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The study is aimed at analyzing the perceptions of students and teachers regarding the effectiveness of massive open online courses (MOOCs) on learning efficiency of students and also evaluating MOOCs as an ideal tool for designing a blended model for education.

The analysis was carried out by using the data gathered from the students as well as teachers of University of Mysore, Karnataka, India. Two separate sets of questionnaires were developed for both the categories of respondents. Also, the respondents were required to have prior experience in MOOCs. Further, the collected data was analyzed using statistical package for social sciences (SPSS).

The study showed that MOOCs have a more positive influence on learning efficiency, as opined by both teachers and students. Negative views such as cheating during the assessment, lack of individual attention to students and low teacher-student ratio were also observed.

Many educational institutions view that the MOOCs do not influence learning efficiency and also do not support in achieving their vision. However, this study provides evidence that MOOCs are positively influencing the learning efficiency and also can be employed in a blended model of education so as to promote collaborative learning.

Technology is playing a pivotal role in all fields of life and the education sector is not an exception. It can be rightly said that the technology-based education models such as MOOCs are the need of the hour. This study may help higher education institutions to adopt MOOCs as part of their blended model of education, and, if already adopted, the outcome of the present study will help them to improve the effectiveness of the MOOCs they are offering.

The purpose of this paper is to propose a new mixed-integer formulation for the time-dependent multi-skilled resource-constrained project scheduling problem (MSRCPSP/t) considering learning effect. The proposed model extends the basic form of the MSRCPSP by three concepts: workforces have different efficiencies, it is possible for workforces to improve their efficiencies by learning from more efficient workers and the availability of workforces and resource requests of activities are time-dependent. To spread dexterity from more efficient workforces to others, this study has integrated the concept of diffusion maximization in social networks into the proposed model. In this respect, the diffusion of dexterity is formulated based on the linear threshold model for a network of workforces who share common skills. The proposed model is bi-objective, aiming to minimize make-span and total costs of project, simultaneously.

The MSRCPSP is an non-deterministic polynomial-time hard (NP-hard) problem in the strong sense. Therefore, an improved version of the non-dominated sorting genetic algorithm II (IM-NSGA-II) is developed to optimize the make-span and total costs of project, concurrently. For the proposed algorithm, this paper has designed new genetic operators that help to spread dexterity among workforces. To validate the solutions obtained by the IM-NSGA-II, four other evolutionary algorithms – the classical NSGA-II, non-dominated ranked genetic algorithm, Pareto envelope-based selection algorithm II and strength Pareto evolutionary algorithm II – are used. All algorithms are calibrated via the Taguchi method.

Comprehensive numerical tests are conducted to evaluate the performance of the IM-NSGA-II in comparison with the other four methods in terms of convergence, diversity and computational time. The computational results reveal that the IM-NSGA-II outperforms the other methods in terms of most of the metrics. Besides, a sensitivity analysis is implemented to investigate the impact of learning on objective function values. The outputs show the significant impact of learning on objective function values.

The proposed model and algorithm can be used for scheduling activities of small- and large-size real-world projects.

Based on the previous studies reviewed in this paper, one of the research gaps is the MSRCPSP with time-dependent resource capacities and requests. Therefore, this paper proposes a multi-objective model for the MSRCPSP with time-dependent resource profiles. Besides, the evaluation of learning effect on efficiency of workforces has not been studied sufficiently in the literature. In this study, the effect of learning on efficiency of workforces has been considered. In the scarce number of proposed models with learning effect, the researchers have assumed that the efficiency of workforces increases as they spend more time on performing a skill. To the best of the authors’ knowledge, the effect of learning from more efficient co-workers has not been studied in the literature of the RCPSP. Therefore, in this research, the effect of learning from more efficient co-workers has been investigated. In addition, a modified version of the NSGA-II algorithm is developed to solve the model.

The purpose of this paper is to present an analytical review of the educational innovation field in the USA. It outlines classification of innovations, discusses the hurdles to innovation, and offers ways to increase the scale and rate of innovation-based transformations in the education system.

The paper is based on a literature survey and author research.

US education badly needs effective innovations of scale that can help produce the needed high-quality learning outcomes across the system. The primary focus of educational innovations should be on teaching and learning theory and practice, as well as on the learner, parents, community, society, and its culture. Technology applications need a solid theoretical foundation based on purposeful, systemic research, and a sound pedagogy. One of the critical areas of research and innovation can be cost and time efficiency of the learning.

Several practical recommendations stem out of this paper: how to create a base for large-scale innovations and their implementation; how to increase effectiveness of technology innovations in education, particularly online learning; how to raise time and cost efficiency of education.

Innovations in education are regarded, along with the education system, within the context of a societal supersystem demonstrating their interrelations and interdependencies at all levels. Raising the quality and scale of innovations in education will positively affect education itself and benefit the whole society.

Originality is in the systemic approach to education and educational innovations, in offering a comprehensive classification of innovations; in exposing the hurdles to innovations, in new arguments about effectiveness of technology applications, and in time efficiency of education.

