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1 – 10 of 49Patrice Silver, Juliann Dupuis, Rachel E. Durham, Ryan Schaaf, Lisa Pallett and Lauren Watson
In 2022, the Baltimore professional development school (PDS) partner schools, John Ruhruh Elementary/Middle School (JREMS) and Notre Dame of Maryland University (NDMU) received…
Abstract
Purpose
In 2022, the Baltimore professional development school (PDS) partner schools, John Ruhruh Elementary/Middle School (JREMS) and Notre Dame of Maryland University (NDMU) received funds through a Maryland Educational Emergency Revitalization (MEER) grant to determine (a) to what extent additional resources and professional development would increase JREMS teachers’ efficacy in technology integration and (b) to what extent NDMU professional development in the form of workshops and self-paced computer science modules would result in greater use of technology in the JREMS K-8 classrooms. Results indicated a statistically significant improvement in both teacher comfort with technology and integrated use of technology in instruction.
Design/methodology/approach
Survey data were collected on teacher-stated comfort with technology before and after grant implementation. Teachers’ use of technology was also measured by unannounced classroom visits by administration before and after the grant implementation and through artifacts teachers submitted during NDMU professional development modules.
Findings
Results showing significant increases in self-efficacy with technology along with teacher integration of technology exemplify the benefits of a PDS partnership.
Originality/value
This initiative was original in its approach to teacher development by replacing required teacher professional development with an invitation to participate and an incentive for participation (a personal MacBook) that met the stated needs of teachers. Teacher motivation was strong because teammates in a strong PDS partnership provided the necessary supports to induce changes in teacher self-efficacy.
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Adebowale Jeremy Adetayo, Augustine I. Enamudu, Folashade Munirat Lawal and Abiodun Olusegun Odunewu
This study investigates the transformative role of Sora in education and libraries. This study aims to explore Sora’s capabilities and potential implications for enhancing…
Abstract
Purpose
This study investigates the transformative role of Sora in education and libraries. This study aims to explore Sora’s capabilities and potential implications for enhancing learning experiences and enriching library resources.
Design/methodology/approach
Using an exploratory approach, this paper analyzes Sora’s functionalities, focusing on its ability to convert textual descriptions into dynamic video content swiftly and accurately. It examines the ways in which Sora can augment learning through interactivity, personalization and accessibility, as well as its capacity to digitize cultural heritage and promote literacy in library settings.
Findings
Sora emerges as a potential powerful tool for education and libraries, offering opportunities for diverse learning modalities, creativity and critical thinking. Its capacity to facilitate immersive storytelling and educational gamification holds promise for engaging users and fostering community involvement. However, ethical considerations such as bias mitigation and equitable access must be addressed to maximize Sora’s benefits.
Originality/value
This study contributes to the understanding of artificial intelligence’s potential in education and libraries, particularly through the lens of Sora.
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The purpose of this study is to propose a research model based on the stimulus-organism-response (S-O-R) model to explore whether gamification and personalization as environmental…
Abstract
Purpose
The purpose of this study is to propose a research model based on the stimulus-organism-response (S-O-R) model to explore whether gamification and personalization as environmental stimuli to learners’ learning engagement (LE) can affect their learning persistence (LP) in massive open online courses (MOOCs) and, in turn, their learning outcomes in MOOCs.
Design/methodology/approach
Sample data for this study were collected from learners who had experience in taking gamified MOOCs provided by the MOOCs platform launched by a well-known university in Taiwan, and 331 usable questionnaires were analyzed using structural equation modeling.
Findings
This study demonstrated that learners’ perceived gamification and personalization in MOOCs positively influenced their cognitive LE and emotional LE elicited by MOOCs, which jointly explained their LP in MOOCs and, in turn, enhanced their learning outcomes. The results support all proposed hypotheses and the research model, respectively, explaining 82.3% and 65.1% of the variance in learners’ LP in MOOCs and learning outcomes.
Originality/value
This study uses the S-O-R model as a theoretical base to construct learners’ learning outcomes in MOOCs as a series of the psychological process, which is influenced by gamification and personalization. Noteworthily, while the S-O-R model has been extensively used in prior studies, there is a dearth of evidence on the antecedents of learners’ learning outcomes in the context of MOOCs, which is very scarce in the S-O-R view. Hence, this study enriches the research for MOOCs adoption and learning outcomes into an invaluable context.
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The purpose of this study is to propose a research model based on the stimulus-organism-response (S-O-R) model to examine whether media richness (MR), human-system interaction…
Abstract
Purpose
The purpose of this study is to propose a research model based on the stimulus-organism-response (S-O-R) model to examine whether media richness (MR), human-system interaction (HSI) and human-human interaction (HHI) as technological feature antecedents to medical professionals’ learning engagement (LE) can affect their learning persistence (LP) in massive open online courses (MOOCs).
Design/methodology/approach
Sample data for this study were collected from medical professionals at six university-/medical university-affiliated hospitals in Taiwan. A total of 600 questionnaires were distributed, and 309 (51.5%) usable questionnaires were analyzed using structural equation modeling in this study.
