Skip to main content

Advertisement

Log in

Leveraging MOOCs for learners in economically disadvantaged regions

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

While Massive Online Open Courses (MOOCs) have seen a surge in enrollments in higher education around the world especially during the Covid-19 pandemic, it is unclear if learners from the economically disadvantaged regions (EDR) are also able to capitalize on them. Specifically, challenges related to using MOOCs in these regions have been reported in the literature. Thus, the objective of this paper is to address the pedagogical challenge by investigating approaches to leverage MOOCs for learners in EDR. Drawing from the ARCS (i.e. Attention, Relevance, Confidence and Satisfaction) model, we proposed an embedded MOOCs approach where bite-sized MOOCs segments are integrated into in-class lectures under the guidance of the instructors. The effectiveness of the embedded MOOCs approach was evaluated and compared with other instructional methods. Results from randomized experiments showed that the embedded MOOCs approach had higher evaluations in terms of attention, relevance and satisfaction than face-to-face learning approach. In addition, the embedded MOOCs approach outperformed asynchronously blended MOOCs in enhancing students’ relevance perception. Regression analysis further revealed that attention, confidence, and satisfaction perceptions were positively associated with students’ intention to adopt the embedded MOOCs approach in their future studies. The findings shed light on how to utilize MOOCs and re-use content in MOOCs for global benefits and enable new pedagogical developments. The findings also underscore the importance of local social support and offline interactions to support the online learning materials.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability statement

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  • Adnan, M. (2020). Online learning amid the COVID-19 pandemic: Students’ perspectives. Journal of Pedagogical Sociology and Psychology, 1(2), 45–51.

    Article  MathSciNet  Google Scholar 

  • Albelbisi, N. A., Al-Adwan, A. S., & Habibi, A. (2021). Self-regulated learning and satisfaction: A key determinants of MOOC success. Education and Information Technologies, 26(3), 3459–3481.

    Article  Google Scholar 

  • Alraimi, K. M., Zo, H., & Ciganek, A. P. (2015). Understanding the MOOCs continuance: The role of openness and reputation. Computers & Education, 80, 28–38.

    Article  Google Scholar 

  • Barclay, C., & Logan, D. (2013). Towards an understanding of the implementation & adoption of massive online open courses (MOOCs) in a developing economy context. Paper presented at the Annual Workshop of the AIS Special Interest Group for ICT in Global Development, Milano, Italy.

  • Barnard, L., Lan, W. Y., To, Y. M., Paton, V. O., & Lai, S. L. (2009). Measuring self-regulation in online and blended learning environments. Internet & Higher Education, 12(1), 1–6.

    Article  Google Scholar 

  • Bowen, W. G., Chingos, M. M., Lack, K. A., & Nygren, T. I. (2014). Interactive learning online at public universities: Evidence from a six-campus randomized trial. Journal of Policy Analysis & Management, 33(1), 94–111.

    Article  Google Scholar 

  • Broadbent, J. (2017). Comparing online and blended learner’s self-regulated learning strategies and academic performance. Internet & Higher Education, 33, 24–32.

    Article  Google Scholar 

  • Broom, D. (2020). Coronavirus has exposed the digital divide like never before. Retrieved July 10, 2021 from https://www.weforum.org/agenda/2020/04/coronavirus-covid-19-pandemic-digital-divide-internet-data-broadband-mobbile/

  • Bruff, D. O., Fisher, D. H., McEwen, K. E., & Smith, B. E. (2013). Wrapping a MOOC: Student perceptions of an experiment in blended learning. Journal of Online Learning and Teaching, 9(2), 187–199.

    Google Scholar 

  • Bulger, M. E., Bright, J., & Cobo, C. (2015). The real component of virtual learning: Motivations for face-to-face MOOC meetings in developing and industrialised countries. Information, Communication & Society, 18(10), 1200–1216.

    Article  Google Scholar 

  • Chakraborty, P., Mittal, P., Gupta, M. S., Yadav, S., & Arora, A. (2021). Opinion of students on online education during the COVID-19 pandemic. Human Behavior and Emerging Technologies, 3, 357–365.

