Self-Report is Indispensable to Assess Students’ Learning

Abstract

Self-report is required to assess mental states in nuanced ways. By implication, self-report is indispensable to capture the psychological processes driving human learning, such as learners' emotions, motivation, strategy use, and metacognition. As shown in the contributions to this special issue, self-report related to learning shows convergent and predictive validity, and there are ways to further strengthen its power. However, self-report is limited to assess conscious contents, lacks temporal resolution, and is subject to response sets and memory biases. As such, it needs to be complemented by alternative measures. Future research on self-report should consider not only closed-response quantitative measures but also alternative self-report methodologies, make use of within-person analysis, and investigate the impact of respondents' emotions on processes and outcomes of self-report assessments.

The Promise and Pitfalls of Self-report

“…Not only do these empirical pieces suggest lateral development is possible, each starts to take us down this journey and provides evidence that these journeys are likely to be fruitful. From empirical multimethod studies (e.g., van Halen et al, 2020;Rogiers, et al 2020) to the theoretically-rich commentaries (Pekrun, 2020;van Meter, 2020;Winne, 2020;), this Special Issue suggests that self-report measures are a unique, valuable -and therefore irreplaceable -source of information about many critical aspects of the learning processes under study here. Clearly, the conclusions drawn from these analyses show that self-report remains critical in our understanding of learning and that educational researchers need to push harder for constant lateral innovation such as these.…”

“…In turn, the better participants are at responding to these types of measurements, the better the interpretations of these data will be. Pekrun (2020) makes the case for the importance of self-report data, and like Winne touches on what they are. Pekrun extends this discussion by focusing primarily on how to improve the validity of the score interpretations of self-report data.…”

“…While each of this Special Issue's articles addresses this question in some form, the syntheses of the three commentaries address it best. All three of these commentaries point out the need to clearly understand the core construct (e.g., Pekrun, 2020), the measurement itself (Winne, 2020), and the conditional nature of their use (Van Meter, 2020). It is critical that the interpretations of self-report data are situated within theoretical frameworks of the core constructs that are being measured.…”

The Differences and Similarities between Curiosity and Interest: Meta-analysis and Network Analyses

“…This can be done by studying the subjective feelings/experience of the two constructs at the momentary level. The subjective feelings of a construct serve as the first gate to the mental state of it (Pekrun, 2020), and the assessment is relatively straightforward and uncontroversial: We can simply ask participants' feelings of curiosity and interest at a specific moment. Although this is just one aspect of the broad concepts of curiosity and interest, this is a way in which we can empirically examine the potential similarities and differences of curiosity and interest.…”

“…First, most studies in the meta-analysis and our Study 2 and 3 assessed curiosity and interest using self-reported questionnaires. Although selfreport may be an indispensable way to access emotional state, affect evaluation, or cognitive appraisal (Pekrun, 2020), there are some inherited problems with self-report measures such as bias or inaccuracy. It is expected that in the future, behavioral as well as neuroscientific research would provide triangulation for self-reports (Hidi & Renninger, 2019).…”