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Rooting in the literature on training and laying on Kirkpatrick model, this paper aims to explore key drivers of corporate training to identify how they can be combined into an integrated framework of learning for human capital development.

By adopting the constructivist grounded theory, this contribution analyzes the experience carried out in the last ten years by Virvelle, an Italian corporate training firm.

Results show the rise of five core categories, g1iving rise to an integrated model of Kirkpatrick. Their dynamic interplay led to a new orientation of Kirkpatrick model giving rise to a metalearning ecosystem.

Managerial implications have identified key factors on which building and implementing appropriate corporate training programmes capable of triggering co-generative processes of value creation. Particularly, the essential role of learning quality culture, digital technology and personalization are detected in integrating not only hard but furthermore soft shades of learning. Concerning theoretical implications, the emergence of key structural and systems enabling dimensions for learning, and contextual mechanisms involved in reshaping training effectiveness and achieving integrated learning outcomes are detected. The main limitation of this study lies in the need to generalize results: the conceptualized framework needs to be empirically tested.

The value of this research is built along three main points. The first is the integration among the core categories that an integrated learning system can be built on, promoting learning quality culture through positive feedback loops. The second is represented by the chance to enhance an integrated mutual knowledge development among engaged actors, thereby shaping a more holistic and multidimensional learning model. The third is related to the transversal role that digital technology plays in all phases of the training process as it integrates and enriches them.

Self-regulated learning (SRL) is crucial for successful learning and lifelong learning in today’s rapidly changing world, yet research has shown that many learners need support for SRL. Recently, learning analytics has offered exciting opportunities for better understanding and supporting SRL. However, substantial endeavors are still needed not only to detect learners’ SRL processes but also to incorporate human values, individual needs and goals into the design and development of self-regulated learning analytics (SRLA). This paper aims to examine the challenges that lifelong learners faced in SRL, their needs and desirable features for SRLA.

This study triangulated data collected from three groups of educational stakeholders: focus group discussions with lifelong learners (n = 27); five teacher interviews and four expert evaluations. The groups of two or three learners discussed perceived challenges, support needs and willing-to-share data contextualized in each phase of SRL.

Lifelong learners in professional development programs face challenges in managing their learning time and motivation, and support for time management and motivation can improve their SRL. This paper proposed and evaluated a set of design principles for SRLA.

This paper presents a novel approach for theory-driven participatory design with multistakeholders that involves integrating learners, teachers and experts’ perspectives for designing SRLA. The results of the study will answer the questions of how learners’ voices can be integrated into the design process of SRLA and offer a set the design principles for the future development of SRLA.

Human resources (HR) management has encountered unforeseen obstacles and issues in recruiting, retaining, training and developing workforces under the “new normal” due to pandemic circumstances followed by the Russo–Ukrainian War and global economic turmoil. As the world is now well-equipped with technological advancements and internet-based connectivity, many pandemic disruptions have been avoided through rapid adaptation of technological systems. Despite the constructive outcomes of this contemporary approach to learning and development (L&D), this study explores the further depths of massive open online courses (MOOC) platform adoption in human resource development initiatives during pandemic times.

A qualitative research approach was adopted to understand the employee and HR perspective on the changes in L&D approaches in organizations. To gather the primary data, respondents were divided into two clusters; different sets of questionnaires were developed for interview sessions.

Results suggest that employee L&D was much more improvised with distance or online learning, including organizational e-learning systems and MOOC platforms. To accomplish their HR development goals, organizations went through significant transformations during the Coronavirus pandemic; organizational attempts to initiate online training and MOOC-based learning fostered positive results in employee capacity development, process improvement, employee engagement and motivation.

This research will assist organizations in developing interactive training methods as an effective replacement for traditional training. Additionally, it will assist readers, practitioners and HR specialists in understanding how MOOCs are changing the L&D ecosystem.

Recent years have witnessed the rapid development of massive open online courses (MOOCs). With more and more courses being produced by instructors and being participated by learners all over the world, unprecedented massive educational resources are aggregated. The educational resources include videos, subtitles, lecture notes, quizzes, etc., on the teaching side, and forum contents, Wiki, log of learning behavior, log of homework, etc., on the learning side. However, the data are both unstructured and diverse. To facilitate knowledge management and mining on MOOCs, extracting keywords from the resources is important. This paper aims to adapt the state-of-the-art techniques to MOOC settings and evaluate the effectiveness on real data. In terms of practice, this paper also tries to answer the questions for the first time that to what extend can the MOOC resources support keyword extraction models, and how many human efforts are required to make the models work well.

