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The purpose of this paper is to investigate the role of peer learning and formative assessment as two creative learning methods in engineering learning environments. The results show that both models have yielded promising results with regard to meeting the criteria for the expected learning outcomes of the curriculum. However, the integrated model has resulted in better results than the model of formative assessment. The paper discusses also some practical and theoretical aspects related to the learning models.

It is advanced two general learning models; in the first model, the formative assessment is integrated with peer learning and in the second one, the formative assessment is solely used to enhance the learning. A field case study is conducted to investigate the effect of using the learning models on the expected learning outcomes of the students in an engineering course.

The results show that both learning models have yielded promising results with regard to meeting the criteria for the expected learning outcomes of the curriculum. However, the integrated model has resulted in better results than the model of formative assessment.

The two models can be applied for the engineering course that has both practical and theoretical aspects. It is expected that areas of expertize in engineering education can be developed very well with the models.

The results show that the two learning models have yielded promising results with regard to meeting the criteria for the expected learning outcomes of the curriculum and that formative assessment link to good learning practice does indeed give improved learning.

Increase the effectiveness of learning in engineering education.

Case study based on observation and planning.

The aim of this article is to develop a holistic model that shows the basic elements of workplace learning and their mutual connections. This is done by adjusting and combining two models that have already been worked out formerly by members of the consortium: a model dealing with the workplace as a learning space, and a general model of the learning process. The most important features of the new model are its distinction between the social and the individual levels of learning, and its pointing to the overlap between the working practice of the organisation and the work identities of the employees as the central area for important workplace learning. Concludes by discussing the purposes to which this model can be put.

This paper presents an information technology based instructional model for effective learning in management information systems programs and modules for students in undergraduate education. This model is primarily centered on the traditional in-class learning model. Information technology is used in a unique and novel way in order to create a learning environment which not only engages students in the learning process but also enhances their problems solving skills, analysis and decision making abilities. The model embeds information technology in projects in order to hide complexities but without compromising achievement of learning objectives. It also uses information technology in a simulation mode in order to demonstrate key concepts or learning themes, to simulate main themes presented in case studies and to simulate problems behaviors. The model employs other creative techniques in order to deliver its overall goals.

The purpose of this paper is to develop a practical model of work integrated learning for undergraduate engineering students in relation to Conceiving-Designing-Implementing-Operating standards 7 (Integrated Learning Experiences) and 8 (Active Learning). Moreover, it is discussed the role of cultural-social perspective and peer learning in enhancing the developed learning model from a pedagogical point of view.

The model is based on an organized collaboration with the industrial partners in the surrounding geographic region. As a part of the collaboration, each participating student is guaranteed internships at a chosen company over the summer period. In the model, company-based projects are integrated with some of the study program courses. Moreover, the participating students are given a possibility to perform their final thesis at the chosen company.

A number of positive effects have been observed and documented as follows: first, the integrated learning improves the learning process for the students, where learning, knowledge and practice are integrated into the engineering curricula; second, the general quality of the study programs in the faculty has been developed and improved based on the professional skills as required by modern industrial companies; and third, the obtained advantage for the industrial partners has been to establish professional contacts with the students as well as the possibility to be acquainted with potential future employees.

The feedback the authors received so far from the industrial partners has been positive. A detailed evaluation will be made at a later stage when more information is available.

The developed learning model supports the expected learning outcomes, especially with regard to interpersonal skills, teamwork and communication. As a part of the collaboration, each participating student is guaranteed internships at a chosen company over the summer periods. The authors consider this collaboration as a “win-win situation” for the three parties involved in the learning model: the students, the university/faculty and the industrial partners.

Case study based on observations and evaluation of a developed learning model.

This study proposes a collaborative federated learning (CFL) framework to address personal data transmission and retention issues for artificial intelligence (AI)-enabled video surveillance in public spaces.

This study examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Based on the requirements, this study proposes a CFL framework to gradually adapt AI models’ knowledge while reducing personal data transmission and retention. The framework uses three different federated learning strategies to rapidly learn from different new data sources while minimizing personal data transmission and retention to a central machine.

The findings confirm that the proposed CFL framework can help minimize the use of personal data without compromising the AI model's performance. The gradual learning strategies help develop AI-enabled video surveillance that continuously adapts for long-term deployment in public spaces.

This study makes two specific contributions to advance the development of AI-enabled video surveillance in public spaces. First, it examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Second, it proposes a CFL framework to minimize data transmission and retention for AI-enabled video surveillance. The study provides comprehensive experimental results to evaluate the effectiveness of the proposed framework in the context of facial expression recognition (FER) which involves large-scale datasets.

This chapter presents five differentiated models of curriculum, each designed with templates created from learning theories. The discipline of distributed leadership is chosen to develop a cognition-based curriculum, a behavior-based curriculum, a performance-based curriculum, a values-based curriculum, and collectively arranged into a competency-based curriculum. The research literature frames the attributes of a competency-based curriculum on psychological competence.

In this chapter, curricula are developed to demonstrate the process of adapting theories of learning, instruction, and environment into design templates with which to differentiate the dimensions and components of a curriculum. In these curricula, multiple conceptual frameworks are employed to translate the content and structure of the discipline into instructional objectives, instructional engagement, instructional experience, and instructional environment to align the instructional processes with the intended learning. For these demonstrations, the discipline of organizational leadership is chosen due to the multidimensional structure of this discipline and the opportunities it presents to differentiate the curriculum and learning. Each component of the curriculum adapts an appropriate framework to align and interconnect the instructional processes into an optimized learning experience. The result is curricula that have a coherent flow horizontally across the components for each outcome as well as interconnectedness vertically between the outcomes. This approach creates coherence, alignment, and interconnectedness to the curricula and order to the learning process for the learners.

