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Highly developed and agile learners who can clearly convey and call on their skills are sought in all walks of life. Diverse demand for these capacities has called attention to how the skills and knowledge gained during doctoral study can be conveyed, translated and leveraged in non-academic settings; however, the complex learning reality underneath doctoral development is challenging to convey.

The data set for this particular analysis was obtained from 245 in-depth telephone interviews with PhD candidates collected prior to COVID-19. Candidates were asked about learning processes, challenges and changes, and both the questions and thematic analysis were guided by theories of doctoral development and transformational learning.

For many participants, learning and development were not familiar topics, while a small proportion deflected questions about learning altogether. One fifth of participants presented rich and lucid accounts of learning in which cognisance of complexity, metacognitive processes and transformational experiences were embedded and multiple avenues of development were in evidence. They were well-placed to convey the complexion of doctoral development. Candidates more deeply engaged in learning also commented more about changes they noticed in themselves. The most identified avenue of development was in understanding and approach to knowledge.

Candidate communication about learning and development is an under-explored dimension of doctoral experience and skill that is relevant to advancing knowledge about doctoral development and illuminating graduate potential both within and outside academe. This must constitute a key element of the re-vitalisation of the doctorate post-pandemic. The salience of framing transferable skills within a learning development perspective is discussed.

This paper elucidates the role of anger in error management (EM) and organizational learning behaviors. The study explores how anger can catalyze learning, emphasizing its strategic implications.

A double-layered moderated-mediated model was developed and tested using data from 744 Chinese CEOs. The psychometric properties of the survey instrument were rigorously examined through structural equation modeling, and hypotheses were tested using Hayes's PROCESS macros.

The findings reveal that anger is a precursor for recognizing the value of significant errors, leading to a positive association with learning behavior among top management team members. Additionally, the study uncovers a triple interaction effect of anger, EM culture and supply chain disruptions on the value of learning from errors. Extensive experience and positive grieving strengthen the relationship between recognizing value from errors and learning behavior.

This study uniquely integrates affect-cognitive theory and organizational learning theory, examining anger in EM and learning. The authors provide empirical evidence that anger can drive error value recognition and learning. The authors incorporate a more fine-grained approach to leadership when including executive anger as a trigger to learning behavior. Factors like experience and positive grieving are explored, deepening the understanding of emotions in learning. The authors consider both negative and positive emotions to contribute to the complexity of organizational learning.

Higher education plays an essential role in achieving the United Nations sustainable development goals (SDGs). However, there are only scattered studies on monitoring how universities promote SDGs through their curriculum. The purpose of this study is to investigate the connection of existing common core courses in a university to SDG education. In particular, this study wanted to know how common core courses can be classified by machine-learning approach according to SDGs.

In this report, the authors used machine learning techniques to tag the 166 common core courses in a university with SDGs and then analyzed the results based on visualizations. The training data set comes from the OSDG public community data set which the community had verified. Meanwhile, key descriptions of common core courses had been used for the classification. The study used the multinomial logistic regression algorithm for the classification. Descriptive analysis at course-level, theme-level and curriculum-level had been included to illustrate the proposed approach’s functions.

The results indicate that the machine-learning classification approach can significantly accelerate the SDG classification of courses. However, currently, it cannot replace human classification due to the complexity of the problem and the lack of relevant training data.

The study can achieve a more accurate model training through adopting advanced machine learning algorithms (e.g. deep learning, multioutput multiclass machine learning algorithms); developing a more effective test data set by extracting more relevant information from syllabus and learning materials; expanding the training data set of SDGs that currently have insufficient records (e.g. SDG 12); and replacing the existing training data set from OSDG by authentic education-related documents (such as course syllabus) with SDG classifications. The performance of the algorithm should also be compared to other computer-based and human-based SDG classification approaches for cross-checking the results, with a systematic evaluation framework. Furthermore, the study can be analyzed by circulating results to students and understanding how they would interpret and use the results for choosing courses for studying. Furthermore, the study mainly focused on the classification of topics that are taught in courses but cannot measure the effectiveness of adopted pedagogies, assessment strategies and competency development strategies in courses. The study can also conduct analysis based on assessment tasks and rubrics of courses to see whether the assessment tasks can help students understand and take action on SDGs.

The proposed approach explores the possibility of using machine learning for SDG classifications in scale.

