Online Lecture As an Alternative Method of Instruction in College Classrooms: Measuring the Effects of Alternating In-class with Online Lectures in Two Sections of an Undergraduate Introduction to Behavior Analysis Course
Bibliographic record
Abstract
Online instruction is becoming increasingly common at universities; however, there is little single subject research concerning the effectiveness of the online lecture format. We investigated whether online lecture could replace in-class lecture in two sections of an undergraduate Introduction to Behavior Analysis course without detrimentally affecting student learning. Using an adapted alternating treatments design, online and in-class lecture formats were counterbalanced across the two course sections. Experimenters collected data on lecture attendance/access, percent correct on the weekly quiz, and student report on lecture format preference. The data show that, within the context of this class, students performed equally in the weekly quiz regardless of lecture format; further, that this is consistent when looking at individual student data and mean data. However, although students stated a preference for online lecture in the questionnaire, a greater percentage of students attended in-class lecture than accessed online lecture.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".