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Informal learning networks are critical to response to calls for practitioners to reskill and upskill in off-site construction projects. With the transition to the coronavirus disease 2019 (COVID-19) pandemic, social media-enabled online knowledge communities play an increasingly important role in acquiring and disseminating off-site construction knowledge. Proximity has been identified as a key factor in facilitating interactive learning, yet which type of proximity is effective in promoting online and offline knowledge exchange remains unclear. This study takes a relational view to explore the proximity-related antecedents of online and offline learning networks in off-site construction projects, while also examining the subtle differences in the networks' structural patterns.

Five types of proximity (physical, organizational, social, cognitive and personal) between projects members are conceptualized in the theoretical model. Drawing on social foci theory and homophily theory, the research hypotheses are proposed. To test these hypotheses, empirical case studies were conducted on two off-site construction projects during the COVID-19 pandemic. Valid relational data provided by 99 and 145 project members were collected using semi-structured interviews and sociometric questionnaires. Subsequently, multivariate exponential random graph models were developed.

The results show a discrepancy arise in the structural patterns between online and offline learning networks. Offline learning is found to be more strongly influenced by proximity factors than online learning. Specifically, physical, organizational and social proximity are found to be significant predictors of offline knowledge exchange. Cognitive proximity has a negative relationship with offline knowledge exchange but is positively related to online knowledge exchange. Regarding personal proximity, the study found that the homophily effect of hierarchical status merely emerges in offline learning networks. Online knowledge communities amplify the receiver effect of tenure. Furthermore, there appears to be a complementary relationship between online and offline learning networks.

Proximity offers a novel relational perspective for understanding the formation of knowledge exchange connections. This study enriches the literature on informal learning within project teams by revealing how different types of proximity shape learning networks across different channels in off-site construction projects.

The study explores the perspectives of college students on the pedagogical shift as well as frequent transitions between online and offline learning modes during the COVID-19 pandemic in Kerala, the most literate state in India.

A descriptive cross-sectional study was conducted among 1,366 college students in Kerala during December 2021. A pre-tested questionnaire was sent using Google Forms to students of arts and science colleges. The authors analyzed quantitative data using descriptive statistics and qualitative data using thematic content analysis.

The reported advantages of online learning were increased technical skill, flexibility in study time, effectiveness in bridging the gap of the missed academic period and provision of attending more educational webinars. Students expressed concerns of increased workload, difficulty in concentration due to family circumstances, academic incompetency, uncleared doubts and addiction to mobile phones and social media during the online classes. The main advantages reported for switching to an offline learning mode were enhanced social interaction, effective learning, better concentration and reduced stress. The reported challenges of offline classes were fear of getting the disease, concern of maintaining social distancing and difficulty in wearing masks during the classes. The shift in offline to online learning and vice versa was perceived as a difficult process for the students as it took a considerable time for them to adjust to the switching process of learning.

Students' concerns regarding transition between different learning modes provide important information to educators to better understand and support the needs of students during the pandemic situations.

Furthermore, the purpose of this study is to compare the student engagement and the learning outcomes in offline and online PBL in the aforementioned course. The COVID-19 pandemic has caused disruption in various sectors, including education. Since it was first announced in mid-March 2020 in Indonesia, teaching and learning activities have been carried out online. In this study, a comparison of the offline (Spring 2019, prior to the pandemic) and online (Spring 2021, during the pandemic) problem-based learning (PBL) method in the sustainable chemical industry course is investigated.

A quantitative analysis was conducted by measuring the students’ engagement, course-learning outcomes (CLOs) and student learning outcomes (SLOs). Difference tests of engagement score, CLOs and SLOs were investigated by using the t-test or Mann–Whitney U-test. Furthermore, the perceived students’ stressors were measured.

It is found that the students’ engagement in offline and online PBL gives similar scores with no significant difference. This is possible because of the PBL structure that demands students to be actively engaged in gaining knowledge, collaboratively working in teams and interacting with other students and lecturers. Although similarly engaged, the CLOs and SLOs of online PBL are significantly lower than offline PBL, except for SLO related to oral and written communication skills and affective aspect. The decrease in CLOs and SLOs could be influenced by students’ academic, psychological and health-related stressors during the COVID-19 pandemic time.

This study provides a recommendation to apply online PBL during the COVID-19 pandemic time and beyond, although some efforts to improve CLOs and SLOs are needed.

Offline reinforcement learning (RL) acquires effective policies by using prior collected large-scale data, while, in some scenarios, collecting data may be hard because it is time-consuming, expensive and dangerous, i.e. health care, autonomous driving, seeking a more efficient offline RL method. The purpose of the study is to introduce an algorithm, which attempts to sample the high-value transitions in the prioritized buffer, and uniformly sample from the normal experience buffer, improving sample efficiency of offline reinforcement learning, as well as alleviating the “extrapolation error” commonly arising in offline RL.

