Towards a Live Anonymous Question Queue To Address Student Apprehension
Bibliographic record
Abstract
In today's university climate many first and second year classes have over a hundred students. Large classrooms make some students apprehensive about asking questions. An anonymous method of submitting questions to an instructor would allow students to ask their questions without feeling apprehensive. In this paper we propose a Live Anonymous Question Queue (LAQQ), a system that facilitates anonymous question submissions in real time to mitigate student apprehension, increase student participation, and provide real-time feedback to the instructor. To study the necessary features of an LAQQ, we conducted a study of a system, namely Google Moderator, which best approached our concept of an LAQQ. We deployed Google moderator in large lectures and studied its support of a number of features that we envisioned for an LAQQ. Through our class observations, interviews with instructors, and surveys with the students, our results suggest that an LAQQ system must provide support for: notification of question submission to provide awareness for the instructor, and context for questions to allow an instructor to easily answer a question. Additionally our results suggest that an LAQQ system must be accessible and usable on multiple platforms. Finally our results suggest that in order to be successful in the classroom an LAQQ system must be fully adopted by the instructor and the classroom organizational structure must change to accommodate the use of the LAQQ.
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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 | 0.051 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".