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The study aims to give a descriptive account of university students’ engagement with non-learning-related activities during class time and explore the relationship between off-task multitasking and learning. The predictive factors for off-task multitasking from individual, social and class-related dimensions are also examined.

Contextualized in a comprehensive university in Hong Kong, the study adopts a survey design and involves 79 samples.

The data show that Hong Kong university students are avid users of mobile phones and heavily engage with digital devices. Off-task multitasking with mobile phones is a common phenomenon, yet not related to learning performance. Among the various media and apps on mobile phones, instant messenger stands out as the most frequently used app on a daily basis and inside the classroom. The individual device-use habit and classroom engagement are significant predictors for off-task multitasking during class time.

This paper will allow teachers and students to be more aware of the causes and effects of off-task multitasking behaviors during class time and derive practical guidance and strategies to pay heed to and resist the disruptive influence of technologies on learning.

The existing scholarly work show a mixed and incomplete picture regarding the effects and determining factors of students’ multitasking. This study includes three variables from individual, social and teaching/learning dimensions and seeks to evaluate their predictive strengths. The results of the study will deepen our understanding of the patterns of off-task multitasking.

In this chapter we study job design. Do organizations plan precisely how the job is to be done ex ante, or ask workers to determine the process as they go? We first model this decision and predict complementarity among these following job attributes: multitasking, discretion, skills, and interdependence of tasks. We argue that characteristics of the firm and industry (e.g., product and technology, organizational change) can explain observed patterns and trends in job design. We then use novel data on these job attributes to examine these issues. As predicted, job designs tend to be “coherent” across these attributes within the same job. Job designs also tend to follow similar patterns across jobs in the same firm, and especially in the same establishment: when one job is optimized ex ante, others are more likely to be also. There is evidence that firms segregate different types of job designs across different establishments. At the industry level, both computer usage and R&D spending are related to job design decisions.

This paper aims to explore university students' multitasking behavior in online classrooms and their influence on academic performance. Also, the study examined students' opinions.

A total of 302 university students fulfilled an online survey. Ten questions were focused on demographic information, five items evaluated online class behavior of students, 9 items evaluated self-efficacy and four items measured academic performance.

Multitasking behavior was found to negatively influence self-efficacy of −0.332, whereas self-efficacy showed a positive influence of 0.325 on academic performance. Cronbach's alpha and average variance extracted values were 0.780 and 0.527 (multitasking behavior), 0.875 and 0.503 (self-efficacy), 0.781 and 0.601 (academic performance). Outcomes of the bootstrapping test showed that the path coefficients are significant.

The research findings may help university managers understand undergraduates’ online and face-to-face behavior and strategies to improve the behavior to ensure the best academic outcomes. The novelty is based on using the partial least square structural equation modeling technique.

The purpose of this study was to design a multitask learning model so that biomedical entities can be extracted without having any ambiguity from biomedical texts.

In the proposed automated bio entity extraction (ABEE) model, a multitask learning model has been introduced with the combination of single-task learning models. Our model used Bidirectional Encoder Representations from Transformers to train the single-task learning model. Then combined model's outputs so that we can find the verity of entities from biomedical text.

The proposed ABEE model targeted unique gene/protein, chemical and disease entities from the biomedical text. The finding is more important in terms of biomedical research like drug finding and clinical trials. This research aids not only to reduce the effort of the researcher but also to reduce the cost of new drug discoveries and new treatments.

As such, there are no limitations with the model, but the research team plans to test the model with gigabyte of data and establish a knowledge graph so that researchers can easily estimate the entities of similar groups.

As far as the practical implication concerned, the ABEE model will be helpful in various natural language processing task as in information extraction (IE), it plays an important role in the biomedical named entity recognition and biomedical relation extraction and also in the information retrieval task like literature-based knowledge discovery.

During the COVID-19 pandemic, the demands for this type of our work increased because of the increase in the clinical trials at that time. If this type of research has been introduced previously, then it would have reduced the time and effort for new drug discoveries in this area.

In this work we proposed a novel multitask learning model that is capable to extract biomedical entities from the biomedical text without any ambiguity. The proposed model achieved state-of-the-art performance in terms of precision, recall and F1 score.

Previous research has demonstrated that students’ participation in class is an important factor in their learning; yet, significant barriers exist to all students’ participation during whole group discussions. These barriers include dynamics related to class size and available time as well as personal dimensions such as gender, age, and learning preferences. The emergence of new forms of social media can help break down those barriers by enabling collaborative construction of understanding. The present study examined whether the concurrent use of a shared learning document during class might provide a means of enhancing participation and learning. Because of the natural tendency of students’ attention to wander over time, the study examined whether providing a parallel learning and sharing space might serve to “focus distraction” in productive ways. During graduate and undergraduate courses in two different universities, the authors used a single Google document, open to every class member. Analysis of these collaborative documents and their use are described, along with student self-reports and videotapes. Data indicate that this approach created the type of participatory space we intended. Its use often broadened the numbers of students involved and increased the quality of spoken and virtual conversations as students negotiated meaning. When attention began to drift, the shared document created new opportunities for students to stay focused and explore course content through its use as an alternative back-channel. This approach also facilitated self-differentiation, as students determined which mix of available media best met their needs.

