Researchers are increasingly exploring educational games in immersive virtual reality (IVR) environments to facilitate students’ learning experiences. Mainly, the effect of IVR on learning outcomes has been the focus. However, far too little attention has been paid to the influence of game elements and IVR features on learners’ perceived cognition. This study examined the relationship between game elements (challenge, goal clarity, and feedback) as pedagogical approach, features of IVR technology (immersion and interaction), and learners’ perceived cognition (reflective thinking and comprehension). An experiment was conducted with 49 undergraduate students who played an IVR game-based application (iThinkSmart) containing mini games developed to facilitate learners’ computational thinking competency. The study employed partial least squares structural equation modelling to investigate the effect of educational game elements and learning contents on learner’s cognition. Findings show that goal clarity is the main predictor of learners’ reflective thinking and comprehension in an educational game-based IVR application. It was also confirmed that immersion and interaction experience impact learner’s comprehension. Notably, adequate learning content in terms of the organisation and relevance of the content contained in an IVR game-based application significantly moderate learners’ reflective thinking and comprehension. The findings of this study have implications for educators and developers of IVR game-based intervention to facilitate learning in the higher education context. In particular, the implication of this study touches on the aspect of learners’ cognitive factors that aim to produce 21st-century problem-solving skills through critical thinking.

Understanding the principles of computational thinking (CT), e.g., problem abstraction, decomposition, and recursion, is vital for computer science (CS) students. Unfortunately, these concepts can be difficult for novice students to understand. One way students can develop CT skills is to involve them in the design of an application to teach CT. This study focuses on co-designing mini games to support teaching and learning CT principles and concepts in an online environment. Online co-design (OCD) of mini games enhances students’ understanding of problem-solving through a rigorous process of designing contextual educational games to aid their own learning. Given the current COVID-19 pandemic, where face-to-face co-designing between researchers and stakeholders could be difficult, OCD is a suitable option. CS students in a Nigerian higher education institution were recruited to co-design mini games with researchers. Mixed research methods comprising qualitative and quantitative strategies were employed in this study. Findings show that the participants gained relevant knowledge, for example, how to (i) create game scenarios and game elements related to CT, (ii) connect contextual storyline to mini games, (iii) collaborate in a group to create contextual low-fidelity mini game prototypes, and (iv) peer review each other’s mini game concepts. In addition, students were motivated toward designing educational mini games in their future studies. This study also demonstrates how to conduct OCD with students, presents lesson learned, and provides recommendations based on the authors’ experience.

Computational thinking (CT) has become an essential skill nowadays. For young students, CT competency is required to prepare them for future jobs. This competency can facilitate students’ understanding of programming knowledge which has been a challenge for many novices pursuing a computer science degree. This study focuses on designing and implementing a virtual reality (VR) game-based application (iThinkSmart) to support CT knowledge. The study followed the design science research methodology to design, implement, and evaluate the first prototype of the VR application. An initial evaluation of the prototype was conducted with 47 computer science students from a Nigerian university who voluntarily participated in an experimental process. To determine what works and what needs to be improved in the iThinkSmart VR game-based application, two groups were randomly formed, consisting of the experimental (n = 21) and the control (n = 26) groups respectively. Our findings suggest that VR increases motivation and therefore increase students’ CT skills, which contribute to knowledge regarding the affordances of VR in education and particularly provide evidence on the use of visualization of CT concepts to facilitate programming education. Furthermore, the study revealed that immersion, interaction, and engagement in a VR educational application can promote students’ CT competency in higher education institutions (HEI). In addition, it was shown that students who played the iThinkSmart VR game-based application gained higher cognitive benefits, increased interest and attitude to learning CT concepts. Although further investigation is required in order to gain more insights into students learning process, this study made significant contributions in positioning CT in the HEI context and provides empirical evidence regarding the use of educational VR mini games to support students learning achievements.

This paper presents iThinkSmart, an immersive virtual reality-based application to facilitate the learning of computational thinking (CT) concepts. The tool was developed to supplement the traditional teaching and learning of CT by integrating three virtual mini games, namely, River Crossing, Tower of Hanoi, and Mount Patti treasure hunt, to foster immersion, interaction, engagement, and personalization for an enhanced learning experience. iThinkSmart mini games can be played on a smartphone with a Goggle Cardboard and hand controller. This first prototype of the game accesses players' competency of CT and renders feedback based on learning progress.  

This study examines the research landscape of smart learning environments by conducting a comprehensive bibliometric analysis of the field over the years. The study focused on the research trends, scholar’s productivity, and thematic focus of scientific publications in the field of smart learning environments. A total of 1081 data consisting of peer-reviewed articles were retrieved from the Scopus database. A bibliometric approach was applied to analyse the data for a comprehensive overview of the trend, thematic focus, and scientific production in the field of smart learning environments. The result from this bibliometric analysis indicates that the first paper on smart learning environments was published in 2002; implying the beginning of the field. Among other sources, “Computers & Education,” “Smart Learning Environments,” and “Computers in Human Behaviour” are the most relevant outlets publishing articles associated with smart learning environments. The work of Kinshuk et al., published in 2016, stands out as the most cited work among the analysed documents. The United States has the highest number of scientific productions and remained the most relevant country in the smart learning environment field. Besides, the results also showed names of prolific scholars and most relevant institutions in the field. Keywords such as “learning analytics,” “adaptive learning,” “personalized learning,” “blockchain,” and “deep learning” remain the trending keywords. Furthermore, thematic analysis shows that “digital storytelling” and its associated components such as “virtual reality,” “critical thinking,” and “serious games” are the emerging themes of the smart learning environments but need to be further developed to establish more ties with “smart learning”. The study provides useful contribution to the field by clearly presenting a comprehensive overview and research hotspots, thematic focus, and future direction of the field. These findings can guide scholars, especially the young ones in field of smart learning environments in defining their research focus and what aspect of smart leaning can be explored.