    Article  Google Scholar 

  • Chang, N. C., & Chen, H. H. (2015). A motivational analysis of the ARCS model for information literacy courses in a blended learning environment. Libri, 65(2), 129–142.

    Article  Google Scholar 

  • Cheng, Y.-C., & Yeh, H.-T. (2009). From concepts of motivation to its application in instructional design: Reconsidering motivation from an instructional design perspective. British Journal of Educational Technology, 40(4), 597–605.

    Article  Google Scholar 

  • Cocquyt, C., Zhu, C., Diep, A. N., De Greef, M., & Vanwing, T. (2019). Examining the role of learning support in blended learning for adults’ social inclusion and social capital. Computers & Education, 142. https://doi.org/10.1016/j.compedu.2019.103610

  • de Moura, V. F., de Souza, C. A., & Viana, A. B. N. (2021). The use of Massive Open Online Courses (MOOCs) in blended learning courses and the functional value perceived by students. Computers & Education, 161. https://doi.org/10.1016/j.compedu.2020.104077

  • Deng, R., Benckendorff, P., & Gannaway, D. (2019). Progress and new directions for teaching and learning in MOOCs. Computers & Education, 129, 48–60.

    Article  Google Scholar 

  • Deschacht, N., & Goeman, K. (2015). The effect of blended learning on course persistence and performance of adult learners: A difference-in-differences analysis. Computers & Education, 87, 83–89.

    Article  Google Scholar 

  • Drits-Esser, D., Bass, K. M., & Stark, L. A. (2014). Using small-scale randomized controlled trials to evaluate the efficacy of new curricular materials. CBE-Life Sciences Education, 13(4), 593–601.

    Article  Google Scholar 

  • Duan, P. (2021). The social presence of online education: How MOOC platforms in China cope with collective trauma during COVID-19. Asian Journal of Communication, 31(5), 436–451.

    Article  Google Scholar 

  • Duan, T. (2022). A new idea for the optimization of MOOC-based teaching. Education and Information Technologies, 27(3), 3623–3650.

    Article  Google Scholar 

  • Eesley, C. E., & Wu, L. (2019). For startups, adaptability and mentor network diversity can be pivotal: Evidence from a randomized experiment on a MOOC platform. Management Information Systems Quarterly, 44(2), 661–697.

    Article  Google Scholar 

  • Field, A. (2018). Discovering Statistics Using IBM SPSS Statistics (5th). SAGE Publications.

    Google Scholar 

  • Giurgiu, L. (2017). Microlearning an evolving elearning trend. Scientific Bulletin, 22(1), 18–23.

    Article  Google Scholar 

  • Griffiths, R., Chingos, M., Mulhern, C., & Spies, R. (2015). Adopting MOOCS on campus: A collaborative effort to test MOOCS on campuses of the university system of Maryland. Online Learning, 19(2), 7–21. https://doi.org/10.24059/olj.v19i2.523

    Article  Google Scholar 

  • Gutiérrez-Santiuste, E., Gámiz-Sánchez, V. M., & Gutiérrez-Pérez, J. (2015). MOOC & B-learning: Students’ Barriers and Satisfaction in Formal and Non-formal Learning Environments. Journal of Interactive Online Learning, 13(3), 88–111.

    Google Scholar 

  • Herzberg, F., Mausner, B., & Snyderman, B. B. (1967). The Motivation to Work (2nd ed.). Wiley.

    Google Scholar 

  • Holotescu, C., Grosseck, G., Cretu, V., & Naaji, A. (2014). Integrating MOOCs in Blended Courses. Paper presented at the Elearning & Software for Education, Bucharest, Romania.

  • Huang, B., & Hew, K. F. (2016). Measuring learners’ motivation level in massive open online courses. International Journal of Information and Education Technology, 6(10), 759–764.

    Article  Google Scholar 

  • Huang, W., Huang, W., Diefes-Dux, H. A., & Imbrie, P. K. (2006). A preliminary validation of attention, relevance, confidence and satisfaction model-based instructional material motivational survey in a computer-based tutorial setting. British Journal of Educational Technology, 37(2), 243–259.

