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Recent advancements in Artificial Intelligence (AI) and, at its core, Machine Learning (ML) offer opportunities for organizations to develop new or enhance existing capabilities. Despite the endless possibilities, organizations face operational challenges in harvesting the value of ML-based capabilities (MLbC), and current research has yet to explicate these challenges and theorize their remedies. To bridge the gap, this study explored the current practices to propose a systematic way of orchestrating MLbC development, which is an extension of ongoing digitalization of organizations.

Data were collected from Finland's Artificial Intelligence Accelerator (FAIA) and complemented by follow-up interviews with experts outside FAIA in Europe, China and the United States over four years. Data were analyzed through open coding, thematic analysis and cross-comparison to develop a comprehensive understanding of the MLbC development process.

The analysis identified the main components of MLbC development, its three phases (development, release and operation) and two major MLbC development challenges: Temporal Complexity and Context Sensitivity. The study then introduced Fostering Temporal Congruence and Cultivating Organizational Meta-learning as strategic practices addressing these challenges.

This study offers a better theoretical explanation for the MLbC development process beyond MLOps (Machine Learning Operations) and its hindrances. It also proposes a practical way to align ML-based applications with business needs while accounting for their structural limitations. Beyond the MLbC context, this study offers a strategic framework that can be adapted for different cases of digital transformation that include automation and augmentation of work.

The paper aims to explore the effect of problem-based learning (PBL) embedding degree and education level on individual perception, as well as the moderating effect of nationality.

The paper first conceptualizes PBL embedding degree which means the extent of applying PBL. It takes an empirical study on an international MBA class in one of the first-class universities in China. An investigation is taken with the designed “PBL-based Cognitive Perception Scale” and an Ordered Probit Model is constructed.

The findings of this study are as follows: PBL embedding degree has a significant effect on the cognitive perception of student, which varies in different dimensions; the educational level of international student positively affects the cognitive perception toward PBL; and nationality may moderate the relationship between the PBL embedding degree and individual perception.

The paper replenishes the investigation and application of Bloom’s Taxonomy of Learning. By conceptualizing PBL embedding degree, the paper extends the research perspectives of PBL and proposes a subjective method on the evaluation of PBL. The paper also may provide a guidance for PBL curriculum design with sustainable development of education.

The purpose of this paper is to present the use of a free code computational tool, Patent2net, in the search of patents for the implementation of distance learning aimed at Continuing Medical Education.

This technical report is based on the extraction, organization and availability, in the format of graphs and dynamic tables, and also based on information in other patents on the subject, made available in the Espacenet database.

As a result, it was possible to identify a Chinese patent, free for reproduction in Brazil, which describes an e-learning system that simulates 3D scenarios for training nursing teams.

The paper has used one unique patent database, but containing more than 100m documents.

The selected patent can contribute to the improvement of care and behavioral techniques of the health professionals.

The training of health professionals can improve the public and supplementary health systems.

This is the first paper in that de technometric analisys of patents was used to solve a problem regarding the training of health professionals.

With the progress of new technologies of information and communication, more and more producers of data exist. On the other hand, the web forms a huge support of all these kinds of data. Unfortunately, existing data is not proper due to the existence of the same information in different sources, as well as erroneous and incomplete data. The aim of data integration systems is to offer to a user a unique interface to query a number of sources. A key challenge of such systems is to deal with conflicting information from the same source or from different sources. We present, in this paper, the resolution of conflict at the instance level into two stages: references reconciliation and data fusion. The reference reconciliation methods seek to decide if two data descriptions are references to the same entity in reality. We define the principles of reconciliation method then we distinguish the methods of reference reconciliation, first on how to use the descriptions of references, then the way to acquire knowledge. We finish this section by discussing some current data reconciliation issues that are the subject of current research. Data fusion in turn, has the objective to merge duplicates into a single representation while resolving conflicts between the data. We define first the conflicts classification, the strategies for dealing with conflicts and the implementing conflict management strategies. We present then, the relational operators and data fusion techniques. Likewise, we finish this section by discussing some current data fusion issues that are the subject of current research.