Abstract

Although knowledge drives organizational growth, limited teamwork skills have hindered effective knowledge sharing and utilization across departments and individuals in higher education institutions (HEIs). Thus, this paper develops a pertinent knowledge-intensive teamworking model for HEIs. Educational background, leadership, and social media adoption were independent variables, while knowledge acquisition, development, application, revision, and knowledge-intensive teamwork were dependent variables. Using an online questionnaire, a quantitative investigation of 918 people across different HEIs tested hypotheses based on PLS-SEM findings. Results showed that educational background, leadership, and social media adoption are integral enablers in smoothing knowledge management operations, while the synergistic combination of process phases drives teamwork. Consequently, outcomes establish foundations for fostering a continuous learning culture, accountability, and cooperation in HEIs.

Introduction

Most management and technology research has focused on the technical efficiency of social media [1]. Information technology revolutionized education over three decades ago, and more recent research has examined various impacts on the workplace, such as training and communication [2,3]. Social media adoption in higher education institutions (HEIs) depends on strategic aims, resources, and cultural factors [4]. Furthermore, the adoption of technology by HEIs is contingent on organizational strategy and differs among HEIs in the same education system [5]. The use of social media in education has drawn interdisciplinary interest [6,7] as HEIs shift communication from one-way to interactive to regularly engage stakeholders [8]. Continual society-wide communication needs frequent updates across media, especially social platforms. Different methods can shape how HEIs utilize social media as key channels, enabling stakeholder conversations. Communication frequently starts on blogging platforms and spreads across media as users access multiple integrated channels, facilitating tailored, multi-way exchanges without time barriers.

The COVID-19 pandemic necessitated remote work and learning, disrupting traditional HEI teamwork models and requiring new virtual collaboration approaches [9]. This research extends knowledge management theory by examining how social media enables leadership, knowledge sharing, and co-development in virtual teams. Specifically, it provides empirical support for the knowledge management cycle model of Kang and Hau [10] by demonstrating how social media can facilitate key stages of knowledge acquisition, sharing, and revision in virtual settings. The findings reveal how social media-enabled leadership, through modeling knowledge-sharing practices and incentivizing participation, is critical for overcoming motivational barriers to collaborative knowledge activities online [11]. This paper advances the theoretical understanding of the interconnected relationships between social media adoption, leadership influence, and iterative knowledge improvement critical for virtual team effectiveness in higher education institutions. Moreover, a key contribution is providing empirical evidence on the affordances of different social media platforms for enabling specific collaborative activities in virtual HEI teams. The findings provide actionable guidance to HEI leaders on effective social media policies and professional development to optimize virtual teamwork. This research also contributes a new conceptual model of social media-enabled knowledge-intensive teamwork tailored to the HEI context.

Conclusion

The COVID-19 pandemic has significantly impacted higher education institutions, catalyzing a shift towards remote and online learning. In this context, social media has become an integral platform enabling knowledge-intensive teamwork, allowing seamless communication and collaboration regardless of location. Specifically, social media can promote the development of such teams in several key ways. Online discussions and forums can be leveraged to share information and resources, discuss ideas, brainstorm solutions, and provide feedback among team members. Video conferencing tools facilitate virtual meetings and presentations, helping members stay connected and engaged. Additionally, knowledge management systems allow the centralized sharing and accessing of documents while tracking progress – optimizing coordination. Consequently, social media facilitates effective knowledge-intensive teamwork in higher education after COVID-19 by providing ubiquitous channels for cooperation.

In conclusion, high-performing knowledge-based teams in higher education are predicated on an innovation-centric framework requiring integrated organizational and procedural alignment. Positive relationships among knowledge skill-building, leadership support, and cultural enablement underscore the need to embed holistically developmental, technological, and structural conditions that foster learning and innovative capacity at individual and team levels. Internally, sustained idea flows via cooperation enable continuous adaptation and improvement as contexts shift.

Abstract

As big data becomes an apparent challenge to handle when building a business intelligence (BI) system, there is a motivation to handle this challenging issue in higher education institutions (HEIs). Monitoring quality in HEIs encompasses handling huge amounts of data coming from different sources. This paper reviews big data and analyses the cases from the literature regarding quality assurance (QA) in HEIs. It also outlines a framework that can address the big data challenge in HEIs to handle QA monitoring using BI dashboards and a prototype dashboard is presented in this paper. The dashboard was developed using a utilisation tool to monitor QA in HEIs to provide visual representations of big data. The prototype dashboard enables stakeholders to monitor compliance with QA standards while addressing the big data challenge associated with the substantial volume of data managed by HEIs’ QA systems. This paper also outlines how the developed system integrates big data from social media into the monitoring dashboard.