This study investigated the role of virtual reality (VR) in computer science (CS) education over the last 10 years by conducting a bibliometric and content analysis of articles related to the use of VR in CS education. A total of 971 articles published in peer-reviewed journals and conferences were collected from Web of Science and Scopus databases to conduct the bibliometric analysis. Furthermore, content analysis was conducted on 39 articles that met the inclusion criteria. This study demonstrates that VR research for CS education was faring well around 2011 but witnessed low production output between the years 2013 and 2016. However, scholars have increased their contribution in this field recently, starting from the year 2017. This study also revealed prolific scholars contributing to the field. It provides insightful information regarding research hotspots in VR that have emerged recently, which can be further explored to enhance CS education. In addition, the quantitative method remains the most preferred research method, while the questionnaire was the most used data collection technique. Moreover, descriptive analysis was primarily used in studies on VR in CS education. The study concludes that even though scholars are leveraging VR to advance CS education, more effort needs to be made by stakeholders across countries and institutions. In addition, a more rigorous methodological approach needs to be employed in future studies to provide more evidence-based research output. Our future study would investigate the pedagogy, content, and context of studies on VR in CS education.

The field of drug discovery has experienced a remarkable transformation with the advent of artificial intelligence (AI) and machine learning (ML) technologies. However, as these AI and ML models are becoming more complex, there is a growing need for transparency and interpretability of the models. Explainable Artificial Intelligence (XAI) is a novel approach that addresses this issue and provides a more interpretable understanding of the predictions made by machine learning models. In recent years, there has been an increasing interest in the application of XAI techniques to drug discovery. This review article provides a comprehensive overview of the current state-of-the-art in XAI for drug discovery, including various XAI methods, their application in drug discovery, and the challenges and limitations of XAI techniques in drug discovery. The article also covers the application of XAI in drug discovery, including target identification, compound design, and toxicity prediction. Furthermore, the article suggests potential future research directions for the application of XAI in drug discovery. This review article aims to provide a comprehensive understanding of the current state of XAI in drug discovery and its potential to transform the field.

The K-means algorithm was deployed to extract clusters within the prevalent cereal foods in West Africa. The West Africa Food Composition Table (WAFCT) presents all the 76 food sources in the cereals class as a single group without considering the similarity or dissimilarity in nutritional values. Using K-means clustering, the Euclidean distance between nutritional values of all cereal food items were measured to generate six subgroups based on similarity. A one-way analysis to validate the results of the extracted clusters was carried out using the mean square values. For every nutrient, the “within groups” and “between groups” values of the mean squares were examined. This was done to ascertain how similar or dissimilar data points in the same or different clusters were to each other. It was discovered that the P values for all “between groups” and “within groups” mean squares for every nutrient was P < 0.01. Additionally, it was observed thatin all cases, the mean square values of the “within groups” were significantly lower than those of the “between groups”. These outcomes are indications that clustering was properly done such that the variability in nutrient values for all food sources within the same clusters was significantly low, while those in different clusters were significantly high. Thus, the ultimate objective of clustering, which is to maximize intra-cluster similarity and minimize inter-cluster similarity was effectively achieved. Cluster analysis in this study showed that all food items within a particular cluster are similar to each other and dissimilar to food items in a different cluster. These findings are valuable in dietaries, food labeling, raw materials selection, public health nutrition, and food science research, when answering questions on the choice of alternative food items. Where original choices are not available or unaffordable, the clusters can be explored to select other similar options within the same cluster as the original choice.

The emergence of artificial intelligence (AI) as a subject to be incorporated into K-12 educational levels places new demand on relevant stakeholders, especially teachers that drive the teaching and learning process. It is therefore important to understand how ready teachers are to teach the emerging subject as the success of AI education would probably be closely dependent on the readiness of teachers. As a result, this study presents an insight into factors influencing the behavioural intention and readiness of Nigerian in-service teachers to teach artificial intelligence. A total of 368 teachers, from elementary to high school participated in the study. We utilised quantitative methodology using variance-based structural equation modelling to understand the relationship among the eight variables (AI anxiety, perceived usefulness, AI for social good, Attitude towards using AI, perceived confidence in teaching AI, relevance of AI, AI readiness, and behavioural intention) considered in the study. The result indicated that confidence in teaching AI predicts intention to teach AI while AI relevance strongly predicts readiness to teach AI. While other factors influence the teaching of AI, anxiety and social good could not predict teachers' intention and readiness to implement AI in classrooms respectively. We discussed the implication of our findings in relation to AI implementation in schools and highlight future directions.

An assisted remote manipulation (ArM) platform has been defined for the Super FragmentSeparator (Super-FRS) main tunnel and hot cell at the High Energy Physics (HEP) Facilityof Anti-proton and Ion Research (FAIR). The designed platform positioned within a VirtualReality (VR) based framework ensures dynamic collaboration and effective humaninteraction to assist with Remote Handling (RH) operations. To visually stimulate operatorassisted intervention in harsh environments, enhanced interaction based on syntheticvision has been adapted with simultaneous localization and mapping (SLAM) techniquesinterlinked with virtual layers representing a three dimensional manipulation of RHmaintenance tasks. The proposed platform also included a sequence mapping toolevaluated with RH task variables specific to the sequence space analyses of pathplanning, motion check, and collision detection performed in both real and virtual RH taskenvironments. Further assistance was envisaged from multimodal feedback categoriesthrough force feedback, in this case, a backpropagation algorithm was tailored to define aforce limit and to send feedback signals to the operator every time the actual patternexceeded the desired output pattern. Overall, the ArM platform ensures the application ofbest engineering practices to RH needs as a basis to maximize information gathering andsharing driven by continuous improvement initiatives.

The educational community continuously seeks ways to improve the learner-centered learning process through new approaches like Learning analytics and its dashboard, which is helpful to enhance the teaching and the learning process. It involves a process whose final goal is presenting results to support decision-making about improving the learning process. However, a descriptive Learning analytics interface for analyzing learning data of students, including the disadvantaged, where to view and interpret learners' historical data is -in general- missing in this research domain. Hence, more research is still required to establish the philosophy of learning analytics on inclusion with an interface for the stakeholders to understand learning and teaching in an inclusive learning environment. This paper fills this gap by providing an inclusive educational learning analytics dashboard to support teachers and students. This study aimed to present a learning analytics implementation in the context of a smart ecosystem for learning and inclusion. We gave the inclusive educational needs and discussed the workflow followed during the descriptive learning analytics dashboard development. Therefore, the study improved existing learning analytics dashboards with a descriptive approach and inclusiveness of students with disabilities. Owing to the software development nature of this study, agile methodology based on five stages was applied: requirement elicitation; data gathering; design and prototyping; implementation; and testing and integration. We performed an initial evaluation, which indicated that the dashboard is suitable for understanding teachers' and students' needs and expectations. Besides, the visualization of inclusive learning characteristics improves engagement and attainment of learning goals.

