Incidental Learning on the London Tube: Evidence from Hypermedia
Bibliographic record
Abstract
Incidental Learning on the London Tube: Evidence from Hypermedia Patricia M. Boechler (patricia.boechler@ualberta.ca) Department of Educational Psychology, University of Alberta, 6-102 Education North, Edmonton, AB, T6G 2G5 Dorothy J. Steffler (dorothy.steffler@concordia.ab.ca) Department of Psychology, Concordia University College of Alberta, 7128 Ada Blvd Edmonton, AB, CANADA T5B 4E4 Ilya Levner (ilya@cs.ualberta.ca) Department of Computer Science, University of Alberta Edmonton, AB, T6G 2G5 Lindsey Leenaars (lindseyleenaars@hotmail.com) Department of Educational Psychology, University of Alberta, 6-102 Education North, Edmonton, AB, T6G 2G5 Keywords: hypermedia, incidental learning Results Incidental learning addresses learning that is unintentional and often occurs automatically while engaging in another, intentional task (Baylor, 2001; Frensch & Runger; 2003). While not necessarily implicit learning, incidental learning is potentially important in a hypermedia environment where learners are exposed to a great deal of information that is peripheral to the target information. We investigated whether information that was presented in two formats, text based and image based, would affect performance on an incidental learning task. Method Participants were introductory psychology students receiving credit for their participation in a two-part study. In the initial test session, participants were tested on a 43-page hypertext document on the topic of historical events on the London Tube. Each hypermedia page (webpage) contained a short text section and a picture to help users differentiate between pages. The image only group received webpages with a textual description of historical events and an accompanying image. The text and image group received webpages that contained the same picture and textual description of historical events plus an additional phrase of text that described the target object in the accompanying image, thus providing the target information in both modes. Students were asked to find the answers to fifteen questions by navigating through the website. They were not explicitly instructed to study or remember the material, in order to induce incidental learning. In the second test session, students were given a multiple- choice test that contained 15 “text” questions (material from the text on the pages where the search answers were found) and 15 “image” questions (material from the images on the pages where the search answers were found). We were interested in whether images alone or images enhanced with text affected incidental learning. One hundred and thirty-four participants were tested, 67 in each condition. Due to equipment failure we were unable to use the data for 19 participants in the image only condition. A two-by-two ANOVA (question type x condition) was computed on number of correct responses on the multiple- choice questions. There was a main effect for question type, F (1, 113) = 40.50, p < .01 (M = 6.98 for image questions and 5.51 for text questions; SD = 2.41 and 2.14, respectively). There was also a main effect for condition, F (1, 113) = 5.22, p < .05 (M = 6.59 for text and image group and 5.77 for the image only group; SD = 2.42 and 1.99, respectively). There was no interaction effect. Discussion In general, regardless of condition, students performed above chance, indicating that incidental learning occurred. In hypermedia environments, image-based information seems to be more conducive to incidental learning than text- based information. However, additional text that supports the image information does enhance incidental learning of image-based material. Acknowledgments This research was supported by a Social Science and Humanities Research Council of Canada grant awarded to Patricia Boechler. References Baylor, A. L. (2001). Perceived disorientation and incidental learning in a web-based environment: Internal and external factors. Journal of Educational Multimedia and Hypermedia, 10, 227-251. Frensch, P. A., & Runger, D. (2003). Implicit learning. Current Directions in Psychological Science, 12, 13-18.
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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 | 0.004 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".