The authors propose a new structure of experience replay architecture, which consists of double experience replies, a prioritized experience replay and a normal experience replay, supplying samples for policy updates in different training phases. At the first training stage, the authors sample from prioritized experience replay according to the calculated priority of each transitions. At the second training stage, the authors sample from the normal experience replay uniformly. The combination of the two experience replies is initialized by the same offline data set.

The proposed method eliminates out-of-distribution problem in an offline RL regime, and promotes training by leveraging a new efficient experience replay. The authors evaluate their method on D4RL benchmark, and the results reveal that the algorithm can achieve superior performance over the state-of-the-art offline RL algorithm. The ablation study proves that the authors’ experience replay architecture plays an important role in terms of improving final performance, data-efficiency and training stability.

Because of the extra addition of prioritized experience replay, the proposed method increases the computational burden and has the risk of changing data distribution due to the combined sample strategy. Therefore, researchers are encouraged to use the experience replay block effectively and efficiently further.

Offline RL is susceptible to the quality and coverage of pre-collected data, which may be not easy to be collected from specific environment, demanding practitioners to handcraft behavior policy to interact with environment for gathering data.

The proposed approach focuses on the experience replay architecture for offline RL, and empirically demonstrates the superiority of the algorithm on data efficiency and final performance over conservative Q-learning across diverse D4RL tasks. In particular, the authors compare different variants of their experience replay block, and the experiments show that the stages, when to sample from the priority buffer, play an important role in the algorithm. The algorithm is easy to implement and can be combined with any Q-value approximation-based offline RL methods by minor adjustment.

The purpose of this paper is to develop an extended Push-Pull-Mooring-Habit (PPMH) framework in order to better understand users’ intention of switching from offline to an online real-person English learning platform service.

Based on 301 valid responses collected from an online survey questionnaire, structural equation modeling was employed to examine the research model.

The causal model was validated using SmartPLS 3.0, and all study hypotheses were supported. The results show that push effects (learning convenience, service quality and perceived price), pull effects (e-learning motivation, perceived usefulness), mooring effects (learning engagement, switching cost and social presences) and habit effects (relationship inertia) all significantly influence users’ switching intentions from offline to an online real-person English learning platform.

The findings should help online English learning service providers and marketers to understand the intention of offline English learning users to switch to an online real-person English learning platform, and develop related theories, services and regulations.

The present study extends the prior research of an online real-person English learning platform by providing PPMH as the general framework and demonstrating its efficacy in explaining user switching intentions.

This article describes how Hyundai Motor Company is successfully developing its high‐potential managers in Korea through an innovative blended learning program. Since 2003, Hyundai has worked in partnership with Educasia to integrate instructor‐led online learning into a program that was previously conducted entirely in the classroom. As a result, Hyundai has been able to deliver an expanded learning curriculum in a more efficient and engaging way to help 150 future Hyundai leaders each year to build a broad range of management and business skills.

The purpose of this paper is to demonstrate a novel use of a generalized Lennard‐Jones (LJ) force law in Physicomimetics, combined with offline evolutionary learning, for the control of swarms of robots moving through obstacle fields towards a goal. The paper then extends the paradigm to demonstrate the utility of a real‐time online adaptive approach named distributed agent evolution with dynamic adaptation to local unexpected scenarios (DAEDALUS).

To achieve the best performance, the parameters of the force law used in the Physicomimetics approach are optimized, using an evolutionary algorithm (EA) (offline learning). A weighted fitness function is utilized consisting of three components: a penalty for collisions, lack of swarm cohesion, and robots not reaching the goal. Each robot of the swarm is then given a slightly mutated copy of the optimized force law rule set found with offline learning and the robots are introduced to a more difficult environment. The online learning framework (DAEDALUS) is used for swarm adaptation in this more difficult environment.

The novel use of the generalized LJ force law combined with an EA surpasses the prior state‐of‐the‐art in the control of swarms of robots moving through obstacle fields. In addition, the DAEDALUS framework allows the swarms of robots to not only learn and share behavioral rules in changing environments (in real time), but also to learn the proper amount of behavioral exploration that is appropriate.

There are significant issues that arise with respect to “wall following methods” and “local minimum trap” problems. “Local minimum trap” problems have been observed in this paper, but this issue is not addressed in detail. The intention is to explore other approaches to develop more robust adaptive algorithms for online learning. It is believed that the learning of the proper amount of behavioral exploration can be accelerated.

In order to provide meaningful comparisons, this paper provides a more complete set of metrics than prior papers in this area. The paper examines the number of collisions between robots and obstacles, the distribution in time of the number of robots that reach the goal, and the connectivity of the formation as it moves.

This paper addresses the difficult task of moving a large number of robots in formation through a large number of obstacles. The important real‐world constraint of “obstructed perception” is modeled. The obstacle density is approximately three times the norm in the literature. The paper shows how concepts from population genetics can be used with swarms of agents to provide fast online adaptive learning in these challenging environments. In addition, this paper also presents a more complete set of metrics of performance.

