Academic Boredom in University Students

The study explored the relationship between academic boredom and course experiences among 179 final-year Education Studies students at a single university in England. A mixed-methods approach was employed, combining quantitative data from questionnaires and qualitative data from individual research interviews. This design allowed for a comprehensive investigation into the multifaceted nature of academic boredom and its impact on student learning. The quantitative data collection involved questionnaires designed to measure academic boredom, learning strategies, and course experiences. The qualitative data, gathered through interviews, provided in-depth insights into student perspectives and experiences, complementing the quantitative findings. The use of both methods ensured a thorough and nuanced understanding of the research problem, providing a more comprehensive picture than either method could offer alone. The student sample was comprised of 179 students in their final year of study. Of these, 41 (22.9%) were male, and 138 (77.1%) were female, reflecting the typical gender ratio of the cohort. The average age of the participants was 24.4 years, with most (66.5%) being first-generation university students. This demographic information provided context for understanding the participants' backgrounds and potential influences on their experiences.

Academic boredom was measured using two key instruments: the Boredom Proneness Scale – UK Higher Education version (BPS-UKHE) and the Shortened Experiences of Teaching and Learning Questionnaire (SETLQ). The BPS-UKHE questionnaire assessed the students' general propensity toward experiencing academic boredom, generating scores ranging from 20 to 74 (with a mean of 43.3 and standard deviation of 9.57). Scores were subsequently rescaled to a 5-point scale (1.1 to 4.1, mean 2.40, SD = 0.532) for easier comparison with other questionnaire variables. Analysis of the BPS-UKHE subscales indicated that respondents were most susceptible to boredom related to tedium. Specific items within the questionnaire revealed that a substantial portion of students found many aspects of university life monotonous and repetitive. For example, 59.8% of respondents reported finding many things they had to do monotonous and repetitive, and even more concerning, 24.6% considered almost everything at university tiresome. The study notes that while almost everyone experiences boredom at some point, the persistent and pervasive nature of academic boredom warrants detailed consideration due to its potential implications for student success. The BPS-UKHE's unique ability to quantify an individual's predisposition towards boredom is crucial to the study's methodology.

Qualitative data were collected through ten semi-structured interviews. Participants were selected to maximize response differentiation; they were chosen based on their BPS-UKHE scores (mean +/- 1 standard deviation). This ensured a balanced representation of high and low boredom proneness levels, adding richness to the quantitative data. Interviews explored various aspects of students' course experiences, focusing on their perceptions of course demands, time allocation for studies, and coping mechanisms used to deal with boredom. The interviews were transcribed and analyzed thematically using content analysis. This involved a combination of first and second-order coding that focused on identifying recurring themes and emotions associated with academic boredom. Interview transcripts revealed several significant insights including the impact of lecture boredom on concentration levels and the coping strategies adopted by students. The qualitative data provided a deeper understanding of student experiences and complemented the quantitative findings, offering a more holistic understanding of the complex relationships under investigation. The insights gained from these interviews enriched the study by adding a layer of nuance and personal experience to the statistical analysis.

A key finding of the research is the significant correlation between levels of academic boredom and the learning strategies employed by students. Students who reported higher levels of boredom (as measured by the BPS-UKHE questionnaire) demonstrated a greater tendency to utilize surface-level learning strategies. This contrasts with students who experienced less boredom, who were more inclined towards deep learning approaches. This relationship highlights the detrimental effect that boredom can have on students' ability to engage in effective and meaningful learning. Students experiencing significant boredom may be less likely to actively process information, make connections between concepts, or critically evaluate materials, instead opting for more superficial methods of engagement. This preference for surface learning strategies is likely a coping mechanism in response to the perceived lack of interest or engagement with the course material, further reinforcing the cyclical nature of boredom and its impact on academic outcomes. The study emphasizes that these surface strategies, while potentially providing short-term relief from boredom, ultimately hinder long-term learning and comprehension. This connection between boredom and learning style underscores the importance of addressing boredom to foster effective learning practices.