Introduction

Quality assurance (QA) in higher education (HE) is emerging as the higher education institutions (HEIs) find it difficult to monitor their levels of quality of services. Governments impose national QA standards in order to achieve the minimum level of quality of services provided by these institutions. In the Kingdom of Saudi Arabia (KSA), Education and Training Evaluation Commission (ETEC) imposes, through its National Centre for Academic Accreditation and Assessment (NCAAA), the QA standards that all HEIs in the KSA are required to comply with for accreditation [1]. To measure compliance with these standards, in 2018, NCAAA developed 23 key performance indicators (KPIs). In 2022, NCAAA introduced a revised version of KPIs. This revised version comprises a total of 17 KPIs, and all HEIs are mandated to monitor and report on compliance. This evaluation process of compliance with these KPIs should take place during the forthcoming accreditation cycle for HEIs [2].

In addition, the increasing amount of data generated from traditional sources and through social media plays negative influence on business intelligence (BI). There is a desire to build low-cost data platforms that will be able to handle this increasing amount of data. This is sometimes known as the big data challenge [3,4].

In this paper, different BI architectures are presented. These BI architectures encompass different BI technologies that HEIs may adopt, taking into consideration their capabilities and specific requirements of HEIs. A proposed architecture is also presented to show how the big data challenge can be addressed to deal with huge amounts of data that the QA system might require particularly if the data is being linked to social media. The recent trend of measuring students’ satisfaction is through students’ comments posted on social media.

Conclusion

This paper showed how the HF-HEQ-BI framework can be applied to develop a BI prototype dashboard for monitoring quality in HEIs and a dashboard has been developed from the framework. The paper has also outlined different BI architectures that HEIs may adopt to handle the big data challenge while monitoring QA. These architectures show different configurations of BI systems that HEIs may adopt based on their requirements and available resources. The development process of the BI dashboard was discussed to show how HF-HEQ-BI FUT may be used to determine the requirements to be presented in the dashboard. HF-HEQ-BI FUT assists in determining the required visualisation for each KPI to be displayed in the dashboard. FUT is customisable and can be adapted by users to reflect the requirements of individual HEI. FUT shows how decision-makers in HEIs may select various visualisation dashboards while monitoring QA compliance regarding NCCAA standards.

A prototype dashboard was developed to illustrate the application of the framework and also to evaluate the HF-HEQ-BI framework. The developed dashboard shows a monitoring display of the mandatory NCAAA KPIs, institutional specific KPIs, additional KPIs, and social media analytics. The social media analytics presented in the dashboard included the sentiment analysis of data drawn from Twitter through Twitter API. Additionally, the proposed dashboard allows the dashboard users to represent additional KPIs related to performance monitoring in the institutions.

The developed dashboard based on the HF-HEQ-BI framework shows how data retrieved from social media is presented in summarised diagrams to handle the big data challenge. The prototype dashboard is dynamic and KPIs can be added or modified easily based on the changes in regulations or institutional directions of the strategic plans. Our future work will focus on the evaluation of the proposed dashboard to meet the big data challenge.

Abstract

Although digital game-based learning (DGBL) has the potential to enhance learning motivation and complex cognitive skills of students, its adoption and effectiveness are heavily dependent on lecturers’ acceptance. Comprehending lecturers’ perceptions and beliefs underlying their decision-making processes is therefore significant. This study examines factors determining the intention of accounting and business lecturers in Indonesia to use digital games in their courses using an extended Technology Acceptance Model (TAM). Based on data collected from 258 lecturers, the research model is analyzed using PLS-SEM approach. The results show that the proposed model can explain 52.4% of the variance in accounting and business lecturers’ behavioral intention to use digital games in class. Particularly, both perceived ease of use and usefulness are the factors significantly determining lecturers’ intention. However, DGBL frequency negatively moderates the positive effect of perceived ease of use on lecturers’ intention.

