Searching as Learning

Ghosh, Souvick; Rath, Manasa; Shah, Chirag

doi:10.1145/3176349.3176386

Cited by 48 publications

(17 citation statements)

References 11 publications

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“…Although the perceived task complexity can change for learners while working on a task ( Liu et al, 2011 ), monitoring and assessing the complexity helps to understand the SAL process. For example, Ghosh et al (2018) observed that with increasing task complexity (ranging from understanding to being able to evaluate strategy/method), the produced search queries increased in length. Walhout et al (2017) compared three search tasks differing in complexity (fact-finding, understanding cause-effect chain, and elaborating a complex topic).…”

Section: Componentsmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Componentsmentioning

confidence: 99%

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

et al. 2022

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“…The research field of "search as learning", recently developed, highlights the connections between the information seeking and the learning processes [4], [5]. Users, generally, have different needs when performing a search especially for knowing and learning, and that is particularly emphasized when users want to shape their understanding on a new topic in the medical domain.…”

Section: Balanced Learning Methodologymentioning

confidence: 99%

“…At the same time, the connection among search processes, learning and knowledge building is emphasized in several works in literature [4], [5]. This is bringing, as a natural consequence, to the development of a new research field which connects the educational sphere with information seeking.…”

Section: Introductionmentioning

confidence: 96%

ULearn: Personalized Medical Learning on the Web for Patient Empowerment

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Taibi

et al. 2019

Lecture Notes in Computer Science

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Health literacy constitutes an important step towards patient empowerment and the Web is presently the biggest repository of medical information and, thus, the biggest medical resource to be used in the learning process. However, at present web medical information is mainly accessed through generic search engines that do not take into account the user specific needs and starting knowledge and so are not able to support learning activities tailored to the specific user requirements. This work presents "ULearn" a meta engine that supports access, understanding and learning on the Web in the medical domain based on specific user requirements and knowledge levels towards what we call "balanced learning". Balanced learning allows users to perform learning activities based on specific user requirements (understanding, deepinning, widening and exploring) towards his/her empowerment. We have designed and developed ULearn to suggest search keywords correlated to the different user requirements and we have carried out some preliminary experiments to evaluate the effectiveness of the provided information.

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“…The extraction of new knowledge can also proceed from the structural components of meaning with related metrics (e.g., Altmetrics: a manifesto http://altmetrics.org/ manifesto/ accessed on 11 March 2023) in systems in which the interactions of the users and the search engine make sense [23]. 'Searching as learning' thus becomes a tandem of interactions between the exploration of search behaviors and outcomes in learning-related tasks [24]. This trend raises questions on the fairness, security and trustworthiness of those models within this type of deep learning model [25] and opens avenues toward meta-heuristics.…”

Section: • Extraction Of New Knowledge From Logsmentioning

confidence: 99%

A Multiverse Graph to Help Scientific Reasoning from Web Usage: Interpretable Patterns of Assessor Shifts in GRAPHYP

et al. 2023

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The digital support for scientific reasoning presents contrasting results. Bibliometric services are improving, but not academic assessment; no service for scholars relies on logs of web usage to base query strategies for relevance judgments (or assessor shifts). Our Scientific Knowledge Graph GRAPHYP innovates with interpretable patterns of web usage, providing scientific reasoning with conceptual fingerprints and helping identify eligible hypotheses. In a previous article, we showed how usage log data, in the form of ‘documentary tracks’, help determine distinct cognitive communities (called adversarial cliques) within sub-graphs. A typology of these documentary tracks through a triplet of measurements from logs (intensity, variety and attention) describes the potential approaches to a (research) question. GRAPHYP assists interpretation as a classifier, with possibilistic graphical modeling. This paper (Paper 2) shows what this approach can bring to scientific reasoning; it involves visualizing complete interpretable pathways, in a multi-hop assessor shift, which users can then explore toward the ‘best possible solution’—the one that is most consistent with their hypotheses. Applying the Leibnizian paradigm of scientific reasoning, GRAPHYP highlights infinitesimal learning pathways, as a ‘multiverse’ geometric graph in modeling possible search strategies answering research questions.

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Searching as Learning

Cited by 48 publications

References 11 publications

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

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

ULearn: Personalized Medical Learning on the Web for Patient Empowerment

A Multiverse Graph to Help Scientific Reasoning from Web Usage: Interpretable Patterns of Assessor Shifts in GRAPHYP

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