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Schools have a significant role in ensuring children's wellbeing as children spend a lot of time at school. Students need to have an active role in their learning and an opportunity to participate in issues concerning wellbeing and studying. This research examines students' wellbeing in an innovative learning environment. The classroom is built with professionals, teachers and students (aged 9). The authors call it Learning Ground.

Students' wellbeing was measured with smart device application for a six-week period. Students answered the questionnaire with a Likert scale of five (very poor – excellent) responses. Two weeks during the six weeks research period, students were able to use digital study aids, EEG-biosensor headsets, to observe the effectiveness of their learning, defined by NeurSky app. The EEG-biosensors enabled students to use a tool to recognise their own learning factors during the lessons. The effectiveness was available to students via tablets all the time.

The students at the Learning Ground are satisfied with wellbeing and the environment support for students' wellbeing experience is notable. They have “good vibes” before and after the school day. When wearing EEG-headsets “study aids”, which enabled them to observe their learning via tablets at lessons, the wellbeing experience in the mornings even increased.

Schools need to be visionaries concerning 21st century learning and children's wellbeing. Building flexible learning environments and bringing innovative technologies into schools to provide active support for students will enable 21st century learning. Wellbeing of children should become first when developing the future schools.

This study aims to discuss science governance in Europe and the network of associated nonprofit institutions. The authors posit that this network, which comprises both (partial) learning organizations and non-learning organizations, has been observed to postpone taking up “responsibility” as an issue in science governance and funding decisions.

This paper discusses the challenge of learning and policy implementation within the European science governance system. By exploring how learning on responsible innovation (RI) in this governance system can be provoked, it addresses the question how Senge’s insights in organizational learning can clarify discourses on and practices of RI and responsibility in research. This study explores the potential of a new organizational form, that of Social Labs, to support learning on Responsible Research and Innovation (RRI) in standing governance organizations.

This study concludes that Social Labs are a suitable format for enacting the five disciplines as identified by Senge, and a Social Lab may turn into a learning organization, be it a temporary one. Responsibility in research and innovation is conducive for learning in the setting of a Social Lab, and Social Labs act as intermediary organizations, which not merely pass on information among actors but also actively give substantive shape to what they convey from a practice-informed, normative orientation.

This empirical work on RRI-oriented Social Labs therefore suggests that Social Lab–oriented temporary, intermediary learning organizations present a promising form for implementing complex normative policies in a networked, nonhierarchical governance setting.

Based on this research funding and governance organizations in research, policy-makers in other domains may take up and create such intermediary organizations to aid learning in (science) governance.

This research suggests that RRI-oriented Social Labs present a promising form for implementing complex normative policies, thus integrate learning on and by responsible practices in various governance settings.

European science governance is characterized by a network of partial Learning Organization (LOs) and Non-Learning Organization (nLOs) who postpone decision-making on topics around “responsibility” and “solving societal challenges” or delegate authority to reviewers and individual actors, filtering possibilities for collaborative transformation toward RRI. social lab (SLs) are spaces that can address social problems or social challenges in an open, action-oriented and creative manner. As such, they may function as temporary, intermediary LOs bringing together diverse actors from a specific context to work on and learn about issues of science and society where standing organizations avoid doing so. Taken together, SLs may offer temporary organizational structures and spaces to move beyond top-down exercise of power or lack of real change to more open, deliberative and creative forms of sociopolitical coordination between multiple actors cutting across realms of state, practitioners of research and innovation and civil society. By taking the role of temporary LOs, they may support existing research and innovation organizations and research governance to become more flexible and adaptive.

Factors like bid price, submission time, and number of bidders influence the procurement process in public projects. These factors and the award criteria may impact the project’s financial compliance. Predicting budget compliance in construction projects has been traditionally challenging, but Machine Learning (ML) techniques have revolutionised estimations.

In this study, Portuguese Public Procurement Data (PPPData) was utilised as the model’s input. Notably, this dataset exhibited a substantial imbalance in the target feature. To address this issue, the study evaluated three distinct data balancing techniques: oversampling, undersampling, and the SMOTE method. Next, a comprehensive feature selection process was conducted, leading to the testing of five different algorithms for forecasting budget compliance. Finally, a secondary test was conducted, refining the features to include only those elements that procurement technicians can modify while also considering the two most accurate predictors identified in the previous test.

The findings indicate that employing the SMOTE method on the scraped data can achieve a balanced dataset. Furthermore, the results demonstrate that the Adam ANN algorithm outperformed others, boasting a precision rate of 68.1%.

The model can aid procurement technicians during the tendering phase by using historical data and analogous projects to predict performance.

Although the study reveals that ML algorithms cannot accurately predict budget compliance using procurement data, they can still provide project owners with insights into the most suitable criteria, aiding decision-making. Further research should assess the model’s impact and capacity within the procurement workflow.

Previous research predominantly focused on forecasting budgets by leveraging data from the private construction execution phase. While some investigations incorporated procurement data, this study distinguishes itself by using an imbalanced dataset and anticipating compliance rather than predicting budgetary figures. The model predicts budget compliance by analysing qualitative and quantitative characteristics of public project contracts. The research paper explores various model architectures and data treatment techniques to develop a model to assist the Client in tender definition.

This paper aims to enrich the scholarly discourse on learning within small social entrepreneurial organisations by examining how leadership can facilitate conditions conducive to collective learning during crises.

A longitudinal single-case study was conducted on a social entrepreneurial organisation in Sweden, operating within the integration field. The study involved comprehensive interviews and observations. Using a longitudinal approach facilitated an in-depth analysis of the organisation’s development over time.

The findings underscore that shifts in leadership can significantly influence collective learning. Specifically, the results suggest that establishing trust between the CEO and team members is a pivotal factor in cultivating conditions for collective learning and fostering the related processes, which persisted even during the pandemic. This trust catalysed inclusive and interactive actions that encouraged team members’ participation in day-to-day decision-making and strategic planning. Consequently, the organisation successfully leveraged its diverse knowledge resources, promoting knowledge sharing and experience exchange, crucial components of successful collective learning.

This paper advocates for a departure from conventional leadership perspectives, proposing that a focus on team–leader relationships – a form of leadership in practice – can offer valuable insights into cultivating collective learning. This approach underscores the significance of collaboration and engagement among team members in promoting collective learning and accentuates the role of leadership in creating these conditions.

The examples provided on structuring, organising and leading virtual meetings could offer valuable insights for leaders. With the increasing adoption of hybrid workplaces combining remote and office environments, communication challenges within teams may arise. Therefore, these examples can aid leaders in formulating effective communication strategies that bridge the gap between remote and in-person team members, ensuring that everyone stays informed and engaged.

This study seized a unique opportunity to explore how leadership can create favourable conditions for collective learning during crises by collecting data both before and during the Covid-19 pandemic.