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Beyond the "c" and the "x": Learning with algorithms in massive open online courses (MOOCs)
Knox, Jeremy

PublishedApril 2018
JournalInternational Review of Education
Volume 64, Issue 2, Pages 161-178
PublisherSpringer Netherlands
CountryUnited Kingdom, Europe

This article examines how algorithms are shaping student learning in massive open online courses (MOOCs). Following the dramatic rise of MOOC platform organisations in 2012, over 4,500 MOOCs have been offered to date, in increasingly diverse languages, and with a growing requirement for fees. However, discussions of learning in MOOCs remain polarised around the "xMOOC" and "cMOOC" designations. In this narrative, the more recent extended or platform MOOC ("xMOOC") adopts a broadcast pedagogy, assuming a direct transmission of information to its largely passive audience (i.e. a teacher-centred approach), while the slightly older connectivist model ("cMOOC") offers only a simplistic reversal of the hierarchy, posing students as highly motivated, self-directed and collaborative learners (i.e. a learner-centred approach). The online nature of both models generates data (e.g. on how many times a particular resource was viewed, or the ways in which participants communicated with each other) which MOOC providers use for analysis, albeit only after these data have been selectively processed. Central to many learning analytics approaches is the desire to predict students' future behaviour. Educators need to be aware that MOOC learning is not just about teachers and students, but that it also involves algorithms: instructions which perform automated calculations on data. Education is becoming embroiled in an "algorithmic culture" that defines educational roles, forecasts attainment, and influences pedagogy. Established theories of learning appear wholly inadequate in addressing the agential role of algorithms in the educational domain of the MOOC. This article identifies and examines four key areas where algorithms influence the activities of the MOOC: (1) data capture and discrimination; (2) calculated learners; (3) feedback and entanglement; and (4) learning with algorithms. The article concludes with a call for further research in these areas to surface a critical discourse around the use of algorithms in MOOC education and beyond.

Au-delà du « c » et du « x » : apprendre avec des algorithmes dans les formations en ligne ouverte à tous (FLOT) – Cet article examine la façon dont les algorithmes influencent l’apprentissage dans les formations en ligne ouverte à tous (FLOT ou MOOC). À la suite de l’essor fulgurant en 2012 des organismes de plateformes FLOT, plus de 4500 de ces formations ont été proposées jusqu’ici, dans un nombre croissant de langues et et avec une demande croissante de participation financière. Les débats sur l’apprentissage dans ces formations demeurent cependant polarisés autour des appellations xMOOC et cMOOC. Dans ce récit, le cours en ligne MOOC étendu ou plateforme (xMOOC), plus récent, adopte une pédagogie diffusée et fondée sur une transmission directe de l’information à un auditoire essentiellement passif (approche centrée sur l’enseignant), tandis que le modèle connectiviste (cMOOC) légèrement plus ancien opère uniquement un renversement simpliste de la hiérarchie, prenant les participants pour des apprenants très motivés, auto-dirigés et collaboratifs (approche centrée sur l’apprenant). La formule en ligne des deux modèles génère des données (par exemple fréquence de consultation d’une ressource donnée, modes de communication entre les participants), que les prestataires des MOOC exploitent à des fins d’analyse, néanmoins uniquement après avoir procédé à leur traitement sélectif. Au cœur de nombreuses approches d’analyse de l’apprentissage se trouve le désir de prédire le comportement futur des apprenants. Les éducateurs doivent être conscients du fait que l’apprentissage dans les cours en ligne ne dépend pas seulement des enseignants et des apprenants, mais aussi des algorithmes : les instructions qui effectuent des calculs automatisés sur les données. La formation devient de plus en plus mêlée à une « culture algorithmique » qui définit les rôles pédagogiques, la réalisation des prévisions, et qui influence la pédagogie. Les théories établies de l’apprentissage semblent totalement inadéquates pour aborder le rôle agentif des algorithmes dans le champ éducatif des MOOC. Cet article identifie et examine quatre principaux domaines d’influence des algorithmes sur les activités du cours en ligne : 1) capture et discrimination de données, 2) apprenants évalués par le système, 3) retour d’information et enchevêtrement, 4) apprentissage avec les algorithmes. L’article conclut par un appel à une recherche complémentaire dans ces domaines pour susciter un discours critique sur l’usage des algorithmes dans les cours en ligne et au-delà.

Keywords algorithm · learning · learning analytics · massive open online course · student data

Published atNetherlands
Rights© The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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