Planning a course that promotes inclusive learning effectively is not a simple task. Each student has different learning goals and specific needs in relation to the level of depth and abstraction of the knowledge to be acquired. To ensure inclusive education, the development of Instructional Design must be carried out carefully to address all these differences. Inclusive education through universal perspectives provides learning for all students, respecting their limitations, whether cognitive or physical. Among the challenges in planning an inclusive course, it is possible to point out the identification of educational needs, the creation of a flexible, personalized curriculum, seeking the most appropriate communication for the student, establishing actions that optimize time for course creation and delivery. In this context, this work aims to discuss the challenges in implementing Universal Instructional Design techniques supported by digital technologies to foster inclusive education.

This research delves into the dynamics of active school transport (AST) by utilizing a two-stage co-design process and leveraging persuasive technology within a game for promoting AST called Battleship-AST. The primary aim of this research is to thoroughly investigate the two-stage game co-design process employed in creating a Battleship-AST game. Moreover, our research aims to evaluate participants’ perceptions regarding the motivating and engaging potential of the persuasive technology and gamification features embedded within the final iteration of the game. This evaluation aims to understand how these features influence participants’ motivation to increase their usage of AST through gameplay. In pursuit of these objectives, the research builds upon the existing Battleship-AST prototype and actively engages school children in a collaborative two-stage co-design process. Their valuable insights and preferences were harnessed in refining the game, which was subsequently tested during a tech event in Skellefteå, Sweden. The findings shed light on various aspects of the game’s impact, from its reception to the gamification features integrated within. Notably, the research highlights the positive impact of the co-design process, with increased motivation and engagement observed among the participants. Their involvement in shaping the game’s design resulted in a more engaging and enjoyable experience. The persuasive technology features, encompassing competition, collaboration, auditory cues, a virtual reward system, and an emphasis on similarity, played a pivotal role in sustaining engagement and motivating players. Elements such as rewards, leaderboard progression, and badges proved highly effective in encouraging continued participation and fostering a positive feedback loop. However, the study also identifies areas for potential improvement, including the need to measure real-life progress and refine the game’s levelling system. The research indicates that refining feedback mechanisms and tailoring game content to individual preferences could create an even more engaging experience. Additionally, long-term playtesting is proposed to assess the game’s extended impact. The findings offer promising avenues for enhancing motivation and engagement in AST, which can contribute to the promotion of healthier and more sustainable transportation choices among school children.

This systematic review aims to assess the effectiveness of virtual reality (VR) and gamification interventions in addressing anxiety and depression. The review also seeks to identify gaps in the current VR treatment landscape and provide guidelines for future research and development. A systematic literature search was conducted using Scopus, Web of Science, and PubMed databases, focusing on studies that utilized VR and gamification technology to address anxiety and depression disorders. A total of 2,664 studies were initially identified, 15 of those studies fulfilled the inclusion criteria for this systematic review. The efficacy of VR in addressing anxiety and depression was evident across all included studies. However, the diversity among VR interventions highlights the need for further investigation. It is advised to incorporate more diverse participant samples and larger cohorts and explore a broader spectrum of therapeutic approaches within VR interventions for addressing anxiety and depression to enhance the credibility of future research. Additionally, conducting studies in varying socioeconomic contexts would contribute to a more comprehensive understanding of their real-world applicability.

Virtual reality is an emerging field in mental health and has gained widespread acceptance due to its potential to treat various disorders, such as anxiety and depression. This paper presents a bibliometric analysis of virtual reality (VR) use in addressing depression and anxiety from 1995 to 2022. The study analysed 1872 documents using the Scopus database, identifying the field’s most relevant journals and authors. The results indicate that using VR for addressing anxiety and depression is a multidisciplinary field with a wide variety of research topics, leading to significant collaborative research in this area. The Annual Review of Cybertherapy and Telemedicine was identified as the most relevant journal, while Behavior Research and Therapy was found to be the most cited journal. The analysis of keywords suggests that there is more research on using VR for anxiety and related disorders than for depression. Riva G. was identified as the top author in producing research outputs on VR-AD, and the University of Washington emerged as the leading institution in scientific outputs on VR-AD. Thematic and intellectual analyses helped identify the main themes within the research domain, providing valuable insight into the current and future directions of the field.

Active school transport (AST) refers to using active means of transport such as walking, cycling, or riding a non-motorised scooter to school. It can help improve adolescents’ physical activity levels and create a more sustainable environment. The study involved 70 adolescents (45 boys and 25 girls) aged 13 to 14 from one school in Skellefteå, in Northern Sweden. In an online questionnaire, they were asked about their perceptions of cycling, walking, and riding a non-motorised scooter to school. This study used descriptive statistics, multiple regression analysis, and hypothesis testing with ANOVA to analyse the collected data and compare the perceptions of different types of transport on safety, environmental, and personal factors among adolescents in Northern Sweden. According to the results, more adolescents walked to school than cycled, and significantly few rode a non-motorised scooter to school. Most adolescents believe walking or cycling to school is a great way to exercise. Furthermore, the study also revealed that many adolescents avoid using AST due to the time it takes. Although the study showed that adolescents felt sufficient support for using AST from schools and parents, the number of adolescents using motorised transport is higher during winter than in summer. Additionally, most of them were more confident about cycling and walking to school than riding a non-motorised scooter and thought using AST was nice. Finally, most adolescents perceived having complete control over their transport options when going to school. The research indicates that it is crucial to implement interventions that inspire children to be interested and excited about using AST. These strategies should include fostering an AST culture that is fun and positive, as well as creating environments that are safe and supportive. The research results will guide the creation of a persuasive game that can motivate adolescents to use AST and measure its effectiveness.

Imbalanced Data (ID) is a problem that deters Machine Learning (ML) models from achieving satisfactory results. ID is the occurrence of a situation where the quantity of the samples belonging to one class outnumbers that of the other by a wide margin, making such models’ learning process biased towards the majority class. In recent years, to address this issue, several solutions have been put forward, which opt for either synthetically generating new data for the minority class or reducing the number of majority classes to balance the data. Hence, in this paper, we investigate the effectiveness of methods based on Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) mixed with a variety of well-known imbalanced data solutions meaning oversampling and undersampling. Then, we propose a CNN-based model in combination with SMOTE to effectively handle imbalanced data. To evaluate our methods, we have used KEEL, breast cancer, and Z-Alizadeh Sani datasets. In order to achieve reliable results, we conducted our experiments 100 times with randomly shuffled data distributions. The classification results demonstrate that the mixed Synthetic Minority Oversampling Technique (SMOTE)-Normalization-CNN outperforms different methodologies achieving 99.08% accuracy on the 24 imbalanced datasets. Therefore, the proposed mixed model can be applied to imbalanced binary classification problems on other real datasets.