This methodology is applied to design the curriculum for five instructional modules. Module 1 focuses on dualistic thinking developed through a cognition-based curriculum. Module 2 presents a multiplistic learning experience through a behavior-based curriculum. Module 3 presents relativistic learning through a performance-based curriculum. Module 4 delivers complex learning through a values-based curriculum. Module 5 compiles these four modules into a competency-based curriculum model.

Each of these modules employs a unique set of theories to configure the components of the curricula to reflect the structure of each discipline. The use of each theory is explained and demonstrated in the design process.

Machine learning (ML), and deep learning in particular, is gaining traction across a myriad of real-life applications. Portfolio management is no exception. This paper provides a systematic literature review of deep learning applications for portfolio management. The findings are likely to be valuable for industry practitioners and researchers alike, experimenting with novel portfolio management approaches and furthering investment management practice.

This review follows the guidance and methodology of Linnenluecke et al. (2020), Massaro et al. (2016) and Fisch and Block (2018) to first identify relevant literature based on an appropriately developed search phrase, filter the resultant set of publications and present descriptive and analytical findings of the research itself and its metadata.

The authors find a strong dominance of reinforcement learning algorithms applied to the field, given their through-time portfolio management capabilities. Other well-known deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN) and its derivatives, have shown to be well-suited for time-series forecasting. Most recently, the number of papers published in the field has been increasing, potentially driven by computational advances, hardware accessibility and data availability. The review shows several promising applications and identifies future research opportunities, including better balance on the risk-reward spectrum, novel ways to reduce data dimensionality and pre-process the inputs, stronger focus on direct weights generation, novel deep learning architectures and consistent data choices.

Several systematic reviews have been conducted with a broader focus of ML applications in finance. However, to the best of the authors’ knowledge, this is the first review to focus on deep learning architectures and their applications in the investment portfolio management problem. The review also presents a novel universal taxonomy of models used.

The Morgan Library at Colorado State University in Fort Collins suffered catastrophic flooding as the result of a historic rain storm and flood that swept through the town on July 28, 1997. This study examines this single library's organizational disaster response and identifies the phenomena that the library's employees cited as their motivation for innovation.

Purpose – This study provides an example of a library where a pre-disaster and post-disaster organizational environment was supportive of experimentation. This influenced the employees’ capacity and motivation to create a new tool meant to solve a temporary need. Their invention, a service now called RapidILL, advanced the Morgan Library organization beyond disaster recovery and has become an effective and popular consortium of libraries.

Design/methodology/approach – This is an instrumental case study. This design was chosen to examine the issues in organizational learning that the single case of Morgan Library presents. The researcher interviewed employees who survived the 1997 flood and who worked in the library after the disaster. The interview results and a book written by staff members are the most important data that form the basis for this qualitative research.

The interviews were transcribed, and key phrases and information from both the interviews and the published book were isolated into themes for coding. The coding allowed the use of NVivo 7, a text analysis software, to search in employees’ stories for “feeling” words and themes about change, innovation, motivation, and mental models.

Three research questions for the study sought to learn how employees described their lived experience, how the disaster altered their mental models of change, and what factors in the disaster response experience promoted learning and innovation.

Findings – This study investigates how the disruptive forces of disaster can influence and promote organizational learning and foster innovation. Analysis of the data demonstrates how the library employees’ feelings of trust before and following a workplace disaster shifted their mental models of change. They felt empowered to act and assert their own ideas; they did not simply react to change acting upon them.

Emotions motivate adaptive actions, facilitating change. The library employees’ lived experiences and feelings influenced what they learned, how quickly they learned it, and how that learning contributed to their innovations after the disaster. The library's supervisory and administrative leaders encouraged staff members to try out new ideas. This approach invigorated staff members’ feelings of trust and motivated them to contribute their efforts and ideas. Feeling free to experiment, they tapped their creativity and provided adaptations and innovations.

Practical implications – A disaster imposes immediate and often unanticipated change upon people and organizations. A disaster response urgently demands that employees do things differently; it also may require that employees do different things.

Successful organizations must become adept at creating and implementing changes to remain relevant and effective in the environments in which they operate. They need to ensure that employees generate and test as many ideas as possible in order to maximize the opportunity to uncover the best new thinking. This applies to libraries as well as to any other organizations.

If library leaders understand the conditions under which employees are most motivated to let go of fear and alter the mental models they use to interpret their work world, it should be possible and desirable to re-create those conditions and improve the ability of their organizations to tap into employees’ talent, spur innovation, and generate meaningful change.

Social implications – Trust and opportunities for learning can be central to employees’ ability to embrace change as a positive state in which their creativity flourishes and contributes to the success of the organization. When leaders support experimentation, employees utilize and value their affective connections as much as their professional knowledge. Work environments that promote experimentation and trust are ones in which employees at any rank feel secure enough to propose and experiment with innovative services, products, or workflows.

Originality/value – The first of its kind to examine library organizations, this study offers direct evidence to show that organizational learning and progress flourish through a combination of positive affective experiences and experimentation. The study shows how mental models, organizational learning, and innovation may help employees create significantly effective organizational advances while under duress.

An original formula is presented in Fig. 1.