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This study aims to identify the gap between the requirements of the accreditation bodies and the widely used learning management systems (LMSs) in assessing the intended learning outcomes (ILOs). In addition, this study aims to introduce a framework, along with the evaluation of the functionality of the LMS, for measuring the ILO.

A qualitative method was deployed to examine the gap between the requirements of the accreditation standards and the LMS functionalities. The researchers collaborated to design a mechanism, develop a system architecture to measure the ILO in alignment with the accreditation standards and guide the development of the Moodle plugin. The appropriateness and effectiveness of the plugin were evaluated within the scope of assessment mapping and design. Focus group interviews were conducted to collect feedback from the instructors and program leaders regarding its implementation.

The results of this study indicate that there is no standardized mechanism to measure course and program ILO objectively, using the existing LMS. The implementation of the plugin shows the appropriateness and effectiveness of the system in generating ILO achievement reports, which was confirmed by the users.

This study proposed a framework and developed a system architecture for the objective measurement of the ILO through direct assessment. The plugin was tested to generate consistent reports during the measurement of course and program ILO. The plugin has been implemented across Gulf University’s program courses, ensuring appropriate reporting and continuous improvement.

The purpose of this study is to examine how five different multi-level governance (MLG) models affect place branding (PB) performance in Saudi Arabia.

In hierarchical administrative systems, central governments exert control on PB, influencing its effectiveness. While PB as such is widely studied, the effect of MLG on PB performance in centralized administrative systems remains understudied. The study is approached as a multiple case study of nine cities.

The study reveals that different MLG models indeed affect PB performance differently. Direct access to central leadership and resources boosts branding performance, while privatization promotes flexibility with similarly positive effects. Study findings, furthermore, show that some cities are considered too big to fail. Cities such as Riyadh and Neom are of prime importance and receive plenty of resources and leadership attention, while others are considered peripheral, are under-resourced and branding performance suffers accordingly. Emerging differences in PB performance associated with different MLG models are thus likely to deepen the gap between urban economic winners and losers.

This paper introduces five MLG models based on the actors involved in PB, their interactions and their access to resources. For each model, this paper assesses other factors which may influence the effectiveness of PB as well, such as access to the national leadership and staff capacity. This research thereby adds to the literature by identifying specific factors within MLG models influencing PB performance in hierarchical administrative systems.

Nearly 75% of EU buildings are not energy-efficient enough to meet the international climate goals, which triggers the need to develop sustainable construction techniques with high degree of resilience against climate change. In this context, a promising construction technique is represented by ventilated façades (VFs). This paper aims to propose three different VFs and the authors define a novel machine learning-based approach to evaluate and predict their energy performance under different boundary conditions, without the need for expensive on-site experimentations

The approach is based on the use of machine learning algorithms for the evaluation of different VF configurations and allows for the prediction of the temperatures in the cavities and of the heat fluxes. The authors trained different regression algorithms and obtained low prediction errors, in particular for temperatures. The authors used such models to simulate the thermo-physical behavior of the VFs and determined the most energy-efficient design variant.

The authors found that regression trees allow for an accurate simulation of the thermal behavior of VFs. The authors also studied feature weights to determine the most relevant thermo-physical parameters. Finally, the authors determined the best design variant and the optimal air velocity in the cavity.

This study is unique in four main aspects: the thermo-dynamic analysis is performed under different thermal masses, positions of the cavity and geometries; the VFs are mated with a controlled ventilation system, used to parameterize the thermodynamic behavior under stepwise variations of the air inflow; temperatures and heat fluxes are predicted through machine learning models; the best configuration is determined through simulations, with no onerous in situ experimentations needed.