There was no known effective approach to implementing information systems in Tanzanian Higher Education Institutions (HEIs) because the requirements of users change frequently and HEIs vary in their requirements. Theoretically, the agile software development process and Design Science Research (DSR) could deliver software that suits such a context, yet no testing had been done priorly. Therefore, this study aimed on investigating the suitability of the combination of Agile Methodologies and the DSR in developing information systems for Tanzania's HEIs. The study used eXtreme Programming as one of the Agile Methodologies and the DSR to develop a timetabling software which easily integrates into the existing academic information system of the College of Business Education for better decision making. Likewise, the evaluation of the developed artifact confirmed that the resulting product raised the user satisfaction with the information system in HEIs. In this regard, the current study advances the frontier of knowledge on using the DSR framework and Agile Methodologies in designing and developing software in Tanzania's HEIs and beyond.

Background: Most children and adolescents in Sweden do not meet the recommended daily physical activity levels of the World Health Organization. Active school transport (AST) and gamification are potential methods for increasing children’s daily physical activity. We previously developed a game named Tic-Tac-Training for promoting active transport at workplaces; however, the game has not been applied to AST.

Objective: The objectives of this study are to investigate how Tic-Tac-Training functions to promote AST among schoolchildren in northern Sweden, improve the game to be more suitable for schoolchildren, and construct a road map for future development based on children’s ideas.

Methods: First, we developed Tic-Tac-Training using the Scrum agile software development method. Second, we conducted a questionnaire-based formative evaluation of the game with schoolchildren (n=16; 9/16, 56% male; 6/16, 38% female; and 1/16, 6% other aged 11-12 years) in Luleå, Sweden. Third, we conducted focus group interviews with 33 children (13/33, 39% male and 20/33, 61% female aged 12-13 years) to gather ideas for gamifying AST. We mapped the interview results to the Octalysis gamification framework and established a road map for future development.

Results: The formative evaluation revealed several issues, including a lack of interesting game features, lack of support for continuous engagement, disliked competitive features, and lack of incentives for discourse and participation. New features such as rewards, collectibles, and levels were implemented based on the results. The focus group interviews revealed additional ideas for gamifying AST, such as using avatars, in-game currency and trading, and context-sensitive tasks.

Conclusions: The results have several potential impacts on how reusable, gamified AST interventions can be developed and what kind of gamification elements schoolchildren in northern Sweden wish to see. These results can interest game researchers and teachers who wish to apply gamification in school contexts. Finally, we aim to continue developing the game based on the road map.

The study employs design-based research (DBR) to design the mathematics teaching and learning activity model (M-TLAM) for blended learning instruction in a Tanzanian higher education context. This model utilises contextual factors, including ICT tool usage, collaborative learning with metacognitive activities, and local culture, to determine how optimising these factors boosts math students' motivation and achievement. Two DBR phases were conducted, of which Phase 1 lasted for two weeks and Phase 2 an entire semester. To evaluate M-TLAM in real-life learning settings, experiments were conducted with 225 first-year undergraduate students and seven lecturers at the  College of Business Education in Tanzania. Experimental data were collected from pre-and post-tests, interviews, and questionnaires administered to students and lecturers. The study examined the perceptions and motivation of the participating lecturers towards using the M-TLAM in mathematics education and the factors influencing student motivation, academic achievement, and experience towards business mathematics courses. The evaluation results are promising and show that the M-TLAM implementation can potentially improve students' motivation and academic achievement. In addition, the pedagogical experiences of students were primarily positive, and student's attitudes towards the business mathematics course through M-TLAM were more favourable than those of students who studied the course via traditional methods. Cultural, technological, and instructional Factors contribute to students' improved motivation. Lectures demonstrated a cheerful disposition towards the potential of the M-TLAM for enhancing teaching and learning activities. The contributions of this study are highlighted through the implementation of the M-TLAM for blended learning and through the design principles and guidelines provided towards its effective implementation.

Student attrition is a long-standing problem in Computer Science (CS), as in many other disciplines, and it has gained momentum in the academic sphere. This study employs bibliometric analysis to shed light on the research stream of student attrition within CS. Bibliometric analysis is a popular technique for evaluating published scientific articles when empirical contributions are producing voluminous research streams. We collected 1310 articles from the Web of Science and Scopus databases, published over a period of 22 years from 2000 to 2022, to analyze the most relevant publication venues in the study of attrition in CS. Further analysis revealed the most cited institutions, countries, key themes, and other conceptual information. Keywords, such as “retention,” “computer science education,” “gender,” “introductory programming,” and “student success” emerged as dominant themes in attrition studies. As researchers work intensively to reduce attrition within CS, these thematic areas may continue to shape the future direction of attrition studies. Our study provides a comprehensive overview of research hotspots, thematic areas, and future directions for attrition studies in CS. This outcome could be valuable for young and emerging scholars who are starting their careers and looking to identify research hotspots in this field of interest.

The advent of blockchain technology over the last decade has led to the development of multiple use-cases of decentralization in various fields including education. This paper presents a unique bibliometric and qualitative analysis of the blockchain in education with novel contributions on temporal development, emerging themes and practical case studies on adoption and integration with existing educational technologies. We focus on identifying the major actors in the space, demographic participation and adoption, current hot topics, grey areas, and potential areas for innovation. Our analysis shows that while the blockchain has been around for about 13 years, blockchain in education only became prominent 5 years ago. This research also reveals that most of the efforts have been focused on reporting and verifying academic certificates and transcripts: only very few research focused on reporting and connecting in-depth academic records such as learning behaviour logs, learning contents and assessment data. This calls for concern as current education blockchain systems do not consider interoperability at the blockchain level and the heterogeneous nature in which institutes create and consume academic data. Finally, we present discussions on the implications of our findings, potential solutions and aspects of education blockchain research that can help to improve educational outcomes for various stakeholders.