Introduction

This study raises a question of what factors affect lecturers’ intention to adopt Digital game-based learning (DGBL)1 in accounting and business courses. The question is based on the issue of DGBL’s low acceptance in the classroom, despite the fact that it is regarded as an effective approach for promoting students’ learning motivation, social skills, and complex cognitive skills (Faisal et al., 2022). Lecturers are the focus of study because they play a critical role in promoting the use of new learning technology in their courses. To answer this question, this study uses a technology acceptance model (TAM) extended by De Grove et al. (2012) as a theoretical foundation to predict the DGBL’s acceptance of accounting and business lecturers. This study also incorporates DGBL frequency into the extended TAM to explain the strength of the relationship between perceived ease of use (PEOU) and behavioral intention.

This question is relevant and timely because comprehending lecturers’ perceptions and beliefs about DGBL could help accounting educators find the appropriate strategies to promote its use in the classroom. There are two advantages of using DGBL over traditional teaching methods. First, the DGBL can provide engaging, interactive, and motivating learning experiences which are essential for an effective learning (Garris et al., 2002; Jabbar & Felicia, 2015; Tennyson & Jorczak, 2008). For example, Loon and Bell (2018) found that positive emotions stimulated by a computer simulation game enhance students’ cognitive skills. Second, the DGBL has proven to be an effective method for students to acquire complex cognitive skills, such as decision making (Pasin & Giroux, 2011; Severengiz et al., 2020; Tawil et al., 2015), critical thinking (Lovelace et al., 2016; Urquidi-Martín et al., 2019; Yang, 2015), problem solving (Eder et al., 2019; Torres & Augusto, 2017; Yang, 2015), and higher-order thinking skills in Bloom’s Taxonomy (Adams et al., 2012; Barab et al., 2012; Hwang et al., 2013; Soflano et al., 2015). These skills are important because research on students’ conceptual understanding and learning approaches revealed that accounting students generally achieved lower-order learning outcomes (Kuang et al., 2021). Therefore, promoting an instructional method that can help students acquire higher-order thinking skills should be a concern for accounting educators.

Conclusions

This study raises the question of what factors affect lecturers’ intention to adopt DGBL in accounting and business courses based on the extended TAM. This study finds that the original TAM variables: PEOU and PU affect behavioral intention, indicating the model is applicable in the context of Accounting and Business education. Additionally, learning opportunities are the only variable proposed in the extended TAM affecting lecturers’ behavioral intention. The new variable introduced in this study, DGBL frequency, was found to significantly weaken the effect of PEOU on behavioral intention, indicating a frequent exposure to DGBL is critical to enhance lecturers’ intention to accept DGBL. Overall, the proposed model can explain 52.4% of the variance in accounting and business lecturers’ behavioral intention to use digital games in the classroom.

This study contributes to theory and practice in several important ways. First, this study extends the literature on DGBL acceptance by evaluating the role of extended TAM in explaining lecturers’ intention behavior to use DGBL in accounting and business education in Indonesia. Second, this study extends the modified TAM (De Grove et al., 2012) by adding the moderating role of DGBL frequency in explaining the relationship between PEOU and lecturer’s acceptance of DGBL. Third, this study addresses curriculum and instruction issues in the field of accounting education, which has rarely been empirically studied in the context of Asian countries. Fourth, this study contributes to empirical research by employing structural model analysis which has not been widely adopted in the accounting curriculum domain. Finally, this study informs educational game developers about the importance of collaborating with lecturers to understand their needs and assist them in developing games that align with curriculum goals. It also encourages regulators to heavily publicize the effectiveness of game-based learning to accountant educators.

Abstract

Nowadays, Java programming is used in a variety of application systems as a highly portable object-oriented programming language. To assist its self-studies by novice students, we have developed the Java programming learning assistant system (JPLAS), and implemented the personal answer platform on Node.js. JPLAS offers several types of exercise problems at different learning levels, including the grammar-concept understanding problem (GUP), the value trace problem (VTP), the mistake correction problem (MCP), the element fill-in-blank problem (EFP), the code completion problem (CCP), and the phase fill-in-blank problem (PFP). Any answer is automatically marked by string matching with the correct one on the platform. In this paper, we present an implementation of the six problem types in a Java programming course in Okayama University. We generated 109 problem instances by following its curriculum and assigned them to 58 third-year students as homework before the final examination. Their solution results reveal the difficulty difference among the problem types and confirm the validity in the Java programming course.

