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In this paper a conception of the model for learning scenario determination is presented. We define the learning scenario as a sequence of the hypermedia pages, representing particular knowledge units, and tests related to them. The scenario determination is a dynamic process that begins when a new student takes up a course. The opening scenario for this student is chosen as the consensus of the final scenarios of the students, who have already finished this course, and who belong to a class of the learners similar to the new one. We have elaborated the consensus‐based procedure for the scenario determination. Since this procedure operates on a set of similar learners, we have developed the conceptions of learner’s profile and students’ classification. The learner’s profile is proposed to include the attributes describing students’ personal data (as name, birthday etc.), their cognitive and learning styles as well as their usage data (represented by the learning scenarios). The students’ classification is based on a set of the basic attributes that seem to influence the learning effects. Their significance is verified during the learning process. We have also elaborated the procedure of reducing undistinguishable values of the attribute and removing useless attributes from the set of basic attributes. A learning procedure proposed, describes generally the situations when the scenario is modified, and the methods used for its modification.

This study aims to explore modern learning (ML) scenarios in Dutch higher education towards 2030 and corresponding consequences for facility management (FM) and corporate real estate strategies (CRES) of universities of applied sciences (UAS).

The designing an accommodation strategy model is used to retrieve the match between demand and supply in real estate of Dutch UAS for both present and future. Application of this model resulted in a scenario-planning session with six education experts to determine future demand in education towards 2030. Subsequently, corporate real estate (CRE) managers of nine Dutch UAS were interviewed concerning this subject (one per UAS).

The results show that CRES of Dutch UAS is only partly aligned to consequences of ML scenarios towards 2030. Most UAS have real estate portfolios that are only partially flexible, so changes in qualitative and quantitative demand due to ML scenarios are not easy to adapt to. The consequences for FM and real estate of the ML scenarios “institute learning” and “mass customization” seem manageable for Dutch UAS, whereas the other ML scenarios, namely, “navigator” and “natural learning”, would create major portfolio problems.

There is an ongoing interest in developments in higher education combined with FM and CRES and its alignment with the corporate vision. Previous studies into modern ways of learning mainly focused on the past and present. However, this study is explorative and explores possible future ML scenarios. These scenarios can contribute to awareness amongst UAS in The Netherlands and abroad regarding their long-term vision. Moreover, these scenarios will help Dutch UAS facility and CRE managers to develop a more future-proof strategic real estate strategy aligned with the corporate vision.

The construction of learning scenarios is a way to plan for teaching activities, promoting the development of skills related to problem solving, collaboration, critical thinking and creativity. Using learning scenarios as a lesson planning strategy becomes a powerful tool in initial teacher education. On the one hand, it mobilizes teaching-related scientific concepts, and on the other hand, it offers opportunities to think on innovative pedagogic approaches involving strategies and capacities essential for the future teacher. Research shows that teacher education programs within real school contexts enriched with digital technologies represent an important factor in increasing the quality of teachers’ preparation and their future professional practice. The paper aims to discuss these issues.

The authors present the analysis of practice of design and implementation of learning scenarios in teachers’ initial education courses developed with students of teaching master degrees. Activity theory is used in the analysis of a case study of a student-teacher in Computer Science.

The results have been analyzed, contributing to the specification of the principles underlying the learning scenarios in initial teacher education.

Results show the affordances and possibilities of using learning scenarios as structuring resources for the initial teacher education practice.

Therefore, the use of learning scenarios brings a set of potentialities to teacher training given its prospective nature.

The paper ' s aim is to report a research study on the mediator and outcome variable sets in scenario planning.

This is a cannonical correlation analysis (CCA)

Twso sets of variables; one as a predictor set that explained a significant amount of variability in the second, or outcome set of variables were found.

The study did not involve random selection or assignment and used perception-based measures.

The findings support scenario planning as a tool to reinforce certain decision styles and learning organization culture.

A critical contribution to scenario planning research, this study brings some order to the variety of variables espoused to be involved in scenario work. Clear outcomes are a learning culture and intuitive/dependent decision styles. The study makes a real contribution to quantitative scenario studies.

The purpose of this approach is to structure and facilitate the development of competency-oriented virtual training scenarios. This approach should help to develop reproducible results.

This publication presents a procedure for the structured, competency-oriented design of virtual teaching–learning environments. The procedure is based on the competency-oriented design of learning factories according to Tisch and consists of a total of three phases with various substeps. The different steps of the method are presented by means of an application example and the generated scenario is evaluated by means of a competency measurement according to Glass regarding its suitability for teaching competencies.

In addition to an application example that introduces the approach, this publication presents concrete results from the research project. In addition to a review of the opportunities and limitations of virtual reality (VR) in continuing education, these also refer to possible, realizable requirements for the use of VR. In addition, results on the suitability of VR learning environments for competency development in the context of value streaming mapping training are presented.

This approach represents a substantial extension of existing concepts for the design of physical learning factory training for lean topics. It can help learning factory operators and other training providers to design competency- and user-oriented virtual training. VR offers a multitude of potentials in this context, but these can only be exploited through conscientious use of the technology.

This paper aims to discuss the experiences designing and conducting an experiential learning virtual incident response tabletop exercise (VIRTTX) to review a business's security posture as it adapts to remote working because of the Coronavirus 2019 (COVID-19). The pandemic forced businesses to move operations from offices to remote working. Given that this happened quickly for many, some firms had little time to factor in appropriate cyber-hygiene and incident prevention measures, thereby exposing themselves to vulnerabilities such as phishing and other scams.

