Browsing by Author "Calle Morales, Darwin Mauricio"

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Desarrollo de un plugin de recomendaciones tipo chatbot orientado a estudiantes para la plataforma Moodle
(Universidad de Cuenca, 2022-05-31) Calle Morales, Darwin Mauricio; Narváez Miranda, Edwin David; Maldonado Mahauad, Jorge Javier
Face-to-face learning is the educational model that has lasted the longest in the history of mankind, however, the emergence and use of virtual spaces such as education platforms or learning spaces have increased in recent years. These platforms, known as learning management systems, bring several benefits, such as ease of access, availability and, as a consequence, the storage of large amounts of data regarding student interactions. Data alone do not provide the expected information, that is why by processing and generating indicators it is possible to improve and understand the learning process of students. In this context appears Moodle, which is a very popular learning management system that offers the possibility of creating courses in virtual mode for free, it is also characterized by collecting and storing a large amount of data on student interactions and by creating plugins to add more functionality to the platform. Moodle has a repository of plugins, most of them are free and open source, however, in the literature reviewed there is no plugin that recommends concrete actions or strategies to improve the learning process. Similarly, in this review there are no plugins that employ novel technologies such as chatbots or conversational agents, which are already being used in different industries as interactive sources of information with high availability and that curiously in education have not been sufficiently explored. That is why, in this degree work we present the development of Miranda, a chatbot-like recommendation plugin oriented to students. The proposed tool helps students in the self-regulation of their learning, providing recommendations of time and sessions, resources and actions within the Moodle platform. Also, with the development of this chatbot plugin we try to fill the technological gap regarding the use of these technologies in the Moodle platform. Miranda's development demonstrates that it is possible to deploy a chatbot plugin that provides various recommendations to students using both Moodle-generated and Moodle-generated learning analytics indicators.
Miranda: a chatbot for supporting self-regulated learning
(Springer Science and Business Media Deutschland GmbH, 2022) Narvaez Miranda, Edwin David; Pérez Sanagustín, Mar; Carvallo Vega, Juan Pablo; Maldonado Mahauad, Jorge Javier; Calle Morales, Darwin Mauricio
Learning Analytics (LA) aims to understand and optimize the learning process in the environments in which they occur. It also offers opportunities for teachers to understand students’ behavior and promote the use of effective strategies that allow them to achieve their goals. Most of current solutions proposed in the literature for supporting students’ SRL are based on dashboards. However, if students do not interact with them, it becomes difficult to understand whether they have an impact in their self-regulated behavior. This demonstration, presents Miranda: A Chatbot that acts as a conversational agent to recommend and make suggestions on SRL strategies based on students’ the behavior. The first version of Miranda has been developed for Moodle, but it could be adapted to any other Learning Management System
Proposal for the design and implementation of Miranda: a chatbot-type recommender for supporting self-regulated learning in online environments
(2021) Narvaez Miranda, Edwin David; Maldonado Mahauad, Jorge Javier; Calle Morales, Darwin Mauricio
The use of virtual platforms as a new space where online learning occurs has experienced a progressive increase in recent years. These platforms, also known as learning management systems (LMS), bring many benefits, not only the intrinsic ones due to their virtual modality: the ease of access and availability, but also due to the large amount of data that they store with respect to student interactions. At present, these data have not yet been processed or exploited in their entirety and if they do so they could provide various indicators that would be oriented to understand the way in which knowledge is acquired, the behavior of students in order to further improve the experience of student learning on online platforms. Fortunately, platforms like Moodle are characterized by storing a large amount of data, for that reason several plugins are developed, which add extra functionalities to the platform and use learning analytics (LA) to monitor and describe the learning process. However, most plugins do not reach a prescription level, that is, they do not delve into specific actions to improve the learning process. Thus, this study proposes the design and implementation of a chatbot-type recommendation system, the proposed tool will help students in self-regulation of their learning, providing recommendations for time and sessions, resources and actions within the platform to obtain better results.