Based on which side generates the data, i.e instructors or learners, the data are classified to teaching resources and learning resources, respectively. The approach used on teaching resources is based on machine learning models with labels, while the approach used on learning resources is based on graph model without labels.

From the teaching resources, the methods used by the authors can accurately extract keywords with only 10 per cent labeled data. The authors find a characteristic of the data that the resources of various forms, e.g. subtitles and PPTs, should be separately considered because they have the different model ability. From the learning resources, the keywords extracted from MOOC forums are not as domain-specific as those extracted from teaching resources, but they can reflect the topics which are lively discussed in forums. Then instructors can get feedback from the indication. The authors implement two applications with the extracted keywords: generating concept map and generating learning path. The visual demos show they have the potential to improve learning efficiency when they are integrated into a real MOOC platform.

Conducting keyword extraction on MOOC resources is quite difficult because teaching resources are hard to be obtained due to copyrights. Also, getting labeled data is tough because usually expertise of the corresponding domain is required.

The experiment results support that MOOC resources are good enough for building models of keyword extraction, and an acceptable balance between human efforts and model accuracy can be achieved.

This paper presents a pioneer study on keyword extraction on MOOC resources and obtains some new findings.

There is some research showing that leadership behaviors could be important antecedents to learning, but knowledge is scarce on the impact of which leadership styles support exploitative and explorative learning. The purpose of this paper is to hypothesize that transformational leadership – more concerned with innovation – will encourage generalist human capital (HC), while transactional leadership – more focused on the efficiency of existing operations – will promote specialist HC.

To test the hypotheses, the authors adopt a structural ambidexterity approach as the authors consider that organizations need units working on both types of learning.

The results show the versatile role of transformational leaders, who are able to promote both types of HC and, in turn, both types of organizational learning. The authors have also found that marketing departments are more willing to explore than production departments.

This study highlights the relevance of considering the department as a unit of analysis (structural ambidexterity approach), the significant role of transformational leaders in organizational learning and the mediating role of HC.

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 purpose of this commentary is to comment on Fischer's et al. (2022)

This commentary responds to Fischer's et al. (2022) call on envisioning alternate conceptualizations of learning for the digital era. In doing so, the author argues for reconsidering learning in its socio-material condition, situated and made of a web of social and technological relations. In this context, the author takes a relational lens on learning to interrogate taken-for-granted views of (1) personalizing data increasingly used for student learning, (2) emerging educational infrastructures for higher education and (3) the student–teacher relationship mediated by data and algorithms.

In this commentary, the author suggested unpacking assumptions about learning that get reflected in the design and discourses about socio-technical arrangements and transformations in education. Taking the example of personalized learning, the author has illustrated a relational mode of thinking that leads the author to argue that, renewed definitions of learning must be discussed multidimensionally and, most importantly, situated in the material world that learning is already part of.

Following Fischer et al. (2022, this issue), the author agrees that the focus should be on finding “new ways of organizing learning by exploring opportunities for radically new conceptualizations and practices.” In order to do that it is of utmost importance to problematize the social and material conditions that actively configure learning today and infrastructure tomorrow's learning. Hopefully, these observations will entice others to discuss further the educational transformations at stake in the age of datafication and algorithmic decision-making.

The author argues for reconsidering learning in its socio-material condition, which is situated and made of a web of social and technological relations. In this context, the author argues that any attempt to reconceptualize learning from a transformational perspective in the 21st century, as mentioned by Fischer et al. (2022), needs to interrogate views and assumptions about the socio-technical relationships researchers, practitioners and educators are contributing to via their practices and discourses.

The purpose of this article is to posit an alternative learning design approach to the technology-led magnification and multiplication of learning and to the linearity of curricular design approaches such as a constructive alignment. Learning design ecosystem thinking creates complex and interactive networks of activity that engage the widest span of the community in addressing critical pedagogical challenges. They identify the pinch-points where negative engagements become structured into the student experience and design pathways for students to navigate their way through the uncertainty and transitions of higher education at-scale.

