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Smart learning analytics (Smart LA) – i.e. the process of collecting, analyzing and interpreting data on how students learn – has great potentials to support opportunistic learning and offer better – and more personalized – learning experiences. The purpose of this paper is to provide an overview of the latest developments and features of Smart LA by reviewing relevant cases.

The paper studies several representative cases of Smart LA implementation, and highlights the key features of Smart LA. In addition, it discusses how instructors can use Smart LA to better understand the efforts their students make, and to improve learning experiences.

Ongoing research in Smart LA involves testing across various learning domains, learning sensors and LA platforms. Through the collection, analysis and visualization of learner data and performance, instructors and learners gain more accurate understandings of individual learning behavior and ways to effectively address learner needs. As a result, students can make better decisions when refining their study plans (either by themselves or in collaboration with others), and instructors obtain a convenient monitor of student progress. In summary, Smart LA promotes self-regulated and/or co-regulated learning by discovering opportunities for remediation, and by prescribing materials and pedagogy for remedial instruction.

Characteristically, Smart LA helps instructors give students effective and efficient learning experiences, by integrating the advanced learning analytics technology, fine-grained domain knowledge and locale-based information. This paper discusses notable cases illustrating the potential of Smart LA.

Most local governance assessment tools are entirely or partially based on stakeholders’ surveys, focus groups and benchmarks of different local governments in the world. These tools remain a subjective way of local governance evaluation. To measure the performance of local good-governance using an unbiased assessment technique, the authors have developed a framework to help automate the design process of a data warehouse (DW), which provides local and central decision-makers with factual, measurable and accurate local government data to help assess the performance of local government. The purpose of this paper is to propose the extraction of the DW schema based on a mixed approach that adopts both i* framework for requirements-based representation and domain ontologies for data source representation, to extract the multi-dimensional (MD) elements. The data was collected from various sources and information systems (ISs) deployed in different municipalities.

The authors present a framework for the design and implementation of a DW for local good-governance assessment. The extraction of facts and dimensions of the DW’s MD schema is done using a hybrid approach, where the extraction of requirement-based DW schema and source-based DW schema are done in parallel followed by the reconciliation of the obtained schemas to obtain the good-governance assessment DW final design.

The authors developed a novel framework to design and implement a DW for local good-governance assessment. The framework enables the extraction of the DW MD schema by using domain ontologies to help capture semantic artifacts and minimize misconceptions and misunderstandings between different stakeholders. The introduction and use of domain ontologies during the design process serves the generalization and automation purpose of the framework.

The presently conducted research faced two main limitations as follows: the first is the full automation of the design process of the DW and the second, and most important, is access to local government data as it remains limited because of the lack of digitally stored data in municipalities, especially in developing countries in addition to the difficulty of accessing the data because of regulatory aspects and bureaucracy.

The local government environment is among the public administrations most subject to change-adverse cultures and where the authors can face high levels of resistance and significant difficulties during the implementation of decision support systems, despite the commitment/engagement of decision-makers. Access to data sources stored by different ISs might be challenging. While approaching the municipalities for data access, it was done in the framework of a research project within one of the most notorious universities in the country, which gave more credibility and trust to the research team. There is also a need for further testing of the framework to reveal its scalability and performance characteristics.

Compared to other local government assessment ad hoc tools that are partially or entirely based on subjectively collected data, the framework provides a basis for automated design of a comprehensive local government DW using e-government domain ontologies for data source representation coupled with the goal, rationale and business process diagrams for user requirements representations, thus enabling the extraction of the final DW MD schema.

In this study, TiO2 is used to enhance the mechanical properties of the composite material containing agave Americana fiber and polyester resin.

Agave Americana fiber was first treated with 5% of NaOH, and the composition of treated and untreated fiber was kept constant, whereas the particulate and resin were alternatively used. The handlay method is used to fabricate the composite plates. The morphology of the composites was studied using scanning electron microscopy (SEM).

The composite was composed of 30% treated agave Americana, 10% of TiO₂ particulates and 60% of a polyester resin for better and enhanced mechanical properties.

The composite can be used for aero-structural components, automobile components and other areas where light-weight components are required.

A new type of agave Americana fiber with TiO₂ and polyester resin composite was fabricated and investigated.

This chapter examines the manner in which a disaster-affected population of artisanal fishers relocated inland to new sites following the Indian Ocean tsunami of 2004 experienced and adapted to problems of water quality, scarcity, sanitation, and drainage. While numerous studies of conflicts over water tend to focus on issues of equitable access (see Anand, 2011), this chapter seeks to link the problem to the contested priorities driving land and resource use and access. I show how inland relocation negatively impacted households, making it harder to sustain livelihoods due to distance from the coast, while imposing new costs including that of commodified and scarce water, locational deficiencies, and the structural weaknesses of new housing. Placed in a historical context, the problem of water can be seen as an aspect of the long-term problem of ecologically unequal exchange pitting local artisanal fisher communities against an aggressively state-supported commercial fishery sector. The continuity I seek to hone in on is the pattern of imposing costs on fishers while enabling the alienation and privatization of coastal resources. Taking water not only as a vital substance presenting questions of access and quality but also as a problem of drainage and effluence enables a fuller consideration of how the unequal distribution of costs on poorer populations became legitimized in the name of recovery. At the same time, the chapter also highlights the manner in which fishers refused to remain docile subjects of power and used their agency and autonomy in adapting to and sometimes refusing the terms of relocation.

