A Short Review of Online Learning Assessment Strategies
Bibliographic record
Abstract
The COVID-19 pandemic has caused a paradigm shift in how teachers, instructors and students approach teaching and learning, especially concerning the migration to online learning environments. One of the most challenging aspects of adapting to online/virtual education is evaluating students’ knowledge acquisition through learning assessments. The lack of face-to-face proctoring renders many of the traditional paper-based assessment techniques impractical, especially in the context of an engineering education that is heavily focused on applied learning. Since virtual education now represents an important evolution in education, it is pertinent for educators to familiarise themselves with the new possibilities of assessment methods in a virtual setting and to design tailored assessment strategies for individual courses. This article reviews and summarises commonly employed virtual assessment methods that are applicable to most engineering educational situations, such as open-book exams, online quizzes, or peer assessments. The paper also discusses some concerns that may arise in implementing these methods. Additionally, there is a particular focus on qualitatively-graded ePortfolios as a unique pedagogical tool in the virtual classroom due to their role as both a repository for storing learning artifacts and a vehicle for advancing students’ learning experience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".