Compared with traditional methods relying on manual teaching or system modeling, data-driven learning methods, such as deep reinforcement learning and imitation learning, show more promising potential to cope with the challenges brought by increasingly complex tasks and environments, which have become the hot research topic in the field of robot skill learning. However, the contradiction between the difficulty of collecting robot–environment interaction data and the low data efficiency causes all these methods to face a serious data dilemma, which has become one of the key issues restricting their development. Therefore, this paper aims to comprehensively sort out and analyze the cause and solutions for the data dilemma in robot skill learning.

First, this review analyzes the causes of the data dilemma based on the classification and comparison of data-driven methods for robot skill learning; Then, the existing methods used to solve the data dilemma are introduced in detail. Finally, this review discusses the remaining open challenges and promising research topics for solving the data dilemma in the future.

This review shows that simulation–reality combination, state representation learning and knowledge sharing are crucial for overcoming the data dilemma of robot skill learning.

To the best of the authors’ knowledge, there are no surveys that systematically and comprehensively sort out and analyze the data dilemma in robot skill learning in the existing literature. It is hoped that this review can be helpful to better address the data dilemma in robot skill learning in the future.

The purpose of this study is to explore the impact of mobile learning platforms on users' study efficiency and develop cognitive indicators to evaluate users' study efficiency on mobile learning platforms.

Layout style was the only independent factor that was investigated. A between-group experimental design was employed. Eye movement data were recorded during the experiment, following which participants were asked to complete an after-scenario questionnaire. This study evaluated the usability of the proposed new design using both subjective and objective data. The computer system usability questionnaire V3 (CSUQ) was used to measure subjective data. For the eye-tracking measure, gaze entropy, the proportion of fixation count and duration of each AOI were calculated. Gaze entropy reflects the complexity of information organization. Fixation counts and AOI duration represent the difficulty of information processing and attention distribution, respectively during the task.

The results indicated that interface layout presents significant effects on user's learning efficiency, usability and cognitive load. Sequential layout improved efficiency and satisfaction among participants and reduced information complexity. The results provided useful insights for designers whose goal is to improve user's learning efficiency under mobile learning scheme.

This study investigated the effects of interface layout on usability, user performance and cognitive load using subjective ratings and eye-tracking technology. Gaze entropy was used to measure the complexity of information organized by the interface design. Fixation count and duration proportion were used to identify the difficulty of information processing and distinguish users' distribution of cognitive resources. The results indicated that a vertical layout panel design was more efficient than a horizontal layout panel design. The design implications of the eye tracking indicators and research results were then summarized. This study is expected to encourage designers to optimize their design proposals using eye tracking testing.

The primary purpose of this study was to predict transfer of learning to workplace among adult learners enrolled in a continuing professional education (CPE) training program, specifically training courses offered through face-to-face, blended and online instruction formats. The study examined the predictive capacity of trainee characteristics, training design and work environment on transfer of learning among the study respondents.

A cross-sectional online survey design was used to collect data from the study respondents, three months after CPE training. Two hundred ninety-seven trainees participated in this study. Data from 46 participants were incomplete and therefore excluded in the preliminary analysis, resulting in 251 valid responses and participants for the data analysis, 43 males (17.1 per cent), 201 females (80.1 per cent) and 7 (2.8 per cent) who did not indicate their gender. To answer the study’s research questions, factor analysis and multiple hierarchical regressions were performed.

The results of the study revealed training efficiency and relevance were critical in the transfer of learning among the study participants. The findings of the study showed combined training efficiency and training relevance enabled training participants to acquire knowledge and skills for application in the workplace and had significantly positive influence in transfer of learning. The work environment, measured by work variability (or flexibility) and work complexity, and the trainee motivation to participate, measured by learning-conducive workplace features, had a positive influence in transfer of learning.

Because the majority of participants were females (80.1 per cent), this could be one of the limitations to this study. Research has identified that, because of the broad expectations based on sex and different family and occupational roles, men and women differ in their social network communication, participation in CPE, personality traits, gender-related occupational preferences, learning preference and methods of handling workplace conflict. The second limitation is related to the study design. The researchers did not have a control group because of practicality issues. This being a cross-sectional online survey study, all extraneous variables were not controlled such as in the case of a true randomized control study. This study is relying on the information obtained from a self-report training transfer instrument completed by the study participants. The accuracy of the obtained data is dependent on the honesty of the participants and their commitment in providing correct responses.

This study provides empirical evidence pertaining to the transfer of learning among adult learners engaged in a continuing professional development training program. The study examines factors related to training design, training delivery, trainee motivation and the workplace environment and how these factors determine transfer of learning among trainee respondents who participated in the study. The findings of the study have practical implications for the design and successful delivery of continuing professional training among adult learners. The study could be replicated at a national level and in international settings.

