Exploring a Rural English Teacher’s Lived Experiences of Assessment Practices in a Blended Learning Enactment: A Narrative Inquiry
Bibliographic record
Abstract
Although a large number of studies have put a focus on the enactment of blended learning in English as a foreign language (EFL) classroom, there is a paucity of research into the teacher’s lived experiences of how they enact assessment in the blended learning activities. To fill such a gap, this paper reports on a narrative inquiry of an EFL teacher’s lived experiences of conducting assessment during blended learning in the pandemic era. The finding of the study shed light on the ineffectiveness of the assessment practice during the blended learning enactment, particularly in the context of rural schools. Albeit the participating teacher in this study was fully engaged to conduct assessment from his past experiences, two major problems hinder such a practice: students’ unsubmitted assignments and poor Internet connection. Based on these findings, teachers are encouraged to find an alternative assessment practice during the blended learning, portfolio assessment can be an option. This suggestion is anchored by the fact that the assessment practice was not technically supported during the blended learning activities.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".