Mental Model Construction in MedlinePlus Information Searching Involves Changes and Developments in Cognition, Emotion, and Behaviour
Notice bibliographique
Résumé
Objectives – To explore the construction of mental models as a dynamic process and how users understand a consumer health information system, MedlinePlus, during a search session. Design – Face-to-face interview. Setting – Large university. Subjects – A total of 38 undergraduate students participated in the study. All majoring in non-medical fields, such as art history, psychology, business, and communication studies. Methods – Participants were randomized into two groups: the simple task group and the complex task group. Simple task group members were asked to perform 12 simple tasks while the complex group members performed three more-involved tasks. Simple tasks were defined as succinct questions with finite answers while complex tasks were open-ended and required more cognitive activity and synthesizing on the part of the individual. Participants in both groups were then given four simple tasks and two complex tasks to perform. Data was derived by video recording search sessions with individuals and interview-like questions for the tasks performed. Participants were given a brief introduction to the search session design and sessions took place in a private lab. Since the aim of the study was to track participants’ mental modeling processes over time, coding of data was caught at three different times throughout the search sessions: T1 (MM1) after five minutes of free exploration, T2 (MM2) after the first search session, and T3 (MM3) after the second search session. Main Results – The author discusses the demographic specifics of the population participating in the study. Although participants were split into two groups, the results were combined to be more meaningful. Out of the 38 participants, 20 were female and 18 were male with ages ranging from 18 to 22. They had, on average, 10 years of computer experience and their average spatial ability score was 12.71. Also on average, they spent about 20 minutes completing the first search session and 12 minutes completing the second search session. The results show that participant-developed mental models of the MedlinePlus web space can be clustered into the following five theoretical components (this information is quantified in tables throughout the paper): system, content, information organization, interface, and procedural knowledge. Conclusion – The study allowed participants to articulate their mental models and representations while conducting predefined searches during private sessions using MedlinePlus. The study also illustrates how users’ mental models of a system developed during interactions with an online system, on a theoretical level. Little is actually known about how mental models are developed when users interact with an information system. The study serves to explore this arena and reveals that the mental model construction involves changes and developments in three parallel dimensions: cognition, emotion, and behaviour. Also, these dimensions are accompanied by three mental activities: assimilating new concepts, phasing out previously perceived concepts, and modifying existing concepts. The mental model construction process could be a useful tool to build user models and make better design decisions for information systems.
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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 | 0,003 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».