Imagen del grupo de investigación

Date of inception 26 September 2024

Leader: Ariadna Angulo Brunet

Unit: Education and eLearning

The Measuring and Improving Student Success (MISS) research group focuses on education research, with an emphasis on improving online higher education. The fundamental objective is to understand the factors and dynamics behind the academic success of students in online learning environments, particularly at the UOC. Academic success is defined, not only in terms of academic execution and performance, but also in relation to the student's experience of well-being, involvement and social connection throughout their studies. In this sense, despite taking the student body as the main object of study, it is understood that educational institutions are responsible for creating and offering inclusive and equitable learning contexts. Thus, MISS proposes a main line of work structured around three axes: the exploration of student expectations regarding online higher education, the study of the learning experience in digital environments and the factors that influence it, and the analysis of student satisfaction with online learning and, specifically, with the educational model of education. Additionally, the group also has special interest in the study of research methodology in education, including both reflection and methodological discussion as well as the analysis of research methodological practices. In parallel, and motivated by the interests of the multidisciplinary team, we propose to go one step further to the study of inclusivity and diversity, both in online higher education and in other related fields. The MISS is a research group that combines experience in the field of online education with the knowledge of quantitative, qualitative and mixed research methods and techniques of the Psychology and Educational Sciences faculty that are part of it, the activity that various doctoral students are developing, and the connection with the Teacher Learning Analysis (eLinC) team.

Researchers

Classifications

  • Application Area: Educació i eLearning

Especialidades predominantes (top 10) Obtenidas a partir de las publicaciones help
Obtenidas a partir de las publicaciones

Las especialidades temáticas mostradas se han obtenido mediante la aplicación de modelos de inteligencia artificial, obtenidos como resultado del Proyecto Hércules.

El listado puede contener errores. En proceso de evaluación y mejora. Compartido para recoger sugerencias de la comunidad.

  1. Education (Social Sciences) Filtrar
  2. General Nursing (Nursing) Filtrar
  3. Applied Psychology (Psychology) Filtrar
  4. Artificial Intelligence (Computer Science) Filtrar
  5. Developmental and Educational Psychology (Psychology) Filtrar
  6. General Health Professions (Health Professions) Filtrar
  7. Health (social science) (Social Sciences) Filtrar
  8. Human-Computer Interaction (Computer Science) Filtrar
  9. Information Systems (Computer Science) Filtrar
  10. Library and Information Sciences (Social Sciences) Filtrar

Former members (1)