher Education, 29(3), 411-431. https://doi.org/10.1007/s12528-017-9135-3
  72. Thornhill, S., Asensio, M., & Young, C. (2002). Video streaming: A guide for educational development. JISC Click and Go Video Project.
  73. van Wermeskerken, M., Ravensbergen, S., & van Gog, T. (2018). Effects of instructor presence in video modeling examples on attention and learning. Computers in Human Behavior, 89, 430-438. https://doi.org/10.1016/j.chb. 2017.11.038
  74. Walji, S., Deacon, A., Small, J., & Czerniewicz, L. (2016). Learning through engagement: MOOCs as an emergent form of provision. Distance Education, 37(2), 208-223. https://doi.org/10.1080/01587919.2016.1184400
  75. 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, 1-16. https://doi.org/10.1016/j.compedu. 2019.103779
  76. Wang, W., Guo, L., He, L., & Wu, Y. J. (2019). Effects of social-interactive engagement on the dropout ratio in online learning: Insights from MOOC. Behaviour & Information Technology, 38(6), 621-636. https://doi.org/10.1080/ 0144929x.2018.1549595
  77. Watson, W. R., Kim, W., & Watson, S. L. (2016). Learning outcomes of a MOOC designed for attitudinal change: A case study of an Animal Behavior and Welfare MOOC. Computers & Education, 96, 83-93. https://doi.org/10.1016/j. compedu.2016.01.013
  78. Wei, X., Saab, N., & Admiraal, W. (2021). Assessment of cognitive, behavioral, and affective learning outcomes in massive open online courses: A systematic literature review. Computers & Education, 163, 104097. https://doi.org/10.1016/j. compedu.2020.104097
  79. Wilson, K. E., Martinez, M., Mills, C., D'Mello, S., Smilek, D., & Risko, E. F. (2018). Instructor presence effect: Liking does not always lead to learning. Computers & Education, 122, 205-220. https://doi.org/10.1016/j.compedu.2018.03.011
  80. Xu, X., Zhu, X., & Chan, F. M. (2023). System design of Pintrich's SRL in a supervised-PLE platform: A pilot test in higher education. Interactive Learning Environments, 31(2), 683-700. https://doi.org/10.1080/10494820.2020.1802296

Related papers

Video Lecture Styles in MOOCs by Malaysian Polytechnics
Muhamad Izzat Rahim

2019

As the utilisation of Massive Open Online Course (MOOC) grows in Technical and Vocational Education and Training (TVET) discipline, TVET students are required to use video lectures uploaded on MOOCs frequently. However, video lectures come in various styles and it may affect students' academic performance and psychology. Acknowledging this issue, this study aims to investigate the styles of TVET video lectures included in MOOCs by Malaysian Polytechnics. The video lectures (N=90) were analysed using video lecture style taxonomy [12]. It was found that the TVET video lectures can be categorised into fifteen styles in which nine of the styles were not listed in the taxonomy. Hence, the findings of this study can benefit TVET students and instructors in utilising video lectures effectively for teaching and learning. Additionally, it can also help researchers in corpus selection to further the study of TVET video lectures.

View PDFchevron_right
The Effects of Video Lecture Delivery Formats on Student Engagement
Marco Gilardi, Paul Newbury

The SAI Conference 2015, 2015

Video lectures are the main teaching tool used in e-learning platforms. Different video lecture delivery formats are used to disseminate course content among students. However, there are a limited number of studies that investigate if and how different video lecture delivery formats affect the way a viewer feels engaged with the video content. This paper presents results from a pilot study aimed at further investigating this area of research. During the experiment participants were exposed to five lecture delivery formats: a one-to-one tutoring session, SussexDL a novel video delivery format being developed at the University of Sussex, and three other video lecture delivery formats commonly used in e-learning platforms. Participants were asked to rate the level of engagement they felt with each format. The results suggest that there is a link between the video lecture delivery format and the engagement felt with the video content.

View PDFchevron_right
The Influence of Video Format on Engagement and Performance in Online Learning
Patrick Charland

Brain Sciences, 2021

Millions of students follow online classes which are delivered in video format. Several studies examine the impact of these video formats on engagement and learning using explicit measures and outline the need to also investigate the implicit cognitive and emotional states of online learners. Our study compared two video formats in terms of engagement (over time) and learning in a between-subject experiment. Engagement was operationalized using explicit and implicit neurophysiological measures. Twenty-six (26) subjects participated in the study and were randomly assigned to one of two conditions based on the video shown: infographic video or lecture capture. The infographic video showed animated graphics, images, and text. The lecture capture showed a professor, providing a lecture, filmed in a classroom setting. Results suggest that lecture capture triggers greater emotional engagement over a shorter period, whereas the infographic video maintains higher emotional and cognitive eng...

View PDFchevron_right
Video Lecture Design and Student Engagement: Analysis of Visual Attention, Affect, Satisfaction, and Learning Outcomes
Lucia Vilela Leite Filgueiras

2022

The growing availability of online multimedia instructions, such as Massive Open Online Courses (MOOCs) mark a revolutionary new phase in the use of technology for education. Considering the high student attrition in MOOCs, it is crucial to study how students engage and disengage during their learning experience in relation to the video lecture design. The present study conducted a pilot user experiment (n = 24) to evaluate in a multimodal way which video lecture design is more effective for learning. Two video lecture designs were scrutinized: voice over slides, and slides overlaid by picture-in-picture instructor video. The experimental setup included different tracking technologies and sensorial modalities to gather synchronized data from the learning experience: eyetracker, Kinect, frontal camera, and screen recording. Among the measures, eye-gaze observational data, facial expressions, and self-reported perceptions were analyzed and compared against the learning assessment results. Based on these results, engagement is discussed regarding the different video lecture designs by connecting the observational and self-reported data to the short-term learning outcomes. • Instructor's presence in multimedia videos provides nonverbal communication cues that can either help students understand the video content or hinder their cognitive load. There is no conclusive results to support one explanation rather than the other. • Multimodal learning analytics is used to analyze students behavior, emotion, and engagement in viewing a video. A setup including different tracking and sensorial modalities tracks the viewing experience, and opinion-based questionnaires add more subjective information. • Low-level learning activities may benefit from instructor's display, whereas learning activities involving higher cognitive load can be fostered in videos without instructor. Instructor's display is more useful and targeted by students when procedures are explained.

View PDFchevron_right
Going 'massive': Learner engagement in a MOOC environment
Stephanie Reid

2015

Student engagement is obviously a key element of designing in an online environment. As the EDUCAUSE “Top ten IT issues 2014” highlights educators must not simply consider whether to engage students online but how to engage students (Grajek, S. & EDUCAUSE, 2013–2014 IT Issues Panel, 2014). Engagement in online courses poses a number of challenges and has a long way to go before it matches levels of engagement in face to face courses (Johnson, Adams Becker, Estrada, & Freeman, 2014). It requires new approaches, new understandings of what constitutes engagement as well as new metrics of levels of engagement (Vu & Fadde, 2014).

View PDFchevron_right

Related topics

  • Student Motivation And Engagement
  • Cognitive Engagement
  • Instructional Video
    • 580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved