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

Citation

Chen, C.-C., Hung, P.C.K., Egrioglu, E., Chiu, D.K.W. and Ho, K.K.W. (2024), "Guest editorial: Contemporary learning behaviors on mobile devices and social media – part II", Library Hi Tech, Vol. 42 No. 2, pp. 381-391. https://doi.org/10.1108/LHT-04-2024-617

Introduction

Following our previous special issues on social robots (Kelner et al., 2022), smart cities (Chang and Abdel-Basset, 2022) and COVID-19 (Huang et al., 2021, 2022, 2023), we are glad to continue with part II of this special issue on contemporary technologies for education from part I (Chiu and Ho, 2023d) with 15 articles. As we have overwhelming responses on our recent special issues and thematic selection of articles to be published in recent issues (Chiu and Ho, 2021, 2022a, b, c, 2023a, b, c, 2024; Ho and Chiu, 2023), we shall continue this practice in organizing our publication and guide readers to thematic research and practices presented by authors of Library Hi Tech.

After COVID-19, e-learning or m-learning has been widely adopted worldwide (Huang et al., 2021, 2022, 2023). However, evidence shows that some teachers with limited information technology skills may have adopted mobile 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 mobile 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 essential 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., 2024; 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.

Artificial intelligence (AI)

The first theme of this special issue focuses on intelligent algorithms for contemporary learning behavior analysis. Artificial intelligence (AI) 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, Chen et al. (2024) show that AI programming self-efficacy, AI literacy and course satisfaction directly affected students’ intention to participate in AI software development. Course playfulness significantly affected course satisfaction and AI literacy, while course usefulness positively affected course satisfaction but did not significantly affect AI literacy and AI programming self-efficacy. Wu and Chung (2024) show that the click-and-mortar teaching environment with assistive videos and problem-based learning can improve learning setting and quality, solidifying learners' proficiency in an Artificial Intelligence of Things (AIoT) course.

On the other hand, researchers use AI for learning analytics. In this issue, Wu (2024) uses Internet real-time intelligent image positioning and opinion monitoring for Internet real-time intelligent image positioning and opinion monitoring in communication teaching and compares the robustness and performance of several optimization algorithms. Meng et al. (2024) used network analysis and deep learning to identify the emotions and topics of misinformation and extracted the structural characteristics of the spreading network from misinformation microblogs and reposts on Weibo,

Readers may be interested in recent research on deep learning for analyzing legal education learners on social media (Chen, 2023), learning of deep learning technologies (Fan and Liu, 2023), incorporating new technologies in various curricula (Ho et al., 2023; Li and Chiu, 2022; Nguyen, 2021; Yew et al., 2022) and text complexity analysis (Liu, 2023).

Virtual reality (VR), augmented reality (AR) and education games

The second 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). Such emerging technologies can engage learners, such as animations and games (Hsu and Lin, 2023; Hsu and Liang, 2022), apart from engaging users with social media (Chan et al., 2020; Fong et al., 2020; Kwan et al., 2023; Lam et al., 2023).

In this issue, Xin (2024) shows that furniture products’ interactive display through VR can improve learner satisfaction through quality design planning and help students better grasp the characteristics of products than paper catalogs, website catalogs and even online videos. Zhang (2024) shows that the relationship between players' motivation in AR-based games and the virtual community has a significant positive impact. Yu et al. (2024a, b) show that the experimental group using the AR board game played aggressively by obtaining the maximum score with the highest number, while the control group focused more on finding the correct answer. Thus, integrating an AR board game into the curriculum should respond to different learning attitudes and behaviors. Yang et al. (2024) also show that student self-efficacy, engagement and learning outcomes for business simulation games were significantly higher with the hierarchical teaching method than with the general teaching method.

Readers may be interested in recent research on immersive and engagement experiences in AR (Dağ et al., 2023; Dalili Saleh et al., 2021; Li and Liu, 2023), VR and AR experiences in libraries (Greene and Groenendyk, 2021), mobile business simulation games (Chen et al., 2023), the metaverse (Feng et al., 2022), game animation design (Wu and Tu, 2023; Zhang and Chang, 2023); and game-based learning with AR (Liu et al., 2023; Yu et al., 2024a, b).

Library and information support of contemporary learning behaviors

Libraries are transforming from a space to store books into integrated cultural, meeting and learning spaces that tend to operate 24/7 (Chan et al., 2020; Ho et al., 2022a, b; Jiang et al., 2023; Kim et al., 2020; Leung et al., 2023; Wong and Chiu, 2023; Yip et al., 2021). Another important direction of transformation of library systems is digitalization and automation (Lin et al., 2022; Lo et al., 2017; Shahzad and Khan, 2022; Sung and Chiu, 2022; Tse et al., 2022).

In this issue, Jiang et al. (2024) construct a conceptual model of influencing factors on mobile library user experiences comprising system quality, interaction quality, content quality, interface quality and function quality. This model helps determine users' perspectives and behavioral characteristics regarding mobile learning engagement. Zhang and Hu (2024) explain that mobile learning technology supports design makers in sharing and co-creating for individual development, the evolution of learning organizations and producing creative value. Such communities of practice require group factors, activity development, physical and technical resources, spatial support and institutional norms to enhance learning behaviors and satisfy learning needs. Dai et al. (2024) show that perceived usefulness, performance expectancy, social influence and facilitating conditions positively influence accepting open data as learning resources, while behavioral intention significantly influences the acceptance of open data as learning resources. Yu et al. (2024a, b) show that information appeal is the main motivation of middle-aged and old-aged users to use short videos. Users with different social attributes have differences in motivation, and a significant relationship exists between motivation and behavior.

Readers may also be interested in recent research on communities of practice (Leung et al., 2023; Wang and Xie, 2023; Zhang and Hu, 2024), MOOCs (Cheng, 2022; Cheng et al., 2023; Wang et al., 2023b), social media information creditability and sharing issues (Khan et al., 2022; Li et al., 2023b; 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).

Transforming learning with mobile technologies

The last theme of this issue focuses on the transformation of learning with contemporary mobile and social media technologies, which help 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). 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., 2022) 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). 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, Cheng et al. (2024) examine the relationship between social media usage (SMU) and the critical thinking ability (CTA) of university students and found that SMU is a double-edged sword, narrowing the digital gap in terms of accessibility but widening the digital gap in terms of usage. Chen and Chen (2024) show that the mobile information environment significantly impacts perceived usefulness and ease of use for learning. The attitude and behavior of using mobile devices, in turn, improve the quality of teaching. Conrad et al. (2024) show that we-intentions are positively associated with recent app use and student satisfaction, group norms significantly influence the formation of we-intentions and social identity is positively associated with both we-intentions and satisfaction. Istek and Ironsi (2024) show that participants perceived enhanced mobile learning as enjoyable and that integrating the CAP(E) framework and m-learning improved their reading skills. Lai (2024) integrated understanding by design-Internet of Things (UbD-IoT) education with design thinking and computational thinking to plan and design an IoT course and showed this approach greatly decreased students' overall cognitive load.

Readers may also be interested in research on mobile vocational information needs (Ezeamuzie et al., 2022), technical vocational training (Abd Majid et al., 2022; Huang, 2023), older adults’ usage (Yang et al., 2023), visual aspects of mobile search (Wu and Zhang, 2022; Zeng et al., 2022), mobile layout design (Zhang et al., 2023a), data usage-based privacy and security issues in mobile app recommendation (Beg et al., 2022), mobile news usage (Wang et al., 2023a; Yu et al., 2022), brain computer interface for brain learning (Wang et al., 2023c) and cloud data service (Hui et al., 2023).