The use of blockchain in education has become one of the trending topics in education technology research. However, only a handful of education blockchain solutions have provided a measure of the impact on students' learning outcomes, teaching, or administrative processes. This work reviews how academic data stored on the blockchain is being visualized across various education blockchain solutions. We argue that education's uniqueness requires a different visualization approach that supports students' learning activities, advances teaching methods, and facilitates administrative procedures. We identify a consistent trend where most of the proposed education blockchain solutions focus on credentials collection and do not provide a way to make sense of the blockchain's data. Thus, we conducted a needs analysis by interviewing four teachers to understand essential features when accessing distributed academic data, report these results and use them to inform the features of our proposed visualizations. Our unique contributions include: presenting typical use cases of distributed learning records from multiple education institutes and demonstrating how past learning records of students stored on the blockchain can be visualized to support current learning. We also propose a method of visualization to increase the data awareness of information owners through the blockchain.

Personality traits play a significant role in many organisational parameters, such as job satisfaction, performance, employability, and leadership for employers. One of the major social networks, the unemployed derives satisfaction from is Facebook. The focus of this article is to introduce association rule mining and demonstrate how it may be applied by employers to unravel the characteristic profiles of the unemployed Facebook users in the recruitment process by employers, for example, recruitment of public relations officers, marketers, and advertisers. Data for this study comprised 3,000 unemployed Facebook users in Nigeria. This study employs association rule mining for mining hidden but interesting and unusual relationships among unemployed Facebook users. The fundamental finding of this study is that employers of labour can adopt association rule mining to unravel job relevant attributes suitable for specific organisational tasks by examining Facebook activities of potential employees. Other managerial and theoretical implications are discussed.

A computer-based simulation is a viable approach for integrating the basics of science and technology in Junior Secondary. This study examined the impact of PhET on students' academic performance as well as students' motivation toward Basic Science and Technology. The study also investigates how PhET influence students' attitudes toward Basic Science and Technology. The study adopted the pre-test, post-test, and non-equivalent control group design. We randomly selected the study population from junior secondary two (JSII) students in private and public schools who are studying Basic Science and Technology. Quantitative data were collected and analyzed using a statistical approach such as the mean, standard deviation, and t-test. Students' post-test academic performance improved significantly (t160 = 36.28, p < 0.05) as a result of teaching Basic Science and Technology with PhET. According to the findings PhET had a substantial effect on the motivation of Basic Science and Technology students (t160 = 29.32, p < 0.05). Furthermore, the results demonstrated that PhET affected students' attitudes toward Basic Science and Technology (t160 = 65.36, p < 0.05). This study contributes to the body of knowledge by providing empirical evidence to support the integration of PhET in the teaching of Basic Science and Technology in Nigeria and other similar contexts. The findings suggest that PhET can be an effective pedagogical tool for improving learning outcomes in science and technology education, particularly in developing countries where resources and infrastructure may be limited.

Recently, virtual reality (VR) technology has shown great potential in advancing education with many pedagogical benefits for building the 21st-century teaching and learning experience. This study conducted a formative evaluation of an immersive VR expedition application with the aim of understanding users' learning processes and how the application facilitates higher education students' computational thinking skills. Six participants were randomly selected to conduct this evaluation. A mixed research approach consisting of quantitative and qualitative methods was employed. The study quantitatively analyzed users' scores from gameplay to understand how the intervention supported computational thinking skills. Participants were also interviewed to collect data after playing the mini-games to investigate users' experiences. The study showcases players' computational thinking competency, assessed automatically during gameplay. Further, this study used inductive content analysis to demonstrate users' reactions to prototyped VR mini-games. The qualitative findings suggest that users found the VR mini-games interactive and immersive, which provided an opportunity to foster learners' computational thinking skills. The quantitative analysis revealed that student's computational thinking competency can be enhanced through consistent playing of the mini-games. Moreover, the expedition aspect of the VR game stimulated learners' curiosity, which sustained their learning progress. Furthermore, users gained new knowledge and found the mini-games educative. Nevertheless, several aspects of the VR mini-games need improvements, according to users' perceptions. This study contributes to the knowledge in terms of the affordances of VR in education research and provides relevant insights that can shape future studies, for example, the recent hype of metaverse in education.

Frameworks for the evaluation of technological instructional tools provide educators with criteria to assess the pedagogical suitability and effectiveness of those tools to address learners’ needs, support teachers’ understanding of learning progress, and recognize the levels of achievement and the learning outcomes of the students. This study applied secondary document analysis and case study to identify five pedagogical indicators for teaching and learning computational thinking, including technology, pedagogical approaches, assessment techniques, data aspect, and teacher professional development. Based on the pedagogical indicators, this study proposed a computational thinking pedagogical assessment framework (CT-PAF) aimed at supporting educators with a strategy to assess the different technological learning tools in terms of pedagogical impact and outcome. Furthermore, three case-study instructional tools for teaching CT in K-12 were analyzed for the initial assessment of CT-PAF. Scratch, Google Teachable Machine, and the iThinkSmart minigames were marched to the underpinning characteristics and attributes of CT-PAF to evaluate the framework across the instructional tools. The initial assessment of CT-PAF indicates that the framework is suitable for the intended purpose of evaluating technological instructional tools for pedagogical impact and outcome. A need for expanded assessment is, therefore, necessary to further ascertain the relevance of the framework in other cases.

ecent studies have shown that children are not adequately physically active and there is a need to increase children’s physical activity. This study describes new opportunities and solutions for using existing games and gamification to increase physical activity among children in Sweden. We adopted the principles of Tic-Tac-Training to redesign, build, and test a classical multiplayer cooperative game, Battleship, to create a PA game that children experience as fun and engaging. The low fidelity prototype of the game was developed using an iterative game development life cycle and tested with 13 young male children aged 8–11 in a real-world informal setting. A mixed-method research approach was used to understand the users’ experiences and the impact of the Battleship-PA game on behavior change regarding physical activity. Research data were collected through audio recordings of interactions, direct observation, and a user experience questionnaire. The results of this study indicate both positive and negative feedback that can be used to improve the game and user experiences. The results from the unfiltered recordings revealed that both teams were competitive, cooperated within their team, and became excited whenever they destroyed opponent’s ships or were close to winning. However, the children felt bored and exhausted when many gamification tasks were repeated several times in a game session. Direct observation indicated that the children enjoyed the physical activities resulting from playing the game. However, participants who had not previously played the classical version of Battleship were confused about the objectives and concept of the game. The analysis of the user experience questionnaire indicated that most children found the game easy to play, motivating, engaging, interactive, fun, cooperative, competitive, and visually appealing. Furthermore, most children agreed that the game helped them to be physically active and strongly agreed that they enjoyed performing the physical activities in the game. Future work is needed to improve the game user interface, gamification elements, and prepare additional physical activity tasks for a rewarding experience.