The purpose of this study is to investigate the impact of the COVID-19 pandemic on the beliefs and attitudes toward the use of information and communication technology (ICT). The study examines the challenges of implementing ICT-based training and provides insights for promoting the acceptance of online training in volunteer sports communities.

The study uses an action design research methodology that combines the implementation of ICT-based training, interviews, and a survey of 523 participants to examine the influence of online training on beliefs and attitudes.

The study shows that before the COVID-19 pandemic, soccer referees had negative beliefs about the use of ICT for learning. However, the experience of being forced to use ICT for training during the pandemic led to a positive shift in their beliefs about ICT.

The study offers four lessons learned for promoting the use of ICT-based training in voluntary sports. Future research should investigate the influence of blended learning approaches on affective, cognitive, and skill-based learning outcomes.

The study has practical implications for those responsible for implementing ICT-based training in voluntary sport. The findings suggest that design features such as usefulness, ease of use and enjoyment should be emphasized to increase the acceptance of online training.

The study contributes to the literature by providing insights into the challenges of implementing ICT-based training in voluntary sport contexts. The findings suggest that the experience of being forced to use ICT can promote the acceptance of online training in volunteer sports communities.

As traditional paper manuals and step-by-step instructions have shown to discourage new product learning because of a lack of exploration, the purpose of this paper is to investigate consumer learning from an online and andragogical, that is, adult learning, perspective by identifying relevant consumer e-learning processes in new product learning.

This paper uses thematic and trace analyses on a multi-method data collection, that is, extant e-learning courses, in-depth interviews and non-participant observations.

Emerging findings give light on customized, interactive and iterative e-learning processes depending on consumers’ previous experiences, their learning orientation as adult learners and the characteristics of the online environment. Results provide evidence for the existence of three learning strategies and show how the online environment comes shifting traditional consumer learning paradigms.

This paper contributes to the literature on consumer behavior on two levels. First, the findings highlight the importance of taking an andragogical standpoint to provide a more nuanced and realistic view on consumers’ learning processes in new product learning. Second, the results show how the exploration and interactivity provided by the online environment present beneficial prerequisites for effective consumer learning. More than just being an alternative, online learning is complementary to offline modes of learning to improve consumers’ overall learning experience.

The research aims to measure the effectiveness of collaborative learning exchanges transpired through digital tools and technologies (DT&Ts) employed by the mentor universities during the COVID-19 pandemic by conducting an empirical study on undergraduate students in Indian higher educational institutions (HEIs) under the mentorship program based on the corporate social responsibility (CSR) initiative. The pandemic scenario, its impact on the mentor university's social responsibility and the way DT&Ts can assist are investigated in this article.

The interactions with experts and students were conducted to explore the DT&Ts for learning exchanges. Next, structural equation modeling (SEM) was performed to validate the model and perform regression analysis. The quantitative data collection was made through questionnaires during the second deadly wave of COVID-19 that hit India.

The independent variables (IVs) such as the IT infrastructure support (IT_IS), virtual collaborative tools (VCTs) and future-oriented technologies (FOTs) have a significant impact on the CSR learning outcomes (CSR_LOs) of undergraduate students under the mentorship program. However, IV research instruments for innovation could not make a significant effect.

The IVs IT_IS, VCTs and FOTs influence the CSR_LOs, while RII does not have an influential impact.

As the online learning environment is expected to stay at least in a blended form, adequate CSR funding in infrastructure is necessitated to harness the full potential of this important resource, technology. The results of this empirical investigation affirm that IT_IS, VOTs and FOTs significantly impact CSR_LOs during the crisis. The study findings would encourage the mendtor universities and their stakeholders, including the mentee universities, to evolve and create an ecosystem for effective management of these resources to attain positive outcomes. The study findings can guide the mentor universities in managing uncertainties like pandemics and effectively using the earlier-mentioned critical resources for social responsibility. This research also allows the development of future applications adnd models in mentor-mentee universities for social responsibility, post-pandemic transformation and resilience.

The DT&Ts came to the immediate rescue during the pandemic and positively affected collaborative CSR_LOs by the mentor universities, but they have not evolved to a level where offline learning can be replaced entirely. Hence, it can be inferred that a hybrid model is preferable. The study also improves the understanding of how DT&Ts are being harnessed to aid collaborative learning in fulfilling the mentors' CSR in fatal emergencies. The purpose is to equip the education system through mentorship so that universities can sustain, innovate and grow even in trying times. Also, it discusses the dynamics of various DT&Ts for creating a sustainable learning environment and utilizing them to make the teaching prolific and influential.

There is a scarcity of literature regarding the learning outcomes realized through CSR initiatives and collaboration between mentor-mentee institutions. There is a need to understand how these knowledge exchanges continued despite the physical restrictions during the pandemic. In this direction, this study helps to understand how the DT&Ts played a critical role in continuing learning and keeping abreast in a knowledge society from the perspective of resource-based view (RBV) in these precarious situations.