Supplementing forestry field instruction with video and online dynamic quizzing
Bibliographic record
Abstract
Abstract Plant identification is a critical skill for students in biological sciences, especially forestry. Many students begin with limited plant identification abilities and struggle to learn this skill. To support student learning of identification and ecological characteristics of important forest plants in an undergraduate forest ecology course at the University of British Columbia, I developed 53 videos, a companion website, and a dynamic quizzing system. The professionally produced, short videos each featured identification and ecological characteristics of a plant species, filmed in the field. The companion information website contained the embedded videos, botanical drawings, photographs, and general information for each species. The online, dynamic practice‐quizzing system allowed students to select which species they wanted to be quizzed on. Questions about those plants were then dynamically generated following several question templates, enabling students to take many practice quizzes with few or no repeated questions. Students were surveyed to gain insight into usage patterns and opinions of these resources. Student feedback was positive, and all three resources were heavily used. The videos are publicly available and have more than 43,000 views. Although this project required significant time and financial resources to produce, I found that field instruction can be supported by optional online resources that are both appreciated and heavily used by students.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.060 | 0.014 |
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".