    Article  Google Scholar 

  • Israel, M. J. (2015). Effectiveness of integrating MOOCs in traditional classrooms for undergraduate students. The International Review of Research in Open and Distributed Learning, 16(5), 102–118.

    Article  Google Scholar 

  • Karakis, H., Karamete, A., & Okçu, A. (2016). The effects of a computer-assisted teaching material, designed according to the ASSURE instructional design and the ARCS model of motivation, on students’ achievement levels in a mathematics lesson and their resulting attitudes. European Journal of Contemporary Education, 15(1), 105–113.

    Google Scholar 

  • Keller, J. M. (1999). Using the ARCS motivational process in computer-based instruction and distance education. New Directions for Teaching and Learning, 1999(78), 37–47.

    Article  Google Scholar 

  • Keller, J. M., & Suzuki, K. (2004). Learner motivation and E-learning design: A multinationally validated process. Journal of Educational Media, 29(3), 229–239.

    Article  Google Scholar 

  • Khan, I. U., Hameed, Z., Yu, Y., Islam, T., Sheikh, Z., & Khan, S. U. (2018). Predicting the acceptance of MOOCs in a developing country: Application of task-technology fit model, social motivation, and self-determination theory. Telematics and Informatics, 35(4), 964–978.

    Article  Google Scholar 

  • Kintu, M. J., Zhu, C., & Kagambe, E. (2017). Blended learning effectiveness: The relationship between student characteristics, design features and outcomes. International Journal of Educational Technology in Higher Education, 14(1), 1–20.

    Article  Google Scholar 

  • Lambert, S. R. (2020). Do MOOCs contribute to student equity and social inclusion? A systematic review 2014–18. Computers & Education, 145, https://doi.org/10.1016/J.COMPEDU.2019.103693

  • Law, K. M., Geng, S., & Li, T. (2019). Student enrollment, motivation and learning performance in a blended learning environment: The mediating effects of social, teaching, and cognitive presence. Computers & Education, 136, 1–12.

    Article  Google Scholar 

  • Lee, C. S., Goh, D. H. L., Chua, A. Y., & Luyt, B. (2009). Choosing communication portfolios to accomplish tasks: The effects of individual differences. Computers & Education, 53(4), 1167–1176.

    Article  Google Scholar 

  • Li, K., & Keller, J. M. (2018). Use of the ARCS model in education: A literature review. Computers & Education, 122, 54–62.

    Article  Google Scholar 

  • Li, K., & Moore, D. R. (2018). Motivating students in massive open online courses (MOOCs) using the attention, relevance, confidence, satisfaction (arcs) model. Journal of Formative Design in Learning, 2(2), 102–113.

    Article  Google Scholar 

  • Littenberg-Tobias, J., & Reich, J. (2020). Evaluating access, quality, and equity in online learning: A case study of a MOOC-based blended professional degree program. The Internet and Higher Education, 47. https://doi.org/10.1016/j.iheduc.2020.100759

  • López-Pérez, M. V., Pérez-López, M. C., & Rodríguez-Ariza, L. (2011). Blended learning in higher education: Students’ perceptions and their relation to outcomes. Computers & Education, 56(3), 818–826.

    Article  Google Scholar 

  • Ma, L., & Lee, C. S. (2019a). Investigating the adoption of MOOCs: A technology - user - environment perspective. Journal of Computer Assisted Learning, 35(1), 89–98.

    Article  Google Scholar 

  • Ma, L., & Lee, C. S. (2019b). Understanding the barriers to the use of MOOCs in a developing country: An innovation resistance perspective. Journal of Educational Computing Research, 57(3), 571–590.

    Article  Google Scholar 

  • Ma, L., & Lee, C. S. (2020). A Motivational Design Approach to Integrate MOOCs in Traditional Classrooms. Paper presented at the The 22nd International Conference on Asia-Pacific Digital Libraries (ICADL 2020), Virtual Conference.