Findings
This study certified that medical professionals’ perceived MR, HSI and HHI in MOOCs positively affected their emotional LE, cognitive LE and social LE elicited by MOOCs, which together explained their LP in MOOCs. The results support all proposed hypotheses and the research model accounts for 84.1% of the variance in medical professionals’ LP in MOOCs.
Originality/value
This study uses the S-O-R model as a theoretical base to construct medical professionals’ LP in MOOCs as a series of the psychological process, which is affected by MR and interaction (i.e. HSI and HHI). Noteworthily, three psychological constructs, emotional LE, cognitive LE and social LE, are adopted to represent medical professionals’ organisms of MOOCs adoption. To date, hedonic/utilitarian concepts are more commonly adopted as organisms in prior studies using the S-O-R model and psychological constructs have received lesser attention. Hence, this study enriches the S-O-R model into an invaluable context, and this study’s contribution on the application of capturing psychological constructs for completely explaining three types of technological features as external stimuli to medical professionals’ LP in MOOCs is well-documented.
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Yaolin Zhou, Zhaoyang Zhang, Xiaoyu Wang, Quanzheng Sheng and Rongying Zhao
The digitalization of archival management has rapidly developed with the maturation of digital technology. With data's exponential growth, archival resources have transitioned…
Abstract
Purpose
The digitalization of archival management has rapidly developed with the maturation of digital technology. With data's exponential growth, archival resources have transitioned from single modalities, such as text, images, audio and video, to integrated multimodal forms. This paper identifies key trends, gaps and areas of focus in the field. Furthermore, it proposes a theoretical organizational framework based on deep learning to address the challenges of managing archives in the era of big data.
Design/methodology/approach
Via a comprehensive systematic literature review, the authors investigate the field of multimodal archive resource organization and the application of deep learning techniques in archive organization. A systematic search and filtering process is conducted to identify relevant articles, which are then summarized, discussed and analyzed to provide a comprehensive understanding of existing literature.
Findings
The authors' findings reveal that most research on multimodal archive resources predominantly focuses on aspects related to storage, management and retrieval. Furthermore, the utilization of deep learning techniques in image archive retrieval is increasing, highlighting their potential for enhancing image archive organization practices; however, practical research and implementation remain scarce. The review also underscores gaps in the literature, emphasizing the need for more practical case studies and the application of theoretical concepts in real-world scenarios. In response to these insights, the authors' study proposes an innovative deep learning-based organizational framework. This proposed framework is designed to navigate the complexities inherent in managing multimodal archive resources, representing a significant stride toward more efficient and effective archival practices.
Originality/value
This study comprehensively reviews the existing literature on multimodal archive resources organization. Additionally, a theoretical organizational framework based on deep learning is proposed, offering a novel perspective and solution for further advancements in the field. These insights contribute theoretically and practically, providing valuable knowledge for researchers, practitioners and archivists involved in organizing multimodal archive resources.
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Tsahi Hayat, Tal Samuel-Azran, Shira Goldberg and Yair Amichai-Hamburger
The 2020 Coronavirus pandemic forced universities to hastily transition to eLearning on a mass scale, necessitating the identification of populations who are more challenged by…
Abstract
Purpose
The 2020 Coronavirus pandemic forced universities to hastily transition to eLearning on a mass scale, necessitating the identification of populations who are more challenged by the transition. This study aims to identify how students’ level of introversion/extraversion and digital literacy come to play in their satisfaction with the eLearning environment.
Design/methodology/approach
The analysis examined 272 Israeli students who moved from a face-to-face learning environment to a Zoom learning environment between March–July 2020, following the outbreak of the pandemic. All the participants completed two rounds of surveys, and 62 of the 272 participants were then interviewed, and their social network was mapped using a sociogram.
Findings
Findings indicated that, in accordance with the “poor get richer” hypothesis, introverts expressed more satisfaction from the transition to the video-conferencing Zoom platform than extraverts. In addition, for highly introverted people, high digital literacy was significantly associated with increased course satisfaction, whereas for highly extraverted people, a high number of social ties with peers from the course was associated with course satisfaction.
Originality/value
As expected, the study’s findings shows that there is no “one size fits all” approach for online learning. Learners with different personalities can benefit from learning environments that foster greater satisfaction with the learning experience. Online platforms can, and should, be designed in a way that offers this needed personalization, and this study provides initial principles that can inform such personalization.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-01-2023-0028
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In an effort to position higher education institutions to survive in this fiercely competitive environment, the paper aims to identify the direct and indirect relationships…
Abstract
Purpose
In an effort to position higher education institutions to survive in this fiercely competitive environment, the paper aims to identify the direct and indirect relationships between higher education institutional positioning and exogenous factors (student engagement, employability, technology adaptation, teaching quality, and moral values).
Design/methodology/approach
A cross-sectional data was collected from 1,015 students studying in the pre-final year of graduation or post-graduate course/program from various educational institutions that were shortlisted based on the Indian NAAC and NIRF rankings. Thereafter, robust assessment criteria of PLS-SEM were used for model assessment and computation of results.
