The Effects of Rubrics and Exemplars as Self-regulated Learning Tools on Students’ Development of Evaluative Judgement of Intercultural Competence: An Exploratory Study
Bibliographic record
Abstract
This study aimed to explore the effects of a pedagogical intervention involving the combined application of rubrics and exemplars, as Self-Regulated Learning (SRL) support tools, on student’s development of the Evaluative Judgement (EJ) of Intercultural Competence (IC). A group of students from the General Required Courses department of a Saudi college participated in a quasi-experimental study involving the administration of an English writing module as part of an intensive English course. Coursework was designed based on Byram’s multidimensional IC model, guided by Liddicoat’s pathway model for IC development, framed within Panadero & Broadbent’s EJ framework, and mapped on Zimmerman’s Self-Reflection Phase (SRP) of the self-regulatory process. A mixed-method design was adopted to gain insights on the learning processes the students went through as well as their perceptions of and attitudes towards the learning experience. The results indicated a variety of SRP strategies at work and provided evidence for the positive effects of rubrics and exemplars on the enhancement of students’ EJ of IC. The results revealed several spheres where SRL, EJ, and IC relate and align to support students’ development and practice of sustainable skills that allow them to take control of their own learning and adapt in ever-changing environments.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".