SIIE16. Proyecto eMadrid: MOOCs y Analítica del Aprendizaje. Carlos Delgado Kloos, Carlos Alario-Hoyos, Carmen Fernández-Panadero, Iria Estévez Ayres, Pedro J. Muñoz-Merino, Edmundo Tovar, Rosa Cabedo, Ruth Cobos, Jaime Moreno, Nelson Piedra
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Proyecto eMadrid: MOOCs y Analítica del Aprendizaje. Carlos Delgado Kloos, Carlos Alario-Hoyos, Carmen Fernández-Panadero, Iria Estévez Ayres, Pedro J. Muñoz-Merino, Edmundo Tovar, Rosa Cabedo, Ruth Cobos, Jaime Moreno, Nelson Piedra, Janneth Chicaiza, Jorge López.15/09/2016.
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SIIE16. Proyecto eMadrid: MOOCs y Analítica del Aprendizaje. Carlos Delgado Kloos, Carlos Alario-Hoyos, Carmen Fernández-Panadero, Iria Estévez Ayres, Pedro J. Muñoz-Merino, Edmundo Tovar, Rosa Cabedo, Ruth Cobos, Jaime Moreno, Nelson Piedra
- 1. Proyecto eMadrid: MOOCs y Analítica del Aprendizaje Carlos Delgado Kloos, Carlos Alario-Hoyos, Carmen Fernández-Panadero, Iria Estévez-Ayres, Pedro J. Muñoz-Merino, UC3M Ruth Cobos, Jaime Moreno, UAM Edmundo Tovar, Rosa Cabedo, UPM Nelson Piedra, Janneth Chicaiza, Jorge López, U. Loja
- 2. UC3M: MOOCs
- 3. UAM: MOOCs ● Pertenece al consorcio edX desde Marzo 2014 Febrero y octubre 2015 Marzo 2016
- 4. UPM: MOOCs
- 5. • Alario-Hoyos, C., Pérez-Sanagustín, M., Delgado-Kloos, C., Gutiérrez-Rojas, I., Leony, D., Parada G. H.A., Designing your first MOOC from scratch: recommendations after teaching "Digital Education of the Future", eLearning papers, From the field, 37(3), 1-7, March 2014. • Alario-Hoyos, C., Pérez-Sanagustín, M., Cormier, D., Delgado-Kloos, C., Proposal for a conceptual framework for educators to describe and design MOOCs, Journal of Universal Computer Science, 20(1), 6-23, January 2014. • Alario-Hoyos, C., Pérez-Sanagustín, M., Delgado-Kloos, C., Are we all on the same Boat? Coordinating Stakeholders for the Design of MOOCs, Proceedings of the 9th European Conference on Technology Enhanced Learning, EC-TEL 2014, Springer, LNCS 8719, 379-384, Graz, Austria, September 2014 http://mooccanvas.com Diseño instruccional y buenas prácticas en MOOCs y SPOCs
- 6. • 4800 contribuciones en 5 herramientas sociales • Herramienta más utilizada: foro • Reducción progresiva de la participación social • Correlación “number of contribution - scores” • Predecir “top contributors” / “leaders” • Alario-Hoyos, C., Pérez-Sanagustín, M., Delgado-Kloos, C., Parada G. H.A., Muñoz-Organero, M., IEEE Transactions on Learning Technologies, 7(3):260-266, July-September 2014. • Alario-Hoyos, C., Pérez-Sanagustín, M., Delgado-Kloos, C., Parada G. H.A., Muñoz-Organero, M., Rodriguez-de-las-Heras, A., Analysing the impact of built-in and external Social Tools in a MOOC on Educational Technologies, Proc. EC-TEL 2013, Springer, LNCS 8095, 5-18, Paphos, Cyprus, September 2013. Note: Best paper award. • Alario-Hoyos, C., Muñoz-Merino, P.J., Pérez-Sanagustín, M., Delgado-Kloos, C., Parada G. H.A., Who are the top contributors in a MOOC? Relating participants' performance and contributions, Journal of Computer Assisted Learning, 32(3):232-243, June 2016. Componente social en MOOCs
- 7. • Alario-Hoyos, C., Estévez-Ayres, I., Pérez Sanagustín, M., Leony, D., Delgado Kloos, C., MyLearningMentor: A Mobile App to Support Learners Participating in MOOCs, Journal of Universal Computer Science, 21(5):735-753, May 2015. • Alario-Hoyos, C., Leony, D., Estévez-Ayres, I., Pérez-Sanagustín, M., Gutiérrez-Rojas, I., Delgado Kloos, C., Adaptive planner for facilitating the management of tasks in MOOCs, Proc. CAFVIR 2014, 517-522, 2014. • Gutiérrez-Rojas, I., Alario-Hoyos, C., Pérez-Sanagustín, M., Leony, D., Delgado-Kloos, C., Scaffolding self-learning in MOOCs, Proc. EMOOCs 2014, 43-49 Lausanne, Switzerland, February 2014. MyLearningMentor Herramientas de apoyo al autoaprendizaje en MOOCs y SPOCs
- 8. 8 LA: Detectores de información Perfiles de aprendizaje Efectividad, eficiencia, interés Comportamientos Destrezas Emociones 1) Pedro J. Muñoz-Merino, J.A. Ruipérez Valiente, C. Delgado Kloos, “Inferring higher level learning information from low level data for the Khan Academy platform.” Proceedings of the Third International Conference on Learning Analytics and Knowledge, pp. 112–116. ACM, New York, (2013)
- 9. 9 LA: Detectores: Efectividad 1) Pedro J. Muñoz-Merino, José A. Ruipérez-Valiente, Carlos Alario-Hoyos, Mar Pérez-Sanagustín, Carlos Delgado Kloos, "Precise effectiveness strategy for analyzing the effectiveness of students with educational resources and activities in MOOCs", Computers in Human Behavior, vol. 47, pp. 108–118 (2015)