It is a conceptual paper drawing on a deep and critical engagement of literature, a reflexive approach to the dominant paradigms and informed by practice.

Learning design ecosystems create spaces within at-scale education for deep learning to occur. They are not easy to design or maintain. They are epistemically and pedagogically complex, especially when deployed within the structures of an institution. As Gough (2013) argues, complexity reduction should not be the sole purpose of designing an educational experience and the transitional journey into and through complexity that students studying in these ecosystems take can engender them with resonant, deeply human and transdisciplinary graduate capabilities that will shape their career journey.

The paper is theoretical in nature (although underpinned by rigorous evaluation of practice). There are limitations in scope in part defined by the amorphous definitions of scale. It is also limited to the contexts of higher education although it is not bound to them.

This paper challenges the dialectic that argues for a complexity reduction in higher education and posits the benefits of complexity, connection and transition in the design and delivery of education at-scale.

Issues around informal, non-formal and formal learning, intended and unintended learning and competencies and capabilities have been considered in work-based learning (WBL). However, demarcated modes of learning, or what can be called strategies or pedagogies of learning, associated with experience of work environments have yet to be examined. One mode of learning which has been highlighted in relation to work is reflective practice, and its centrality to learning at work has been established. But reflective practice as a core skill, and its relation to other approaches to learning and research in WBL, remains uncovered. The purpose of the present study therefore is to identify different modes of learning as they appear in the literature and to present a proto-theoretical “learning by …” model for WBL and research founded on learning by reflection.

Proto-theoretical modelling and qualitative descriptions of each mode of learning.

Work environments, and the higher degree WBL programmes which support them, should provide learning via every available mode of learning, thereby allowing students to find their own best orientation to learning and encourage it by any means.

The proto-theoretical model and 12 modes of learning applied to WBL are unique to this study. WBL provides participants of work with multiple opportunities and approaches to learn and similarly provides multiple modes through which learning can occur on the basis of knowledge and skills in reflective practice.

The purpose of this paper is to describe an online faculty development pilot course on how to engage students online. A framework was used, referred to as the Trifecta of Student Engagement, for the design of the course. The Trifecta of Student Engagement proposes that students, in order to be fully engaged in a course, need to be engaged with their course content, with their peers and with their instructor. The course has three units of content that each correspond to the Trifecta of Student Engagement. This course has gone through one pilot with faculty and has impacted students and faculty positively.

An online faculty development course was piloted with eight faculty members across a range of disciplines who participated in the program. After taking the course, they had to apply the Trifecta of Student Engagement framework to a course they taught and share what they did via written report, webinar, or web presentation. This study summarized the faculty participants’ written reports and presentations as well as provided a qualitative evaluation on the impact this course had on students and faculty.

After faculty applied the Trifecta of Student Engagement framework to courses taught, faculty saw an improvement in student engagement, satisfaction, learning and achievement. Three faculty surveyed students to determine their engagement and satisfaction and found students to respond positively to the use of tools and activities for student-to-content engagement, student-to-student engagement and student-to-instructor engagement. Two faculty examined student grades to determine if there were changes in student outcomes. One professor saw average grades increase by 11 percent. Another professor saw grades improve by 8 percent. She also found that student assessment of learning increased by 0.57. Both faculty attributed the improvement to the effectiveness of the teaching strategies employed.

This research is limited to the eight faculty who participated in the pilot. Some faculty used methods to attempt to measure the impacts of their teaching practices by surveying students and looking at student performance data. A second pilot is needed for additional faculty to take the course and apply the Trifecta of Engagement framework to generate more data for impact.

Institutions looking to create an online teaching professional development course for faculty can utilize the Trifecta of Student Engagement framework for their course design. Additionally, faculty can read about tools and strategies that they can immediately apply to create more student-to-content engagement, student-to-student engagement and student-to-instructor engagement.

Faculty can be more intentional in how they engage students in their online course experience.

This paper adds to the literature on faculty development regarding student-centered teaching practices. Other institutions looking to create a faculty development course or program that utilizes a student-centered framework may find aspects of this paper useful for their own online teaching professional development initiatives.