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With the continuous growth of online education, the quality issue of online educational videos has become increasingly prominent, causing students in online learning to face the dilemma of knowledge confusion. The existing mechanisms for controlling the quality of online educational videos suffer from subjectivity and low timeliness. Monitoring the quality of online educational videos involves analyzing metadata features and log data, which is an important aspect. With the development of artificial intelligence technology, deep learning techniques with strong predictive capabilities can provide new methods for predicting the quality of online educational videos, effectively overcoming the shortcomings of existing methods. The purpose of this study is to find a deep neural network that can model the dynamic and static features of the video itself, as well as the relationships between videos, to achieve dynamic monitoring of the quality of online educational videos.

The quality of a video cannot be directly measured. According to previous research, the authors use engagement to represent the level of video quality. Engagement is the normalized participation time, which represents the degree to which learners tend to participate in the video. Based on existing public data sets, this study designs an online educational video engagement prediction model based on dynamic graph neural networks (DGNNs). The model is trained based on the video’s static features and dynamic features generated after its release by constructing dynamic graph data. The model includes a spatiotemporal feature extraction layer composed of DGNNs, which can effectively extract the time and space features contained in the video's dynamic graph data. The trained model is used to predict the engagement level of learners with the video on day T after its release, thereby achieving dynamic monitoring of video quality.

Models with spatiotemporal feature extraction layers consisting of four types of DGNNs can accurately predict the engagement level of online educational videos. Of these, the model using the temporal graph convolutional neural network has the smallest prediction error. In dynamic graph construction, using cosine similarity and Euclidean distance functions with reasonable threshold settings can construct a structurally appropriate dynamic graph. In the training of this model, the amount of historical time series data used will affect the model’s predictive performance. The more historical time series data used, the smaller the prediction error of the trained model.

A limitation of this study is that not all video data in the data set was used to construct the dynamic graph due to memory constraints. In addition, the DGNNs used in the spatiotemporal feature extraction layer are relatively conventional.

In this study, the authors propose an online educational video engagement prediction model based on DGNNs, which can achieve the dynamic monitoring of video quality. The model can be applied as part of a video quality monitoring mechanism for various online educational resource platforms.

This case study explores how universal design for learning (UDL)-informed online instruction modules developed during COVID-19 can better support student information literacy outcomes. This study will also examine how hybrid learning lends itself to UDL and may resolve some of the issues within library instruction.

This case study explores how a team of librarians at Utah State University developed three UDL-informed modules to support library instruction and hybrid learning during the height of the COVID-19 pandemic. A survey was sent to composition instructors to understand how they utilized the three new UDL-informed modules and if the modules helped their students reach information literacy outcomes.

Findings from this case study describe how academic libraries should adopt the UDL framework to support best practices for online learning as well as inclusive pedagogies. The findings indicate that the UDL-informed modules developed for hybrid instruction help students meet information literacy outcomes and goals.

The authors present a case study examining the current climate of information literacy instruction and UDL while providing actionable instructional practices that can be of use to librarians implementing hybrid instruction.

Business intelligence (BI) systems and tools are deemed to be a transformative source with the potential to contribute to reshaping the way different healthcare organizations’ (HCOs) services are offered and managed. However, this emerging field of research still appears underdeveloped and fragmented. Hence, this paper aims to reconciling, analyzing and synthesizing different strands of managerial-oriented literature on BI in HCOs and to enhance both theoretical and applied future contributions.

A literature-based framework was developed to establish and guide a three-stage state-of-the-art systematic literature review (SLR). The SLR was undertaken adopting a hybrid methodology that combines a bibliometric and a content analysis.

In total, 34 peer-review articles were included. Results revealed significant heterogeneity in theoretical basis and methodological strategies. Nonetheless, the knowledge structure of this research’s stream seems to be primarily composed of five clusters of interconnected topics: (1) decision-making, relevant capabilities and value creation; (2) user satisfaction and quality; (3) process management, organizational change and financial effectiveness; (4) decision-support information, dashboard and key performance indicators; and (5) performance management and organizational effectiveness.

To the authors’ knowledge, this is the first SLR providing a business and management-related state-of-the-art on the topic. Besides, the paper offers an original framework disentangling future research directions from each emerged cluster into issues pertaining to BI implementation, utilization and impact in HCOs. The paper also discusses the need of future contributions to explore possible integrations of BI with emerging data-driven technologies (e.g. artificial intelligence) in HCOs, as the role of BI in addressing sustainability challenges.

Black boys report experiencing more school-based racial discrimination than any other group (Butler-Barnes et al., 2019). Additionally, Black boys are viewed as older and less innocent than their peers beginning as early as 10 years old (Goff et al., 2014). Black boys are also suspended and expelled at much higher rates than other students (Graves & Wang, 2022). As such, there needs to be an investment in asset-based research designed to understand the factors that can help Black boys cope with these perceptions. Consequently, this chapter will discuss strengths based protective factors that will aid in the promotion of positive outcomes in Black boys.