The purpose of this study is to theorize that computer-assisted language learning (CALL) can be integrated in English language learning with a focus on cultural learning of both home and target language.

The present study used a systematic methodology to conceive the language and home-culture integrated online learning (LHIOL) curriculum design based on relevant conceptual frameworks and gather qualitative data from focused group interviews of 30 teachers and 3,000 students’ open-ended questionnaires, along with learning artifacts to identify major themes.

CALL, used as cultural and linguistic material, helps students embrace their cultural identities, especially ethnic minorities, capitalize on their distinctive values, and appreciate and empathize with other languages and cultures. The instructors advocate for localizing intercultural communicative competence (ICC) educational content into Vietnamese culture, using real multimedia resources. However, the LHIOL curriculum faced systemic constraints regarding competitions between linguistic and cultural instruction, teachers’ refusal to recognize ICC’s importance and recognition of an explicit link between virtual cultural learning and their lives.

LHIOL is a preliminary practical effort to suggest how a cultural education from one’s native tongue can be integrated into a culture-focused English/Western language environment. By incorporating fundamental concepts that underpin the integration of language and culture as well as CALL, improving ICC offers a framework that can be applied to elucidate cultural learning.

In collaboration with their home cities, universities increasingly develop courses in which students investigate urban sustainability challenges. This paper aims to understand how far-reaching the collaboration with urban stakeholders in these courses is and what students are meant to learn from the transdisciplinary pedagogies.

This research is designed as a qualitative multiple-case study into the intentions of transdisciplinary courses in which universities collaborate with their home cities: Delft University of Technology in Delft and Amsterdam Institute for Advanced Metropolitan Solutions in Amsterdam. The study compares the written intentions of eight courses in course descriptions with the ideal intentions that teachers describe in interviews.

First, seven of the eight investigated courses were designed for urban stakeholders to participate at a distance or as a client but rarely was a course intended to lead to a collaborative partnership between the city and students. Second, the metacognitive learning objectives, such as learning to deal with biases and values of others or getting to know one’s strengths and weaknesses in collaboration, were often absent in the course descriptions. Learning objectives relating to metacognition are at the heart of transdisciplinary work, yet when they remain implicit in the learning objectives, they are difficult to teach.

This paper presents insight into the levels of participation intended in transdisciplinary courses. Furthermore, it shows the (mis)alignment between intended learning objectives in course descriptions and teachers’ ideals. Understanding both the current state of transdisciplinarity in sustainability courses and what teachers envision is vital for the next steps in the development of transdisciplinary education.

This paper aims to shed light on how the composition of future blended learning (BL) courses can be changed to provide students with quality academic learning experiences. The model suggested in this study will guide instructors on how to design their course learning outcomes to ensure effective delivery.

The new model has been developed by combining Bloom's taxonomy and Carman's model. Later, a new framework entitled “PATHCO” based on an extensive literature review is applied to enhance the quality of all five components of Carman's model.

The PATHCO conceptual framework has been developed to ensure quality in the five main teaching and learning factors. This framework covers criteria like pedagogical, assessments, technical, health care and organizational. Further research is required to broaden the main elements of the suggested framework and to validate this research through a case study.

The COVID-19 pandemic has transformed the landscape of the education sector by encouraging an extensive acceptance of technology-enhanced learning and teaching. Blended learning (BL) has become the most appropriate medium to deliver online learning (OL). However, educators and students have reported dissatisfaction with the BL mode of delivery. To address this dissatisfaction, this study outlines, using the PATHCO model, all the essential building blocks which are required to find the right blend of both face-to-face and online components.

The emphasis on short-term by project-based firms (PBFs) implies the adoption of project efficiency and impact on the team as project success drivers in PBFs context. Good performance by employees, as individuals in a team, can be explained by their behaviors, associated with goal orientation theory. Learning and performance orientations are associated with teams’ effectiveness and overall project performance. The purpose of this paper is to analyze the relationships between the dimensions of goal orientation, especially learning orientation, and project efficiency and impact on the team, in PBFs context.

A quantitative approach was adopted, based on data from a survey of 714 respondents, representing project managers, that turned into a valid sample of 315 composed only by PBFs respondents. The results were analyzed through multiple linear regression and, mainly, mediation analysis methods.

