Guest editorial: Contemporary learning behaviors on mobile devices and social media

Citation

Chen, C.-C., Hung, P.C.K., Egrioglu, E., Chiu, D.K.W. and Ho, K.K.W. (2023), "Guest editorial: Contemporary learning behaviors on mobile devices and social media", Library Hi Tech, Vol. 41 No. 5, pp. 1265-1274. https://doi.org/10.1108/LHT-10-2023-589

Introduction

Following our previous special issues on social robots (Kelner et al., 2022) and smart cities (Chang and Abdel-Basset, 2022), we are glad to present this special issue on contemporary technologies for education after four decades of Library Hi Tech's publication. As we have overwhelming responses on our recent special issues and thematic selection of articles to be published in recent issues (Chiu and Ho, 2022a, b, c), we shall continue this practice in organizing our publication and guide readers to thematic research and practices presented by authors of Library Hi Tech.

Although e-learning or mobile learning (m-learning) has been adopted in many countries for many years, its popularity is limited. Some teachers with limited information technology skills may not adopt m-learning in their courses or use social media as learning aids (Leung et al., 2023). Many schools and households still lack the necessary information technology infrastructure and facilities (Chan and Chiu, 2023; Lo et al., 2018; Tse et al., 2022; Wong and Chiu, 2023). Most teachers prefer traditional teaching environments and pedagogies because online learning or m-learning technologies may increase their workload. They might also be unwilling to work further on social media to augment classroom teaching or recommend supplementary learning materials.

Even so, e-learning, m-learning and social media have become increasingly important methods to help prevent infection during the current global pandemic and massive lockdowns (Guo et al., 2021; Huang et al., 2021, 2022, 2023; Meng et al., 2023; Nadi-Ravandi and Batooli, 2023; Yi and Chiu, 2023). These technologies are becoming more prevalent for teaching and learning support to enhance infection prevention measures and support various new modes of social behavior (Conrad et al., 2022; Fasae et al., 2021; Yu et al., 2023). Governments, schools, universities, librarians, teachers, parents, tutors and multiple stakeholders are increasingly adopting such technologies and developing different functions and digital content to satisfy the diverse learning needs of students.

Theories, models and algorithms for learning analytics

The first theme of this special issue focuses on intelligent algorithms for contemporary learning behavior analysis. Artificial intelligence has received wide attention and has been put into practice in recent library and education contexts (Asemi et al., 2021; Borgohain et al., 2022; Harisanty et al., 2022; Lin et al., 2022; Ma et al., 2022; Okunlaya et al., 2022; Wu and Yang, 2022; Yoon et al., 2022).

In this issue, Zhang and Chang (2023) integrated the Analysis Hierarchy Process (AHP) method and the consistent fuzzy preference relationship to investigate the critical factors for a mobile animation game. The results showed that the most important critical factor considered by the player was the information system, followed by promotional activities, game motivation and finally, the game itself. Chen (2023) presented a hybrid intelligence model based on deep learning (DL), fuzzy clustering analysis (FCA) and bidirectional encoder and decoder (ENDEC) to detect mobile learners' behaviors under current social media conditions. The proposed model obtained a high level of accuracy and was shown to be applicable in practice. Liu (2023) used an artificial neural network (ANN) with DL approach to analyze the text complexity of Chinese and foreign academic English writings. The experimental results showed that the deep neural network algorithm could achieve better performance in feature extraction and text mining in comparison to a conventional neural network.

Readers may also be interested in other recent analytics methods such as digital media recording and broadcasting using intelligent imaging in classrooms (Wu, 2022), curriculum reform with data analytics (Ho et al., 2023), corpora creation from the social web (de Carvalho and Costa, 2022), structure-function recognition with DL (Liu and Zhao, 2022) and wisdom-based decision methods (Nguyen, 2021). See also Yew et al. (2022) for a recent review of technological components for Library and Information Science curricula and Li and Chiu (2022) for that concerning archival education.

Mobile technologies for education

The second theme of this issue focuses on mobile for contemporary learning behaviors analysis and Apps. Ubiquitous mobile Internet access has changed the reading and learning habits of the younger generation (Banshal et al., 2022; Chan et al., 2022; Ding et al., 2021; Pianzola et al., 2022; Wang et al., 2016; Yu et al., 2022a) and facilitated anytime, anywhere learning activities (Fan et al., 2020; Lau et al., 2020; Law et al., 2019; Yip et al., 2021; Zhang et al., 2021), especially under the constraints of COVID-19 restrictions (Li et al., 2021a; Sung and Chiu, 2022).

Chen et al. (2023) investigated the effect of mobile business simulation games on entrepreneurship education. The results pointed out that the experience of flow is the most important factor in mobile business simulation games, which can improve entrepreneurial attitudes and self-efficacy. Yang et al. (2023) examined the impact of a 16-h smartphone training program on the correlations among different constructs of smartphone use in a sample of older adults based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Their findings indicated that, for older adults, technology training has a positive impact on smartphone use. Fan and Liu (2023) adopted DL technology to implement a voice evaluation system and investigated how mobile device-based learning aids impact students' learning behavior. The experimental results showed that learning aids delivered via a mobile platform could improve students' learning efficiency. Wang et al. (2023a) presented a mechanism for examining the relationship between news learning and news sharing on mobile platforms. The proposed mechanism includes factors at three levels: personal, interpersonal and social. They found that news learning on mobile platforms is affected by self-efficacy and self-enhancement. They also found that news-sharing intention is influenced positively by self-efficacy, interpersonal trust, interpersonal reciprocity, online community identity and social norms. Zhang et al. (2023a) designed cognitive indicators to evaluate users' study efficiency on mobile learning platforms. The results showed that interface layout has the most significant effect on the system's usability, as well as on the user's learning efficiency and cognitive load.

