Empowering teachers in LMOOC design by using a taxonomy of participants’ temporal patterns
A decade of research into MOOCs (massive online open courses) for language learning (LMOOCs) shows that they seem to have consolidated their position as a subfield of computer-assisted language learning (CALL). Since the appearance of LMOOCs in 2013, 3 key systematic reviews have been carried out; t...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universidad Nacional de Educación a Distancia |
| Repositorio: | e-spacio. Repositorio Institucional de la UNED |
| Idioma: | inglés |
| OAI Identifier: | oai:e-spacio.uned.es:20.500.14468/26001 |
| Acceso en línea: | https://hdl.handle.net/20.500.14468/26001 |
| Access Level: | acceso abierto |
| Palabra clave: | 5505.10 Filología LMOOCs Temporal Access Patterns Learning Analytics (LA) Participants Profiles LMOOC Teachers Learning Pathways |
| Sumario: | A decade of research into MOOCs (massive online open courses) for language learning (LMOOCs) shows that they seem to have consolidated their position as a subfield of computer-assisted language learning (CALL). Since the appearance of LMOOCs in 2013, 3 key systematic reviews have been carried out; these confirm that research into student profiles is a recurring trend, with the focus on avoiding dropout rates by creating personalized learning pathways. One of the challenges for teachers and LMOOC developers is that they are not cognizant of their students or their study habits. If we could learn how students organize their study in LMOOCs, a taxonomy could be established according to their profiles. This would enable teachers and LMOOC developers to improve their course design and so create personalized learning pathways, making the courses better suited to students’ specific learning preferences. In this study, we use techniques of learning analytics (LA) to explore the temporal patterns of LMOOC participants in order to understand the way they manage and invest their time during their online courses. As a result of this study, we propose a new taxonomy of LMOOC participant profiles based on temporal patterns—one which would provide teachers with a tool to support them when personalizing the design and development of LMOOCs and which would, therefore, help them adapt their courses to the specific learning preferences of each profile. |
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