This study uses the quantitative research approach to examine the connection between students’ teamwork experience, self-regulated learning, technology self-efficacy, and performance in an online educational technology course. Sixty-three (63) students participated in this study. The study data were collected through an online questionnaire that included background information, course satisfaction, motivation strategies for learning, and online technology self-efficacy, to study the variables’ interactions using quantitative research. To realize this study’s aims, multivariate regression and correlation approaches were employed to analyze the online students’ data. The multivariate regression analysis results show a relationship between self-regulated learning, the online course level, and the number of online courses that the students have completed. Right self-regulated learning strategies in online courses motivate students to strive for a good teamwork experience, leading to increased interest in online learning. In addition, the results also show that there is a relationship between satisfaction and the level of the online course. Achieving good grades makes the student more satisfied and improves the level of technology use. Finally, this study established a relationship between the students’ motivation and the online course level. Therefore, teachers and course designers should implement learning objects that promote students’ engagement and motivation in online learning environments.

Artificial Intelligence (AI) for K-12 education has been considered a global initiative. However, evidence of Africa’s inclusion in globalization across schools is lacking in the literature. Besides, resources, including materials and content, are developed across Hong Kong, Japan, Europe, and the USA. These suggest that contextualized resources are effective for AI implementation in schools. Since appropriate pedagogical approaches, sound instructional methods, materials, tools, and activities familiar to the student for instruction lead to effective learning, we embark on a literature survey to unravel the approaches and kind of AI resources utilized across contexts. A systematic literature review methodology was used in this paper to understand the trends of teaching AI at the K-12 educational level. Scientific databases such as IEEE, ACM, Web of Science, and Scopus were searched to gather relevant literature in tandem with our research aim. Out of the 451 articles that were retrieved, only 54 fit well into the inclusion criteria and were reviewed for further analysis. This study revealed several existing approaches and resources used to teach AI in schools.

The academic world has experienced rapid growth in the adoption of social media that can constructively complement traditional education and even replace it in distance/online learning. Social media is used in many institutions for educational purposes in numerous innovative ways, even to the extent of being utilized in traditional face-to-face classrooms. A wealth of academic research has been published related to social media in education. The purpose of this special issue is to highlight research studies of social media in computing education, with the aim to discuss research findings, share good practices and practical experiences, and address the challenges of using social media in computing education. The special issue focuses on how social media in computing education is being used to transform teaching and learning practices in various educational contexts and settings. Additionally, the special issue covers a wide range of aspects related to the use of social media in computing education, such as the adoption of social media in instructional activities, the applicability of different social media tools in computing education, pedagogical frameworks, theoretical approaches, managerial perspectives, and possible ethical issues. An overview of the special issue papers is presented, exemplifying the importance of social media from a computing education perspective in a diverse educational context.

Personalized or precision education (PE) considers the integration of multimodal technologies to tailor individuals’ learning experiences based on their preferences and needs. To identify the impact that emerging multimodal technologies have on personalized education, we reviewed recent implementations and applications of systems (e.g., MOOCs, serious games, artificial intelligence, learning management systems, mobile applications, augmented/virtual reality, classroom technologies) that integrate such features. Our findings revealed that PE techniques could leverage the instructional potential of educational platforms and tools by facilitating students’ knowledge acquisition and skill development. The added value of PE is also extended beyond the online digital learning context, as positive outcomes were also identified in blended/face-to-face learning scenarios, with multiple connections being discussed between the impact of PE on student efficacy, achievement, and well-being. In line with the recommendations and suggestions that supporters of PE make, we provide implications for research and practice as well as ground for policy formulation and reformation on how multimodal technologies can be integrated into the educational context

Predicting academic performance for students majoring in computer science has long been a significant field of research in computing education. Previous studies described that accurate prediction of students’ early-stage performance could identify low-performing students and take corrective action to improve performance. Besides, adopting machine learning algorithms with predictive analytics has proven possible and meaningful. The traditional approach of looking after students without uncovering the root causes of poor performance has shifted dramatically into improving the quality of the educational processes of students, teachers, and stakeholders. Thus, this study employed predictive analytics to develop an early warning prediction model using computing science degree performance data at a public institution. Predictive models based on our data analysis revealed that low, medium, and high-performing students could be predicted with an accuracy of 88% using only the grades of the courses they took in the second year. Moreover, 96% accuracy was achieved when all course grades were used in predictive models. The courses that are important in determining the overall performance of the students were also analyzed. By employing a multi-method approach, utilizing a large dataset spanning four academic years, and including a diverse sample of 430 students, our study offers a robust foundation to researchers, designers, and computer science educators for understanding and predicting student performance. The enhanced generalizability and implications for educational practice position our study as a valuable contribution to the field, paving the way for further advancements in predictive analytics.

In Tanzania, street traders face the challenge of limited markets caused by employing weak marketing and promotion strategies. This study developed a mobile application to solve the problem addressed using participatory design. Qualitative data were collected using focus group discussions and brainstorming from 80 respondents involving both street traders and customers in different workshops and meetings. Data were used for the design and development of the Machinga application. Furthermore, quantitative data for application evaluation were collected from 96 respondents using questionnaires. In addition, 20 interviews were conducted to validate the evaluation results. Thematic and descriptive analysis were performed for both qualitative and quantitative data. The results show that the mobile application has prospective features which solve the problem of limited markets in the street trading community. The application is perceived positively by end-users because of embracing their prior requirements and meeting the evaluation criteria of usefulness, ease-of-use, learnability, and user satisfaction. The study recommends further training of users to enable the application to attain its multiplier effect on the vast population. This study confirms the relevance of participatory design in ICT4D projects for informal workers as it allowed the involvement of end-users and reflected their voices in terms of the technology they desire. 