The exercise was designed and facilitated through Microsoft Teams. The approach used included a literature review and an experiential learning method that used scenario-based, active pedagogical strategies such as case studies, simulations, role-playing and discussion-focused techniques to develop and evaluate processes and procedures used in preventing, detecting, mitigating, responding and recovering from cyber incidents.

The exercise highlighted the value of using scenario-based exercises in cyber security training. It elaborated that scenario-based incident response (IR) exercises are beneficial because well-crafted and well-executed exercises raise cyber security awareness among managers and IT professionals. Such activities with integrated operational and decision-making components enable businesses to evaluate IR and disaster recovery (DR) procedures, including communication flows, to improve decision-making at strategic levels and enhance the technical skills of cyber security personnel.

It maintained that the primary implication for practice is that they enhance security awareness through practical experiential, hands-on exercises such as this VIRTTX. These exercises bring together staff from across a business to evaluate existing IR/DR processes to determine if they are fit for purpose, establish existing gaps and identify strategies to prevent future threats, including during challenging circumstances such as the COVID-19 outbreak. Furthermore, the use of TTXs or TTEs for scenario-based incident response exercises was extremely useful for cyber security practice because well-crafted and well-executed exercises have been found to serve as valuable and effective tools for raising cyber security awareness among senior leadership, managers and IT professionals (Ulmanová, 2020).

This paper underlines the importance of practical, scenario-based cyber-IR training and reports on the experience of conducting a virtual IR/DR tabletop exercise within a large organisation.

This paper aims to describe how, during the period after the scenarios have been sketched out but before the company leadership reaches the strategy‐development or investment‐decision stage, a continuous learning process can help planners and senior decision‐makers identify and track the unfolding events that are most relevant to the challenges and big issues facing their organization.

In the case example, a web‐connected Event, Pattern, Structure (EPS) tool allows all members of a multi‐stakeholder Scenario Planning Steering Group, as well as invited experts, to submit news articles, conference reports, research, scholarly articles and other published information into a database that is organized with links to four scenarios and their driving forces.

As the case demonstrates, an ongoing EPS process related to the scenarios and key drivers can support strategic monitoring of the business environment with the scenarios serving an organizing structure.

This continuous learning tool is particularly useful for companies that develop scenarios that evolve over long periods of time, a practice that is usually determined by the investment window or marketing cycle of an organization.

Research collected in the EPS system can be used to watch for developments that portend operating‐environment changes. Planners can generate early indicators that signal whether a particular scenario or trends within a scenario are emerging.

The purpose of this research is to achieve multi-task autonomous driving by adjusting the network architecture of the model. Meanwhile, after achieving multi-task autonomous driving, the authors found that the trained neural network model performs poorly in untrained scenarios. Therefore, the authors proposed to improve the transfer efficiency of the model for new scenarios through transfer learning.

First, the authors achieved multi-task autonomous driving by training a model combining convolutional neural network and different structured long short-term memory (LSTM) layers. Second, the authors achieved fast transfer of neural network models in new scenarios by cross-model transfer learning. Finally, the authors combined data collection and data labeling to improve the efficiency of deep learning. Furthermore, the authors verified that the model has good robustness through light and shadow test.

This research achieved road tracking, real-time acceleration–deceleration, obstacle avoidance and left/right sign recognition. The model proposed by the authors (UniBiCLSTM) outperforms the existing models tested with model cars in terms of autonomous driving performance. Furthermore, the CMTL-UniBiCL-RL model trained by the authors through cross-model transfer learning improves the efficiency of model adaptation to new scenarios. Meanwhile, this research proposed an automatic data annotation method, which can save 1/4 of the time for deep learning.

This research provided novel solutions in the achievement of multi-task autonomous driving and neural network model scenario for transfer learning. The experiment was achieved on a single camera with an embedded chip and a scale model car, which is expected to simplify the hardware for autonomous driving.

Higher education institutions (HEIs) have a great responsibility to put education for sustainable development at the centre of their work. Curricula should therefore start to incorporate the sustainable development goals (SDGs) and key competencies in sustainability, and research should be carried out to determine effective learning methods for this. This study aims to explore the usefulness of problem-based learning (PBL) approaches to train biomedical students in sustainability and to provide some recommendations for the design and implementation of new PBL-SDG scenarios.

Two PBL-SDG scenarios were designed, implemented and evaluated for 110 students of human biology degree. Learning outcomes and student perceptions of this approach were analysed through questionnaires, student productions, non-participant observation and focus groups.

The results show that the PBL-SDG scenarios effectively addressed several SDGs and sustainability competencies in a transversal, collaborative and innovative manner. According to student perceptions, the elements that contributed most to the development of these competencies were emotional involvement with the scenario, reflection on their own actions, freedom to approach the problem and tutors who empowered them with their proposals.

The PBL-SDG approach presented in this study is an example of a pedagogical strategy that can help HEIs educate their learners as key change agents. The findings of this study provide evidence for this important aspect and give guidelines and strategies to successfully designing and implementing such methodologies in biomedical education.

Immersive and collaborative virtual worlds can offer educationalists a future-focused solution to enhancing the learning experience they provide. Problem-based learning (PBL) is one option by which virtual worlds can provide a creative solution to providing physical-world experience within a safe and controlled environment free from the consequences associated with typical physical-world experiences. This collaborative approach to teaching and learning can be run synchronously or asynchronously and is based on sound pedagogical principles. PBL within virtual worlds can be used to provide an active and engaging learning experience that enables individuals to learn safely and effectively within a complex and realistic environment, allowing the student to be at the centre of, and in relative control of, their own learning experience.