A New Dilemma for Language Teachers and Students: Self-assessment or Teacher Assessment (Research Article)
Bibliographic record
Abstract
Transitioning smoothly from traditional learning of language to independent learning and consequently, moving from teacher-assessment to self-assessment faces teachers with a dilemma of deciding on learners’ final improvement. To assist to eliminate this dilemma and to compare learners’ self-assessment of reading comprehension skills with those of teacher assessment, the present study was set out. To this end, 190 B.S. Iranian engineering students were selected based on intact classes. The participants’ proficiency was determined by the Oxford Quick Placement Test. Prior to the instruction, the participants’ ability to use two reading skills, i.e. scanning and skimming was assessed by their instructor and by themselves through using a Likert Scale questionnaire. After instructing each skill, the participants received post-tests, both self-assessment and teacher assessment. Following the post-self-assessment, the participants answered an open-ended questionnaire to reflect on their assessment. To analyze the data and understand the differences and correlations between the two types of assessments, SPSS was performed. Intriguingly, the results from self- and teacher-assessment were pro-self-assessment. Besides, the outcomes of the open-ended questionnaire indicated that it is time to trust learners and allow them to assess their own learning and decide on their learning process.
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 | 0.135 | 0.170 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".