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The purpose of this study is to analyse and discuss K-12 mathematics and technology teachers' perceptions on integrating programming in their teaching and learning activities, and perceptions on different programming tools.

The approach of a case study was used, with data collected from three instances of a professional development programming course for K-12 teachers in mathematics and technology.

The findings show that there are perceived challenges and opportunities with learning and integrating programming, and with different programming tools. Many teachers perceive programming as fun, but lack the time to learn and implement it, and view different programming tools as both complementary to each other and with individual opportunities and challenges.

The practical implication of the research is that it can provide guidance for teachers and other stakeholders that are in the process of integrating programming in K-12 education. Further, the research provides useful information on teachers' experiences on working with different programming tools.

The social implication of the research is that the overall aim of the nation-wide integration process might not succeed if the challenges identified in this study are not addressed, which could have negative effects on the development of students' digital competence.

The value of the research is that it identifies important challenges and opportunities for the integration of programming. That is, that many teachers perceive the different programming tools available as complimentary to each other, but are hesitating about what is expected of the integration. Findings could also be valuable for future course design of the teacher professional development.

The conducted examination of programming affordances and constraints had the purpose of adding knowledge and value that facilitate the on-going national curricula revision; knowledge that also could be of general interest outside the Swedish K-12 context.

With a qualitative approach, the study was conducted as a document analysis where submitted lesson plans were the base for a directed content analysis.

This study presents findings on how the involvement of programming in mathematics and technology have potential to foster engagement and motivation among students. Findings also indicate that the implementation of programming can develop important general skills that go beyond the boundaries of mathematics and technology. Moreover, the identified constraints could be valuable to improve the on-going curriculum development for K-12 mathematics and technology.

This qualitative study was conducted on a relatively small number of teachers where the majority has taken the courses on a voluntary basis. An important complement would be to conduct a larger quantitative study with data from a more general sample of K-12 teachers.

Results and discussions provide guidance for K-12 teachers and other stakeholders who want to introduce programming as a complementary tool in teaching and learning activities.

The study has a contribution to the on-going implementation of the Swedish national curricula for K-12 mathematics and technology.

During the last years, many studies have been published on teacher training in programming, and how the training can be improved. This study goes beyond the actual teacher training and examine aspects teachers translate to theirs daily work after completing the training.

Programming education is being rapidly transformed by generative AI tools and educators must determine how best to support students in this context. This study aims to explore the experiences of programming educators and students to inform future education provision.

Twelve students and six members of faculty in a small technology-focused university were interviewed. Thematic analysis of the interview data was combined with data collected from a survey of 44 students at the same university. Self-determination theory was applied as an analytical framework.

Three themes were identified – bespoke learning, affect and support – that significantly impact motivation and learning outcomes in programming education. It was also found that students are already making extensive use of large language models (LLMs). LLMs can significantly improve learner autonomy and sense of competence by improving the options for bespoke learning; fostering emotions that are conducive to engendering and maintaining motivation; and inhibiting the negative affective states that discourage learning. However, current LLMs cannot adequately provide or replace social support, which is still a key factor in learner motivation.

Integrating the use of LLMs into curricula can improve learning motivation and outcomes. It can also free educators from certain tasks, leaving them with more time and capacity to focus their attention on developing social learning opportunities to further enhance learner motivation.

To the best of the authors’ knowledge, this is the first attempt to explore the relationship between motivation and LLM use in programming education.

The geographic information systems (GIS) sector is witnessing significant growth in recent times at the global level and in the Kingdom of Saudi Arabia at the same pace. In this research, the authors aim to measure the GIS market employability in the Kingdom of Saudi Arabia, the required skills of those seeking GIS employees, existing knowledge and abilities of graduates of relevant academic programs and gaps, if any, between the graduates and the labor market requirements in terms of GIS. Research on GIS education and GIS employability from an information system perspective is scarce; thus, this study is needed.

In this study, a questionnaire was distributed manually and electronically to a specific group of GIS students, teachers and employees. The questionnaire was prepared in Arabic and English language. It was distributed manually to the attendees of the 12th GIS Symposium, organized by Imam Abdulrahman Bin Faisal University at Dammam city, Saudi Arabia in April of 2018. The event is a yearly one that gathers local GIS community and outside speakers and guests. An electronic version of the questionnaire was also available and distributed via personal contacts. Responses were received until the end of 2019.

