ABSTRACT
Massive Open Online Courses (MOOCs) have increased the accessibility of quality educational content to a broader audience across a global network. They provide access for students to material that would be difficult to obtain locally, and an abundance of data for educational researchers. Despite the international reach of MOOCs, however, the majority of MOOC research does not account for demographic differences relating to the learners' country of origin or cultural background, which have been shown to have implications on the robustness of predictive models and interventions. This paper presents an exploration into the role of nation-level metrics of culture, happiness, wealth, and size on the generalizability of completion prediction models across countries. The findings indicate that various dimensions of culture are predictive of cross-country model generalizability. Specifically, learners from indulgent, collectivist, uncertainty-accepting, or short-term oriented, countries produce more generalizable predictive models of learner completion.
- Adamopoulos, P. 2013. What Makes a Great MOOC? An Interdisciplinary Analysis of Student Retention in Online Courses. Thirty Fourth International Conference on Information Systems. 2013, (2013), 1--21. DOI:https://doi.org/10.1145/1164394.1164397.Google ScholarDigital Library
- Alamri, A. et al. 2021. MOOC next week dropout prediction: weekly assessing time and learning patterns. Intelligent Tutoring Systems: 17th International Conference, ITS 2021, Virtual Event, June 7--11, 2021, Proceedings 17 (2021), 119--130.Google ScholarDigital Library
- Andres, J.M.L. et al. 2017. Replicating 21 findings on student success in online learning. Technology, Instruction, Cognition, and Learning. 10, 4 (2017), 313--333.Google Scholar
- Baker, R. et al. 2022. Research Using the MOOC Replication Framework and E-TRIALS. 2022 IEEE Learning with MOOCS (LWMOOCS) (2022), 131--136.Google Scholar
- Baker, R.S. et al. Culture in Computer-Based Learning Systems: Challenges and Opportunities. Computer-Based Learning In Context. 1, 1, 1--13.Google Scholar
- Benjamini, Y. and Hochberg, Y. 1995. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Controlling the False Discovery Rate: a Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. 57, 1 (1995), 289--300. DOI:https://doi.org/10.2307/2346101.Google Scholar
- Brinton, C.G. and Chiang, M. 2015. MOOC performance prediction via clickstream data and social learning networks. Proceedings - IEEE INFOCOM. 26, (2015), 2299--2307. DOI:https://doi.org/10.1109/INFOCOM.2015.7218617.Google Scholar
- Brooks, C. et al. 2015. Who you are or what you do: Comparing the predictive power of demographics vs. activity patterns in massive open online courses (MOOCs). Proceedings of the Second (2015) ACM Conference on Learning@ Scale (2015), 245--248.Google Scholar
- Buolamwini, J. and Gebru, T. 2018. Gender shades: Intersectional accuracy disparities in commercial gender classification. Conference on fairness, accountability and transparency (2018), 77--91.Google Scholar
- Crossley, S. et al. 2017. Predicting success in massive open online courses (MOOCs) using cohesion network analysis. Proceedings of the International Conference on Computer-Supported Collaborative Learning (2017), 103--110.Google Scholar
- Dunn, O.J. 1961. Multiple comparisons among means. Journal of the American statistical association. 56, 293 (1961), 52--64.Google ScholarCross Ref
- Fogarty, J. et al. 2005. Case studies in the use of ROC curve analysis for sensor-based estimates in human computer interaction. Proceedings of Graphics Interface 2005 (2005), 129--136.Google ScholarDigital Library
- Gardner, J. et al. 2018. Replicating MOOC predictive models at scale. Proceedings of the Fifth Annual ACM Conference on Learning at Scale (2018), 1--10.Google ScholarDigital Library
- Guo, P.J. and Reinecke, K. 2014. Demographic differences in how students navigate through MOOCs. Proceedings of the first ACM conference on Learning@ scale conference (2014), 21--30.Google ScholarDigital Library
- Helliwell, J.F. et al. eds. 2015. World Happiness Report 2015. Development Solutions Network.Google Scholar
- Henrich, J. et al. 2010. The weirdest people in the world? Behavioral and Brain Sciences. (2010). DOI:https://doi.org/10.1017/S0140525X0999152X.Google Scholar
- Hofstede, G. 1986. Cultural differences in teaching and learning. International Journal of intercultural relations. 10, 3 (1986), 301--320.Google ScholarCross Ref
- Hofstede, G. et al. 2010. Culture and organizations: software of the mind, intercultural cooperation and its importance for survival. McGraw Hill.Google Scholar
- House, R.J. et al. 2004. Culture, leadership, and organizations: The GLOBE study of 62 societies. Sage publications.Google Scholar
- Hutt, S. et al. 2022. Controlled outputs, full data: A privacy-protecting infrastructure for MOOC data. British Journal of Educational Technology. 53, 4 (2022), 756--775. DOI:https://doi.org/10.1111/bjet.13231.Google ScholarCross Ref
