Using Online Assessments in the Age of Covid-19: An Exploratory Study of Cognitive Load in Higher-Education Teachers
Bibliographic record
Abstract
Cognitive load is based on the principle that working memory is limited in its capacity to hold and store information. The demands of assessment on teachers; designing, implementing, and providing feedback to students, may create a strain on working memory. With the onset of the Covid-19 pandemic, post-secondary systems world-wide shifted teaching, learning and assessments to online platforms. Considering the sudden shift from in-person to online modes, this paper explores cognitive load in higher-education teachers who use online assessment in a pandemic context, to answer two research questions; What is the process of using online assessment among higher-education teachers? How does this process influence cognitive load? The study uses a case study approach to explore cognitive load in teachers, wherein the major case is higher-education assessment during the Covid-19 institutional closures, with each participant presenting a unique instance of the case. The study included a heterogenous sample of six post-secondary level teachers (n = 6) from a public university in Canada and used an online questionnaire, assessment artefacts and a semi-structured interview to address the two research questions proposed. The findings of this paper show the process of using online assessment as including experiences that tied to affect, moments that required support and moments that were high and/or low in terms of workload. There may not be a unidirectional influence on cognitive load. Rather, the load may also influence the process, potentially suggesting a relationship between the two that could build on each other until sufficient mental resources are acquired to address the load. The findings ultimately support the conclusion that regardless of new technologies or new features, cognitive resources were still potentially exhausted with how to use the technologies rather than what technologies were available to use.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".