Student engagement and wellbeing over time at a higher education institution
Autoři:
Chris A. Boulton aff001; Emily Hughes aff002; Carmel Kent aff001; Joanne R. Smith aff002; Hywel T. P. Williams aff001
Působiště autorů:
Computer Science, University of Exeter, Exeter, United Kingdom
aff001; School of Psychology, University of Exeter, Exeter, United Kingdom
aff002
Vyšlo v časopise:
PLoS ONE 14(11)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0225770
Souhrn
Student engagement is an important factor for learning outcomes in higher education. Engagement with learning at campus-based higher education institutions is difficult to quantify due to the variety of forms that engagement might take (e.g. lecture attendance, self-study, usage of online/digital systems). Meanwhile, there are increasing concerns about student wellbeing within higher education, but the relationship between engagement and wellbeing is not well understood. Here we analyse results from a longitudinal survey of undergraduate students at a campus-based university in the UK, aiming to understand how engagement and wellbeing vary dynamically during an academic term. The survey included multiple dimensions of student engagement and wellbeing, with a deliberate focus on self-report measures to capture students’ subjective experience. The results show a wide range of engagement with different systems and study activities, giving a broad view of student learning behaviour over time. Engagement and wellbeing vary during the term, with clear behavioural changes caused by assessments. Results indicate a positive interaction between engagement and happiness, with an unexpected negative relationship between engagement and academic outcomes. This study provides important insights into subjective aspects of the student experience and provides a contrast to the increasing focus on analysing educational processes using digital records.
Klíčová slova:
Behavior – Happiness – Human learning – Learning – Lectures – Motivation – Surveys – Teaching methods
Zdroje
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PLOS One
2019 Číslo 11
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