Understanding effects of COVID-19 on undergraduate academic stress,motivation and coping over time

The rapid and unprecedented shift from face-to-face instruction to remote online learning as a consequence of the COVID-19 pandemic had a substantial impact on teaching and learning in Higher Education: students had to adapt to a new way of learning, away from typical campus settings and their peers, and to new forms of assessments. This study examined academic stress, learning strategies, motivation and ways of coping from a sample of 177 unique students from a large London university, collecting primary data via survey at three time points during the academic year 2020/21 when teaching was remote and online only. Our findings show how patterns in academic stress, learning strategies, motivation and coping vary over the course of the academic year giving novel insight into how student learning and adaptation to the situation changed over time. We also report on differences in these patterns according to year group and for those students who are the first-in-family to attend university and those who are not. Based on these findings we identify priority areas where higher education institutions should support undergraduate students and provide evidence that some groups of students may need more and targeted support to secure their ongoing learning and well-being.     

Desarrollo del aprendizaje de la escritura en alumnos y alumnas discapacitados

En este artículo se pasa el énfasis de los alumnos con discapacidad desde su limitación a su proceso de construcción y desarrollo, analizando desde ahí muchas de las estrtegias de tareas y destrezas y sus efectos, con frecuencia negativos, apoyándose por el contrario en su proceso de construcción y viendo sus enores como mecanismos constructivos en la Zona de Desarrollo Próximo hacia formas progresivamente más elaboradas de escritura. El artículo, aún siguiendo el hilo expositivo de una investigación, ofrece un modelo alternativo claro para el diseño didáctico.     

The first steps for adapting an artificial intelligence emotion expression recognition software for emotional management in the educational context

To test the suitability of an automatic system for emotional management in the classroom following the control-value theory of achievement emotions (CVT) framework, the performance of an emotional expression recognition software of our creation is evaluated in an online synchronous context. Sixty students from the Faculty of Education at the University of Alicante participated in 16 educational activities recording close-ups of their faces and completing the AEQ emotional self-report, as well as detailed reports from the subsequent review of their videos. In addition, they completed the VCQ-36 test to measure their volitional competencies and relate their influence on their emotional response. The results indicate a high coherence between the emotional expressions detected by the automatic system and the detailed emotional self-reports, but insufficient precision to meet the CVT requirements. On the other hand, both the AEQ test results and the emotion expression recognition software suggest students' preference for participative activities as opposed to passive ones. Meanwhile, statistical analysis results indicate that volitional competencies seem to influence the emotional response of students in the educational context, although the AI system does not show sufficient sensitivity in this field. Implications and limitations of this study for future work are discussed.

Practitioner notes

What is already known about this topic

• Student motivation and involvement in the learning process are highly related to appropriate emotional regulation, which can be associated with particular educational activities, strategies and methodologies.
• Deep learning technology based on convolutional neural networks feeds automatic systems focused on facial expression recognition from image analysis.

What this paper adds

• There is high coherence between the emotional expressions detected by the AI system and the students' emotional self-reports, but the AI system provides just emotional valences, insufficient to meet the CVT framework.
• Both emotional self-reports and the emotion recognition software suggest students' preference for active educational activities as opposed to passive ones.
• Volitional competencies seem to influence the emotional response of students in the educational context.

Implications for practice and/or policy

• It is possible to use automatic systems to effectively monitor the emotional response of students in the learning process.
• Only if sensitivity improved, a real-time, easy-to-interpret emotional expression recognition software interface could be implemented to assist teachers with the emotional management of their classes within the CVT framework, maximizing their motivation and engagement.