Academic boredom(s): a person-centered investigation.

Should we refer to boredom or boredoms? Research on the emotion of boredom sets itself apart from studies on other emotions by posing the question: is boredom a singular concept or does it have multiple facets? In this manuscript presenting empirical research on academic boredom, our aim is to demonstrate the justification for claiming a distinct position. Person-centered models examining university students' achievement emotions reveal the existence of multiple types of boredom, in contrast to other learning emotions that are typically represented as singular constructs. Using data generated by dispositional learning analytics applications, we further investigate the progression of learning boredom over time, exploring the impact of the pandemic and analyzing how various student learning aptitudes, such as mindsets, epistemological beliefs, epistemic emotions, learning motivation, engagement, as well as demographic factors like gender and culture, can be considered as potential antecedents or triggers of boredom. Consistent with the control-value theory of achievement emotions, we conclude that control and value constructs serve as proximal antecedents of boredom, alongside epistemic boredom as a distal antecedent. However, the relationships between boredom and its antecedents exhibit notable variations across different types of boredom.

Personality traits and academic performance: Correcting self-assessed traits with vignettes.

In this study, we investigate whether Conscientiousness, Emotional Stability and Risk Preference relate to student performance in higher education. We employ anchoring vignettes to correct for heterogeneous scale use in these non-cognitive skills. Our data are gathered among first-year students at a Dutch university. The results show that Conscientiousness is positively related to student performance, but the estimates are strongly biased upward if we use the uncorrected variables. We do not find significant relationships for Emotional Stability but find that the point estimates are larger when using the uncorrected variables. Measured Risk Preference is negatively related to student performance, yet this is fully explained by heterogeneous scale use. These results indicate the importance of using more objective measurements of personality traits.

Subjective data, objective data and the role of bias in predictive modelling: Lessons from a dispositional learning analytics application.

For decades, self-report measures based on questionnaires have been widely used in educational research to study implicit and complex constructs such as motivation, emotion, cognitive and metacognitive learning strategies. However, the existence of potential biases in such self-report instruments might cast doubts on the validity of the measured constructs. The emergence of trace data from digital learning environments has sparked a controversial debate on how we measure learning. On the one hand, trace data might be perceived as "objective" measures that are independent of any biases. On the other hand, there is mixed evidence of how trace data are compatible with existing learning constructs, which have traditionally been measured with self-reports. This study investigates the strengths and weaknesses of different types of data when designing predictive models of academic performance based on computer-generated trace data and survey data. We investigate two types of bias in self-report surveys: response styles (i.e., a tendency to use the rating scale in a certain systematic way that is unrelated to the content of the items) and overconfidence (i.e., the differences in predicted performance based on surveys' responses and a prior knowledge test). We found that the response style bias accounts for a modest to a substantial amount of variation in the outcomes of the several self-report instruments, as well as in the course performance data. It is only the trace data, notably that of process type, that stand out in being independent of these response style patterns. The effect of overconfidence bias is limited. Given that empirical models in education typically aim to explain the outcomes of learning processes or the relationships between antecedents of these learning outcomes, our analyses suggest that the bias present in surveys adds predictive power in the explanation of performance data and other questionnaire data.