COVID‐19 as the tipping point for integrating e‐assessment in higher education practices
Bibliographic record
Abstract
Abstract The COVID‐19 pandemic provoked an urgency for many educators to integrate digital information and communication technologies in their educational practices. We explored how faculty members tackled the task of adapting their assessment practices during the pandemic to identify what is required to sustain and favour future quality development and implementation of e‐assessment in higher education. Employing a qualitative descriptive approach, we conducted semi‐structured interviews with thirty‐one individuals six months into the COVID‐19 pandemic. We identified four major themes in participants' discourse about the integration of e‐assessment during the COVID‐19 pandemic: (a) the considerations they had for the potential consequences on students and how they considered this while deciding how to move forward, (b) the preoccupations for the potential for cheating, (c) the importance of pedagogical alignment, and (d) the affordances available to them. While the COVID‐19 pandemic highlighted the fact that higher education institutions were not prepared for a pivot to‐ or greater integration of‐ e‐assessment, it also provided the tipping‐point to do so. In other words, it offered an unprecedented opportunity to critically appraise and change assessment practices, this opportunity was also a very challenging balancing act of considering the social consequences of assessment, and the alignment within set affordances. Practitioner notes What is already known about this topic The advantages of e‐assessment include the opportunity for rapid‐, even immediate‐, feedback. The integration of digital information and communication technologies to assessment practices is a source of stress and anxiety for some students. The move towards e‐assessment increases faculty members' workload. What this paper adds How cheating, anxiety and workload were considered in the context of a forced and rushed change to e‐assessment because of the COVID‐19 pandemic. The COVID‐19 pandemic offered an opportunity to faculty members to take a critical look at their assessment practices and increase their alignment with learning outcomes. Faculty members considered the consequences of their decisions on students when making changes to their assessment. Implications for practice and/or policy Moving to e‐assessment is an opportunity to align the assessment practice with students' future professional practice, or in other words it is an opportunity to increase the authenticity of the assessment. A better integration of information and communication technology to assessment practices will require to provide educators with skills, time, evidence‐informed recommendations, and institutional support among others.
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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 | 0.058 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.028 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.003 | 0.034 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".