Describing the Information Seeking Behavior: An Investigation on Comparing Learning Models Using Experimental Data Sets
Bibliographic record
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
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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 | 0.105 | 0.286 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".