The results of 107 respondents indicates good GIS foundation (for example in topology, geospatial data processing and cartography); however, programming and business skills (namely cost benefit analysis, organizational perspective and business understanding) are lacking from Saudi GIS graduates. About 67% of respondents acknowledge directly that it is difficult to find locally qualified GIS graduates. Moreover, 55% of respondents classify GIS job candidates as underqualified. This research identifies under qualification in business competencies and computer programming skills for GIS students and workers in Saudi Arabia. This research did not find a statistically significant different response between GIS teachers and GIS employees/employers; however, students’ response was significantly different from these two groups.

Research on GIS education and GIS employability from an Information System perspective is scarce; thus, this study is needed. Moreover, research about this topic in Saudi Arabia and the Arabian Gulf is almost nonexistent.

This pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and embedded cameras) that aids in manual lifting human pose deduction, analysis and training in the construction sector.

Using a pragmatic approach combined with the literature review, this study discusses the SVBM. The research method includes a literature review followed by a pragmatic approach and lab validation of the acquired data. Adopting the practical approach, the authors of this article developed an SVBM, an AI program to correlate computer vision (recorded and live videos using mobile and embedded cameras).

Results show that SVBM observes the relevant events without additional attachments to the human body and compares them with the standard axis to identify abnormal postures using mobile and other cameras. Angles of critical nodal points are projected through human pose detection and calculating body part movement angles using a novel software program and mobile application. The SVBM demonstrates its ability to data capture and analysis in real-time and offline using videos recorded earlier and is validated for program coding and results repeatability.

Literature review methodology limitations include not keeping in phase with the most updated field knowledge. This limitation is offset by choosing the range for literature review within the last two decades. This literature review may not have captured all published articles because the restriction of database access and search was based only on English. Also, the authors may have omitted fruitful articles hiding in a less popular journal. These limitations are acknowledged. The critical limitation is that the trust, privacy and psychological issues are not addressed in SVBM, which is recognised. However, the benefits of SVBM naturally offset this limitation to being adopted practically.

The theoretical and practical implications include customised and individualistic prediction and preventing most posture-related hazardous behaviours before a critical injury happens. The theoretical implications include mimicking the human pose and lab-based analysis without attaching sensors that naturally alter the working poses. SVBM would help researchers develop more accurate data and theoretical models close to actuals.

By using SVBM, the possibility of early deduction and prevention of musculoskeletal disorders is high; the social implications include the benefits of being a healthier society and health concerned construction sector.

Human pose detection, especially joint angle calculation in a work environment, is crucial to early deduction of muscoloskeletal disorders. Conventional digital technology-based methods to detect pose flaws focus on location information from wearables and laboratory-controlled motion sensors. For the first time, this paper presents novel computer vision (recorded and live videos using mobile and embedded cameras) and digital image-related deep learning methods without attachment to the human body for manual handling pose deduction and analysis of angles, neckline and torso line in an actual construction work environment.

This paper examines the educational environment of indigenous Gulf Arab students at a UAE institution, many from rural families and with English skills that need further development. The influences on a group of freshman Engineering students, their developing learning skills and the coaching they are given is explored. Practical methods for motivating such students to cross boundaries of world-view and sentiment are discussed, whereby a focus is maintained on their personal context and objectives whilst aiming to achieve excellence in academic accomplishment. A pre-freshman stage is introduced to fill the language void, easing the transition between High School, College and University. A personal view is presented from an educator who himself is crossing many boundaries.

This paper aims to introduce an information and communication technology (ICT) artifact that uses text mining to support the innovative and standardized assessment of professional competences within the validation of prior learning (VPL). Assessment means comparing identified and documented professional competences against a standard or reference point. The designed artifact is evaluated by matching a set of curriculum vitae (CV) scraped from LinkedIn against a comprehensive model of professional competence.

A design science approach informed the development and evaluation of the ICT artifact presented in this paper.

A proof of concept shows that the ICT artifact can support assessors within the validation of prior learning procedure. Rather the output of such an ICT artifact can be used to structure documentation in the validation process.

Evaluating the artifact shows that ICT support to assess documented learning outcomes is a promising endeavor but remains a challenge. Further research should work on standardized ways to document professional competences, ICT artifacts capture the semantic content of documents, and refine ontologies of theoretical models of professional competences.

Text mining methods to assess professional competences rely on large bodies of textual data, and thus a thoroughly built and large portfolio is necessary as input for this ICT artifact.

