Human behavior analysis for library and information science
| Date | 20 November 2017 |
| Published date | 20 November 2017 |
| DOI | https://doi.org/10.1108/LHT-10-2017-0213 |
| Pages | 442-444 |
| Author | Mu-Yen Chen,Edwin David Lughofer,Neil Y. Yen,Chia-Chen Chen |
| Subject Matter | Library & information science,Librarianship/library management,Library technology,Information behaviour & retrieval,Information user studies,Metadata,Information & knowledge management,Information & communications technology,Internet |
Guest editorial
Human behavior analysis for library and information science
Introduction
This special issue investigates the topics of human behavior analysis, data mining, and
ambient intelligence technologies in library and information sciences. It is considered one of the
most important issues to investigate the interaction between librarians and technology.
Recently, the emerging technologies like Internet of Things, big data, and deep learning
technologies, along with the public’s embracing of wireless sensor networks generates new
opportunities for situation-aware library systems and services. The realization of big data
covers the main kernel of database management technology, giving rise to the development of
raw data gathering, data preprocessing, data warehouse, specific hardware devices, computer
clouds, parallel processing techniques, and data mining. Compared to traditional library
systems and services, a situation-aware, computing-based library application has the
advantage of changing from the on-spot experiences to the mobile and ubiquitous environment.
Many challenges, however, must be addressed for the development of consistent,
suitable, safe and flexible real-time library and information systems. Deficiencies in human
behavior analysis and situation-aware care may raise issues in the collection of streamed
data. The analysis and use of such data refers to as social mining, web mining and
sentiment mining, the last of which has recently become highly popular. Situation-aware
technology involves the creation of smart spaces and this technology can be applied to
systems that handle information retrieval, recommendations, trust, agent behavior,
environmental conditions and changes and security, etc., and the surrounding issues have
important implications to library and information science.
Fewer research questions, diverse fields
Human behavior analysis refers to the interaction between individual and technology to the
existence of individuals. They are usually hidden in our daily living environments, and are
situation awareness, personalized, and adaptive in the environment. In this issue, authors
provided more particular and more diverse objectives. These papers can be grouped into
three major fields.
The first field describes the “Learning behavior.”In this special issue, Wu et al. (2018) use
Kolb’s learning style theory and investigate the learning effectiveness of user’s different
learning style (including accommodators, divergers, convergers, and assimilators) on
web-based learning system. In addition, Bhardwaj and Kumar (2018) present a detailed
investigation of visually impaired students’problems, and how the proposed approach can
be useful to evaluate the digital infrastructure and services. Lin and Huang (2018) evaluate
the students’learning achievement based on flow experience and AR technique in
U-learning environment. Finally, Tsai and Tang (2018) adopt blended problem-based
learning method to apply into university biotechnology courses and evaluate the
relationship between learning attitudes and learning achievement.
The second field focuses on “Context-aware and intelligent system.”In this special issue,
Liao and Chang (2018) propose the context-aware annotation system for Hakka culture-specific
language learning in the U-learning environment. Besides, Bouchrika et al. (2018) adopt the
technology-to-performance chain model to evaluate the relationship between the perceived
performance, software usability, and attitude to use the online educational system. Finally,
Sangaiah et al. (2018) figure out the global optimization and intelligent system issues and also
compare the performance for cuckoo search and flower pollination algorithm.
Library Hi Tech
Vol. 35 No. 4, 2017
pp. 442-444
© Emerald PublishingLimited
0737-8831
DOI 10.1108/LHT-10-2017-0213
442
LHT
35,4
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