Using Self-Assessment as a Tool for English Language Learning
Bibliographic record
Abstract
A very important element of formative assessment is giving and receiving feedback. However, most teachers face difficulty in giving students feedback due to various reasons, such as the large number of students in class that makes it time consuming for them to do so. Fortunately, students themselves can be excellent sources of feedback through self-assessment, through which the students would reflect on the quality of their work, judge the degree to which their work reflects explicitly stated goals or criteria, and revise their work if necessary. Under the right conditions, student self-assessment can provide accurate, useful information to promote learning. Self-assessment can also be effective in English language learning, such as: motivating students to learn and reflect on their own English learning; promote critical thinking and reflective practices in learning English; scaffold knowledge of English learning from different English language skills; develop a sense of autonomy in their own learning English; and foster commitment in learning English among many others. This conceptual paper thus seeks to explore the potentials of using self-assessment in English language learning. In this paper, the concept and underlying principles of self-assessment will be introduced. Next, the review of past studies on self-assessment in the context of teaching and learning English as a second or English as a foreign language (ESL/EFL) will be explained. Later, the advantages and disadvantages of using self-assessment in the classroom will be discussed. In the final section, recommendations will be given for the implementation of self-assessment in learning English as a second language (ESL) classrooms.
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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 | 0.036 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".