Describing the Information Seeking Behavior: An Investigation on Comparing Learning Models Using Experimental Data Sets/DESCRIPTION DU COMPORTEMENT DE LA RECHERCHE D'INFORMATION : UNE INVESTIGATION SUR LA COMPARAISON DES MODÈLES D'APPRENTISSAGE EN UTILISANT L'ENSEMBLE DE DONNÉES EXPÉRIMENTALES
Bibliographic record
Abstract
Abstract: The purpose of this paper is to investigate the rule and characteristics of ACADEMIC users' information seeking behavior, as well as primary factors which influencing satisfaction and behavior outcomes as consequences of the value of information seeking. To examine the users' behavior, several learning models were adopted, such as Bush-Mosteller model, Bayesian model,, fictitious play and EWA model. Here we employ both qualitative and quantitative approaches to examine the phenomenon of information seeking. We tried to compare the consistence of different models on the basis of experimental data. In Nanjing University of Science and Technology, 120 students were randomly assigned to three different teams and provided different communicating environment according to different learning models when seeking same assign for two hour. The result of a series of confirmatory factor analyses reveals that users' satisfaction and behavior outcomes had correlated factors with moderate to good reliability. The findings from model analyses showed that EWA are more adapted to the others. Key words: learning model, information seeking, academic user Resume: Le present article vise a etudier les regles et caracteristiques du comportement de la recherche d'information des utilisateurs academiques, et les facteurs essentiels influant sur les resultats de satisfaction et de comportement en raison de la valeur de la recherche d'information. Afin d'examiner le comportement des utilisateurs, plusieurs modeles d'apprentissage ont ete adoptes, tels que modele Bush-Mosteller, modele Bayesian, jeu fictif et modele EWA. Nous employons ici a la fois les approches qualitatives et quantitatives pour etudier le phenomene de la recherche d'information. On tente de comparer la coherence de differents modeles sur la base des donnees experimentales. A l'Universite de Sciences et Technologie de Nanjing, 120 etudiants distribues au hasard dans 3 groupes ont offert, pour les memes tâches de recherche de 2 heures, de differents environnements de communication en vertu des modeles d'apprentissage distincts. Le resultat d'une serie d'analyses sur les facteurs confirmatoires montre que les resultats de satisfaction et de comportement ont correlation avec la moyenne et la grande fiabilite. Les resultats des analyses de modeles indiquent que EWA s'adapte mieux a d'autres. Mots-Cles: modele d'apprentissage, recherche d'information, utilisateur academique 1. INTRODUCTION More and more database companies pay attention to academic users and it is becoming an important issue. As for database companies, in order to attract more users, they have to put in tremendous effort to concerning their needs and preference. Because of the importance of that problem, this paper investigated academic users' information seeking behavior from the learning behavior perspective and also concerned the consistence of learning models. Generally speaking, learning is an universal phenomenon in the world. It is a process that all kinds of animals obtain individual behavior experience. Narrowly speaking, learning only mean the human learning behavior. Mainly through language as intermediary to master social experience and it is a positive process. Psychologists started to study learning processes extensively approximately 100 years ago. At that time, psychology was dominated by the view that processes within the brain cannot be studied and that explanations of behavior should be based purely on observable variables. In the 1950s psychologists started a new line of research into learning processes. They studied the impact of social interaction and observation on learning and divided learning only into two fundamentally different ways(Thomas Brenner, 2005). First, humans share with other animals a simple way of learning, which is usually called reinforcement learning. This kind of learning seems to be biologically fixed. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.025 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".