Introduction

Java is a powerful, general-purpose object-oriented programming language. Java was created in 1995, and is running on more than three billion devices worldwide as one of the most popular programming languages. Nowadays, Java is used in a variety of application systems, such as web applications, mobile applications, desktop applications, games, IoT application systems, and cloud service systems, as a highly portable object-oriented programming language. Thus, many universities and professional schools are offering the courses to cultivate Java programming engineers.

To assist self-studies of Java programming by novice students, we have developed the Java programming learning assistant system (JPLAS) and implemented the personal answer platform on Node.js that will be distributed to students on Docker [1].

Evaluation

In this section, we present the implementation results of the 109 instances to 58 third-year students taking a Java programming course in Okayama University.

A. Solution Performances by Problem Types

First, we analyze differences of solution performances of the students by the six problem types. Table II shows the number of students who submitted answer files, the average correct answer rate, and the average number of answer submission times for each instance by them for each problem type. From this table, it can be observed that GUP, VTP, MCP, and EFP are easier for these novice students. The average correct answer rate and the average number of answer submission times are similar in them. CCP and PFP are harder for them, where both the correct rate and the number of submission times become worse. The average correct answer rate of two CCP instances at ID = 8 and 10, and one PFP instance at ID = 12 is less than 90%. According to Table I, they are on override, overload, structure, inheritance, abstract class, and interface, which can be difficult for novices to understand. The hint function should be implemented for them, which will be in future works.

Abstract

Teachers are at the front lines of implementing artificial intelligence (AI) in education. They are expected to develop an adequate understanding of AI and become educated users as well as educators. Their readiness for the use of AI is critical for the success of AI-enhanced education. The present study conceptualized AI readiness from four components: cognition, ability, vision, and ethics in the educational use of AI, and investigated their interrelationships and their implications for teachers’ work. The data from 3164 primary school teachers were collected and analyzed by partial least square structural equation modelling and cluster analysis. This study found that cognition, ability, and vision in the educational use of AI were positively associated with ethical considerations. The four components of AI readiness all positively predicted, whereas perceived threats from AI negatively predicted, AI-enhanced innovation, which in turn positively predicted teachers’ job satisfaction. This study identified three clusters of teachers based on their AI readiness levels. Teachers with high levels of AI readiness tended to perceive low threats from AI and demonstrate high AI-enhanced innovation as well as high job satisfaction. However, teachers from different socio-economic regions and of different genders showed no significant differences regarding AI readiness and its impact on their jobs. This study empirically validated the importance of AI readiness for teachers’ work and has important implications for the development of strategies and policies facilitating successful AI-enhanced education.

Introduction

Artificial intelligence (AI) has been increasingly used in a variety of fields (e.g., industry, finance, and education) to promote innovation and increase work efficiency (Ng et al., 2021). In education, AI is touted as a seemingly almighty tool, supporting or even replacing teachers’ work by automatically tracking students’ progress, assessing their performance, and providing personalized help (Albacete et al., 2019; Chounta et al., 2022; Tarus et al., 2018). Teachers can rely on AI to make informed decisions on orchestrating teaching practice so as to better support student learning (Van Leeuwen & Rummel, 2020).

Nonetheless, in reality, intelligent tools for education are rarely used consistently in K-12 classrooms (Ferguson et al., 2016). Schiff (2021) found that much practice and research related to the educational use of AI did not deliver promised changes and benefits. Among the multiple reasons leading to this controversy, for instance, the quality of AI and users’ preferences (Luckin et al., 2022) and ethical concerns (Holmes et al., 2022), an essential culprit could be the techno-centric approach vehemently promoted by some in the educational field, which stresses the role of AI but ignores the agency of teachers who can decide whether, what, when, and how AI technologies are used in the first place (Luckin et al., 2022). Teachers are on the front lines of AI deployment, bridging schools’ AI policies and students’ needs, thereby the critical role in the successful implementation of AI in schools (Felix, 2020). However, many teachers may not be actually ready for AI-enhanced education, though they are mostly aware of the potential benefits that AI can bring to education (Chounta et al., 2022). Their inadequate AI readiness may partially contribute to the gap between rapid advances in AI technologies and comparatively slow and unsatisfactory adoption of them in education (Luan et al., 2020; Luckin et al., 2022).