Following the recent call of European policymakers to develop standardized and ICT-based approaches for the assessment of professional competences, an ICT artifact that supports the automatized assessment of professional competences within the validation of prior learning is designed and evaluated.

Skilled graduates delivered through vocational programmes are critical to ensure the future growth of emerging economies. This study explored students' reflections and experiences of online teaching and learning (T&L) during the COVID-19 pandemic in South Africa. The study specifically focused on the transferable skills students acquired and their relevance to working in the local retail and hospitality industries.

Following a case study research design, this study retrospectively delved into the multi-perspectives of students enrolled in vocational programmes. A total of 145 students completed reflective questions via a Qualtrics link regarding the topic in question. Student reflections were grouped and analysed for recurring themes using Atlas.ti. Through thematic analysis, two topical themes emerged related to transferable skills development and the usefulness of skills for future work.

The findings suggest that although students had to rely on online classes during the COVID-19 pandemic, they still developed vital transferable skills, including communication, teamwork, organisational, self-management, flexibility, technology, metacognition and problem-solving.

The findings offer valuable input into planning and developing student-centric online courses to facilitate the development of desired transferable skills. Findings could also guide best T&L practices regarding how education and training across digital platforms could be used to ensure that graduates are prepared to navigate the future complexities of working in ever-changing globalised industries.

This study provides new insights into the evolution of T&L and how unexpected situations could provide an opportunity to hone desired skills and prepare students for employment and the 21st century workplace.

Public libraries play a pivotal role in supporting education and literacy. They provide numerous services, activities, collections and resources for education and leisure. Bibliotheca Alexandrina (BA) is an international renowned public library that provides numerous services for different users worldwide. E-learning is an emergent and promising method for teaching and learning different subjects such as the science, technology, engineering and mathematics (STEM). The e-learning educational system is quite novel in Africa and the Middle East; hence, this paper presents the whole concept to the reader. In addition, it demonstrates number of e-courses tackling different domains provided by different educational institutions, national and public libraries worldwide.

In 2017, the BA inaugurated its e-learning services to cope with the new educational trend and to consolidate the lifelong learning concept in the community. The author showed special interest to the case of e-learning in the BA, as it is a regional public library. The main idea of this paper is to attract attention toward public libraries as a promising venue for e-learning implementation for general knowledge, library information sciences, soft skills, elementary and informal STEM education. The paper discusses in details e-learning and its characteristics.

In addition, the paper compares traditional education (face-to-face) with e-learning education, mentions both their pros and cons and recommends blending the two educational methods as they complement each other. Furthermore, the author has selected a sample of different STEM e-courses (203 different e-courses). These e-courses were selected to assert the possibility of presenting STEM topics in the form of e-courses.

This study would be one of the emergent research studies that connect e-learning to both STEM disciplines and public libraries. Additionally, this research highlights the importance of public libraries and all the services they provide. In the mean time, it shed light on the important and unique role of specialized librarians. Briefly, public libraries with all their resources, services and expert librarians could provide an exceptional e-learning experience to their community and be of great help to educational institutions and organizations.

The assessment of the Real Estate (RE) prices depends on multiple factors that traditional evaluation methods often struggle to fully understand. Housing prices, in particular, are the foundations for a better knowledge of the Built Environment and its characteristics. Recently, Machine Learning (ML) techniques, which are a subset of Artificial Intelligence, are gaining momentum in solving complex, non-linear problems like house price forecasting. Hence, this study deployed three popular ML techniques to predict dwelling prices in two cities in Italy.

An extensive dataset about house prices is collected through API protocol in two cities in North Italy, namely Brescia and Varese. This data is used to train and test three most popular ML models, i.e. ElasticNet, XGBoost and Artificial Neural Network, in order to predict house prices with six different features.

The models' performance was evaluated using the Mean Absolute Error (MAE) score. The results showed that the artificial neural network performed better than the others in predicting house prices, with a MAE 5% lower than the second-best model (which was the XGBoost).

All the models had an accuracy drop in forecasting the most expensive cases, probably due to a lack of data.

The accessibility and easiness of the proposed model will allow future users to predict house prices with different datasets. Alternatively, further research may implement a different model using neural networks, knowing that they work better for this kind of task.

To date, this is the first comparison of the three most popular ML models that are usually employed when predicting house prices.