How Korean Language Arts Teachers Adopt and Adapt Open Educational Resources: A Study of Teachers’ and Students’ Perspectives
Bibliographic record
Abstract
Since 2005, open educational resources (OER) have played a key role in K-12 education in South Korea; so far, however, there has been little discussion about OER efficacy in South Korean K-12 education. In the meantime, South Korean education has been attracting a lot of interest around the world. Former U.S. President Obama’s comments about South Korean education might also be caused by South Korean students’ academic performance evaluated by international large-scale assessments such as the Program for International Student Assessment (PISA). This article uses an ethnographic perspective to explore the experiences of teachers and students in the Korean context. The analysis of the findings shows how teachers adopt and adapt OER for their 12th grade (the final year of secondary school) Korean language arts classes. Through classroom observations, interviews, and questionnaires, this exploration revealed that nearly 92% of the students perceived OER as beneficial to their studies and that teachers were spurred on to orchestrate differentiated instructional plans by OER. We argue that there is significant value to using OER in the formal educational curriculum, but that a lack of knowledge of how to adapt OER restricts how their potential is realized in practice. We identify implications for maximizing OER adaptation and successful usage of OER in K-12 education.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".