- Jordan, K. 2014. Initial trends in enrolment and completion of massive open online courses. International Review of Research in Open and Distributed Learning. 15, 1 (2014), 133--160.Google ScholarCross Ref
- Kizilcec, R.F. et al. 2013. Deconstructing disengagement: analyzing learner subpopulations in massive open online courses. Proceedings of the third international conference on learning analytics and knowledge (2013), 170--179.Google ScholarDigital Library
- Kizilcec, R.F. and Cohen, G.L. 2017. Eight-minute self-regulation intervention raises educational attainment at scale in individualist but not collectivist cultures. Proceedings of the National Academy of Sciences. 114, 17 (2017), 4348--4353.Google ScholarCross Ref
- Kulik, J.A. and Fletcher, J.D. 2016. Effectiveness of Intelligent Tutoring Systems: A Meta-Analytic Review. Review of Educational Research. (2016). DOI:https://doi.org/10.3102/0034654315581420.Google Scholar
- Lan, X. et al. 2019. Parental autonomy support and psychological well-being in Tibetan and Han emerging adults: A serial multiple mediation model. Frontiers in Psychology. 10, (2019), 621.Google Scholar
- Le, C.V. et al. 2018. Communication at scale in a MOOC using predictive engagement analytics. Artificial Intelligence in Education: 19th International Conference, AIED 2018, London, UK, June 27--30, 2018, Proceedings, Part I 19 (2018), 239--252.Google ScholarCross Ref
- Li, X. et al. 2021. On the limits of algorithmic prediction across the globe. arXiv preprint arXiv:2103.15212. (2021).Google Scholar
- Liu, Z. et al. 2016. MOOC Learner Behaviors by Country and Culture; an Exploratory Analysis. International Educational Data Mining Society (2016).Google Scholar
- Moore, R.L. and Wang, C. 2021. Influence of learner motivational dispositions on MOOC completion. Journal of Computing in Higher Education. 33, 1 (2021), 121--134.Google ScholarCross Ref
- Ogan, A. et al. 2015. Towards understanding how to assess help-seeking behavior across cultures. International Journal of Artificial Intelligence in Education. 25, (2015), 229--248.Google Scholar
- San Pedro, M.O.C.Z. et al. 2011. Detecting carelessness through contextual estimation of slip probabilities among students using an intelligent tutor for mathematics. Artificial Intelligence in Education (2011), 304--311.Google Scholar
- Shah, Dhawal 2019. Online Degrees Slowdown: A Review of MOOC Stats and Trends in 2019. Class Central.Google Scholar
- Sndergaard, M. 1994. Hofstedes Consequences: A Study of Reviews. Citations and. (1994).Google Scholar
- Soares, A.M. et al. 2007. Hofstede's dimensions of culture in international marketing studies. Journal of business research. 60, 3 (2007), 277--284.Google ScholarCross Ref
- Steenkamp, J.-B.E. 2001. The role of national culture in international marketing research. International marketing review. (2001).Google ScholarCross Ref
- Trompenaars, F. and Hampden-Turner, C. 2011. Riding the waves of culture: Understanding diversity in global business. Nicholas Brealey International.Google Scholar
- Trumbore, A. 2021. Learner behavior and career benefits in business massive open online courses. Proceedings of the 15th International Conference of the Learning Sciences-ICLS 2021. (2021).Google Scholar
- Veeramachaneni, K. et al. 2013. Moocdb: Developing data standards for mooc data science. AIED 2013 workshops proceedings volume (2013).Google Scholar
- Wang, Y. and Baker, R. 2018. Grit and intention: Why do learners complete MOOCs? The International Review of Research in Open and Distributed Learning. 19, 3 (2018).Google ScholarCross Ref
Index Terms
- Exploring Cross-Country Prediction Model Generalizability in MOOCs
Recommendations
Exploring Factors Affecting Learners' Acceptance of MOOCs Based on Kirkpatrick's Model
IC4E '17: Proceedings of the 8th International Conference on E-Education, E-Business, E-Management and E-LearningSince Massive Open Online Courses (MOOCs) are evolving rapidly, many researches have been conducted to explore the structure, effectiveness and issues arise in MOOCs. The free accessibility of MOOCs is believed will soon replace the traditional teaching ...
Studying MOOC completion at scale using the MOOC replication framework
LAK '18: Proceedings of the 8th International Conference on Learning Analytics and KnowledgeResearch on learner behaviors and course completion within Massive Open Online Courses (MOOCs) has been mostly confined to single courses, making the findings difficult to generalize across different data sets and to assess which contexts and types of ...
Factors Influencing Learning and Factors Influencing Persistence: A Mixed-method Study of MOOC Learners' Motivation
ICISDM '17: Proceedings of the 2017 International Conference on Information System and Data MiningMassive open online courses have opened up access to courses from many highly-regarded universities to learners of diverse backgrounds, such as students, in-service staffs, and senior citizens. This study examined the specific factors that spurred ...
Comments