Incidental Learning on the London Tube: Evidence from Hypermedia
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,051 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».