Combining flipped class sessions with traditional lectures in a non-computational upper level economic geology class
Bibliographic record
Abstract
Flipped classrooms have been shown to be useful in both introductory and advanced computational Earth Science courses. However, to date they have not been implemented in advanced level non-computational courses. Here we assess a three-year study into the use of flipped classroom techniques in a fourth year undergraduate Mineral Deposits class. One to two flipped classrooms were used to teach porphyry deposits, iron oxide copper gold and/or volcanogenic massive sulfide deposits. The effectiveness of the technique was assessed using a combination of student feedback forms, comparison of students’ ability to answer exam questions from topics taught by flipped classroom versus other topics taught by traditional methods, and interviews with students 6 to 36 months after completion of the course. The students’ ability to answer lecture exam questions was slightly higher in topics taught as flipped classrooms compared to traditional techniques. Whereas the students’ ability to answer laboratory exam questions was slightly lower between topics taught as flipped classrooms. However, most students did think that the flipped classrooms were useful and aided in their learning of the material. The use of video lectures was particularly appreciated by some who found that it increased their flexibility and ability to absorb the material at their own pace. As such we determined that flipped classrooms are an effective technique for teaching upper-level non-computational Earth Science courses and they increased behavioral, emotional, and cognitive engagement.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".