Delivering forestry courses online: experiences, lessons learned, and future of forestry online education in the Asia Pacific
Bibliographic record
Abstract
Innovation in forestry education is needed to address changing contexts of the positionality of forests. This is particularly significant in the Asia-Pacific region, where deforestation and degradation are high. However, the accessibility of high-quality forestry education to address changing regional and global contexts is lacking. A series of innovative sustainable forest management (SFM) open education resource (OER) courses were developed and implemented to improve the accessibility of SFM education to enhance teaching quality, curriculum, and research capacity of universities in the Asia-Pacific Region. To evaluate the SFM-OER program in terms of student experiences, this study investigated student achievement, perceived success of the pedagogical approach and instructional design, and perceived effectiveness of the learning activities in promoting active and transformative learning through the assessment of a 1,191-course feedback survey between 2018 and 2020, including the global pandemic. This study revealed that the program attracted diverse student demographics, including a higher proportion of female students majoring in forestry, ecology, and other environmental studies. Their primary motivation to participate in the courses was to gain international experience, followed by the flexibility of online learning, mandatory course requirements, and earning course credits. Students were satisfied with the Canvas learning management system. Most students spent less than 5 to 10 h of their weekly time in the course and agreed or strongly agreed that the workloads were manageable. Students reflected positively on various learning activities and assignments, such as watching lecture videos, taking quizzes, reading and summarizing, having discussions, and peer review writing. However, they did not clearly prefer specific learning activities, signifying the importance of using diverse learning activities to satisfy diverse individual learning styles in online settings. This analysis contributes to the further development of student-centered pedagogical development for online learning and provides insight into the ways forward for online higher forestry education, while repurposing existing OER courses in a post-Covid-19 era.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".