Discussion

Although AI has been increasingly utilized in education and has been found to be beneficial and powerful for student learning (Smakman et al., 2021; Xia et al., 2022), little attention has been paid to the needs and challenges faced by teachers in AI-enhanced teaching (Celik et al., 2022; Langran et al., 2020). Teachers are seen as one of the crucial stakeholders in AI-enhanced education (Celik et al., 2022; Seufert et al., 2021). Therefore, their perspectives, needs, and experiences are vital to the successful integration of AI in school settings (Holmes et al., 2022). In order to effectively implement AI in the classroom, teachers must be prepared in terms of cognition, ability, vision, and ethical considerations related to the use of AI in education (Luckin et al., 2022). This study therefore aimed to empirically examine the concept of AI readiness among 3164 primary school teachers who had experiences using AI technologies in their work. In what follows, we discuss the important findings of this study by relating them to previous research on similar topics.

Abstract

     One of the most critical areas of concern in the online learning process is to design engaging courses that are easy to follow and interactive to keep the audience attracted when there is no in-person communication between instructor and learner. Online courses may be challenging for learners, instructors, and institutions as well. However, the employment of creative approaches incorporates into online training strategies aims to make learning more interactive for learners. This case study discusses the employment of a range of creative e-learning strategies and online technological tools to deliver knowledge in a virtual environment based on collaborative learning, learning by doing, and team-based learning techniques. Common challenges of e-learning faced by the instructor and learners and the way they are approached are also discussed. The introduced approach has been employed in a Management Information Systems course as a case study.

Introduction

     Efficient knowledge transfer is the key to sustaining success and development. The advancement of technology has made knowledge transfer and learning processes faster and more convenient and online learning through the Internet and web applications has enabled individuals to study globally. The Internet has provided people from all over the world to gain equal education opportunities and simply transfer knowledge. Online learning through the Internet and web applications enables individuals to study and teach from anywhere in the world in a cost-effective and time-saving manner [1]. Especially with the coronavirus outbreak and the limitation of in-person classes, virtual education has been welcomed. Furthermore, the hectic lifestyle, ease of internet access, and increase in access to educational tools are other reasons for the popularity of online courses [2]. The major customers of e-learning services are schools, universities, educational institutions, and the training department of industries. In industries, employees’ efficient performance is significantly reliant on frequent and regular education; however, since employees are almost busy attending in-person courses, delivering the training material by e-learning makes a significant contribution to keeping them updated. E-learning is an online learning platform that emerges in a formal context and employs a variety of multimedia and communication technologies for a convenient learning process [3]. E-Learning is moving toward more inclusive developmental approaches with the necessity to deliver more interesting, effective, and engaging e-learning courses [4].

     Flexibility is the main advantage of online learning. Unlike the traditional or face-to-face learning method, online learning and distance education have improved businesses and education systems in many aspects [5]. Distance education provides students with the opportunity to get connected to their counterparts in different parts of the world while the instructor assigns them in teams to deliver group assignments remotely and engage in video calls and distance meetings unlimitedly [3].

Conclusion

     The importance of delivering creative approaches in teaching is highlighted since a considerable shift has been witnessed in the classroom environment by the spread of e-learning. E-learning courses are required to assure the attendee’s improvement in competency, knowledge, and skill by providing accurate and applicable processes for knowledge transfer. In this case history, methods that have been employed in an online MIS course to ensure that the class is collaborative, and students learn by doing in a team-based environment have been introduced. The feedback received from students by taking technology-based approaches in teaching MIS and students’ evaluation records has demonstrated the success of the approach; however, to realize the reliability of the approach in comparison to similar teacher-oriented e-learning courses should be analyzed using students’ evaluation records in different courses. Also, a systematic feedback collection is required to ensure the proposed e-learning methods are convenient and useful. Feedback collection after each program helps identify the barriers to effective learning, select effective methods of problem-solving, and improve the system.

Abstract

     The existing evidence shows that parental engagement is one of the most effective educational interventions. Most parents, carers, and teachers are aware of that and wish to engage with their children’s education. However, most parents are still only peripherally involved through parent–teacher evenings, school activities, or by helping their children keep up with their homework. In this review paper, we summarize the evidence about the impact of parental engagement, as opposed to involvement, on the learning of children. Via that, we critically look at the design choice of most western mainstream public education systems to distance parents from their children’s education, which, as the review results indicate, can be detrimental to children’s learning. Based on these results, we reframe parental engagement in the light of two global shifts: (1) the implications of the school closures during the COVID-19 pandemic for the role of parents in their children’s learning; and (2) the increased use of educational technologies for learning, and specifically, the rise of artificial intelligence (AI) technologies. We conclude by calling for a renewed conversation about parents’ and families’ roles in their children’s learning and their interface with schools and teachers.