  • Magen-Nagar, N., & Cohen, L. (2017). Learning strategies as a mediator for motivation and a sense of achievement among students who study in MOOCs. Education and Information Technologies, 22(3), 1271–1290.

    Article  Google Scholar 

  • Means, B., Toyama, Y., Murphy, R. F., & Baki, M. (2013). The effectiveness of online and blended learning: A meta-analysis of the empirical literature. Teachers College Record, 115(3), 1–47.

    Article  Google Scholar 

  • Mooney, C. Z. & Duvall, R. (1993). Bootstrapping: A nonparametric approach to statistical inference. SAGE

  • Ocak, M. A. (2011). Why are faculty members not teaching blended courses? Insights from faculty members. Computers & Education, 56(3), 689–699.

    Article  Google Scholar 

  • Onah, D. F., Pang, E. L., & Sinclair, J. E. (2022). Investigating self-regulation in the context of a blended learning computing course. The International Journal of Information and Learning Technology, 39(1), 50–69.

    Article  Google Scholar 

  • Ossiannilsson, E. (2021). MOOCS for Lifelong Learning, Equity, and Liberation. In D. M. Cvetković (Ed.), MOOC (Massive Open Online Courses): Intechopen. https://doi.org/10.5772/intechopen.99659

  • Owston, R., York, D. N., & Malhotra, T. (2019). Blended learning in large enrolment courses: Student perceptions across four different instructional models. Australasian Journal of Educational Technology, 35(5), 29–45.

    Google Scholar 

  • Patru, M., & Balaji, V. (2016). Making Sense of MOOCs: A Guide for Policy-Makers in Developing Countries. UNESCO.

    Google Scholar 

  • Pinto, M., Mariscal, D. C., & Segura, A. (2022). Experiences of information literacy and mobile technologies amongst undergraduates in times of COVID. A qualitative approach. Aslib Journal of Information Management, 74(2), 181–201.

    Article  Google Scholar 

  • Pittenger, A. L., & Doering, A. H. (2010). Influence of motivational design on completion rates in online self-study pharmacy-content courses. Distance Education, 31(3), 275–293.

    Article  Google Scholar 

  • Rasheed, R. A., Kamsin, A., & Abdullah, N. A. (2020). Challenges in the online component of blended learning: A systematic review. Computers & Education, 144,. https://doi.org/10.1016/j.compedu.2019.103701

  • Shapiro, H. B., Lee, C. H., Roth, N. E. W., Li, K., Çetinkaya-Rundel, M., & Canelas, D. A. (2017). Understanding the massive open online course (MOOC) student experience: An examination of attitudes, motivations, and barriers. Computers & Education, 110, 35–50.

    Article  Google Scholar 

  • Smith, J. G., & Suzuki, S. (2015). Embedded blended learning within an Algebra classroom: A multimedia capture experiment. Journal of Computer Assisted Learning, 31(2), 133–147.

    Article  Google Scholar 

  • So, H. J., & Brush, T. A. (2008). Student perceptions of collaborative learning, social presence and satisfaction in a blended learning environment: Relationships and critical factors. Computers & Education, 51(1), 318–336.

    Article  Google Scholar 

  • Song, S. H., & Keller, J. M. (2001). Effectiveness of motivationally adaptive computer-assisted instruction on the dynamic aspects of motivation. Educational Technology Research and Development, 49(2), 5–22.

    Article  Google Scholar 

  • Statistics, G. P. B. o. (2019). Gansu Development Yearbook 2019. China Statistics Press.

    Google Scholar 

  • Stockwell, B. R., Stockwell, M. S., Cennamo, M., & Jiang, E. (2015). Blended learning improves science education. Cell, 162(5), 933–936.

    Article  Google Scholar 

  • Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), 1273–1296.

    Article  Google Scholar 

  • Thai, N. T. T., Wever, B. D., & Valcke, M. (2017). The impact of a flipped classroom design on learning performance in higher education. Computers & Education, 107(1), 113–126.