The study aims to explore the effectiveness of performance-oriented practices like high-performance work practices (HPWPs) in higher education (HE), given its explicit focus on performance these days.

The study uses hierarchical linear modeling using statistical package for social sciences (SPSS 22.0) to test the hypotheses. An intertwined framework of the ability–motivation–opportunity (AMO) model and the job demand-resources (JD-R) model was proposed. The study considered strategic hiring, recognition and participatory decision-making as ability, motivation and opportunity-enhancing practices respectively. Further, the study addressed the impact of institutional level moderators, like administrative workload (AWL) and support staff (SS).

The findings based on the responses of 385 faculties and 443 students from 36 Indian institutes, indicated that HPWPs enhanced the education performance (EP) of HE institutes. Further, results revealed that both AWL and SS had differential effects on the relationship between HPWPs and EP. Contrary to authors’ expectations, SS showed a negative effect of the relationship between HPWPs and EP.

The increased AWL was debilitating the beneficial effects HPWPs. The negative interaction effect of SS sheds light on the hidden issues surrounding SS in HE institutes. Based on findings, the study offered important theoretical and practical implications.

To the best of authors’ knowledge, the impact of innovative human resource (HR) practices in academia remains relatively under-researched, and the current study is an attempt to fill this void.

This study aims to establish a comprehensive understanding of the intricacies of how individuals engage with deepfakes, focusing on limiting adverse effects and capitalizing on their benefits.

This study conducted a meta-synthesis of qualitative studies on deepfakes, incorporating study-specific analysis followed by a cross-study synthesis.

Based on the meta-synthesis, the study developed an integrated conceptual framework based on the perspectives from the social shaping of technology theory embedding deepfake-related assertions, motivations, the subtleties of digital platforms, and deepfake-related repercussions.

The study offers crucial insights into the evolving nature of deepfakes as a socio-technical phenomenon and the significance of platform dynamics in deepfake production. It enables researchers to comprehend the cascading effects of deepfakes and positions them to evaluate deepfake-related risks and associated mitigation mechanisms.

The framework that emerges from the study illustrates the influence of platforms on the evolution of deepfakes and assists platform stakeholders in introducing effective platform governance structures to combat the relentless proliferation of deepfakes and their consequences, as well as providing guidance for governments and policymakers to collaborate with platform leaders to set guardrails for deepfake engagement.

Deepfakes have been extensively contested for both their beneficial and negative applications and have been accused of heralding an imminent epistemic threat that has been downplayed by some quarters. This diversity of viewpoints necessitates a comprehensive understanding of the phenomenon. In responding to this call, this is one of the first to establish a comprehensive, theoretically informed perspective on how individuals produce, process, and engage with deepfakes through a meta-synthesis of qualitative literature on deepfakes.

The purpose of this paper is to understand the market for various e-learning platforms existing globally. The global pandemic transformed the traditional education sector into an e-learning industry and impacted the digital education sector proportionally. The wide impact on people to maintain a social distance shortened the space and enhanced student engagement with digital screens. The merchandise of electronic gadgets and e-learning platforms boosted global revenues ever before.

A digital data analytical tool retrieved the data for e-learning companies. While screening companies, the authors came up with more than 150 and later narrowed it to 71. Furthermore, revenues and market capitalisation data were collected from open-source websites of respective e-learning platforms at national and international stock exchanges. The data from the year 2018 to 2021 was used to predict the compound annual growth rate (CAGR) of the 2022–2027 market value of e-learning companies. The demographics of the statistical data were plotted using GraphPad Prism and Python Plotly. Also, the study attempted to underpin the association of annual revenue with market capitalisation for e-learning companies using Statistical Package for the Social Sciences (SPSS).

The financials of the e-learning platforms were compared from 2018 to 2021 based on the stock market exchange, which showed a gradual increase in revenues and influenced the market capitalisation by the 2022 financial year. Also, simultaneously validating the CAGR of 13.16%, the expected revenues from 2022 to 2027 were $830bn. The data plotted on the atlas choropleth exhibiting the e-learning users increased globally by 2022. The Web traffic data validated by the browsing gadget helped to validate the increase in revenues of the electronic gadget companies.

To the best of the authors’ knowledge, this is one of the pioneer studies postulating the study of e-learning demographics comparing the e-learning influence on the global digital market, Web user traffic and the education sector. The demographics of e-learning users by age, gender and gadget distribution validate the e-learning induction globally.

That knowledge acquisition from external sources can play a pivotal role in product design is a well-known fact. However, knowledge acquisition need not play a pivotal role in every context; it is also documented to have a dark side (i.e. negative impacts). Specifically, given that product stewardship, by definition, calls on each party in the product life cycle – including suppliers – to share responsibility for the environmental impact of products, the purpose of this study is to answer the question “whether knowledge acquired from suppliers plays a beneficial role in the context of product stewardship?”