Street trading is an economic activity that is conducted by street traders in numerous urban parts of Tanzania. Street traders use mobile technology to search for markets. This study mapped the wants and needs of Tanzanian street traders and their customers in order to better understand the potential and pitfalls of technology to help their trade activities. Qualitative data were collected using in‐depth interviews with 22 street traders and 22 customers. In addition, two focus group discussions with 20 participants, including street traders and customers, were conducted. Data were analyzed through content analysis. The results identified a number of technology wants and needs shared by traders and customers that would ease the customers' access to products they want, and support the traders to promote their products, locate where the users are, support business growth, and predict sales potential. This research study contributes to understanding the technology needs of one marginalized group in Tanzania. The study facilitates discussion on the suitability of design research in the context of informal worker communities, and it points toward a path for design research of information systems that are grounded on the needs and knowledge of end‐users in their communities and contexts of use.

Machine learning (ML) literacy has recently been identified as one of critical skills students need to succeed as future creators and innovators. While the significance of introducing ML basics at kindergarten to twelfth grade (K-12) levels is increasingly acknowledged, there is limited research that focuses specifically on collaborative design of ML applications with middle school students. We posit that engaging young children to co-invent and make concrete prototypes improves their ideas, encourages them to become active participants, and allows them to establish the implications of the technology in their everyday lives. In order to lay the foundation for middle school ML education, we collaboratively designed and prototyped ML applications with 43 eighth grade students (ages 11 to 14) in a Nigerian school. The ideas generated by the students indicate that they began to identify the applicability of ML to their daily lives and as a solution to a plethora of societal challenges. This study provides learners’ input and preliminary insights into approaches that could be adopted to promote ML within the compulsory level of education in an African setting. The research contributes to the limited body of knowledge available on effectively teaching ML to young learners using design-oriented pedagogy, especially in the context of an emerging country.

The benefits of teaching machine learning to K-12 pupils include building foundational skills, useful mental models and inspire the next generation of AI researchers and software developers. However, introducing machine learning in schools has been a challenge even though several initiatives, curriculum design, platforms, projects, and tools exist to demystify the concept. The existing resources are scattered and sometimes overlap. Thereby selecting the appropriate tools to adopt in teaching becomes an arduous task for the teachers and other practitioners. More so, despite the increasing number of papers published in this field, there are still gaps in identifying specific tools and resources for teaching machine learning in K-12 settings. This study presents a literature review on machine learning in K-12 by selecting articles published from 2010 to 2021. Therefore, this paper presents a resource catalog and surveys of tools to help teachers find suitable teaching paths and make the decision to introduce activities that help students understand the basic concepts of machine learning. Based on the research objective, we utilized six databases to extract relevant information, while thirty-nine peer-reviewed articles were collected based on a systematic literature search and were analyzed. This study identified resources, tools, and instructional methods as the main categories of pedagogical items needed to ensure impactful teaching of machine learning in K-12 settings. Besides, the mode of operation, benefits and the challenges of the pedagogical tools for teaching machine learning in K-12 settings were unraveled. The findings also show the increased number of initiatives resulting in tools development to support machine learning teaching. Finally, this study provides recommendations for future research directions to help researchers, policymakers, and practitioners in the education sector identify and apply various resources to aid decision-making in practice and to democratize machine learning practices in schools.

The teaching of machine learning is now considered essential and relevant in schools globally. Despite the ongoing discourse and increased research in the emerging field, teachers' conceptions of machine learning remain under-researched. This study aims at filling the gap by describing the initial conceptions of teaching machine learning by 12 African in-service teachers. We detailed the result of a phenomenographic analysis of teachers' pre-conceptions on teaching machine learning in K-12 settings. Twelve high school (Grades 10–12) computer science teachers in some selected African countries were recruited for a semi-structured interview. Five categories emerged from the analysis of the semi-structured interviews as follows: supporting student technical knowledge, having knowledge of the concept, focusing on professional development practices, contextualizing teaching resources and tools, and sustainability for development goals. These involve the relevance of teaching machine learning, the pedagogical approaches, strategies, and sustainability relating to practical implementation in schools. The results suggest the need to train in-service teachers to use existing tools designed for introducing machine learning. The teachers should also be involved in the co-designing process of resources considering contextual factors and, significantly, the curriculum to integrate machine learning into mainstream education. Involving teachers in the development process would help contextualize machine learning, contributing to real impact and societal changes.

Researchers' efforts to build a knowledge base of how middle school students learn about machine learning (ML) is limited, particularly, considering the African context. Hence, we conducted an experimental classroom study (N = 32) within the context of extracurricular activities in a Nigerian middle school to discern how students engaged with ML activities. Furthermore, we explored whether participation in our intervention program elicit changes in students' ML comprehension, and perceptions. Using multiple qualitative data collection techniques including interviews, pre-post open-ended surveys and written assessments, we uncover evidence that indicated evolution of students’ ML understanding, ethical awareness, and societal implication of ML. In addition, our findings showed that a middle school student can learn and understand ML, even when one had no prior knowledge or interest in science related careers. The findings have implication for pedagogical design of AI instruction in middle school context. We discuss the implication of our results for researchers and relevant stakeholders, highlight the limitations and chart future work paths.

The increasing attention to Machine Learning (ML) in K-12 levels and studies exploring a different aspect of research on K-12 ML has necessitated the need to synthesize this existing research. This study systematically reviewed how research on ML teaching and learning in K-12 has fared, including the current area of focus, and the gaps that need to be addressed in the literature in future studies. We reviewed 43 conference and journal articles to analyze specific focus areas of ML learning and teaching in K-12 from four perspectives as derived from the data: curriculum development, technology development, pedagogical development, and teacher training/professional development. The findings of our study reveal that (a) additional ML resources are needed for kindergarten to middle school and informal settings, (b) further studies need to be conducted on how ML can be integrated into subject domains other than computing, (c) most of the studies focus on pedagogical development with a dearth of teacher professional development programs, and (d) more evidence of societal and ethical implications of ML should be considered in future research. While this study recognizes the present gaps and direction for future research, these findings provide insight for educators, practitioners, instructional designers, and researchers into K-12 ML research trends to advance the quality of the emerging field. 

Background and context

Researchers have been investigating ways to demystify machine learning for students from kindergarten to twelfth grade (K–12) levels. As little evidence can be found in the literature, there is a need for additional research to understand and facilitate the learning experience of children while also considering the African context.

Objective

The purpose of this study was to explore how young children teach and develop their understanding of machine learning based technologies in playful and informal settings.

