Time-Turner: A Bichronous Learning Environment to Support Positive In-class Multitasking of Online Learners
Bibliographic record
Abstract
University students engage in a substantial amount of multitasking in online classes despite being aware of its negative impacts on their learning. Depending on the learner’s goals, in-class multitasking can be a positive strategic behavior to increase productivity. In a formative pilot study (N=10), we established the structure and scope for our design by exploring students’ motivations, perceptions, and challenges in in-class multitasking and identified several promising design elements. Our design facilitates multitasking in online synchronous classes by providing a novel bichronous (blending of synchronous and asynchronous) learning environment manifested in Time-Turner that enables asynchronous guided accelerated viewing of past content during synchronous classes. A summative evaluation of our prototype showed significant improvement in learning outcomes when multitasking (N=20). Furthermore, 95% of users found Time-Turner helpful and expressed interest in having it in their online classes. Our findings show the great potential of supporting positive multitasking in synchronous online classes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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; both teacher heads agree on what is shown here.
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".