- 12. Analítica visual ● Open-DLAs: An Open Dashboard for Learning Analytics (Cobos, R, Gil, Silvia, Lareo, A., Vargas, F. Open-DLAs: An Open Dashboard for Learning Analytics. L@S: Third Annual ACM Conference on Learning at Scale April 25-26, 2016, The University of Edinburgh )
- 13. Lack of data at a European level Launched by European Commission’s Institute of Prospective Technological Studies (IPTS) Audiences MOOCKnowledge project Led by Open University in the Netherlands Collaboration: Technical University of Madrid (UPM) Universitat Oberta de Catalunya (OUC) Establishing a large-scale, cross-provider data collection about MOOC participants Analysis of Open Education impact of participants’ subpopulation, such as language learners Standardized & multilingual survey: Pre-, Post- and Follow-up questionnaires Policy-making by the European Commission MOOC providers to build open online education
- 14. MOOCKnowledge project MOOC format is characterized by the great diversity of enrolled people There is a lack of prior knowledge of participants’ background It is needed to provide a good picture of the heterogeneity of MOOC participants Problem analysis Data collection Real-world data from MOOCKnowledge data collection Main research focus on sub-population of language MOOCs (LMOOC) within MOOCKnowledge This research, under the umbrella of MOOCKnowledge project, aims to have a deeper knowledge of the diversity of participants' profiles (and particularly of language MOOCs) by running unsupervised clustering techniques. The features that build up the profiles cover sociological, linguistic and motivational perspectives, as well as prior satisfaction of participants with MOOC format
- 15. MOOC format is characterized by the great diversity of enrolled people (heterogeneity) There is a lack of prior knowledge of participants’ background It is needed to provide a good picture of the diversity of MOOC participants Problem analysis Real-world data from MOOCKnowledge data collection Main research focus on sub-population of language MOOCs (LMOOC) within MOOCKnowledge Data collection Use of unsupervised learning techniques applied to four reduced sets of features that shape the participant’s profilesData mining Clustering interpretatio n MOOCKnowledge project The identification of underlying relationships in internal structure of input data could help stakeholders in their decision-making processes for (L)MOOC format Participant's profile features = sociological + linguistic + motivational + prior satisfaction with MOOC format
- 16. 16 Learning Analytics - Predicción ● ELASA: E-Learning Analytics for Student's Absenteeism (Alvaro Andujar, Trabajo Fin de Grado. Julio 2016. Departamento de Ingeniería Informátca. Universidad Autónoma de Madrid. Tutora: Ruth Cobos) Datos anonimizadosDatos anonimizados IndicadoresIndicadores Extracción de datos anonimizados kNNkNN TrainingTraining TestTest
Editor's Notes
- 3 cursos
- 3 cursos
- As a consequence of our experience we published a set of recommendations on the different aspects of creating a MOOC from the selection of the platform to the learning contents and assessment activities. Also, we created the MOOC Canvas, which is a template or canvas that helps teachers reflect and discuss about the different aspects that should be taken into account when creating a MOOC. The Canvas includes two types of aspects (available resources in light grey and design decisions in white). Each aspect is addressed through a set of driving questions to help teachers reflect about that particular issue. The aspects have a number so the recommendation is to fill the MOOC Canvas in order
- Analysis of five social tools throughout the MOOC - Built-in tools are more popular - Different tools, different purposes - It is good having alternatives for the students - Many social tools decentralize the conversations - Some students become “leaders”
- MLM is a mobile application for guiding and advising learners that enroll in MOOCs. It includes an adaptive planner that suggests the tasks that need to be accomplished depending on user information and the information of the MOOC. Profile (more time for those without qualifications or unrelated backgrounds) Preferences (available hours, times in the week) Priorities (of the MOOCs enrolled) Previous performance (completed on time? Tasks (mandatory, recommended, optional) and estimated workload