    Article  Google Scholar 

  • Tseng, H., & Walsh, E. J. (2016). Blended versus traditional course delivery: Comparing students’ motivation, learning outcomes, and preferences. The Quarterly Review of Distance Education, 17(1), 43–52.

    Google Scholar 

  • Turel, Y. K., & Sanal, S. O. (2018). The effects of an ARCS based e-book on student’s achievement, motivation and anxiety. Computers & Education, 127, 130–140.

    Article  Google Scholar 

  • Ustun, A. B., & Tracey, M. W. (2020). An effective way of designing blended learning: A three phase design-based research approach. Education and Information Technologies, 25(3), 1529–1552. https://doi.org/10.1007/s10639-019-09999-9

    Article  Google Scholar 

  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.

    Article  Google Scholar 

  • Wu, H., & Luo, S. (2022). Integrating MOOCs in an undergraduate english course: Students’ and Teachers’ perceptions of blended learning. SAGE Open, 12(2). https://doi.org/10.1177/21582440221093035

  • Yousef, A. M. F., Chatti, M. A., Schoeder, U., & Wosnitza, M. (2015). A usability evaluation of a blended MOOC environment: An experimental case study. International Review of Research in Open & Distributed Learning, 16(2), 69–93.

    Article  Google Scholar 

  • Zhou, M. (2016). Chinese university students’ acceptance of MOOCs: A self-determination perspective. Computers & Education, 92, 194–203.

    Article  Google Scholar 

  • Zhou, Q., Lee, C. S., Sin, S.-C.J., Lin, S., Hu, H., & FahmiFirdaus Bin Ismail, M. (2020). Understanding the use of YouTube as a learning resource: A social cognitive perspective. Aslib Journal of Information Management, 72(3), 339–359.

    Article  Google Scholar 

  • Zhu, M., Sari, A. R., & Lee, M. M. (2022). Trends and issues in MOOC learning analytics empirical research: A systematic literature review (2011–2021). Education and Information Technologies. https://doi.org/10.1007/s10639-022-11031-6

    Article  Google Scholar 

Download references

Funding

This work was supported by the Philosophy and Social Science Research Project of Zhejiang Province (Grant numbers: 21NDJC076YB).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Long Ma.

Ethics declarations

Ethical approval

Ethical approval is not applicable for this article.

Informed consent

Written informed consent was obtained from the participants for their anonymized information to be published in this article.

Conflicts of interest

The authors have no competing interests to declare that are relevant to the content of this article.

Additional information

Publisher's note

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

Appendix

Appendix

1.1 Measurements

Constructs

Questions

Cronbach’s α

Attention

This class has very little in it that captures my attention. (R)

0.630

The students in this class seem curious about the subject matter

The instructor uses an interesting variety of teaching techniques

I often daydream while in this class. (R)

My curiosity is often stimulated in this class

Relevance

The things I am learning in this course will be useful to me

 

The subject matter of this course seems important

The content of this course relates to my expectations and goals

To accomplish my goals, it is important that I do well in this course

I do NOT think I will benefit much from this course. (R)

Confidence

I feel confident that I will do well in this course

0.600

The subject matter of this course is just too difficult for me. (R)

As I am taking this class, I believe I can succeed if I try hard enough

Satisfaction

I feel that this course gives me a lot of satisfaction

0.819

I enjoy working for this course

I am pleased with the design of this class

I feel satisfied with what I am getting from this course

I feel rather disappointed with this course. (R)

Self-regulation ability

I am good at resisting temptation

0.744

I am self-disciplined at times

People can count on me to keep on schedule

Intention to adopt

Using the MOOC before/during class is a good idea

0.776

I dislike the idea of using the MOOC before/during class. (R)

I’d like to continue this kind of course design in I take the course

  1. “R” indicates that the response of the question item needs to be reversed

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ma, L., Lee, C.S. Leveraging MOOCs for learners in economically disadvantaged regions. Educ Inf Technol 28, 12243–12268 (2023). https://doi.org/10.1007/s10639-022-11461-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-022-11461-2

Keywords