TURNING A NEGATIVE INTO A POSITIVE WITH MODERN ELECTRONIC TECHNOLOGIES
Bibliographic record
Abstract
In today’s society, electronic devicesrepresent an important part of the cultural fabric, especiallyfor the younger generation. Professors and lecturersteaching at post-secondary levels express concern whenstudents appear to be more interested in electronic devicesthan the content being presented in the classroom. Thesedevices permit the student to locate small snippets of dataand information leaving them largely incapable ofintegrating this data into comprehensive concepts. Anotherdrawback of using modern electronic devices in theclassroom relates to their misuse during examinations. Theargument from an educator’s perspective is: suppressionversus celebration, as electronic devices assume aubiquitous place and role in the classroom. Unable toeffectively compete with the apparent entertainment valueof iPods, iPads, cell phones and laptops, educators andpolicy makers enact a range of measures designed toenforce student engagement during class time. Institutionalresponses to the use of electronic devices include: policylanguage enshrined in course outlines, bans on suchdevices and confiscation of offending devices. Conversely,given that every conceivable subject is available via theinternet, these electronic tools are effective in ‘bringing theworld into the classroom’. This paper explores and presentsactionable strategies that educators can employ to leveragethe power of electronic devices in stimulating students’participation during classroom delivery. Based onliterature search, personal experience and field interviews,suggested best practices are advanced for the enhancementof the learning environment (both lecture and lab) in thepursuit of improved learning outcomes. The paper will alsopresent the limitations and drawbacks in the use of thesedevices.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.036 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".