Bibliographic record
Abstract
Students often complain of overload in online learning environments. Discussions here consider online design and organization factors that might contribute to students’ reports of overload. This study explored predictions that 1) students’ past online experience, 2) the organization of online environments and relevance of online material with which students work, and 3) the level of task difficulty impact (a) student learning outcomes, (b) students’ reports of overload, and (c) students’ perceptions of having enough time to complete assigned course work. A total of 346 participants were tested in two experiments that manipulated the organization of the online environment and the material that students had to learn. Experiment 1 tested how the organization of the online environment impacted learning outcomes. Findings suggested that online environments that are overly busy and that contain irrelevant information (i.e., stimulus-rich or “stimulus-noisy” online environments) had a negative impact on experienced, savvy online learners, but no impact on students less experienced with online learning environments. Surprisingly, results here suggest that overload affected only experienced students. Experiment 2 tested how the organization of the online material (that students had to learn) impacted learning outcomes. Findings suggested that online learning environments that used hypertext to organize material had a negative impact on student learning outcomes, misconceptions of information, and perceived overload. This chapter examines literature that considers design and organization factors that can impact online learning, and considers design strategies for online teaching environments and strategies for avoiding factors that can leave students feeling overloaded.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.050 | 0.028 |
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".