Introduction

     The idea of a mandatory-attendance public education system based on out-of-home institutions called schools has its roots from at least the 16th century [1]. This initiated the daily routine of school-aged children exiting their homes every morning, leaving their parents, cultures, and familial beliefs behind to go into schools. Once there, they adhere to the schools’ standards of curriculum and conform to their behavioural schemas, rewards, and implications. This journey was, and still is, designed and realised by various stakeholders: such as governments, employers, religious institutions, industry leaders, and education policymakers [1], each with their own agenda, led by their own political, economic, or pedagogical interests, not all of which have to do with children’s wellbeing and learning.

     The original aims of national educational systems from around the world differed in time and location. These ranged from producing obedient soldiers in Prussia to efficient assembly line workers in the US during the 19th century, to eradicating the original culture of “first nation” children in Australia in the 20th century, and onto today’s national education systems in western countries such as the US and the UK, who strive to prepare young people as future members of a workforce, whose needs are hard to predict (see, for example [2,3]).

Concluding Remarks

     This review paper’s aim is to bring a non-systematic critical overview of the current state of research and practice around parents’ and carers’ roles in their children’s education. We do that by synthesizing the existing evidence in light of recent global shifts that are challenging the current design principles and opening new opportunities for radical changes in schooling–parents relationships. In this concluding section, we summarize our findings and provide recommendations.

     Most western governments and schools today would state that supporting their students’ wellbeing and effective learning are their first priority. Whether or not schools are best designed to support either is debatable, but what is evidentially clear is that actively engaged parents and pulling the learning experience away from being regulated solely within schools into a home–school–community continuum are what would most benefit children. School is just one element in every child’s ZPD, just one form of education out of many, and just one milestone in a life-long journey. Supportive parents, carers, families, and the community surrounding children shape and hold their ZPD way before school starts and are likely to play a significant role long after school ends. If we accept that at least some of our educational system’s aims are to support learners in developing self-regulated skills for learning, we should bring their ZPD “closer to home”. In normative and supportive homes reside those people who understand where the children are coming from, what they have gone through, whether they slept tight at night, and whether they had a nutritious breakfast in the morning. Moreover, the students themselves, with the support of their parents are eventually those who will be making decisions regarding their potential future. Therefore, if schools’ priority is children’s learning, it is essential that children’s ZPD space is integratively built on top of a partnership, and, in that, it would be influenced by those factors that are most personal and relevant to each child.

Abstract

Introduction

Methods

Analysis and Results

Discussion

Conclusion

Data Availability

Ethical Approval

Consent

Conflicts of Interest

References

Copyright

Related articles

Abstract

The novel coronavirus pandemic has made life significantly more stressful for large populations of people. As one such demographic, university students worldwide have experienced a sudden shift toward the provision of socially distanced online education, often in the absence of a coherent institutional plan. The mechanisms of stress appraisal and response differ between individuals in part determined by personality. With a sample of 293 undergraduate students at a Japanese university operating under prohibitions relating to face-to-face education, this article examines the impact of personality on the affordance of socially distanced online education mediated through generalized life stress and online learning stress appraisal. A retrimmed structural model returned an acceptable goodness of fit accounting for 31.6% of the criterion variance. The model indicates that conscientiousness (positive) and neuroticism (negative) hold a significant mediated impact on the affordance of socially distanced online education through generalized life stress and online learning stress appraisal. Moreover, and in the absence of face-to-face social interaction, the model shows that extroverted students experience greater online learning stress appraisals than neurotic students. Neurotic students were, however, negatively impacted by appraisals of generalized life stress but not online learning stress. Informed by personality characteristics and stress appraisals, the outcomes are discussed in relation to educational improvements and appropriate pedagogies for the delivery of socially distanced online education.

1. Introduction

The novel coronavirus pandemic has exerted a profound impact on public mental health contributing to an increase in depression, anxiety, insomnia, and stress appraisal [1–5]. As a defensive physiological, psychological, and behavioral reaction, stress can be understood as the disruption arising from a perception that threat demands exceed one’s adaptive mitigation capacity [6]. The transactional stress theory [7, 8] contends that stress as an objective variable is present neither within the individual nor the environment but rather is emergent via an abstract relationship wherein “the separate person and situation elements are joined together to form a new relational meaning” ([9]: 294). The mechanisms of stress appraisal and coping are known to differ significantly between individuals [10] and are at least partially determined by personality [11–13]. Personality “not only affects the appraisal of and coping with stress, but it is also crucial with regard to the selection and shaping of stressful situations” ([14]: 335). Certain individuals are therefore more predetermined to experience and respond to stress based on personality characteristics [15, 16].