The purpose of this paper is to investigate the tradeoffs between efficiency and flexibility in production processes involve a cross-trained workforce. The study quantifies production losses that stem from worker learning and forgetting in a cross-training environment.

The paper examines multiple levels of cross-training in the context of several workplace factors including absenteeism, turnover, process change frequency, and process change magnitude using discrete event simulation. The parameters of the simulation model are informed by data from a operating manufacturing system.

Results suggest that productivity gains obtained from a moderate level of cross-training (e.g. two tasks) can outweigh the production losses from additional training, and that adding further cross-training beyond this may negatively affect system performance.

Production systems exist in an environment of process change and competitive pressure. Cross-training is an often-used operational technology for managing process knowledge in an environment of change as well as providing employees with a richer work environment. While the potential benefits of enriching the workplace experience include greater motivation, less boredom and fatigue, greater task vigilance, and other effects generally regarded as beneficial, productivity losses brought about by training and retraining disruptions associated with cross-training have not been examined as widely.

The paper aims to analyse the application of a pedagogy-based approach to designing online language learning courses. It aims to evaluate course efficiency from three perspectives – provider, recipient and wider community perspective and define the extent to which the target course could be applied for developing tourism and hospitality industry students’ and employees’ language competences in Europe.

In line with pragmatism paradigm, a summative/outcome evaluation research in provider, recipient and wider community perspective was conducted in Latvia, Croatia, Slovenia, Romania and Italy. It comprised a focus group interview of 8 language teachers (the provider perspective), a survey of 505 students (the recipient perspective) and a survey of 106 language teachers and 161 industry representatives and 10 expert interviews (the wider community perspective).

The results highlight the main strengths of online learning environments and emphasise the efficiency of the pedagogy-based approach applied in designing an online course. The created courses are beneficial and may be used to foster the development of tourism and hospitality industry students’ and employees’ language competences.

The research sample is composed of language learners from five countries, but it is not proportionally distributed. Another limitation refers to the languages piloted. Further in-depth study concerning less widely used languages is required.

The research results enable understanding certain aspects of designing online language learning courses, provide evaluators’ feedback, suggest the course application for language learning and reveal the European value added.

The research explores the specifics for designing online language learning courses for Languages for Special Purposes for the application in tourism and hospitality industry. The course efficiency and quality are evaluated from provider, recipient and wider community perspective.

The purpose of this research is to achieve multi-task autonomous driving by adjusting the network architecture of the model. Meanwhile, after achieving multi-task autonomous driving, the authors found that the trained neural network model performs poorly in untrained scenarios. Therefore, the authors proposed to improve the transfer efficiency of the model for new scenarios through transfer learning.

First, the authors achieved multi-task autonomous driving by training a model combining convolutional neural network and different structured long short-term memory (LSTM) layers. Second, the authors achieved fast transfer of neural network models in new scenarios by cross-model transfer learning. Finally, the authors combined data collection and data labeling to improve the efficiency of deep learning. Furthermore, the authors verified that the model has good robustness through light and shadow test.

This research achieved road tracking, real-time acceleration–deceleration, obstacle avoidance and left/right sign recognition. The model proposed by the authors (UniBiCLSTM) outperforms the existing models tested with model cars in terms of autonomous driving performance. Furthermore, the CMTL-UniBiCL-RL model trained by the authors through cross-model transfer learning improves the efficiency of model adaptation to new scenarios. Meanwhile, this research proposed an automatic data annotation method, which can save 1/4 of the time for deep learning.

This research provided novel solutions in the achievement of multi-task autonomous driving and neural network model scenario for transfer learning. The experiment was achieved on a single camera with an embedded chip and a scale model car, which is expected to simplify the hardware for autonomous driving.

Today's logistics industry is facing severe challenges since global transportation demand increases substantially. Carriers are urged to reduce empty loads and CO2 emissions through collaboration. Therefore, the concept of Physical Internet (PI) came into being. However, PI is still in its infancy. It is difficult to understand its sophisticated coordination mechanism, which makes learning of the concept more complicated.

Gamification is an effective approach to help students improve their learning curve. At the same time, the psychological and behavioral changes in learning will also pose an impact on learning efficiency. This paper introduces a PI transportation game and designs a set of gamification teaching experiments. In the experiment, a control group and three experimental groups are set up, and the experiment was designed to respond to a plethora of research questions using the methods of T-test, correlation analysis and regression analysis. Experimental results were analyzed through the method of multivariate statistics.

This paper looks for superior pedagogical methods and procedures for students to learn PI while providing suggestions for PI's learning among undergraduates. The authors found (1) gamification teaching will make participants feel more satisfied and master more knowledge points; (2) the scores of logistics testing have been significantly improved after gamification teaching and (3) flow experience has a significant impact on game revenue.

This is the first study about the impact of gamification on teaching and learning PI. The authors apply the methods of T-test, correlation analysis and regression analysis to analyze the collected data. The paper proves that gamification can help students learn PI and that flow experience can improve the efficiency of students learning PI.