Advances in Human-Computer Interaction
 Journal metrics
Acceptance rate21%
Submission to final decision42 days
Acceptance to publication49 days
CiteScore3.600
Journal Citation Indicator0.270
Impact Factor-

A Framework of the Training Module for Untrained Observers in Usability Evaluation Motivated by COVID-19: Enhancing the Validity of Usability Evaluation for Children’s Educational Games

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 Journal profile

Advances in Human-Computer Interaction is an interdisciplinary journal that publishes theoretical and applied papers covering the broad spectrum of interactive systems.

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Advances in Human-Computer Interaction maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.

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Research Article

Smartphone Use, Experience of Learning Environment, and Academic Performance among University Students: A Descriptive Appraisal

In our contemporary digital society, the smartphone is at the center of a powerful technological revolution affecting multiple domains. In the context of higher learning, the use of smartphones among students has been an area of interest. Previous studies on smartphone use and academic performance have generally focused on measuring the impact that smartphone use has on the academic performance of students. The purpose of this study is to examine the extent to which gender differences and the experience of a particular learning environment contributes to the use of smartphones for academic purposes. Data were collected through the use of a standardized self-report questionnaire completed by 300 first-year and 203 fourth-year undergraduate students from the University of Botswana. Our analysis is guided by the following specific objectives: first, to explore gender and the patterns of smartphone use for academic purposes; second, to appraise the contributing value of the experience of a learning environment on the use of smartphones to enhance academic achievement; and third, to examine smartphone use and its possible contribution to the performance outcome of students. Overall, we argue that the use of a smartphone for academic purposes is partly influenced by the extent to which a student is familiar with or understands the multiple contexts that shape his/her learning environment. For further studies in the field of smartphone use and academic performance, we suggest using multiple methods of data collection to uncover how students attach meanings to the use of smartphones and the role of smartphone use in improving their academic performance outcomes.

Research Article

Utilizing Structural Network Positions to Diversify People Recommendations on Twitter

Social recommender systems, such as “Who to follow” on Twitter, utilize approaches that recommend friends of a friend or interest-wise similar people. Such algorithmic approaches have been criticized for resulting in filter bubbles and echo chambers, calling for diversity-enhancing recommendation strategies. Consequently, this article proposes a social diversification strategy for recommending potentially relevant people based on three structural positions in egocentric networks: dormant ties, mentions of mentions, and community membership. In addition to describing our analytical approach, we report an experiment with 39 Twitter users who evaluated 72 recommendations from each proposed network structural position altogether. The users were able to identify relevant connections from all recommendation groups. Yet, perceived familiarity had a strong effect on perceptions of relevance and willingness to follow-up on the recommendations. The proposed strategy contributes to the design of a people recommender system, which exposes users to diverse recommendations and facilitates new social ties in online social networks. In addition, we advance user-centered evaluation methods by proposing measures for subjective perceptions of people recommendations.

Review Article

A Systematic Review of Greenhouse Humidity Prediction and Control Models Using Fuzzy Inference Systems

Cultivating in greenhouses constitutes a fundamental tool for the development of high-quality crops with a high degree of profitability. Prediction and control models guarantee the correct management of environment variables, for which fuzzy inference systems have been successfully implemented. The purpose of this review is determining the various relationships in fuzzy inference systems currently used for the modelling, prediction, and control of humidity in greenhouses and how they have changed over time to be able to develop more robust and easier to understand models. The methodology follows the PRISMA work guide. A total of 93 investigations in 4 academic databases were reviewed; their bibliometric aspects, which contribute to the objective of the investigation, were extracted and analysed. It was finally concluded that the development of models based in Mamdani fuzzy inference systems, integrated with optimization and fuzzy clustering techniques, and following strategies such as model-based predictive control guarantee high levels of precision and interpretability.

Review Article

A Brief Study of Binaural Beat: A Means of Brain-Computer Interfacing

The human brain tends to follow a rhythm. Sound has a significant impact on our physical and mental health. This sound technology uses binaural beat by generating two tones of marginally different frequencies in each individual ear to facilitate the improved focus of attention, emotion, calming, and sensory organization. Binaural beat helps in memory boosting, relaxation, and work performance. Again because of hearing a binaural beat sound, brainwave stimuli can be diagnosed to pick up a person’s sensitive information. Using this technology in brain-computer interfacing, it is possible to establish a communication between the brain and the computer. Thus, it enables us to go beyond our potential. The aim of this study is to assess the impact and explore the potential contribution of binaural beat to enhancement of human brain performance.

Research Article

Enhancing Human-Computer Interaction in Digital Repositories through a MCDA-Based Recommender System

Digital repositories contain a large amount of content, which is available to heterogeneous groups of people. As such, in many cases people encounter difficulties in finding specific content which is related to their preferences. In view of this compelling need and towards advancing human-computer interaction, this paper presents a recommender system which is incorporated in a digital repository. The recommender system is designed using multiple-criteria decision analysis (MCDA) and more specifically the weighted sum model (WSM) in order to refine the delivered content to the users. It also considers several users’ characteristics (their preferences as depicted by the content they visited or searched and by the frequency of searches/visits) and features of the content (content types and traffic). The recommender system outputs the suggestions of content to users based on their preferences and interests. The presented recommender system was evaluated by real users, and the results show a high degree of accuracy in the recommended content and satisfaction by users.

Research Article

Controlling an Anatomical Robot Hand Using the Brain-Computer Interface Based on Motor Imagery

More than one billion people face disabilities worldwide, according to the World Health Organization (WHO). In Sri Lanka, there are thousands of people suffering from a variety of disabilities, especially hand disabilities, due to the civil war in the country. The Ministry of Health of Sri Lanka reports that by 2025, the number of people with disabilities in Sri Lanka will grow by 24.2%. In the field of robotics, new technologies for handicapped people are now being built to make their lives simple and effective. The aim of this research is to develop a 3-finger anatomical robot hand model for handicapped people and control (flexion and extension) the robot hand using motor imagery. Eight EEG electrodes were used to extract EEG signals from the primary motor cortex. Data collection and testing were performed for a period of 42 s timespan. According to the test results, eight EEG electrodes were sufficient to acquire the motor imagery for flexion and extension of finger movements. The overall accuracy of the experiments was found at 89.34% (mean = 22.32) at the 0.894 precision. We also observed that the proposed design provided promising results for the performance of the task (grab, hold, and release activities) of hand-disabled persons.

Advances in Human-Computer Interaction
 Journal metrics
Acceptance rate21%
Submission to final decision42 days
Acceptance to publication49 days
CiteScore3.600
Journal Citation Indicator0.270
Impact Factor-
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