Claims

Overconfidence in self-assessment is especially concerning in online learning environments where limited external feedback widens gaps between perceived and actual performance

Claim · All claims
Evidence · 1 study · associational r? · q1
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person

Subclaims

Reading the evidence codes

q is the design tier, i the impact, n the sample, and the kind with its r the rigour judged within that kind: what each code means.

This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.

q1 i? The challenges students face in self-assessing their understanding are particularly pronounced in online learning environments where limited external feedback can exacerbate calibration discrepancy. → HaeJin Lee · Nigel Bosch 2025

Evidence

HaeJin Lee · Nigel Bosch 2025

HaeJin Lee · Nigel Bosch. (2025). Calibration Discrepancy Predicts Students’ Subsequent Metacognitive Strategy Use in Computer-based Learning Environments. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-025-00514-5

q1 · i? · associational · r?

Theoretical argument in the article's review: "limited external feedback can exacerbate the issue", because online learners miss real-time interactions with instructors and peers that are crucial for evaluating performance. The article offers this as literature-grounded motivation, citing Bringula et al. (2021).

"The challenges students face in self-assessing their understanding are particu - larly pronounced in online learning environments (Bringula et al., 2021), where lim - ited external feedback can exacerbate the issue"

Discussion

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