Findings
The findings revealed that to develop the greatest platform for upcoming young talent, higher educational institutional positioning ought to be addressed as a priority, which in turn will result in better living standards for upcoming generations.
Research limitations/implications
Framing strategies for urban students can never match those living in rural areas, as they are deprived of money due to their level of upbringing from childhood, which creates a high difference in the psychological mindset of students while choosing a career path.
Practical implications
The higher positioning of educational institutions clearly reflects the authentic learning environment, with professionalism leading to better student engagement with best industry practice.
Originality/value
Research novelty is highlighted as a more focused and streamlined approach to students’ career development and institution branding by reanalyzing and grouping various concepts of institutional positioning into a single model.
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Mengxi Zhou, Selena Steinberg, Christina Stiso, Joshua A. Danish and Kalani Craig
This study aims to explore how network visualization provides opportunities for learners to explore data literacy concepts using locally and personally relevant data.
Abstract
Purpose
This study aims to explore how network visualization provides opportunities for learners to explore data literacy concepts using locally and personally relevant data.
Design/methodology/approach
The researchers designed six locally relevant network visualization activities to support students’ data reasoning practices toward understanding aggregate patterns in data. Cultural historical activity theory (Engeström, 1999) guides the analysis to identify how network visualization activities mediate students’ emerging understanding of aggregate data sets.
Findings
Pre/posttest findings indicate that this implementation positively impacted students’ understanding of network visualization concepts, as they were able to identify and interpret key relationships from novel networks. Interaction analysis (Jordan and Henderson, 1995) of video data revealed nuances of how activities mediated students’ improved ability to interpret network data. Some challenges noted in other studies, such as students’ tendency to focus on familiar concepts, are also noted as teachers supported conversations to help students move beyond them.
Originality/value
To the best of the authors’ knowledge, this is the first study the authors are aware of that supported elementary students in exploring data literacy through network visualization. The authors discuss how network visualizations and locally/personally meaningful data provide opportunities for learning data literacy concepts across the curriculum.
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Wei Zhang, Hui Yuan, Chengyan Zhu, Qiang Chen, Richard David Evans and Chen Min
Although governments have used social media platforms to interact with the public in an attempt to minimize anxiety and provide a forum for public discussion during the pandemic…
Abstract
Purpose
Although governments have used social media platforms to interact with the public in an attempt to minimize anxiety and provide a forum for public discussion during the pandemic, governments require sufficient crisis communication skills to engage citizens in taking appropriate action effectively. This study aims to examine how the National Health Commission of China (NHCC) has used TikTok, the leading short video–based platform, to facilitate public engagement during COVID-19.
Design/methodology/approach
Building upon dual process theories, this study integrates the activation of information exposure, prosocial interaction theory and social sharing of emotion theory to explore how public engagement is related to message sensation value (MSV), media character, content theme and emotional valence. A total of 354 TikTok videos posted by NHCC were collected during the pandemic to explore the determinants of public engagement in crises.
Findings
The findings demonstrate that MSV negatively predicts public engagement with government TikTok, but that instructional information increases engagement. The presence of celebrities and health-care professionals negatively affects public engagement with government TikTok accounts. In addition, emotional valence serves a moderating role between MSV, media characters and public engagement.
Originality/value
Government agencies must be fully aware of the different combinations of MSV and emotion use in the video title when releasing crisis-related videos. Government agencies can also leverage media characters – health professionals in particular – to enhance public engagement. Government agencies are encouraged to solicit public demand for the specific content of instructing information through data mining techniques.
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This study aims to construct a sentiment series generation method for danmu comments based on deep learning, and explore the features of sentiment series after clustering.
Abstract
Purpose
This study aims to construct a sentiment series generation method for danmu comments based on deep learning, and explore the features of sentiment series after clustering.
Design/methodology/approach
This study consisted of two main parts: danmu comment sentiment series generation and clustering. In the first part, the authors proposed a sentiment classification model based on BERT fine-tuning to quantify danmu comment sentiment polarity. To smooth the sentiment series, they used methods, such as comprehensive weights. In the second part, the shaped-based distance (SBD)-K-shape method was used to cluster the actual collected data.
Findings
The filtered sentiment series or curves of the microfilms on the Bilibili website could be divided into four major categories. There is an apparently stable time interval for the first three types of sentiment curves, while the fourth type of sentiment curve shows a clear trend of fluctuation in general. In addition, it was found that “disputed points” or “highlights” are likely to appear at the beginning and the climax of films, resulting in significant changes in the sentiment curves. The clustering results show a significant difference in user participation, with the second type prevailing over others.
Originality/value
Their sentiment classification model based on BERT fine-tuning outperformed the traditional sentiment lexicon method, which provides a reference for using deep learning as well as transfer learning for danmu comment sentiment analysis. The BERT fine-tuning–SBD-K-shape algorithm can weaken the effect of non-regular noise and temporal phase shift of danmu text.
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