- This visualization intends to count the number of times that each second of a video has been watched. When showing information regarding all the class, it can be used to detect problems in videos. When analyzing each student individually, instructors can infer that some students might be having problems, e.g. Figure shows Video 1 has a peak of visualizations around second 70, that might indicate that students are struggling with a concept there.
- The MOOCKnowledge project is an initiative funded by the Institute for Prospective Technology Studies (Joint Research Centre of the European Commission) and executed by the Open University of the Netherlands in cooperation with the Open University of Catalunya (UOC) and the Technical University in Madrid. The goal of the MOOCKnowledge project is to assess the current perspective of learners as participants of European MOOCs. The goal of the project is to collect a large-scale data basis about participants of (European) MOOCs with respect to their demographic background, lifelong-learning profile, ICT competences and motivation. These data will inform policy-making by the European Commission and can also inform MOOC providers to build open online education. Problem analysis MOOCKnowledge project has the purpose of building a large scale and cross-provider data collection that provides information related to profiles, experiences and behaviors of (European) MOOCs participants from an European perspective, as well as analyzing the Open Education impact of participants' subgroups such as those with a specific cultural background. In order to perform this process, an online multilingual survey comprised by a pre-, post- and follow up-questionnaire has been implemented. It is was expected the results reflects the high level of heterogeneity of MOOC participants' profiles.
- A more realistic understanding of the profiles of people is a critical issue for many disciplines that call for an in-depth knowledge of their customers and Open Education is no exception, as it might be positively impacted by a deeper knowledge of the heterogeneity of profiles that can be found in MOOC format. This format, characterized by the great diversity of enrolled people, is shaped by different personal and professional backgrounds, many knowledge levels and dissimilar motivations, among many other features. The lack of their prior knowledge constitutes an important barrier in order to identify and get a better understanding of the underlying relationships that make up the participants’ profiles. This diversity of MOOC participants represents an opportunity for applying unsupervised clustering algorithms with real-world data from hundreds, even thousands of people. Clustering could be discovered as a useful exploratory technique for identifying and analyzing MOOC participants' profiles, a format characterized by the great diversity of enrolled people that come from different personal and professional backgrounds, have a range of knowledge levels very large, with dissimilar motivations and goals, as well as many other heterogeneous issues that make more challenging the clustering process. The identification of underlying relationships in this internal structure of participants' features might help designers and other stakeholders to identify the trully defining features that impact in a decisive way on the design in MOOC format. MOOC participants' perspective, and specifically the set of their profiles, has little prominence in research on MOOC format. This research has the final purpose of deepening in the different profiles from the population who decide to enroll in a (language) MOOC. The identification and further analysis of these profiles allow the recognition of different potential target audience from the four identified perspectives (sociological, linguistic, motivational and prior satisfaction). This information is relevant for people involved in the design process and other stake-holders related to (language) MOOCs format. CONCLUSIONS Clustering is highlighted as a feasible technique for the identification and analysis of the diversity of participants’ profiles in MOOC format The identification of underlying relationships in this internal structure of participants' features might help stake-holders to identify the trully defining features that impact in a decisive way on MOOC design Open Education could be positively impacted by a more realistic understanding of the set of profiles that can be found in MOOC format
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