How to Make Classes, Tutorials and Lab Manuals Stretch Further-A Look at Echo 360!
Bibliographic record
Abstract
Students today often find first year university to be a vastly different experience than high school. Lectures move at a frightening pace and chapters are finished before students have had a chance to open the book. Students are playing catch-up and hence tutorial sessions are often not as helpful as we might like them to be. If students have not absorbed the material from a previous chapter sufficiently to be able to do problems, they will likely not attend a tutorial on problem-solving for the next chapter. As instructors, if we fall behind, we feel as if we are disadvantaging students, yet if we go ahead, we are not helping them learn. How can we address this perennial dichotomy? One useful tool is Echo 360. It provides a method to record class sessions, tutorial sessions, and even allows for the upload of small sessions of 5-10 min. Echo 360 allows students to view or review material at their OWN pace and to study problem-solving when they are ready to do so. It also allows instructors to provide just in time teaching (JITT) sessions to students on an as-needed basis. In this session, I will discuss some of the ways Echo 360 has been used at the University of Ottawa in general, and in particular in teaching introductory chemistry. The session will be interactive with plenty of occasion for discussion and questions.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.158 | 0.213 |
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".