Towards a model for the assessment of student boredom and boredom proneness in the UK higher education context

Abstract

Recently identified as an academic ‘achievement emotion’, boredom has long been implicated as a factor contributing adversely to student attainment across a diverse range of formal educational settings. Despite this, the study of boredom, particularly among students in higher education, remains a relatively neglected and underdeveloped field. In this article, and following a systematic review of the research literature, we present details of a new research instrument and diagnostic tool derived from Farmer and Sundberg’s Boredom Proneness Scale (BPS), specifically intended to assess or measure the recurring propensity or habitual disposition of students to becoming bored particularly within the UK higher education context (e.g. further education, university college and university provision). Referred to here as the BPS-UKHE, to distinguish it from its predecessor, the statistical validity and reliability as well as educational relevance and meaningfulness of the BPS-UKHE is established, confirming its multidimensional nature with subscales (Tedium, Time, Challenge, Concentration and Patience) reflecting boredom’s acknowledged cognitive, affective, motivational and behavioural components. Full-scale, five-factor and three-factor ‘short-form’ solutions are offered, the immediate utility of which in identifying students more prone to boredom than others and gauging emotional response is demonstrated. The BPS-UKHE has application in many areas and is considered a valuable tool in relation to the UK student engagement agenda, its scales and potential, as well as theoretical underpinning, available for empirical use and critical comment.

Keywords:

• boredom
• boredom proneness
• academic trait boredom
• achievement emotion
• higher education

Acknowledgements

The authors are particularly grateful to Professor Stephen J. Vodanovich, an international authority in the field of boredom and boredom proneness, School of Psychological and Behavioral Sciences, University of West Florida, for his assistance in directing us towards relevant materials which informed the preparation of this article.

Notes

1. RMSEA (Root Mean Square Error of Approximation) is typically used to measure model fit (see, for example, Byrne 2001; Hu and Bentler 1999).

2. CFI (Comparative Fit Index), TLI (Tucker-Lewis Index) and p are commonly used with RMSEA as criteria for evaluating model fit (see, for example, Bentler 1990; Byrne 2001).