Enhance learners experience in e-learning based scenarios using intelligent alerting systems and learning analytic

  1. Meira Ferrão Luis, Ricardo Manuel
unter der Leitung von:
  1. Manuel J. Fernández Iglesias Doktorvater/Doktormutter
  2. Martín Llamas Nistal Doktorvater/Doktormutter

Universität der Verteidigung: Universidade de Vigo

Fecha de defensa: 02 von Oktober von 2023

Gericht:
  1. Luis E. Anido Rifón Präsident/in
  2. Adriana Gewerc Barujel Sekretärin
  3. Luis Manuel Cerqueira Barreto Vocal

Art: Dissertation

Zusammenfassung

This PhD research work is focused on technologies aimed at E-learning based scenarios, Intelligent Alerting Systems (IAS) and Learning Analytics (LA), and its main objective is to enhance the learners¿ experience and reduce dropout rates in E-learning based scenarios. For this, establish as the first objective to study the background and state of the art, mainly with the analysis, exploration and comparison between existing interactive video platforms and technologies, their pros, cons and specifications. As E-learning grows in popularity, the relatively low completion rates of students has been a dominant criticism (between 2% and 10%). Therefore, this study seeks also to identify, in a first phase, through a survey within a student community, the key reasons (causes) for dropouts when using E-learning as a learning platform. And, at a second phase, deepen these acknowledgements by interviewing teachers, counselors and platform administrators. This first research and analysis will be used to detect motives and behavior patterns of students with dropout thoughts. A very important part in a Learning Management System (LMS) is the tracking and recording of students progresses with the use of learning analytics. These measurements, analysis and reporting of data about learners and their contexts is essential in determining dropout patterns (such as used in finding patterns in banking or insurance clients). So, another objective of this thesis is to, with the usage of learning analytics, determine different stages, levels and patterns in dropout students and suggest appropriate interventions in order to prevent, in advance, these closure actions. Based on Intelligent Alerting Systems, and with the knowledge of abandonment patterns, computer intelligent services may be generated, in a first reaction to a dropout profile, by automatically messaging motivational phrases (online messaging and/or subliminal video messaging) in an early phase, by alerting the guidance counselor and/or teacher in a middle stage and by the usage of e-tutoring technologies (one-to-one) on an advanced stage (only within a final phase and not by student demand). Though the thesis has a technological approach, its main objective is to prevent dropouts and raise completion rates within E-learning. It also seeks that proposals follow, to the extent possible, to existing educational models and concepts. It is important to note, that the methods can vary substantially with regard to the technologies used and the target audience.