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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.

The purpose of this study was to understand perspectives of doctoral students about their compulsory online experience and aspects of their compulsory online experiences which were strongly associated as a result of the COVID-19 pandemic, a disorienting dilemma.

Exploratory descriptive survey research was the approach taken. Notable descriptors and associations were interpreted based on statistical analysis complimented by respondent comments.

Respondents included students who were at various stages of completing their doctoral degree. Overall findings indicated preference for face-to-face classes, the switch to online learning was well-received, primarily as a result of perceptions of quick and supportive communication from doctoral program leadership, strong student and instructor connections, and high-quality collaborative opportunities.

The COVID-19 pandemic represented a disorienting dilemma provoking cognitive dissonance among doctoral students who were compelled to move from a hybrid to a completely online learning model. Fear, anger and discontent induced by broken assumptions were mitigated through shared experiences creating new meaning and habits of mind in the process of adjusting to new expectations. Study results reveal that engagement, collaboration and support among instructors and classmates eased the transformative process transitioning into online learning.

The results of this study provided real-time understanding of students' needs in order to be successful in the quest and persistence of doctoral study online. Though the process of seeking official and state approvals to move the educational leadership doctoral program fully online, program faculty made a series of teaching and program adaptations informed by these results.

Research about doctoral student experiences during a compulsory transition from a hybrid to online delivery model has not been explored and offers original perspective to improve future practice transitioning into online programs for student acceptance, engagement and retention.

This study aims to answer pertinent questions related to the quality of the organisational learning capability measurement model.

A time-separated design informed data collection. The organisational learning capability was exposed to classical higher-order and bifactor confirmatory factor analyses. Multigroup confirmatory factor analysis facilitated measurement invariance testing. This study assessed the predictive validity of the organisational learning capability subscales using hierarchical regression analysis.

This study replicated the second-order organisational learning capability model with four subscales. Bifactor modelling confirmed the multidimensionality of the organisational learning capability. The organisational learning capability was invariant between gender groups. The organisational learning capability subscales accounted for a significant variance in innovative work behaviour.

The organisational learning capability exhibits robust properties, making it a plausible option for monitoring the quality of organisational learning. Organisations must appreciate the quality of this dynamic capability and leverage it to generate new sources of value.

This study fills a critical gap in organisational learning-related capabilities in sub-Saharan African contexts, providing a base to influence innovation-related trajectories positively.

Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural network (PINN), which was proposed to encode physical laws into neural networks, is a less data-demanding approach for flow field reconstruction. However, when the fluid physics is complex, it is tricky to obtain accurate solutions under the PINN framework. This study aims to propose a physics-based data-driven approach for time-averaged flow field reconstruction which can overcome the hurdles of the above methods.

A multifidelity strategy leveraging PINN and a nonlinear information fusion (NIF) algorithm is proposed. Plentiful low-fidelity data are generated from the predictions of a PINN which is constructed purely using Reynold-averaged Navier–Stokes equations, while sparse high-fidelity data are obtained by field or experimental measurements. The NIF algorithm is performed to elicit a multifidelity model, which blends the nonlinear cross-correlation information between low- and high-fidelity data.

Two experimental cases are used to verify the capability and efficacy of the proposed strategy through comparison with other widely used strategies. It is revealed that the missing flow information within the whole computational domain can be favorably recovered by the proposed multifidelity strategy with use of sparse measurement/experimental data. The elicited multifidelity model inherits the underlying physics inherent in low-fidelity PINN predictions and rectifies the low-fidelity predictions over the whole computational domain. The proposed strategy is much superior to other contrastive strategies in terms of the accuracy of reconstruction.

In this study, a physics-informed data-driven strategy for time-averaged flow field reconstruction is proposed which extends the applicability of the PINN framework. In addition, embedding physical laws when training the multifidelity model leads to less data demand for model development compared to purely data-driven methods for flow field reconstruction.

Framed within the bioecological model, this autoethnographic case study explores the author’s experiences as a working mother of two children with disability prior to, during and after emerging from compulsory remote learning arrangements in Victoria, Australia due to the COVID-19 pandemic. The purpose of this paper is to address this issue. The intention of this paper is to share the author's experiences.

Bronfenbrenner’s bioecological model was overlaid on the author’s experiences to explore direct and indirect impacts on her agency, educational priorities and personal values through each level of the model.

This research presents a new perspective for examining how the global COVID-19 pandemic has disrupted the equilibrium typically experienced by individuals across the education system.

This research presents a new perspective for examining how the global COVID-19 pandemic has disrupted the equilibrium typically experienced by individuals across the education system.

This article shares an academic library's transition from traditional reference services to a peer support model during a campus reopening post-COVID-19 closure. It examines the conception, implementation and implications of the Peer Mentor program amidst shifts in campus priorities, Library research and reference paradigms and the Library workforce, from the perspectives of library faculty and staff, alongside feedback from student workers.

The article uses a case study approach to detail the Library's Peer Mentor Program by authors involved in various stages of the program's lifespan and discusses the beginnings, implementations and challenges of the program and introduces the unique curriculum used to train student Peer Mentors.

The authors chronicle their own experience after two semesters of the Peer Mentor program alongside qualitative feedback from the first cohort of Peer Mentors. The Peer Mentor feedback points to positive impacts in other academic pursuits and in gaining a greater understanding of the information landscape and the library field.

The case study presented is a valuable example for academic librarians considering beginning peer-to-peer learning models within their own research and reference services units, especially those who may be creating these peer learning networks in the wake of library service disruption or restructure (such as due to the COVID-19 pandemic).

This case study documents the move to emergency virtual learning at the University of Johannesburg. Prefacing promoting social justice and care, the authors explain how key challenges unique to a university in a developing world context were considered. The findings highlight the importance of agility, adaptability and the role of Ubuntu (i.e. values of compassion, solidarity and sharing) in higher education institutional leadership in a crisis. Furthermore, the authors highlight the need to address how structural inequality and poverty impact the pace of technological infusion in higher education in the Global South.