Unpacking 'the Next Black Box': Investigating the Cognitive and Affective Underpinnings of Student Self-Assessment
Bibliographic record
Abstract
Despite theoretical and empirical arguments for its essential role in learning, and its consequent centrality in classroom assessment frameworks and policy globally, little is known about student self-assessment’s internal cognitive and affective processes. Left without a theory to inform teachers in supporting productive student self-assessment (SSA), many students will never learn to be sources of their own feedback, the foundation for independent, lifelong learning. This research responds to calls to examine how students in K-12 contexts think and feel while engaged in SSA tasks that are supported by literature – the next black box of classroom assessment research. First, I synthesized relevant self-assessment literature within a highly granular self-regulated learning (SRL) model. Drawing on this theoretical foundation, I employed a collective case study using digital trace data to infer the ways in which a class of Year 12 students (n=16) in a UK secondary school thought and felt during an evidence informed self-assessment activity. Matomo, a web analytics platform, collected session recording and heatmap data which elucidated participants’ cognitive and affective operations as they completed a writing task, self-assessed their work using exemplars and rubrics, and revised their writing accordingly. I analyzed log files of trace data to a) infer which SRL subprocesses participants activate, b) generate self-assessment process graphs and profiles for each participant, and c) investigate how participants engaged in each process based on the content of their work. Findings highlight the recursive and weakly sequenced nature of SSA processes, as well key cognitive and affective trends. Moreover, SSA profiles for each participant demonstrate how trace data and learning analytics can support advances in student learning through SSA. Forming the basis for an initial theory of SSA cognition and affect, this research advances SSA theory, a core component of classroom assessment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".