Editorial: Special selection on advances in learning technologies

Kevin K.W. Ho (University of Tsukuba, Tokyo, Japan)
Dickson K.W. Chiu (Faculty of Education, University of Hong Kong, Pokfulam, Hong Kong)

Library Hi Tech

ISSN: 0737-8831

Article publication date: 7 November 2023

Issue publication date: 7 November 2023

475

Citation

Ho, K.K.W. and Chiu, D.K.W. (2023), "Editorial: Special selection on advances in learning technologies", Library Hi Tech, Vol. 41 No. 6, pp. 1621-1630. https://doi.org/10.1108/LHT-12-2023-590

Publisher

:

Emerald Publishing Limited

Copyright © 2023, Emerald Publishing Limited


Recently, we have started selecting articles of similar themes for regular issues and writing editorials to guide readers better to the Library Hi Tech and other related literature, such as “Bibliometrics and literature review” (Chiu and Ho, 2021, 2022a, 2023a) in 39(4), 40(3) and 41(2), “Information and Knowledge Management” (Chiu and Ho, 2023c) in 41(4), “Digital culture and reading” (Chiu and Ho, 2022b) in 40(5), “Social Media and Social Network Research” (Chiu and Ho, 2023b) in 41(3) and “40th anniversary: contemporary library research” (Chiu and Ho, 2022a, b) in 40(6). Continuing our previous special issue on contemporary technologies for education (Chan et al., 2022) in 41(5), we select seven more papers on recent advances in learning technology, which should be interesting to the broad readers of Library Hi Tech.

In this issue, Dai and Chiu (2023) revealed the effectiveness and problems of high school students' use of electronic resources and discovered changes in the reading behavior of high school students and their parents' attitudes during COVID-19. COVID-19 has seriously affected educational institutions, libraries and museums (Conrad et al., 2022; Fasae et al., 2021; Huang et al., 2021, 2022, 2023; Guo et al., 2021; Meng et al., 2023; Yu et al., 2023; Zhang et al., 2023c), though we are now recovering (Abbass et al., 2022; Suh and Alhaery, 2022; Kumar et al., 2021). Various high-quality online information services have provided information access (Cheng et al., 2023; Cheng, 2023a, b; Ding et al., 2021b; Hsieh et al., 2023; Lo and Chiu, 2015; Lo et al., 2015, 2017; Suen et al., 2020; Sun et al., 2022; Wang et al., 2016; Wong and Chiu, 2023b; Fan and Liu, 2022; Zuo et al., 2023; Wang et al., 2022; Wong et al., 2023b), facilitating access during the lockdown, as supported by ubiquitous mobile Internet services, social media, apps and devices (Dong et al., 2021; Dukic et al., 2015; Ezeamuzie et al., 2022; Fan et al., 2020; Fung et al., 2016; Gong et al., 2017; Hui et al., 2023; Lau et al., 2017, 2020; Law et al., 2019; Ni et al., 2022; Yip et al., 2021; Zhang et al., 2021, 2022). Thus, students and teachers have changed their information and communication habits to digital, particularly for the newer generation (Cheung et al., 2023; Guo et al., 2022; Ho et al., 2023; Nguyen and Le, 2021; Leung et al., 2022; Li et al., 2021; Lu et al., 2023; Soroya and Faiola, 2023; Sung and Chiu, 2022; Yin et al., 2023; Yu et al., 2022; Yuan et al., 2023; Wang and Xie, 2022; Chiu and Wong, 2023; Wong et al., 2023a; Wu and Zhang, 2022; Zhou et al., 2022).

Next, in this issue, Senthil Kumaran and Latha (2023) improved the accuracy and efficiency of predicting and recommending personalized learning resources in digital libraries with a novel method using an ontology-supported collaborative filtering recommendation system to facilitate a personalized digital learning environment. Using ontological framework knowledge for semantic similarity and considering multiple attributes apart from learner preferences for the learning resources improve the accuracy of the proposed model. Readers, please note digitalization technology enables libraries and scholars to share links and content on social media to attract users (Banshal et al., 2022; Lo et al., 2019; Ocran and Afful-Arthur, 2022; Pianzola et al., 2022; Yang et al., 2022; Wu et al., 2022a, b; Xu and Du, 2022). In particular, e-loyalty and e-trust are essential for digital service adoption and usage continuance (Chan et al., 2022; Wang et al., 2021, 2022; Yin et al., 2023), where good personalization and recommendations are often good strategies.