The study establishes a clear link between academic boredom and academic performance, specifically focusing on final-year degree outcomes. A negative correlation was observed between reported boredom levels and final grades. Students with higher levels of trait boredom (as measured using the BPS-UKHE) tended to receive lower marks. This inverse relationship confirms the damaging influence of academic boredom on overall academic achievement. The research goes on to explain that final-year degree marks ranged from 43% to 80%, with a mean of 60.6%. This spread of scores provides a broad range of performance levels within the study cohort and allows for a clearer correlation with reported levels of academic boredom. The distribution of degree classifications (13.4% first-class, 48.6% upper second-class, 32.4% lower second-class, and 5.6% third-class) also supports this finding. The systematic decrease in average marks across the clusters identified through cluster analysis further reinforces the connection between boredom, learning strategies, and academic outcomes. These findings underline the importance of addressing academic boredom to improve student success and overall academic performance.

To further investigate the relationship between academic boredom and other variables, a hierarchical cluster analysis was conducted. This statistical method grouped the students into five distinct clusters (C1-C5) based on their scores on the BPS-UKHE, and measures of their learning approaches and organized effort. This allowed for a detailed examination of different student profiles based on their levels of boredom, learning strategies, and academic performance. Each cluster presented a distinct profile. For instance, Cluster 1 (C1) comprised high-achieving students with low boredom scores and a preference for deep learning approaches. In contrast, Cluster 5 (C5) contained students experiencing high levels of boredom, favoring surface-level learning, and demonstrating lower academic achievement. The significant differences between clusters (ANOVA F=65.351, df=4,174 p<.001; ɳ2 =.600) validated the clustering strategy. The cluster analysis provides valuable insights into the diverse ways in which academic boredom manifests and impacts students, highlighting the need for tailored interventions to support students with varying needs and profiles. The significant contrast between clusters also illustrates that different student groups may react to boredom differently and that targeted support strategies may be crucial for optimizing their academic journey.

The study examined student engagement levels in different teaching formats, revealing a significant disparity between engagement in lectures and tutorials. A substantial portion (70.4%) of respondents reported being engaged in tutorials most or all of the time. This high engagement rate is likely attributed to the interactive nature of tutorials, allowing for more direct interaction with instructors and peers. In contrast, a much smaller percentage (41.3%) of respondents felt engaged during lectures. The low engagement in lectures is linked to various factors reported by the students, notably the overuse of PowerPoint presentations and a lack of interaction. Student comments highlighted the monotony of lectures where lecturers simply dictated from PowerPoint slides, lacking enthusiasm and leading to disengagement and boredom. Coping mechanisms employed by students during lectures varied, ranging from daydreaming and texting to doodling. The data shows a stark difference in engagement levels between lecture and tutorial settings, highlighting the impact of teaching style and interaction opportunities on student experience and engagement with course materials. The significant difference in engagement (ANOVA F=3.611, df=4,174 p<.01; ɳ2 =.077) between clusters further emphasizes this impact.

The research also investigated student perceptions of course demands and their relationship to student engagement and boredom. While a large majority (79.9%) of students found the use of information technology straightforward, a significantly smaller number (27.4%) felt the same about the overall workload. This suggests that the perceived workload was a significant source of stress for many students, impacting their engagement with their studies. Interview data revealed that course demands were often linked to achievement motivation and yearly progression, with students feeling more pressure as their studies advanced. The discrepancy between positive perceptions of technological aspects of the course and negative perceptions of workload suggests a potential area of course improvement. Students reported that this demanding workload had a significant impact on their ability to manage their time effectively, potentially exacerbating feelings of stress and contributing to academic boredom. The finding that workload received the lowest overall scores suggests this is a significant factor influencing the student experience and should be an area of focus for course improvement.

The degree of student choice and autonomy over their learning was explored, revealing a potential conflict between course structure and student satisfaction. A relatively low percentage (54.7%) of students agreed that they were given sufficient choice in what to focus on during learning. This indicates a constraint on individual learning styles and preferences. Interviews provided insights into the impact of this limited choice on student experience, revealing a sense of self-fulfillment conflict particularly during assignments. Students valued having freedom and choice in their final year projects where they felt productive and creative. This highlights the importance of balancing structured learning activities with opportunities for student autonomy and choice to enhance engagement and mitigate boredom. The study points toward the potential benefits of giving students greater control over their learning journey to improve their overall experience and engagement with their studies. Providing increased choice, where possible, could lead to improved motivation and reduced academic boredom. This is a crucial consideration in promoting more positive student experiences.

