Browsing by Author "Moreno Marcos, Pedro Manuel"

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Can feedback based on predictive data improve learners' passing rates in MOOCs? a preliminary analysis
(Association for Computing Machinery, 2021) Pérez Sanagustín, María del Mar; Pérez Álvarez, Ronald Antonio; Maldonado Mahauad, Jorge Javier; Villalobos, Esteban; Hilleger, Isabel; Hernández Correa, Josefina; Sapunar, Diego; Moreno Marcos, Pedro Manuel; Muñoz Merino, Pedro; Delgado Kloos, Carlos; Imaz, Jon
This work in progress paper investigates if timely feedback increases learners’ passing rate in a MOOC. An experiment conducted with 2,421 learners in the Coursera platform tests if weekly messages sent to groups of learners with the same probability of dropping out the course can improve retention. These messages can contain information about: (1) the average time spent in the course, or (2) the average time per learning session, or (3) the exercises performed, or (4) the video-lectures completed. Preliminary results show that the completion rate increased 12% with the intervention compared with data from 1,445 learners that participated in the same course in a previous session without the intervention. We discuss the limitations of these preliminary results and the future research derived from them.
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.