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The lecture method has been compared with teaching methods such as flip learning, cooperative learning and simulations to establish which holds the key to students' understanding of concepts. What is bereft in the education literature is its comparative efficiency with the culturo-techno contextual approach (CTCA) in the teaching of computer science education.

This study adopted the quasi-experimental design to determine the efficacy of the CTCA in breaking difficulties related to the study of spreadsheets as a difficult concept in the Nigerian computer science education curriculum. Junior high school students studying computer science education participated in the study. The control group had 30 students, with 35 students in the experimental group. The experimental group was taught using CTCA, while the control group used the lecture method. The spread sheet achievement test, which had 40 items on spreadsheet, was used to collect data.

The results showed that the experimental group significantly outperformed the control group [F (1,60) = 41.89; p < 0.05]. The findings showed the potential of CTCA in improving students' performance in spreadsheets in the computer science education curriculum.

The originality of this study is hinged on its ground-breaking test of the CTCA to the study of the spreadsheet. The findings of this study indicate its efficacy in improving students' understanding of spreadsheet and computer science education.

This study aims to investigate the performance analysis, science mapping and future direction of artificial intelligence (AI) technology, applications, tools and software used to preserve, curate and predict the historical value of cultural heritage.

This study uses the bibliometric research method and utilizes the Scopus database to gather data. The keywords used are “artificial intelligence” and “cultural heritage,” resulting in 718 data sets spanning from 2001 to 2023. The data is restricted to the years 2001−2023, is in English language and encompasses all types of documents, including conference papers, articles, book chapters, lecture notes, reviews and editorials.

The performance analysis of research on the use of AI to aid in the preservation of cultural heritage has been ongoing since 2001, and research in this area continues to grow. The countries contributing to this research include Italy, China, Greece, Spain and the UK, with Italy being the most prolific in terms of authored works. The research primarily falls under the disciplines of computer science, mathematics, engineering, social sciences and arts and humanities, respectively. Document types mainly consist of articles and proceedings. In the science mapping process, five clusters have been identified. These clusters are labeled according to the contributions of AI tools, software, apps and technology to cultural heritage preservation. The clusters include “conservation assessment,” “exhibition and visualization,” “software solutions,” “virtual exhibition” and “metadata and database.” The future direction of research lies in extended reality, which integrates virtual reality (VR), augmented reality (AR) and mixed reality (MR); virtual restoration and preservation; 3D printing; as well as the utilization of robotics, drones and the Internet of Things (IoT) for mapping, conserving and monitoring historical sites and cultural heritage sites.

The cultural heritage institution can use this result as a source to develop AI-based strategic planning for curating, preservation, preventing and presenting cultural heritages. Researchers and academicians will get insight and deeper understanding on the research trend and use the interdisciplinary of AI and cultural heritage for expanding collaboration.

This study will help to reveal the trend and evolution of AI and cultural heritage. The finding also will fill the knowledge gap on the research on AI and cultural heritage.

Some similar bibliometric studies have been conducted; however, there are still limited studies on contribution of AI to preserve cultural heritage in wider view. The value of this study is the cluster in which AI is used to preserve, curate, present and assess cultural heritages.

Climate change is a problem that concerns all of us. Despite the information produced by organizations such as the Expert Team on Climate Change Detection and Indices and the United Nations, only a few cities have been planned taking into account the climate changes indices. This paper aims to study climatic variations, how climate conditions might change in the future and how these changes will affect the activities and living conditions in cities, specifically focusing on Mexico city.

In this approach, two distinct machine learning regression models, k-Nearest Neighbors and Support Vector Regression, were used to predict variations in climate change indices within select urban areas of Mexico city. The calculated indices are based on maximum, minimum and average temperature data collected from the National Water Commission in Mexico and the Scientific Research Center of Ensenada. The methodology involves pre-processing temperature data to create a training data set for regression algorithms. It then computes predictions for each temperature parameter and ultimately assesses the performance of these algorithms based on precision metrics scores.

This paper combines a geospatial perspective with computational tools and machine learning algorithms. Among the two regression algorithms used, it was observed that k-Nearest Neighbors produced superior results, achieving an R² score of 0.99, in contrast to Support Vector Regression, which yielded an R² score of 0.74.

