Topic-independent modeling of user knowledge in informational search sessions

De-contextual communication: Factors influencing usage intentions of metaverse technology in digital library services

This study aims to investigate the factors influencing the usage intentions of metaverse technology in digital library services within higher learning institutions, using the unified system information theory. To achieve this, an online survey was conducted among university staff and students, utilizing a link-scale measurement. Various factors affecting the usage intention of metaverse technology in library services were computed through transformation models such as UTAUT, DM, ISS, and TTF. Subsequently, the model parameters were empirically tested using partial least squares structural equation modeling (PLS-SEM) to identify the significant factors influencing the usage intention of metaverse technology. The results of the study reveal that users' intentions to use metaverse technology in digital library systems are influenced by perceptions of system use, perceived interaction, perceived usefulness, and perceived ease of use. Notably, these influences vary depending on the user's intended task. These findings provide valuable insights into the factors that affect the adoption and usage intentions of metaverse technology in the context of digital library services in higher learning institutions. This research contributes to enhancing understanding and guiding future strategies for leveraging metaverse technology effectively in educational environments.

Retrieving Adversarial Cliques in Cognitive Communities: A New Conceptual Framework for Scientific Knowledge Graphs

The variety and diversity of published content are currently expanding in all fields of scholarly communication. Yet, scientific knowledge graphs (SKG) provide only poor images of the varied directions of alternative scientific choices, and in particular scientific controversies, which are not currently identified and interpreted. We propose to use the rich variety of knowledge present in search histories to represent cliques modeling the main interpretable practices of information retrieval issued from the same “cognitive community”, identified by their use of keywords and by the search experience of the users sharing the same research question. Modeling typical cliques belonging to the same cognitive community is achieved through a new conceptual framework, based on user profiles, namely a bipartite geometric scientific knowledge graph, SKG GRAPHYP. Further studies of interpretation will test differences of documentary profiles and their meaning in various possible contexts which studies on “disagreements in scientific literature” have outlined. This final adjusted version of GRAPHYP optimizes the modeling of “Manifold Subnetworks of Cliques in Cognitive Communities” (MSCCC), captured from previous user experience in the same search domain. Cliques are built from graph grids of three parameters outlining the manifold of search experiences: mass of users; intensity of uses of items; and attention, identified as a ratio of “feature augmentation” by literature on information retrieval, its mean value allows calculation of an observed “steady” value of the user/item ratio or, conversely, a documentary behavior “deviating” from this mean value. An illustration of our approach is supplied in a positive first test, which stimulates further work on modeling subnetworks of users in search experience, that could help identify the varied alternative documentary sources of information retrieval, and in particular the scientific controversies and scholarly disputes.

The Search as Learning Spaceship: Toward a Comprehensive Model of Psychological and Technological Facets of Search as Learning

Using a Web search engine is one of today's most frequent activities. Exploratory search activities which are carried out in order to gain knowledge are conceptualized and denoted as Search as Learning (SAL). In this paper, we introduce a novel framework model which incorporates the perspective of both psychology and computer science to describe the search as learning process by reviewing recent literature. The main entities of the model are the learner who is surrounded by a specific learning context, the interface that mediates between the learner and the information environment, the information retrieval (IR) backend which manages the processes between the interface and the set of Web resources, that is, the collective Web knowledge represented in resources of different modalities. At first, we provide an overview of the current state of the art with regard to the five main entities of our model, before we outline areas of future research to improve our understanding of search as learning processes.