A path analysis was conducted to examine the relationships between academic boredom, learning approaches, and academic achievement. The analysis, rooted in Control-Value Theory (C-VT), revealed complex interconnections. Academic boredom emerged as a significant predictor of learning strategies and overall academic performance. Specifically, trait boredom negatively predicted organized effort and positively predicted surface approaches to learning. The path coefficient for the direct effect of academic boredom on organized effort was -.40, while the path coefficient for the direct effect on surface approaches was .49. This indicates that students prone to boredom are significantly less likely to engage in organized study habits and more likely to adopt superficial learning methods. The model as a whole accounted for 19% of the variance in final-year degree outcome, highlighting the substantial impact of academic boredom on academic success. Though the model doesn't definitively prove causality, the strong correlations suggest a reciprocal and reinforcing relationship between boredom and academic outcomes. While more complex models remain possible, the findings strongly indicate the need to address academic boredom to improve learning outcomes.

The study's findings have significant implications for higher education practices and interventions aimed at enhancing student learning and well-being. The strong correlation between academic boredom and negative learning outcomes highlights the need for pedagogical approaches that actively engage students and minimize the incidence of boredom. The research suggests that strategies to improve student engagement and reduce academic boredom should involve a multifaceted approach including improving lecture delivery methods and course design, promoting deeper learning approaches, and addressing factors contributing to students' perceptions of high workloads. The research clearly points to the need for further study into interventions focused on mitigating the effects of academic boredom, such as attribution retraining and goal-setting techniques. The insights of the research also provide opportunities for learning developers to better address academic boredom by promoting metacognitive awareness and encouraging students to take greater responsibility for their learning. Institutions can implement interventions to help students develop better study habits, manage time more effectively, and adopt strategies to maintain focus and motivation. This includes strategies to mitigate assignment boredom, such as providing students greater choice and autonomy. The study makes a strong case for a proactive approach to managing academic boredom to enhance student success.

While the study provides valuable insights, several limitations are acknowledged, particularly the exploratory nature of the research and the sample size. The cross-sectional design limits the ability to establish definitive causal relationships between variables. The reliance on self-reported data raises potential concerns regarding social desirability bias and response accuracy. The study acknowledges the limitations imposed by the sample size, which restricted more complex modeling such as structural equation modeling. This highlights the need for further research with larger, more diverse samples. Future studies could employ longitudinal designs to better understand the development of academic boredom and its impact on students over time. Future research should address these limitations by using larger samples, implementing longitudinal studies, and exploring different data collection methods to strengthen findings and increase the generalizability of results. The identification of distinct student clusters also suggests a need for more targeted interventions tailored to specific student groups. This focused approach may be more effective in addressing the diverse ways in which students experience and respond to academic boredom.

The research acknowledges several limitations that affect the generalizability and interpretive scope of its findings. Firstly, the study's design is largely exploratory and inductive, rather than explanatory and deductive, meaning it focuses on describing relationships rather than definitively establishing cause and effect. The relatively small sample size (179 students) limits the statistical power and generalizability of the results to a wider population of university students. The cross-sectional nature of the data collection means that causal inferences cannot be definitively made, as all variables were measured simultaneously. There's a potential for various biases to influence the results, such as social desirability bias, where participants might respond in ways they perceive as socially acceptable, rather than truthfully. Furthermore, questionnaire fatigue may have led to lower response rates in later stages of data collection, potentially impacting the representativeness of the sample, especially in Cluster 5. The reliance on retrospective self-reporting in both questionnaires and interviews introduces the possibility of recall bias and subjective interpretations of experiences. These limitations highlight the need for future research to address these methodological challenges to strengthen the study's conclusions.

The study concludes by proposing several avenues for future research to build upon and extend the current findings. Given the limitations, particularly the sample size and cross-sectional design, longitudinal studies are recommended. These studies should track the same individuals over time to examine the development of academic boredom and its long-term effects on academic performance. The identification of distinct student clusters (C1-C5) suggests a need for future research focused on those specific clusters. This targeted approach would allow for more in-depth investigations into the unique characteristics and experiences within each cluster, leading to more effective and tailored interventions. Employing different instruments and analytical techniques would improve the robustness of the research. Experimental studies, manipulating specific teaching and learning conditions to investigate their impact on academic boredom, would provide further insights. Furthermore, exploring the influence of contextual factors, such as the characteristics of individual lecturers, should be addressed in future studies. Investigating these areas will help to refine existing interventions and develop effective strategies to combat academic boredom in higher education.