The full potential of machine learning algorithms has not been fully harnessed for predicting climate indices. This paper also identifies the strengths and weaknesses of each algorithm and how the generated estimations can then be considered in the decision-making process.

This paper aims to consolidate empirical studies between 2013 and 2022 to investigate the impact of artificial intelligence (AI) in higher education. It aims to examine published research characteristics and provide insights into the promises and challenges of AI integration in academia.

A systematic literature review was conducted, encompassing 44 empirical studies published as peer-reviewed journal papers. The review focused on identifying trends, categorizing research types and analysing the evidence-based applications of AI in higher education.

The review indicates a recent surge in publications concerning AI in higher education. However, a significant proportion of these publications primarily propose theoretical and conceptual AI interventions. Areas with empirical evidence supporting AI applications in academia are delineated.

The prevalence of theoretical proposals may limit generalizability. Further research is encouraged to validate and expand upon the identified empirical applications of AI in higher education.

This review outlines imperative implications for future research and the implementation of evidence-based AI interventions in higher education, facilitating informed decision-making for academia and stakeholders.

This paper contributes a comprehensive synthesis of empirical studies, highlighting the evolving landscape of AI integration in higher education and emphasizing the need for evidence-based approaches.

Based on the reception of the principle of self-organization, the core of Heinz von Foerster's operational theories, I hypothesize how Heinz von Foerster's theory can be an orientation model for the epistemological problem of complexity. I have chosen this study to demonstrate complexity as an epistemological problem. This is because the question of how order arises - the core problem of complexity - is an epistemological question for which Heinz von Foerster developed an epistemology of self-organization. I do not present new research because HvF already had the complex organization of systems in mind. Rather, I build a critical approach to complexity on the research and work on operational epistemology in HvF.

This article aims to provide an orientation for a philosophical and epistemological understanding of complexity through a reading of Heinz von Foerster's operational theory. The article attempts to establish complexity as an epistemological phenomenon through the following method: (1) a conceptual description of the science of complexity based on the turn to thermodynamic time, (2) a genealogy of complexity going back to the systemic method, and (3) Heinz von Foerster's cybernetic approach to self-organization.

Based on the reception of the principle of self-organization, the core of Heinz von Foerster's operational theories, the conclusion is drawn that complexity as a description is based on language games.

The results present complexity not as an object of science, but as a description that stands for the understanding of complex description.

The hypothesis that complexity is a question of description or observation, i.e. of description for what language serves, has enormous social implications, in that the description of complexes and the recognition of their orders (patterns) cannot be left to algorithmic governmentality, but must be carried out by a social agency.

HvF's operational epistemology can serve as an epistemological model for critical complexity theory.

The purpose of this study is to determine the funding ratio of BRICS nations in various research areas. The leading funding institutions that support research in the developing world have also been researched.

This study involves the funding acknowledgment analysis of the data retrieved from the “Clarivate Analytics' InCites database” under “22 specific research areas” to determine whether the publication was funded.

This study shows that China achieves the highest funding ratio of 88.6%, followed by Brazil (73.74%), Russia (72.93%) and South Africa (70.94%). However, India has the lowest funding ratio of 58.2%. For the subject areas, the highest funding ratio is by microbiology in Russia (86.6%), India (84.3%) and China (96.9%) and space science in Brazil (93.7%) and South Africa (94.82%). However, economics and business achieves the lowest funding ratio in Brazil (38.6%), India (20.1%) and South Africa (30.24%). Moreover, the regional funding agencies are the leading research sponsors in the BRICS nations.

This study implies increasing the funding ratio across various research areas, including arts, humanities and social sciences. The nations, particularly India, also need to gear up sponsoring the research to improve the funding ratio for scientific development, bringing overall good.

This study efforts to show the status of countries and research subjects in terms of funding ratio and reveals the prominent funders working toward scientific growth.

There is a need for precollege learning designs that empower youth to be epistemic agents in contexts that intersect burgeoning areas of computing, big data and social media. The purpose of this study is to explore how “sandbox” or open-inquiry data science with social media supports learning.

This paper offers vignettes from an illustrative youth study case that highlights the pedagogical prospects and obstacles tied to designing for open-ended inquiry with computational data science to access or “scrape” Twitter/X. The youth case showcases how social media can be taken up productively and in ways that facilitate epistemological agency, an approach where individuals actively shape understanding and knowledge-creation processes, highlighting the potentially transformative impact this approach might have in empowering learners to engage productively.

