When knowing more means knowing less: Understanding the impact of computer experience on e‑learning and e‑learning outcomes
Pourquoi ce travail est dans la base
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Notice bibliographique
Résumé
Students often report feeling more overloaded in courses that use e-learning environments compared to traditional face-to-face courses that do not use such environments. Discussions here consider online design and organizational factors that might contribute to students’ reports of information overload. It was predicted that certain online factors might contribute to stimulus overload and possibly students’ perceived overload, rather than information overload per se. User characteristics and a range of design and organizational factors that might contribute to perceived overload are discussed and hypotheses of how such factors might affect learning outcomes are also discussed. An experiment was conducted to test predictions that (i) students’ past online experience, (ii) the organization and relevance of online information, and (iii) the level of task difficulty affect (i) learning outcomes, (ii) students’ perceptions of information overload, and (iii) students’ perceptions of having enough time to complete experimental tasks. A total of 187 participants were tested in four experimental conditions that manipulated the organization and relevance of online material that students had to learn (ie, (i) a stimulus-low environment, where the material to be learned was presented as scrolling text, with no other stimuli present; (ii) a familiar environment, where the material to be learned was set within the borders of a familiar course Web site; (iii) a stimulus-rich or stimulus-noisy environment, where the material to be learned was set within the borders of an Amazon.com Web page (a Web site where you can search for, and buy books, videos and other products online); (iv) a PDF file environment, where the material to be learned was presented as a PDF file that resembled an online duplicate of the same material in the course textbook). Findings suggested that overly busy online environments that contain irrelevant information (ie, stimulus-rich or stimulus-noisy online environment) had a negative impact on learning for students ranked “high” on experience with e-learning technologies, but no impact on learning for other students (as measured by a knowledge test of material studied during experimental sessions). There is no doubt that online environments contain vast amounts of information and stimuli; often some of which are irrelevant and distracting. How one handles irrelevant or distracting information and stimuli can have a significant impact on learning. Surprisingly, results here suggest that overload affected only experienced students. Perceptual load hypotheses are discussed to explain what initially seemed to be counterintuitive results. This paper examines literature that considers factors that can affect learning online, strategies for how teachers can ensure positive outcomes for the technology-based classroom, and strategies for avoiding online pitfalls that might leave students frustrated or burdened with feelings of overload.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,007 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 découle