IWILDS 2021 : 2nd International Workshop on Investigating Learning During Web Search
Call For Papers
Call for papers: 2nd International Workshop on Investigating Learning During Web Search (IWILDS 2021)
Workshop Website: https://iwilds2021.wordpress.com
Submission deadline: 2021-07-09
Acceptance notification: 2021-08-15
Camera-ready submission: tba
Workshop date: 2021-11-01 (preliminary)
Web search is one of the most ubiquitous online activities and often used as a starting point to learn, i. e., to acquire or extend one's knowledge about certain topics or procedures. When learning by searching the Web, individuals are confronted with an unprecedented amount of information in various forms and varying quality. Thus, successful learning on the Web requires high degrees of self-regulation and should be supported by the adequate design of search, recommendation, and training tools. This creates a highly interdisciplinary research area at the intersection of information retrieval, human-computer interaction, psychology, and educational sciences. Search as Learning (SAL) research examines the relationships between querying, navigation, media consumption behavior, and the learning outcomes during Web search, how they can be measured, predicted, and supported.
Building on the growing SAL research community, IWILDS’21 provides an interdisciplinary forum that includes keynotes, paper presentations, and discussion. Additionally to traditional IWILDS topics (see below), we specifically invite submissions focusing on challenges caused by the Covid-19 pandemic, investigating, for instance, health-related information acquisition on the Web and the pandemic-induced digitalization of formal learning.
include (but are not limited to):
* Understanding learning during Web search
* Role of personal characteristics & attitudes in Web-based learning, e. g.
* Confidence, engagement, & affect
* Educational background
* Technology acceptance & experience
* Security concerns
* Modeling, recognizing, measuring & predicting learning processes and outcomes in Web search
* Methods of data collection to understand online learning
* Data acquisition (e. g. crowdsourcing, lab experiments)
* Data sources (e.g., query & navigation logs, eye-tracking) and their interpretation
* Information literacy & source credibility, authenticity, relevance, and usefulness evaluation during Web search
* Role of Web search in formal & semi-formal learning scenarios
* Resource features influencing online learning trajectories & outcomes
* (Multi-)media composition (e.g., learning with videos, comics/pictograms, texts)
* Supporting learning during Web search
* Interventions, tools & user interfaces to foster SAL
* Information/multimedia retrieval for SAL
* Learning analytics & educational data mining in search-based learning
* Fusion & summarization techniques for aggregating learning resources
* Evaluation & benchmarking of SAL systems
* Transparency of retrieval & ranking in SAL
* COVID-related information learning, retrieval & use in health decision making (e.g., COVID prevention, treatment, vaccination)
IWILDS’21 welcomes papers ranging from 2 to 8 pages plus references. Any page length in between is allowed. Authors are expected to adapt the length of their
submission based on contribution size; appropriateness of the chosen length will be a reviewing criterion.
All submissions must be written in English and formatted according to CEUR-WS format. All identifying attributes must be removed to allow for double-blind review. Each submission will be reviewed based on (a) quality of its contribution, (b) quality of presentation and suitable length, (c) fit to the workshop’s topics.
Accepted papers will be invited for presentation during the workshop. Presentation time and format (talk or poster) will be allocated based on the presented
Format guidelines: http://ceur-ws.org/Vol-XXX/samplestyles/ (two-column style, with page numbers)
Submission page: https://easychair.org/conferences/?conf=iwilds2021
Irina Brich, IWM - Leibniz-Institut für Wissensmedien, Germany,
Anett Hoppe, TIB - Leibniz Information Centre for Science & Technology, Germany,
Jiqun Liu, University of Oklahoma, USA,
Ran Yu, GESIS – Leibniz Institute for the Social Sciences, Germany.