Prior to the novel coronavirus pandemic, a declassified Organization for Economic Co-operation and Development (OECD) report contextualized education within an emergent volatile, uncertain, complex, and ambiguous global environment [17]. The pandemic has exacerbated these concerns and situated education as a stressful domain for various stakeholders including students. In the context of Japanese society, a recent Japan Times (2021, February 28) [18] article entitled “COVID-19 pushes 1,300 Japanese university students to drop out” documents how many students have quit university due to an inability to pay tuition fees, loneliness and isolation, and an inability to make friends through online education, factors which also had a negative impact upon motivation to study. Beyond Japan, a plethora of recent studies have highlighted increases in stress among students brought about by conditions surrounding the pandemic [13, 19–27]. These studies are important as mental health problems among students are associated with reduced academic performance, substance abuse, poor physical health, increased dropout rates, and avoidance-based coping strategies [28–33].

Abstract

     At the beginning of 2020, COVID-19 began to sweep through China, major schools adhering to the concept of “no classrooms instead of classes” and launching online education. The major online education platforms received the “epidemic advantage” and grew. However, on the night of 24 July 2021, when Chinese authorities have introduced a guideline to ease the burdens of excessive homework and off-campus tutoring for students undergoing compulsory education, so that including online education platforms, received a severe blow. As part of the digital economy, the current transformation of the education sector to education informatization, quality education and vocational education may become the vane of the new era.

Abstract

1. Introduction

2. Related Work

3. VoRtex—Metaverse System Description

4. Evaluation of Metaverse Platforms

5. Conclusions

References

Abstract

     Metaverse platforms are becoming an increasingly popular form of collaboration within virtual worlds. Such platforms provide users with the ability to build virtual worlds that can simulate real-life experiences through different social activities. In the paper, we introduce a novel platform that provides assistive tools for building an educational experience in virtual worlds and overcoming the boundaries caused by pandemic situations. Therefore, the authors developed a high-level software architecture and design for a metaverse platform named VoRtex. VoRtex is primarily designed to support collaborative learning activities with the virtual environment. It is designed to support educational standards and it represents an open-source accessible solution developed using modern technology stack and metaverse concepts. For this study, we conducted a comparative analysis of the implemented VoRtex prototype and some popular virtual world platforms using Mannien’s matrix. Afterwards, based on the comparison, we evaluated the potential of the chosen virtual world platform and the VoRtex platform for online education. After an interactive demonstration of the VoRtex platform, participants were asked to fill out a questionnaire form. The aim was to enable participants to identify the main advantages of online teaching using the VoRtex platform. Finally, the authors analyzed benefits and disadvantages of collaborative learning between the metaverse platform and real-world classroom sessions.

Introduction

     Over the years, people have used various technologies to adapt education to modern mediums. Online learning using personal computers (PC) and mobile devices has become very popular in the last few years. Modern learning platforms include sharing, monitoring, personalized experience, and different client applications (desktop, mobile, web-based) for accessing the content. Moreover, new mediums that we can use for online learning such as metaverse or virtual worlds (VWs) platforms are emerging. We can define VWs as follows: “Simulated persistent space based on the interaction with a computer, inhabited by several users, who are represented by iconic images called avatars, who can communicate with each other in a synchronized way” [1]. VW integrates several technologies into one platform, which includes audio, video, webcam, text chat, voice chat using voice over Internet protocol (VoIP), graphical editors, and digital avatars. For building VWs, we can use software development tools such as game engines. Game engines provide basic building blocks (rendering, object manipulation, scripting, and other computer graphics features) for VW development. In addition, for designing a high-quality computer graphics-based software, we should follow the best practices and standards regarding user experience design and software optimization [2]. Collaborative virtual environments (CVEs) are VWs that provide a richer collaborative arena for social encounters and community building. Another name for CVEs is virtual learning environments (VLEs), the universal term in the literature associated with education. Examples of CVEs include social entertainment, multiplayer games, interactive art, simulations, distance learning, architectural walk-throughs, cultural heritage, etc.