The authors identify three key affordances for learning that emerged from the illustrative case: (1) flexible opportunities for content-specific domain mastery, (2) situated inquiry that embodies next-generation science practices and (3) embedded computational skill development. The authors discuss these findings in relation to contemporary education needs to broaden participation in data science and computing.

To address challenges in current data science education associated with supporting sustained and productive engagement in computing-based data science, the authors leverage a “sandbox” approach – an original pedagogical framework to support open inquiry with precollege groups. The authors demonstrate how “big data” drawn from social media with high school-aged youth supports learning designs and outcomes by emphasizing learner interests and authentic practice.

This study aims to focus on carrying out a quantitative analysis of publications related to augmented reality (AR)-based gaming. AR combines the real world with the digital world and is a large basis of data in an ICT-based digital society. Therefore, there is a need for a comprehensive bibliometric review of research papers on AR-based gaming to identify the potential for the advancement of knowledge in this field.

In the experimental part, reference data from Scopus was extracted according to a search string formulated with great care. The retrieved data were then analyzed through open-source, sophisticated bibliometric tools, VOSviewer, along with Biblioshiny-R.

After running the analysis, it turns out that the years with the biggest number of publications were in 2010 and 2012. The focus areas of study include mixed reality, surveys and helmet-mounted displays. The USA was the most connected country in the research collaboration network; it has strong links with other nations including the UK, Canada and Greece, which cluster around it in the map. This trend of monetarily increasing number of papers depicts the growing interest and sustainable productivity in this research domain.

The purpose of this paper is to present a novel perspective on AR-based gaming, which has not been so far examined by bibliometric analysis. The results provide important information for researchers and stakeholders. In addition to filling a void in the literature, this study is an important reference for those researchers who are interested in exploring evolution and trends in AR-based gaming research.

This study aims to investigate the usability of a public university website for measuring its efficiency, users’ satisfaction or anxiety while searching for and retrieving information through different devices.

A task-based approach was adopted for the study. Twenty-eight participants were asked to complete 11 information-searching tasks on the website. The participants were divided into two groups. The tasks were carried out by members of each group, using desktop and mobile devices in a rotating fashion. Volunteers observed the participants' actions and recorded information regarding their productivity, time usage (using a timer), satisfaction or annoyance while performing each task. Finally, based on the use of the devices, a comparison was established between the participants' performance accuracy, efficiency and anxiety.

The study provides an overview of a task-based user experience carried out on the university website using a combination of qualitative and quantitative research methods. According to the results, participants' satisfaction levels were generally high, and their anxiety levels were low while completing the tasks on a mobile device. In comparison to the desktop, it took less time overall to complete all tasks. On the other hand, using a desktop computer (97.1%) resulted in better task completion success rates for participants than using a mobile device (85.7%).

No previous task-based evaluation study of this kind has been conducted to assess the usability of any university website in Bangladesh.

With the growth and profound influence of technology on our life, it is important to address the ethical issues inherent to the development and deployment of technology. Researchers and practitioners submit the need to inspect: how technology and ethics interact, how ethical principles regulate technology and what could be the probable future course of action to execute techno-ethical practices in a socio-technical discourse effectively. To address the thoughts related to techno-ethics, the authors of the present study conducted exploratory research to understand the trend and relevance of technology ethics since its inception.

The study collected over 679 documents for the period 1990–2022 from the Scopus database. A quantitative approach of bibliometric analysis was conducted to study the pattern of authorship, publications, citations, prominent journals and contributors in the subject area. VOS viewer software was utilized to visualize and map academic performance in techno-ethics.

The findings revealed that the concept of techno-ethics is an emerging field and requires more investigation to harness its relevance with everchanging technology development. The data revealed substantial growth in the field of techno-ethics in humanities, social science and management domain in the last two decades. Also, most of the prominent cited references and documents in the database tend to cover the theme of Artificial Intelligence, Big data, computer ethics, morality, decision-making, IT ethics, human rights, responsibility and privacy.

The article provides a comprehensive overview of scientific production and main research trends in techno-ethics until 2022. The study is a pioneer in expanding the academic productivity and performance of embedding ethics in technology.