As for using bibliometrics technology in learning, Inaam ul haq et al. (2023) reflected the trends in the academic landscape of special education using probabilistic dynamic topic modeling. They identified the hottest topic of research in this domain, including children's communication and speech using gaming techniques, mental retardation, cost effect on infant birth, involvement of special education children and their families, assistive technology information for special education, syndrome epilepsy and the impact of group study on skill development peers or self. Zárate et al. (2023) presented SciBR-M, a novel method to identify scientific interest evolution from bibliographic material based on Formal Concept Analysis for Educational Data Mining, considering 23 years since the first publications. In the digital libraries context, SciBR-M allows the integration of the academy, education and cultural memory concerning a study domain. Readers may also be interested in other bibliometrics research and reviews on library-related subjects (Alcaraz Martínez et al., 2022; Borgohain et al., 2022; Nadi-Ravandi and Batooli, 2023; Ding et al., 2021a; Feng et al., 2022a, b; Kim et al., 2021; Lee, 2021; Nugroho et al., 2023; Nwankwo et al., 2022; Shahzad and Khan, 2022; Wijewickrema, 2023; Zhang et al., 2023a).

Moreover, the pandemic has accelerated service transformation (Chan and Chiu, 2022; Cheng et al., 2023; Chin and Chiu, 2023; Hsu and Lin, 2021; Khan and Ayesha, 2022; Kelner et al., 2022; Li et al., 2023a, b; Lin et al., 2022; Lo et al., 2020; Noh, 2022; Nwagwu and Matobako, 2022; Okunlaya et al., 2022; Tsang and Chiu, 2022; Tse et al., 2022; Yao et al., 2023; Wong and Chiu, 2023a; Wu et al., 2022a, b, c, 2023; Xue et al., 2023), leading to recent advancements in learning technologies. In this issue, Shang and Du (2023) identified with interpretive structural modeling and cross-impact matrix multiplication 50 factors that potentially affect the quality of online courses, together with the hierarchical relationships and dependent correlations between the factors. Readers may also be interested in other online education aids, platforms and related issues (Cheng, 2023a, b; Cheng et al., 2023; Chung et al., 2020; Kabigting et al., 2023; Jiang et al., 2023a, b; Lei et al., 2021; Leung et al., 2023; Mak et al., 2022; Wang et al., 2023; Xie et al., 2023a, b) as well as service promotion issues (Chan et al., 2020; Cheng et al., 2020; Fong et al., 2020; Kwan et al., 2023; Lam et al., 2019, 2023; Liu et al., 2023; Nabi et al., 2023; Sahli et al., 2023; Wójcik, 2022; Wu and Yang, 2022; Zhang et al., 2023b).

As for Massive Open Online Courses (MOOC) technologies, Cheng (2023a) discovered with a survey and structure equation modeling that students' perceived knowledge quality, system quality, interface design quality, learner–instructor interaction quality and collaboration quality all positively caused students' perceived usefulness, confirmation and learning effectiveness in MOOCs, which jointly explained students' satisfaction with MOOCs and subsequently resulted in students' continuance intention of MOOCs. Cheng (2023b) further demonstrated that learners' perceived gamification, learner–instructor interaction and learner–learner interaction in MOOCs positively affected their cognitive presence, teaching presence and social presence elicited by MOOCs, which jointly expounded their continuance intention of MOOCs. Readers may also be interested in Cheng et al. (2023), which summarized the research foci (e.g. themes, methods, contexts, etc.) and discussed the new trends of massive open online courses (MOOCs) research in Mainland China.

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Further reading

Batool, S.H., Rehman, A.u. and Sulehri, I. (2022), “The current situation of information literacy education and curriculum design in Pakistan: a discovery using Delphi method”, Library Hi Tech, Vol. 40 No. 6, pp. 1705-1720.

Dai, W., Zhou, Y., Zhang, C. and Zhang, H. (2023), “Open data: an innovative learning resource for postgraduates”, Library Hi Tech, ahead-of-print, doi: 10.1108/LHT-05-2022-0228.

Khan, A.U., Zhang, Z., Chohan, S.R. and Rafique, W. (2022), “Factors fostering the success of IoT services in academic libraries: a study built to enhance the library performance”, Library Hi Tech, Vol. 40 No. 6, pp. 1796-1818.

Sun, X., Wang, G., Xu, L. and Yuan, H. (2021), “Data replication techniques in the Internet of Things: a systematic literature review”, Library Hi Tech, Vol. 39 No. 4, pp. 1121-1136.

Wai, I.S.H., Ng, S.S.Y., Chiu, D.K.W., Ho, K.K.W. and Lo, P. (2018), “Exploring undergraduate students' usage pattern of mobile apps for education”, Journal of Librarianship and Information Science, Vol. 50 No. 1, pp. 34-47.

Wu, H. (2022), “Digital media recording and broadcasting classroom using Internet intelligent image positioning and opinion monitoring in communication”, Library Hi Tech, ahead-of-print, doi: 10.1108/LHT-09-2021-0315.

Yi, Y. and Chiu, D.K.W. (2023), “Public information needs during the COVID-19 outbreak: a qualitative study in mainland China”, Library Hi Tech, Vol. 41 No. 1, pp. 248-274.

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