Browsing by Author "Alario Hoyos, Carlos"

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Adaptation of a process mining methodology to analyse learning strategies in a synchronous massive open online course
(Springer Science and Business Media Deutschland GmbH, 2022) Maldonado Mahauad, Jorge Javier; Pérez Sanagustín, Mar; Delgado Kloos, Carlos; Alario Hoyos, Carlos
The study of learners’ behaviour in Massive Open Online Courses (MOOCs) is a topic of great interest for the Learning Analytics (LA) research community. In the past years, there has been a special focus on the analysis of students’ learning strategies, as these have been associated with successful academic achievement. Different methods and techniques, such as temporal analysis and process mining (PM), have been applied for analysing learners’ trace data and categorising them according to their actual behaviour in a particular learning context. However, prior research in Learning Sciences and Psychology has observed that results from studies conducted in one context do not necessarily transfer or generalise to others. In this sense, there is an increasing interest in the LA community in replicating and adapting studies across contexts. This paper serves to continue this trend of reproducibility and builds upon a previous study which proposed and evaluated a PM methodology for classifying learners according to seven different behavioural patterns in three asynchronous MOOCs of Coursera. In the present study, the same methodology was applied to a synchronous MOOC on edX with N = 50,776 learners. As a result, twelve different behavioural patterns were detected. Then, we discuss what decision other researchers should made to adapt this methodology and how these decisions can have an effect on the analysis of trace data. Finally, the results obtained from applying the methodology contribute to gain insights on the study of learning strategies, providing evidence about the importance of the learning context in MOOCs
MOOC-Maker: three years building MOOC management capacities in Latin America
(CEUR-WS, 2018) Alario Hoyos, Carlos; Pérez Sanagustín, Mar; Morales, Miguel; Delgado Kloos, Carlos; Hernández Rizzardini, Rocael; Román, Mariela; Ramirez Gonzalez, Gustavo; Luna, Teresa; Jerez Yáñez, Oscar; Gütl, Christian; Moreira Teixeira, António; Maldonado Mahauad, Jorge Javier; Amado Salvatierra, Héctor R.; Meléndez, Alejandra; Solarte, Mario Fernando
MOOC-Maker is a project co-financed by the European Union Erasmus+ programme, developed between October 2015 and October 2018 with the aim of building MOOC (Massive Open Online Course) Management capacities in Latin America. The consortium of this project, made up of three experienced European higher education institutions (HEIs) in the field of MOOCs and 6 Latin American HEIs with different levels of experience in relation to MOOCs, has, among others: (1) trained more than 300 people in Latin America on MOOCs through face-to-face workshops; (2) offered more than a dozen MOOCs through the MOOC-Maker Campus, with more than 10,000 registered students; and (3) contributed to the debate and research in the field of MOOCs in Latin America through the organization of 6 international conferences and other dissemination events.
Predicting learners’ success in a self-paced MOOC through sequence patterns of self-regulated learning
(Springer Verlag, 2018) Maldonado Mahauad, Jorge Javier; Pérez Sanagustín, Mar; Moreno Marcos, Pedro Manuel; Alario Hoyos, Carlos; Muñoz Merino, Pedro; Delgado Kloos, Carlos
In the past years, predictive models in Massive Open Online Courses (MOOCs) have focused on forecasting learners’ success through their grades. The prediction of these grades is useful to identify problems that might lead to dropouts. However, most models in prior work predict categorical and continuous variables using low-level data. This paper contributes to extend current predictive models in the literature by considering coarse-grained variables related to Self-Regulated Learning (SRL). That is, using learners’ self-reported SRL strategies and MOOC activity sequence patterns as predictors. Lineal and logistic regression modelling were used as a first approach of prediction with data collected from N = 2,035 learners who took a self-paced MOOC in Coursera. We identified two groups of learners (1) Comprehensive, who follow the course path designed by the teacher; and (2) Targeting, who seek …
Temporal analysis for dropout prediction using self regulated learning strategies in self paced MOOCs
(2020) Moreno Marcos, Pedro Manuel; Muñoz Merino, Pedro J.; Maldonado Mahauad, Jorge Javier; Pérez Sanagustín, Mar; Alario Hoyos, Carlos; Delgado Kloos, Carlos
(Massive Open Online Courses) have usually high dropout rates. Many articles have proposed predictive models in order to early detect learners at risk to alleviate this issue. Nevertheless, existing models do not consider complex high-level variables, such as self-regulated learning (SRL) strategies, which can have an important effect on learners' success. In addition, predictions are often carried out in instructor-paced MOOCs, where contents are released gradually, but not in self-paced MOOCs, where all materials are available from the beginning and users can enroll at any time. For self-paced MOOCs, existing predictive models are limited in the way they deal with the flexibility offered by the course start date, which is learner dependent. Therefore, they need to be adapted so as to predict with little information short after each learner starts engaging with the MOOC. To solve these issues, this paper contributes with the study of how SRL strategies could be included in predictive models for self-paced MOOCs. Particularly, self-reported and event-based SRL strategies are evaluated and compared to measure their effect for dropout prediction. Also, the paper contributes with a new methodology to analyze self-paced MOOCs when carrying out a temporal analysis to discover how early prediction models can serve to detect learners at risk. Results of this article show that event-based SRL strategies show a very high predictive power, although variables related to learners' interactions with exercises are still the best predictors. That is, event-based SRL strategies can be useful to predict if e.g., variables related to learners' interactions with exercises are not available. Furthermore, results show that this methodology serves to achieve early powerful predictions from about 25 to 33% of the theoretical course duration. The proposed methodology presents a new approach to predict dropouts in self-paced MOOCs, considering complex variables that go beyond the classic trace-data directly captured by the MOOC platforms.