Performance-avoid orientation is a predictor of project efficiency; performance-prove orientation, a predictor of impact on the team. Learning orientation relates positively to both project success criteria. Project managers should balance/induce the proper orientation within the team, favoring learning orientation according to the results, to have short-term project success in PBFs.

There is a direct relationship between learning orientation and project efficiency, but it is fully mediated by impact on the team, which it was not found in previous studies. This study argues that they are not parallel constructs, constituent parts of equal weight in project success, but that impact on the team precedes project efficiency when learning orientation is considered.

This paper aims to systematically review and synthesize existing research on learning-driven strategic renewal and examines the findings to elucidate the dimensions, antecedents, mechanisms and consequences associated with learning-driven strategic renewal, thereby addressing gaps in the existing literature.

This research covers learning-driven strategic renewal from 1992 to 2022, using hybrid snowball sampling techniques and Boolean searches on the Scopus and Web of Science databases to extract 49 papers.

This review proposes an organizing framework for learning-driven strategic renewal, building upon existing literature. The framework identifies various dimensions of the process, including antecedents, mechanisms and consequences. The antecedents are categorized into individual, organizational and external factors. The mechanisms for learning-driven strategic renewal were explored within the context of Crossan’s established 4I framework, which serves as a lens for emphasizing the balance between exploratory and exploitative learning. Within this framework, intuiting, interpreting, integrating and institutionalizing are the four “Is” that guide the renewal process. These mechanisms require a robust system to enforce the prescribed processes effectively, thereby contributing to long-term firm performance and sustainability.

Despite using search terms similar to those in existing literature on strategic renewal, the scope and depth of this study may be limited. Further research may benefit from bibliometric screening or more refined inclusion criteria.

While there has been extensive research into both organizational learning and strategic renewal, no coherent framework links them. This study fills this gap by building a framework that identifies connections between these two concepts, providing valuable insights that may be used to foster successful strategic renewal efforts. The review offers valuable knowledge and understanding of the subject matter, serving as useful guidance for effectively driving renewal initiatives within organizations.

The purpose of this study is to examine the continuance intentions of working professionals to pursue e-learning courses as a path for career advancement. The primary objective of this study is to ascertain the predictors of continued intentions of working professionals to pursue e-learning courses and examine if this is a trend in career development.

Perceived usefulness of e-learning, motivation and satisfaction are independent variables which are examined using a regression model as potential determinants of continued intentions to use various e-learning platforms. Data from 240 working professionals in different sectors was collected. In addition, satisfaction, motivation and perceived usefulness among the male and female respondents are compared using ANOVA.

The findings showed that motivation, satisfaction and perceived usefulness of e-learning are significant predictors and have a strong influence on the continued intentions of working professionals to pursue e-learning courses. In addition, the results showed that motivation levels while pursuing e-learning and satisfaction derived from them were higher for female professionals.

This study identifies the antecedents of the continued intentions of working professionals to pursue e-learning courses on the path of career advancement. The outcome of the study can be used by educators and e-content creators to make e-learning more engaging. Corporates can also use the results of this study to identify initiatives that can encourage the pursuit of e-learning.

This study provides an important insight exploring the antecedents of continued intentions of working professionals to pursue e-learning courses as a path of career advancement. The research contributes significantly to the understanding thought process of working professionals towards their careers.

The purpose of this study is to identify the main perspectives and trends in educational data mining (EDM) in the e-learning environment from a managerial perspective.

This paper proposes a systematic literature review to identify the main perspectives and trends in EDM in the e-learning environment from a managerial perspective. The study domain of this review is restricted by the educational concepts of e-learning and management. The search for bibliographic material considered articles published in journals and papers published in conferences from 1994 to 2023, totaling 30 years of research in EDM.

From this review, it was observed that managers have been concerned about the effectiveness of the platform used by students as it contains the entire learning process and all the interactions performed, which enable the generation of information. From the data collected on these platforms, there are improvements and inferences that can be made about the actions of educators and human tutors (or automatic tutoring systems), curricular optimization or changes related to course content, proposal of evaluation criteria and also increase the understanding of different learning styles.

This review was conducted from the perspective of the manager, who is responsible for the direction of an institution of higher education, to assist the administration in creating strategies for the use of data mining to improve the learning process. To the best of the authors’ knowledge, this review is original because other contributions do not focus on the manager.