Socio-Emotional Competencies and School Performance in Adolescence: What Role for School Adjustment?

There is growing evidence in the literature of positive relationships between socio-emotional competencies and school performance. Several hypotheses have been used to explain how these variables may be related to school performance. In this paper, we explored the role of various school adjustment variables in the relationship between interpersonal socio-emotional competencies and school grades, using a weighted network approach. This network approach allowed us to analyze the structure of interrelations between each variable, pointing to both central and mediatory school and socio-emotional variables within the network. Self-reported data from around 3,400 French vocational high school students were examined. This data included a set of interpersonal socio-emotional competencies (cognitive and affective empathy, socio-emotional behaviors and collective orientation), school adjustment measures (adaptation to the institution, school anxiety, self-regulation at school, and self-perceived competence at school) as well as grades in mathematics and French language. The results showed that self-regulation at school weighted the most strongly on the whole network, and was the most important mediatory pathway. More specifically, self-regulation mediated the relationships between interpersonal socio-emotional competencies and school grades

Helping psychology students improve their statistics performance: A test of the Exo-St@ts web-based training environment

International audienc

The Point Illusion: Incorrect Weighting of Absolute Performance in Self-assessments

Abstract- People spend much of their life in an attempt to assess their aptitude for numerous tasks. For example, students expend a great deal of effort to determine their academic standing given a distribution of grades. This research finds that students use their absolute performance, or percentage correct as a yardstick for their self-assessment, even when relative standing is much more informative. An experiment shows that this reliance on absolute performance for self-evaluation causes a misallocation of time and financial resources. Reasons for this inappropriate responsiveness to absolute performance are explored. I

Analizando el comportamiento de los alumnos más allá del MOOC: un estudio exploratorio

La mayor parte de la literatura sobre cursos en línea abiertos masivos (MOOC) se ha centrado en describir y predecir el comportamiento del alumno con datos de seguimiento del curso. Sin embargo, se sabe poco sobre los recursos externos más allá del MOOC que utilizan para dar forma a su experiencia de aprendizaje, y cómo estas interacciones se relacionan con su éxito en el curso. Este documento presenta los resultados de un estudio exploratorio que analiza datos de 572 alumnos en 4 MOOC para comprender (1) cuáles son las actividades de los alumnos más allá de los MOOC y (2) cómo se relacionan con el rendimiento de su curso. Analizamos las frecuencias de las actividades individuales de los estudiantes dentro y fuera del MOOC, y las transiciones entre estas actividades. Luego, analizamos el tiempo que pasamos fuera del contenido MOOC, así como la naturaleza de este contenido. Finalmente, predecimos qué transiciones predicen mejor las calificaciones de los alumnos finales. Los resultados muestran que podemos predecir con precisión las calificaciones del curso de los estudiantes utilizando solo datos detallados del curso interno de las interacciones de los estudiantes con video conferencias y exámenes combinados con datos de seguimiento de interacciones con contenido fuera de los MOOC. Además, los datos muestran que los alumnos pasan el 75% de su tiempo en el MOOC, pero recurren con frecuencia a otro contenido, principalmente sitios de redes sociales, buzones de correo y motores de búsqueda. © 2019, Springer Nature Switzerland AG.Most of literature on massive open online courses (MOOCs) have focused on describing and predicting learner’s behavior with course trace data. However, little is known on the external resources beyond the MOOC they use to shape their learning experience, and how these interactions relate with their success in the course. This paper presents the results of an exploratory study that analyzes data from 572 learners in 4 MOOCs to understand (1) what the learners’ activities beyond the MOOC are, and (2) how they relate with their course performance. We analyzed frequencies of the students’ individual activities in and beyond the MOOC, and the transitions between these activities. Then, we analyzed the time spent on outside the MOOC content as well as the nature of this content. Finally, we predict which transitions better predict final learners’ grades. The results show that we can predict accurately students’ grades of the course using only internal-course fine-grained data of student’s interactions with video-lectures and exams combined with trace data of interactions with content outside the MOOCs. Also, data shows that learners spent 75% of their time on the MOOC, but go frequently to other content, mainly social networking sites, mail boxes and search engines

Developing Learning Analytics for TUT Circle

Part 4: Learning Analytics in Higher EducationInternational audienceIn this article, we introduce the concept of learning analytics in the context of TUT Circle, a social media enhanced web service for learning in use at Tampere University of Technology. Through three case studies, we apply the methods of learning analytics for insight into the bursty nature of learning activities, procrastination, peer learning, and co-operation between two academic tribes. We found learning analytics useful in providing information to improve the pedagogical practices of online courses, as well as the quality of web-based learning in general