This study focuses on the effect of knowledge acquisition on product stewardship and its subsequent effect on environmental performance. Given that the effect of knowledge acquisition could be moderated by firm-specific and relational factors, this study also considers the moderating role of knowledge exploitation and supplier opportunism. Using primary data, the hypotheses are tested using two-stage hierarchical ordinary least squares regression models involving valid instruments.

Though extant research doubts that knowledge acquisition will always be beneficial, this study adheres to the tenets of knowledge-based view and hypothesize that knowledge acquisition is pivotal to product stewardship and its subsequent impact on environmental performance. But the results suggest an intriguing double-edged effect of knowledge acquisition; while its direct effect on product stewardship is nonsignificant, it seemed to have a significant positive moderating effect on the relationship between product stewardship and environmental performance. But whenever knowledge exploitation and supplier opportunism are maintained at ideal levels, this double-edged effect of knowledge acquisition is successfully negated.

While knowledge acquisition is key for new product design, its specific role in the product design that incorporates environmental considerations is still not clear. By proposing that knowledge acquisition could instead have a double-edged effect within the unique context of product stewardship, the study makes an invaluable contribution to the extant literature on knowledge management within supply chain relationships.

This study aims to examine the scope and contribution of Forensic Clinical Psychology (FCP) advice from the National Crime Agency (NCA) to criminal investigations in the UK to address the gap in current knowledge and research.

The 36 FCP reports reviewed were written between 2017 and 2021. They were analysed using Toulmin’s (1958) application of pertinent arguments to the evaluation process. The potential utility of the reports was analysed in terms of the advice provided.

Most of the reports involved murder and equivocal death. The reports focused primarily on understanding the offender’s psychopathology, actions, motivation and risk to self and others using a practitioner model of case study methodology. Out of the 539 claims, grounds were provided for 99% of the claims, 91% had designated modality, 62% of the claims were potentially verifiable and 57% of the claims were supported by a warrant and/or backing. Most of the reports provided either moderate or high insight into the offence/offender (92%) and potential for new leads (64%).

The advice provided relied heavily on extensive forensic clinical and investigative experience of offenders, guided by theory and research and was often performed under considerable time pressure. Flexibility, impartiality, rigour and resilience are essential prerequisites for this type of work.

To the best of the authors’ knowledge, this study is the first to systematically evaluate forensic clinical psychology reports from the NCA. It shows the pragmatic, dynamic and varied nature of FCP contributions to investigations and its potential utility.

The topic of student engagement (SE) has received considerable attention in the literature for its robust correlation with many positive educational outcomes in higher education (HE). However, among these desirable outcomes, student competencies have rarely been examined despite their importance in employability. While universities are expected to improve graduates' competencies and prepare them to adapt in an uncertain and complex workplace, the results are still limited. Therefore, this study attempts to examine the impacts of SE on student competencies in HE in Vietnam, focusing on the out-of-class environment. It also aims at developing a measurement instrument for out-of-class SE.

Based on student self-reports, this study collected two different datasets of 492 and 490 undergraduate business students in Hanoi to implement separate exploratory and confirmatory factor analyses. The measurement instrument of out-of-class engagement was adapted from the literature review and developed based on qualitative research, which combined the four subconstructs of participatory, emotional, cognitive and agentic engagement.

Findings confirms four subconstructs of SE in the out-of-class context where agentic engagement is a valid and distinct aspect. Findings further show that, among these four subconstructs, cognitive and agentic engagement have significant impacts on all four important student competencies for business students, while participatory and emotional engagement do not.

This study confirmed out-of-class SE has significant positive impacts on student competencies in HE, especially with out-of-class cognitive and agentic engagement. The findings show evidence of different impacts between participatory and agentic engagement, in which while participatory has no impacts on student competencies, agentic engagement significantly contributes to competencies of managing self, communicating and managing people and tasks. This implies different outcomes can be expected from different levels of engagement.

From a managerial point of view, the findings of this study strongly suggest that higher education institutions (HEIs) should view the out-of-class environment as an important extended learning context for students' complete development. Besides, as student out-of-class participatory engagement is not sufficient to the formation of student competencies, HEIs should strengthen out-of-class cognitive and agentic engagement among students. From the leadership perspective, building a good campus environment with various opportunities to involve students in diversified extra-curricular activities is beneficial to university students.

This paper contributes to the body of knowledge in SE by establishing a theoretical linkage between out-of-class engagement and student competencies and provides sufficient statistical significance. In addition, it validated out-of-class agentic engagement as a separate and distinct subcomponent of out-of-class SE and confirmed the positive impact of out-of-class engagement on student competencies for business students.