Method

Using a qualitative methodological approach through fine-grained analysis of video recordings and interviews, we analysed how 18 children aged 3–13 years constructed their interactions with a machine-based technology (Google’s Teachable Machine).

Findings

This study provides empirical support for the claim that Google’s Teachable Machine contributes to the development of data literacy and conceptual understanding across K–12 irrespective of the learners’ backgrounds. The results also confirmed children’s ability to infer the relationship between their own expressions and the output of the machine learning-based tool, thus, identifying the input-output relationships in machine learning. In addition, this study opens a discussion around differentials in emerging technology use across different contexts through participatory learning.

Implications

The results provide a baseline for future research on the topic and preliminary evidence to discern how children learn about machine learning in the African K–12 context.

The momentum around computational thinking (CT) has kindled a rising wave of research initiatives andscholarly contributions seeking to capitalize on the opportunities that CT could bring. A number of literaturereviews have showed a vibrant community of practitioners and a growing number of publications. However,the history and evolution of the emerging research topic, the milestone publications that have shaped itsdirections, and the timeline of the important developments may be better told through a quantitative, scientometric narrative. This article presents a bibliometric analysis of the drivers of the CT topic, as well as itsmain themes of research, international collaborations, influential authors, and seminal publications, and howauthors and publications have influenced one another. The metadata of 1,874 documents were retrieved fromthe Scopus database using the keyword “computational thinking.” The results show that CT research has been US-centric from the start, and continues to be dominated by US researchers both in volume and impact. International collaboration is relatively low, but clusters of joint research are found between, for example, anumber of Nordic countries, lusophone- and hispanophone countries, and central European countries. The results show that CT features the computing’s traditional tripartite disciplinary structure (design, modeling, and theory), a distinct emphasis on programming, and a strong pedagogical and educational backdrop including constructionism, self-efficacy, motivation, and teacher training.

Many empirical studies have shown that educational games and recent technologies impact education and increase learning effectiveness, students’ motivation and engagement. The overall aim of this study is to evaluate the usability of Imikode, a virtual reality (VR) game that was developed to introduce the concepts of object-oriented programming to novices. The improved version of the Imikode VR game consists of three features: An artificial intelligence component designed to provide real-time error feedback to users, an intelligent agent that guides and teaches users how to play the game and finally, the integration of multiple game play that gives learners more opportunities to explore the VR environment for greater immersive learning experience. This study adopted a survey approach and recruited first-year computer science students to measure learner satisfaction with educational virtual reality games and examined the correlations among the attributes of the Usefulness, Satisfaction and Ease of Use questionnaire of usage of Imikode. The results showed that the students were satisfied with Imikode and perceived the virtual reality educational game as very useful for learning object-oriented programming concepts. In addition, there was a correlation among the questionnaire variables, which means that researchers can use the instrument for future usability studies in the context. We further proffered some design recommendations for building software tools.

There is a growing number of initiatives for teaching artificial intelligence or machine learning in the compulsory levels of education. However, more research and development is required to understand technological and pedagogical aspects of AI teaching especially in K-12 level. In the context of a two day workshop in a science festival, we introduced the concept of Convolution neural network (CNN) and examined how children learn about the way CNN performs image recognition. The concept was presented through hands-on practice with DoodleIt, a simple app for introducing the fundamental ideas behind CNN.

The aim of the research was to investigate teachers' perspectives on the elements of emergency e-learning during the COVID-19 pandemic. The research was conducted with 134 teachers from different types of schools in Poland during the first wave of crisis e-learning (March -May 2020). The variables included in the analysis comprise teachers' use of differentiated teaching methods, student collaboration, school support for modern ICT, and teachers' digital competence. The findings are summarized as follows: (1) about a third of students did not develop the ability to work together in emergency e -learning; (2) more than two-thirds of teachers underlined that their schools actively promoted the idea of implementing ICT in education; (3) more than two-thirds of teachers emphasized that their school principals had systematically modernized the IT facilities necessary for effective teaching; (4) approximately half of the teachers were supported by the school authorities in strengthening their digital competence; and (5) teachers used various teaching methods in emergency e-learning, and the most popular methods were videos, presentations, e-learning platforms, and interactive games and applications; (6) teachers who did not differentiate digital teaching methods did not believe in the development of opportunities for soft skills among students; (7) the schools invested in technological facilities and supported the development of digital competence among teachers; and (8) the intensive use of e-learning platforms by teachers increased their positive attitude towards the development of soft competences (e.g., collaboration skills) among students.

This research presents the results of an exploration of special education teachers' understanding of how their participation in workshop-based digital storytelling (DST) would enhance their professional development concerning inclusive education. This study evaluates the usability of the Smart Ecosystem for Learning and Inclusion (SELI) platform for supporting teachers during the workshop-based digital storytelling process. We used a convergent parallel mixed-method research design approach with 47 secondary school teachers working with disabled people in the Dominican Republic. The results of this study indicated that the SELI smart learning platform had shown good usability in supporting teachers during the workshop-based digital storytelling pedagogical process. Besides, two themes emerge regarding how workshop-based digital storytelling can contribute to teacher professional development for promoting inclusive education. The resulting themes are expressing, listening, and learning through digital storytelling; and driving change with digital storytelling to create more inclusive environments. Teachers who participated in the interviews were optimistic about DST implementation. They expressed that the workshop worked for multiple ways of expression, listening from and connecting with other stories, and learning through DST. Moreover, teachers could reflect their idea about using DST in terms of its potential impact on inclusion in the classrooms for driving change, building meaningful learning, and influential practice when used in the classroom.

The entire globe is battling the novel coronavirus disease (COVID-19) outbreak, which has caused a downward spiral in many nations’ economies, particularly in the higher education contexts. A growing number of universities have either postponed or cancelled academic activities. A few universities have intensified measures to prevent face-to-face interactions, intending to protect staff members and students from this highly contagious disease. This study investigates the COVID-19 impact on the higher education sector in Nigeria. Interview sessions involving seven lecturers across five universities in three geographical locations of Nigeria were conducted. The interview data were gathered using digital applications, such as Zoom cloud meetings and Skype, transcribed into a textual format, and further analysed. Six themes with corresponding sub-themes emerged from the study. In the final analysis, results revealed that the COVID-19 negatively impacted several universities. This study presents opportunities for responding issues, problems and trends that are currently arising and will arise in the future due to the impact of the COVID-19 pandemic in the Nigerian higher education system.