Journal Information
Computers in Human Behavior
https://www.journals.elsevier.com/computers-in-human-behavior
Impact Factor:
6.829
Publisher:
Elsevier
ISSN:
0747-5632
Viewed:
15397
Tracked:
3
Call For Papers
Computers in Human Behavior is a scholarly journal dedicated to examining the use of computers from a psychological perspective. Original theoretical works, research reports, literature reviews, software reviews, book reviews and announcements are published. The journal addresses both the use of computers in psychology, psychiatry and related disciplines as well as the psychological impact of computer use on individuals, groups and society. The former category includes articles exploring the use of computers for professional practice, training, research and theory development. The latter category includes articles dealing with the psychological effects of computers on phenomena such as human development, learning, cognition, personality, and social interactions. The journal addresses human interactions with computers, not computers per se. The computer is discussed only as a medium through which human behaviors are shaped and expressed. The primary message of most articles involves information about human behavior. Therefore, professionals with an interest in the psychological aspects of computer use, but with limited knowledge of computers, will find this journal of interest. 
Last updated by Dou Sun in 2022-01-29
Special Issues
Special Issue on Learning Analytics Ten Years After: A Retrospective and Research Agenda
Submission Date: 2024-05-01

Overview of developments and trends in Learning Analytics. Guest editors: Dr. Nicolae Nistor University details - Ludwig-Maximilians-Universität München, Germany nic.nistor-chb@lrz.uni-muenchen.de Dr. Àngel Hernandez-García Universidad Politécnica de Madrid, Spain angel.hernandez@upm.es Dr. Miguel Ángel Conde-Gonzalez Universidad de León, Spain mcong@unileon.es Special issue information: A decade ago, Learning Analytics (LA) was established as a discipline. Meanwhile, many other disciplines include a specialized LA section. Will LA in another decade be completely integrated in other disciplines as a standard methodology and thus dissolve? This question is addressed in our special issue. We start with an overview over a decade of learning analytics, and focus on representative subdomains. We conclude by proposing a research agenda.
Last updated by Dou Sun in 2023-10-03
Special Issue on Recent Trends and Future Advances in Personalised/Adaptive Ubiquitous Learning
Submission Date: 2024-07-20

The changes in education and teaching techniques have resulted in incorporating new technology into both teaching and learning models. With the emergence of new illnesses such as the Covid-19, lockdowns were used that limited direct contact between individuals. The imposition of constraints on human movement resulted in the development of new technologies that efficiently remove the challenges associated with daily tasks. One such technology is the ubiquitous device, which adds computer capacity to all devices via embedded technology. Due to advancements in wireless computing, technologies such as IoT, wearable devices, robotic devices, and pervasive gadgets continue to grow in popularity, which contributes to the development of ubiquitous systems. The learning models applied in conjunction with the ubiquitous adaptive devices analyse data acquired from students and correlate it to the learning techniques. Guest editors: Dr. Faheem Khan,Gachon University, Seongnam, South Korea, faheem@gachon.ac.kr Dr. Umme Laila, Sir Syed University of Engineering & Technology, Pakistan, ulaila@ssuet.edu.pk Dr. Muhammad Adnan Khan, Riphah International University, Pakistan, khanadnan.khan@riphah.edu.pk Dr. Inam Ullah, Chungbuk National University, inam@chungbuk.ac.kr Special issue information: The benefit of pervasive or ubiquitous computing is that communication may occur everywhere and at any time, regardless of the communication channel. Additionally, customised ubiquitous devices have applications in e-learning, corporate settings, geographical applications, electronic highway tolls, smartphones, and wearables. Furthermore, with ubiquitous learning, several contemporary technologies, such as machine learning models, are included in the analysis of acquired data. Additional advancements include fuzzy approaches, augmented/virtual reality technologies for device interaction, a four-dimensional modelling environment, and IoT in learning settings. Further, advancements in data analytics, such as reinforcement learning, fuzzy learning models, neural networks, and deep learning techniques, may allow the development of efficient data models. With distributed processing methods such as blockchain models, edge/fog computing techniques may efficiently process and store data. As a result of the adaptive technologies employed in education, pupils may continuously perceive learning via ubiquitous gadgets. Furthermore, mobile devices, personal assistants, RFID, and 6G communications allow efficient data transfer between devices through wireless media. This special issue examines the use of personalised/adaptive learning in a contactless learning environment and new developments linked with ubiquitous gadgets. Several coupled learning models may be exploited via successful wireless deployment and current advancements in artificial intelligence technology. Thus, researchers and academics might provide their perspectives on different technological implementations that could be combined with ubiquitous learning models like the digital twins, MIMO, vehicular networks and personal ubiquitous computing models related to learning. LIST OF TOPICS AREAS INCLUDE, BUT ARE NOT LIMITED TO: Pervasive computing environment and the implementation of the technology in learning models Enhanced deployment of context-aware systems in the adaptive ubiquitous learning environments Enhanced wireless 5G/6G communications with effective ubiquitous learning models Personalisation and adaptive learning models for effective learning deployment Federated learning models with the effective deployment of blockchain in an adaptive learning environment Reinforced learning methods for data prediction and analysis in ubiquitous learning Frameworks and architectures for the deployment of ubiquitous learning Design and development of ubiquitous embedded systems related to learning Deployment of ubiquitous learning models with effective data analysis with fuzzy neural methods
Last updated by Dou Sun in 2023-10-03
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