Readers may also be interested in research on the influence of the performance of mobile devices on teaching quality (Chen and Chen, 2022), mobile vocational information needs (Ezeamuzie et al., 2022), technical vocational training (Abd Majid et al., 2022), visual aspects for mobile search (Wu and Zhang, 2022; Zeng et al., 2022) and data usage-based privacy and security issues in mobile app recommendation (Beg et al., 2022) and cloud data service (Hui et al., 2023).

Social media and MOOC communities

The third theme focuses on pedagogy innovation based on social media and massive open online course (MOOC) platforms that form communities of learning and practice (Chung et al., 2020; Gupta et al., 2022; Lei et al., 2021; Li et al., 2023b; Wang et al., 2021; Yang et al., 2022), which extends efforts to more traditional online education platforms (Cheung et al., 2023; Li et al., 2021b; Mak et al., 2022; Yao et al., 2023). In particular, the younger generation is generally accustomed to such new platforms and technologies, which can be accessed anytime, anywhere, with mobile technologies (Deng and Chiu, 2023; Dong et al., 2021; Jiang et al., 2023).

In this issue, Wang and Xie (2023) investigated what factors affect users' knowledge and information learning and sharing on social media platforms. The results showed that users' trust and satisfaction would affect their knowledge and information learning and sharing on the platform. Factors that affect users' trust in social platforms include privacy protection effectiveness and network effects. Perceived usefulness and perceived ease of use are found to be related to users' satisfaction with social media platforms. Leung et al. (2023) investigated the advantages and weaknesses of using Facebook to facilitate learning Japanese as a foreign language. The results showed that Facebook is especially effective for Japanese learning when learners fall into any one of the following groups: young, female, or intermediate (N2/3) learners. Cheng et al. (2023) summarized the research foci (e.g. themes, methods, contexts, etc.) and discussed the new trends of MOOCs research in Mainland China. They found that new technology tools, such as big data and machine learning approaches for learning analytics, may change traditional MOOC research into new practices.

Readers may also be interested in recent research on design-maker communities of practice (Zhang and Hu, 2022), MOOCs continuance intention and dropout (Cheng, 2022; Wang et al., 2023b), social media information creditability and sharing issues (Khan et al., 2022; Li et al., 2023a; Ye and Ho, 2022; Yuan and Bi, 2023; Zhang et al., 2022, 2023b) and the use of social media for promotion (Liao et al., 2022; Lu et al., 2023; Wójcik, 2022a; Wu and Yang, 2022).

Virtual and augmented technologies for education

The fourth theme of this issue focuses on the application of emerging technologies to education, such as virtual reality (VR) (Suen et al., 2020) and augmented reality (AR) (Lo et al., 2017, 2019).

Li and Liu (2023) applied AR with context awareness technology to build the m-learning augmented reality (MLAR) system in this issue. The system allowed students to learn about campus plants as part of the college life technology curriculum. Finally, the statistical results of the questionnaires showed that learners who used MLAR scored higher on Likert scales measuring learning behavioral intention, cognitive usefulness, cognitive ease of use and environmental interactivity. Liu et al. (2023) developed a digital learning system for a multiplayer online animated game coupled with 3D and VR technologies for firefighting education. The results show that it can increase learners' understanding of the content being taught and stimulate the willingness to learn.

Readers may be interested in recent research on immersive and engagement experiences in AR (Dağ et al., 2023; Dalili Saleh et al., 2021), VR and AR experiences in libraries (Greene and Groenendyk, 2021) and the metaverse (Feng et al., 2022).

Engaging learning with animations and games

The fifth theme of this issue focuses on emerging technologies to engage learners, such as animations and games (Hsu and Lin, 2023; Hsu and Liang, 2022), apart from engaging users with social media (Lam et al., 2023; Chan et al., 2020; Fong et al., 2020; Kwan et al., 2023).

In this issue, Huang (2023) explored different m-learning strategies and investigated the relationship between learning attitudes and achievement in the context of animated games. Based on their results, a higher level of digital experience and a more positive attitude toward using mobile phones are both associated with better learning performance. Wu and Tu (2022) designed teaching strategies for alternating peer teaching and progressive project-oriented learning to design a digital animation game curriculum in advance, allowing them to conduct experimental research in a teaching environment. As a result, they found that learners can acquire the necessary knowledge and skills of digital animation game production under the guidance of their proposed strategies. Wang et al. (2023c) proposed a hybrid research model integrating the decomposed theory of planned behavior, perceived playfulness, risk and task–technology fit (TTF) to examine adoption intention in students concerning brain-computer interface (BCI) games in a learning context. The results showed that attitude, subjective norms and TTF have a significant impact on the intention to play the BCI game, while perceived behavioral control did not show a significant impact.

Readers may also be interested in recent research on BCIs regarding library information retrieval (Wójcik, 2022b) and game-based learning with AR (Yu et al., 2022b).

Conclusion

We would like to thank all those who contributed to this special issue for their excellent participation, valuable scientific contributions and for their kind support for this special issue. We are confident that readers of Library Hi Tech, practitioners and scholars researching mobile technologies, social media and educational fields will find this special issue of great interest and benefit.