Conclusions

     In this paper, we have compared and evaluated the potential of a VW platform for educational purposes. Furthermore, we evaluated the user experience on the VoRtex platform. Firstly, we reviewed the related work regarding collaboration and learning in VWs. Secondly, we described the concept and purpose of VW platforms. Thirdly, we introduced the VoRtex software architecture for collaboration and e-learning inside VWs. Afterwards, we compared VW platforms Vircadia, Sansar, and the VoRtex prototype using Mannien’s matrix. Based on the comparison, features offered by the emerging VW platforms enabled simulation of real-life interaction and experience through the support of desktop PCs or VR devices. Afterwards, we provided a survey in which we evaluated the user experience in the VoRtex prototype for Windows PC. Based on the results of survey, we found that the users respond positively to the VoRtex platform, and the usage of VWs in online learning could overcome boundaries during the COVID-19 pandemic.

     In the end of the comparison process, Vircadia provided an overall slightly better educational experience, based on the listed criteria, in comparison to the VoRtex and Sansar with a sufficiently suitable solution (74 scores) for educational purposes. VoRtex has less content than the other two platforms. However, the VoRtex platform regarding the results from Mannien’s matrix comparative analysis has a security advantage, the most important aspect in the educational sector [29]. In addition, VoRtex’s innovative design provides out-of-the-box assistive tools for teachers such as presentation and video panels that are not common features in the other two platforms. Planned improvements after the next technology milestone using Unreal Engine 5 and MetaHuman will bring the platform graphically to a hyper-photorealistic level and initially provide more assets to content creators [32]. In our future work, we will measure and evaluate the VW software architecture and improve the prototype to simplify the VW platform design process for educational purposes. Furthermore, one of the top priorities for moving forward is to have users carry out routine assessments of VoRtex to help the development of the platform and make it more feature-rich. VoRtex source code and project updates are available on GitHub [36].

Highlights

Abstract

Keywords

1. Introduction

2. Methodological approach

3. Findings

4. Discussion

5. Conclusion

Declaration of Competing Interest

Changes after transfer to open-access

Appendix. Supplementary materials

References

Abstract

Behaviorist methodological considerations in the learning sciences are rare compared to the 20th century. As a result, researchers may be conditioned towards an unbalanced and fragmented evidence base for the improvement of classroom teaching in primary education. The presented systematic literature review relates research of digital technology and learning in primary education to expected learning in primary education. In total, 641 articles published 2011–2020 were included and 2777 were excluded by full-text criteria review using radical behaviorist methodological presuppositions.

Findings show a low but increasing frequency of articles with behaviorist approaches, optimistic expectancy and evidence of learning in primary education through use of digital technology, and a trend for research to emphasize representational functions of digital technology. The relevancy of radical behaviorist methodology is discussed based upon the implications of the findings and other 21st century themes.

1. Introduction

In the learning sciences, behaviorism is considered a well-established approach. As a major part of foundational scientific theorizing, behaviorism dominated many methodological approaches for most of the 20th century [49]. These decades of widespread behaviorist methodological approaches are nowadays regarded by scientists in different light, often dismissed as an important but obsolete part in historical overviews presenting research [48]. The period has even been referred to as the “dark ages” where “nothing worthwhile was discovered” ([5], p. 171) on the motivational influences to learning due to narrow methodological approaches. For example, radical behaviorists reject so-called inner influences often derived from self-reported data without contextual relations [51]. In contrast to these dark ages, studies of digital technology may relate the use of such technology by emphasizing motivational influences and optimistically expect improved learning in primary education [25]. However, there are indications that such improvements might not extend to educational achievement outcomes, despite the widespread use of digital technology [54].

As educational achievement outcomes are a common way for teachers to determine the learning of their students, researchers may need to consider such narrow limitations [43]. This is especially true for research in primary education, as self-assessment may be a difficult task for children [39]. Further, recent technological developments have enabled education policy-making based on highly sought-after large-scale data with bodily and biological conditions [64]. These kinds of conditions for data use could relate an approach to a genetic epistemology. Radical behaviorism presupposes a genetic epistemology [7]. The scientific underutilization of such data may pose an ethical risk of leaving that which might have been scientifically controlled in other hands [52]. In the years 2011–2020, many important and valid literature reviews in the learning sciences have been published (e.g. [17,25,54,59]) but very few have explicitly had a behavioristic emphasis.