Student-Centered Online Assessment in Foreign Language Classes
Bibliographic record
Abstract
In 2020 educational institutions in many countries had to implement online learning due to quarantine restrictions caused by the coronavirus pandemic. The research aims to study the peculiarities of the distance learning technologies used by Ukrainian foreign language teachers for formative and summative assessment and their impact on students. At the end of 2020, a survey about online resources used for creating different types of tasks for foreign language classes in Ukraine was conducted by the authors of the study, and the main characteristics of the most popular online resources were analyzed. According to the survey, to create assessment tasks the majority of Ukrainian teachers use the following platforms: Kahoot, Google Forms, Quizlet, Classtime, Quizizz, Socrative, Quizalize, Gimkit, Blooket, Liveworksheets, and Wizerme. Some of these resources provide a strong element of competition that makes them perfect for formative assessment, while the others better suit summative assessment as they have a clear interface without distracting elements. When designing online tasks for the assessment, teachers should also take into consideration the possibilities online resources provide to reduce their students’ anxiety and stress caused by performing test tasks.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".