Due to the large and fast growing sentiment analysis (SA) area recently, many new concepts and different nomenclatures have emerged without the desired organization. This confusion in the research field makes the understandability of the concepts hard and also hampers the comparison of different approaches. Thus, this paper aims to propose a hierarchical taxonomy to help the consolidation of SA area. The taxonomy aims at covering the addressed problems and methods in the SA field.

This taxonomy is a filtered union of various classifications found in the literature with a proposal of nomenclatures standardization. As instance, a case study is presented with 20 SA-related articles classified according to the proposed taxonomy.

This taxonomy is very expressive because it covers many concepts and is also effective once it allows the distinction and categorization of the previous SA works.

To the best of the authors’ knowledge, the literature does not present such an expressive and effective classification as proposed in this paper. This new taxonomy allows for the navigation between the existing concepts in the SA field as well as, facilitates the search, comparison and indexing of papers already published.

The study aims to investigate employees' overall justice perception and job stress as multiple mediators in the relationship between perceived talent management (TM) practices and turnover intention.

Covering various industries in Bangkok, Thailand, the sample of the study consists of 552 employees, including both talented employees and normal employees as defined by their organization. Online questionnaires were distributed internally by selected organizations, and structural equations modeling was used to analyze the data.

The results indicate that both overall justice perception and job stress mediate the relationship between perceived TM practices and turnover intention.

The study contributes to the lack of empirical evidence in TM literature and aids in the design of better TM strategies to deliver effective organizational investments in its people.

Education 4.0 is one of the major transformations in the field of education to improve the quality of teaching and learning processes and prepare students as 21st-century learners. This study aims to examine the effect of Educations 4.0 on creative and collaborative learning of students.

In this correlational research study, a quantitative data collection tool such as a questionnaire is used. This research is conducted on university students (N = 365), and purposive sampling technique is used to achieve the purpose. Statistical Package for Social Sciences (SPSS) version 27.0 is used to analyze the data.

The findings of the study reveal that there is a strong positive relationship between education 4.0 and the two major 21st-century skills, i.e. collaboration and creativity. The value of correlation coefficient value of the variables is 0.597, which indicates positive and strong relationship; hence, null hypothesis is rejected and an alternative hypothesis is accepted.

The results of the current study are beneficial for the school managers to enhance students' two significant 21st-century skills, i.e. “Creativity” and “Collaboration.” Also, the school managers can make efforts to develop teachers' competence to adapt the technological advancements for improving students' creativity and collaboration to benefit their learning.

This is a unique study because there are only a few recent studies that show a connection between Education 4.0 and 21st-century skills. Also, there is no other study that presents the direct association of Education 4.0 and students' creativity and collaboration specifically in the context of Pakistan.

This paper explores the indigenous climate knowledge (ICK) of the Gwadar fishing community in Pakistan. The main purpose of this paper is to explore the accuracy of ICK and how climatic change brings changes to it and the social lives of local fishers.

Qualitative research methods, including participant observation, in-depth interviews and oral histories, were used to collect the data.

Finding from this long fieldwork shows that this fishing community has a harmonious relationship with nature and local ecology. Their knowledge of local ecology enables them to have equal access to natural resources, sustainable resource management, disaster risk reduction and strong social organization on the coast of Gwadar. Recently their deep relationship with local ecology and sociocultural organization has been disturbed due to huge climate changes caused by human manipulation of the environment. Their ability to foresee climatic events has been reduced. They are finding it impossible to estimate fish availability due to massive climate changes. Local communities are losing their traditional livelihoods and socioeconomic autonomy as a result of growing climate change. Climatic change adds to the existing poverty situation and increases political instability in the region.

The study suggests using the fishermen’s valuable indigenous knowledge of local ecology, climate and its ties to local traditions, culture and resource management for a scientific understanding of climate change and marine resource management in Gwadar, Pakistan.

This is an ethnographic study based on a long term field work. Fishing community is passing through catastrophic climatic changes in the region. This community has been ignored by both government and researchers to record their problems and bring them to academia and media. Therefore, this study will help them raise their voices.

The purpose of this paper is to present a deep-learning-based beamforming method for phased array weather radars, especially whose antenna arrays are equipped with large number of elements, for fast and accurate detection of weather observations.

The beamforming weights are computed by a convolutional neural network (CNN), which is trained with input–output pairs obtained from the Wiener solution.

To validate the robustness of the CNN-based beamformer, it is compared with the traditional beamforming methods, namely, Fourier (FR) beamforming and Capon beamforming. Moreover, the CNN is compared with a radial basis function neural network (RBFNN) which is a shallow type of neural network. It is shown that the CNN method has an excellent performance in radar signal simulations compared to the other methods. In addition to simulations, the robustness of the CNN beamformer is further validated by using real weather data collected by the phased array radar at Osaka University (PAR@OU) and compared to, besides the FR and RBFNN methods, the minimum mean square error beamforming method. It is shown that the CNN has the ability to rapidly and accurately detect the reflectivity of the PAR@OU with even less clutter level in comparison to the other methods.

Motivated by the inherit advantages of the CNN, this paper proposes the development of a CNN-based approach to the beamforming of PAR using both simulated and real data. In this paper, the CNN is trained on the optimum weights of Wiener solution. In simulations, it is applied on a large 32 × 32 planar phased array antenna. Moreover, it is operated on real data collected by the PAR@OU.