Search results

During the Coronavirus crisis (COVID-19) that started in 2019 and at the extensive quarantine regulations, educational institutions, companies, and individuals have reacted by shifting their teaching and learning activities to virtual spaces. Yet, although the use of online learning has increased, it has not been able to achieve the long-promised transformative effect. The COVID-19 crisis has the potential to boost online education overall or at least enable better preparation of the system for the next crisis. Ultimately, to make a digital transformation sustainable, appropriate skills are required. In this study, we adapt the dynamic capabilities foundations creating a theoretical approach to explain how educational institutions have responded to the changing environmental conditions during the COVID-19 pandemic.

Introduction: Numerous decision-making situations are faced in education where Artificial Intelligence may be prevalent as a decision-making support tool to capture streams of learners’ behaviours.

Purpose: The purpose of the present study is to understand the role of AI in student performance assessment and explore the future role of AI in educational performance assessment.

Scope: The study tries to understand the adaptability of AI in the education sector for supporting the educator in automating assessment. It supports the educator to concentrate on core teaching-learning activities.

Objectives: To understand the AI adaption for educational assessment, the positives and negatives of confidential data collections, and challenges for implementation from the view of various stakeholders.

Methodology: The study is conceptual, and information has been collected from sources comprised of expert interactions, research publications, survey and Industry reports.

Findings: The use of AI in student performance assessment has helped in early predictions for the activities to be adopted by educators. Results of AI evaluations give the data that may be combined and understood to create visuals.

Research Implications: AI-based analytics helps in fast decision-making and adapting the teaching curriculum’s fast-changing industry needs. Students’ abilities, such as participation and resilience, and qualities, such as confidence and drive, may be appraised using AI assessment systems.

Theoretical Implication: Artificial intelligence-based evaluation gives instructors, students, and parents a continuous opinion on how students learn, the help they require, and their progress towards their learning objectives.

The European Union’s Digital Markets Act (DMA) calls for far-reaching changes to the way economic activity will occur in EU digital markets. Before its remedies are imposed, it is critical to assess their impacts on individual markets, the digital sector, and the overall European economy. The European Commission (EC) released an Impact Assessment in support of the DMA that purports to evaluate it using cost/benefit analysis.

An economic evaluation of the DMA should consider its full impacts on dynamic competition. The Impact Assessment neither assesses the DMA's impact on dynamic competition in the digital economy nor evaluates the impacts of specific DMA prohibitions and obligations. Instead, it considers benefits in general and largely ignores costs. We study its benefit assessments and find they are based on highly inappropriate methodologies and assumptions. A cost/benefit study using inappropriate methodologies and largely ignoring costs cannot provide a sound policy assessment.

Instead of promoting dynamic competition between platforms, the DMA will likely reinforce existing market structures, ossify market boundaries, and stunt European innovation. The DMA is likely to chill R&D by encouraging free riding on the investments of others, which discourages making those investments. Avoiding harm to innovation is critical because innovation delivers large, positive spillover benefits, driving increases in productivity, employment, wages, and prosperity.

The DMA prioritizes static over dynamic competition, with the potential to harm the European economy. Given this, the Impact Assessment does not demonstrate that the DMA will be beneficial overall, and its implementation must be carefully tailored to alleviate or lessen its potential to harm Europe’s economic performance.

The COVID-19 pandemic has disrupted the ‘real’ world and substantially impacted the virtual world and thus the Internet ecosystem. It has caused a significant exogenous shock that offers a wealth of natural experiments and produced new data about broadband, clouds, and the Internet in times of crisis. In this chapter, we characterise and evaluate the evolving impact of the global COVID-19 crisis on traffic patterns and loads and the impact of those on Internet performance from multiple perspectives. While we place a particular focus on deriving insights into how we can better respond to crises and better plan for the post-COVID-19 ‘new normal’, we analyse the impact on and the responses by different actors of the Internet ecosystem across different jurisdictions. With a focus on the USA and Europe, we examine the responses of both public and private actors, with the latter including content and cloud providers, content delivery networks, and Internet service providers (ISPs). This chapter makes two contributions: first, we derive lessons learned for a future post-COVID-19 world to inform non-networking spheres and policy-making; second, the insights gained assist the networking community in better planning for the future.

Today, pedagogy does not innovate by proposing new methods but by creating learning conditions conducive to the autonomy of learners. During training, students learn to set goals for acquiring knowledge, control their activity and persevere in the face of difficulties. Innovative teachers favour an integrative approach to human activity to jointly develop the relationship with the learning environment or work environment, the socio-cognitive characteristics of learners and their sense of responsibility for the consequences of action and metacognitive management of the activity.

Learners thus evolve in a learning ecosystem that includes the learner himself and his physical and social environment: his tools available (notepad, tablet, etc.), his resources (procedures, methods, instructions, course materials, notes, documentation, etc.) and his partners who also have some knowledge (pair, teachers, expert network, colleagues, etc.). This ecosystem can be seen as a virtual learning space in which technologies that contribute to learning (hardware, software and network) are used with the aim of fostering interactions between stakeholder and content communities. The knowledge is distributed and accessible through the memory of the learner himself or through his tools, resources or partners. It is therefore in the elaboration of the learning mechanism mobilising all the actors to meet the needs of the learners that the innovation can be efficient and effective.

Within this frame of reference, we propose some reflections from pedagogical practice that can develop the learners’ power to act – Which IT environments are needed for specified actions? What pedagogies need to be implemented using these IT environments? What collaborative and reflective tools are needed for professional and university training? Which methods energise learners’ agentivity in the digital age? Three case studies focussing on these questions will offer some recommendations for innovation in pedagogy.

The growing uses of digital technologies have been creating several new business opportunities. Recently, a new concept has emerged in the literature, the “digital entrepreneurship ecosystem” (DEE). However, it has been empirically understudied. Thus, this study aims to analyze the influence of DEE on entrepreneurial activity. The meta-organization theory was used as a perspective of analysis. A quantitative methodology was applied in a sample that includes data from 28 European countries through the generalized method of moments. It was concluded that the DEE pillars, informal and formal institutions, market conditions, physical infrastructure, human capital and talent and networking and support positively influence entrepreneurial activity. It was also found that the variables knowledge, creation and dissemination and finance have a negative impact on entrepreneurial activity. Several theoretical and empirical contributions are also left for the various stakeholders. The present study is original, as no known studies analyze the influence of DEE on entrepreneurial activity in European countries from the perspective of meta-organization theory.