The "Dunning-Kruger effect" refers to the tendency of poor performers to overestimate test outcomes. Although a widespread phenomenon, questions exist regarding its source and sensitivity to countermeasures. The present field study aimed to (a) examine whether practice with tests used in previous classes can enhance students' ability to estimate test outcomes, (b) determine the main source of the effect (i.e., is it unawareness of one's readiness or wishful thinking?), and (c) assess the extent to which particular individual differences can be used as predictors of test performance. In this study, participants practiced with old tests and then completed the final exam. Before and after the exam, they predicted their grades and indicated their subjective confidence in the predictions made. Furthermore, participants' emotional intelligence and self-efficacy about their academic abilities were surveyed. Results suggested that poor performers were not unaware of their test preparation, but rather engaged in wishful thinking. In fact, although they overestimated their test grades, their estimates not only improved after completing the final test but also were regarded with little confidence. Overall, estimation bias was a good predictor of students' final test performance, whereas subjective confidence and emotional intelligence only weakly predicted such performance. Thus, if proactive interventions are to be developed for at-risk students, performance-estimation tasks may offer valuable information regarding such students' future performance in a course much more than emotional intelligence and self-efficacy measures.
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http://dx.doi.org/10.3390/bs13030275 | DOI Listing |
Do machines and humans process language in similar ways? Recent research has hinted at the affirmative, showing that human neural activity can be effectively predicted using the internal representations of language models (LMs). Although such results are thought to reflect shared computational principles between LMs and human brains, there are also clear differences in how LMs and humans represent and use language. In this work, we systematically explore the divergences between human and machine language processing by examining the differences between LM representations and human brain responses to language as measured by Magnetoencephalography (MEG) across two datasets in which subjects read and listened to narrative stories.
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Methods: This was a prospective study.
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