This chapter presents a thorough review on the mobile learning concept. It also explores how businesses are using mobile learning (m-learning) technologies for the training and development of their human resources. The research involved semi-structured interviews and an online survey. The research participants were expected to share their opinions about the costs and benefits of using m-learning applications (apps). The findings reported that the younger course participants were more likely to embrace the m-learning technologies than their older counterparts. They were using different mobile devices, including laptops, hybrids as well as smartphones and tablets to engage with m-learning applications at work, at home and when they are out and about. This contribution has identified the contextual factors like the usefulness and the ease of use of m-learning applications (apps), individual learning styles and their motivations, time, spatial issues, integration with other learning approaches as well as the cost and accessibility of the m-learning technology. In conclusion, this contribution identifies future research avenues relating to the use of m-learning technologies among businesses and training organizations.

The purpose of this paper is to present a novel tactile sensor and a visual-tactile recognition framework to reduce the uncertainty of the visual recognition of transparent objects.

A multitask learning model is used to recognize intuitive appearance attributes except texture in the visual mode. Tactile mode adopts a novel vision-based tactile sensor via the level-regional feature extraction network (LRFE-Net) recognition framework to acquire high-resolution texture information and temperature information. Finally, the attribute results of the two modes are integrated based on integration rules.

The recognition accuracy of attributes, such as style, handle, transparency and temperature, is near 100%, and the texture recognition accuracy is 98.75%. The experimental results demonstrate that the proposed framework with a vision-based tactile sensor can improve attribute recognition.

Transparency and visual differences make the texture of transparent glass hard to recognize. Vision-based tactile sensors can improve the texture recognition effect and acquire additional attributes. Integrating visual and tactile information is beneficial to acquiring complete attribute features.

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.

As a foundational issue of social mining, sentiment classification suffered from a lack of unlabeled data. To enhance accuracy of classification with few labeled data, many semi-supervised algorithms had been proposed. These algorithms improved the classification performance when the labeled data are insufficient. However, precision and efficiency are difficult to be ensured at the same time in many semi-supervised methods. This paper aims to present a novel method for using unlabeled data in a more accurate and more efficient way.

First, the authors designed a boosting-based method for unlabeled data selection. The improved boosting-based method can choose unlabeled data which have the same distribution with the labeled data. The authors then proposed a novel strategy which can combine weak classifiers into strong classifiers that are more rational. Finally, a semi-supervised sentiment classification algorithm is given.

Experimental results demonstrate that the novel algorithm can achieve really high accuracy with low time consumption. It is helpful for achieving high-performance social network-related applications.

The novel method needs a small labeled data set for semi-supervised learning. Maybe someday the authors can improve it to an unsupervised method.

The mentioned method can be used in text mining, image classification, audio processing and so on, and also in an unstructured data mining-related field. Overcome the problem of insufficient labeled data and achieve high precision using fewer computational time.

Sentiment mining has wide applications in public opinion management, public security, market analysis, social network and related fields. Sentiment classification is the basis of sentiment mining.

According to what the authors have been informed, it is the first time transfer learning be introduced to AdaBoost for semi-supervised learning. Moreover, the improved AdaBoost uses a totally new mechanism for weighting.

The rapid advancement of technology in online communication and fingertip access to the Internet has resulted in the expedited dissemination of fake news to engage a global audience at a low cost by news channels, freelance reporters and websites. Amid the coronavirus disease 2019 (COVID-19) pandemic, individuals are inflicted with these false and potentially harmful claims and stories, which may harm the vaccination process. Psychological studies reveal that the human ability to detect deception is only slightly better than chance; therefore, there is a growing need for serious consideration for developing automated strategies to combat fake news that traverses these platforms at an alarming rate. This paper systematically reviews the existing fake news detection technologies by exploring various machine learning and deep learning techniques pre- and post-pandemic, which has never been done before to the best of the authors’ knowledge.

The detailed literature review on fake news detection is divided into three major parts. The authors searched papers no later than 2017 on fake news detection approaches on deep learning and machine learning. The papers were initially searched through the Google scholar platform, and they have been scrutinized for quality. The authors kept “Scopus” and “Web of Science” as quality indexing parameters. All research gaps and available databases, data pre-processing, feature extraction techniques and evaluation methods for current fake news detection technologies have been explored, illustrating them using tables, charts and trees.

The paper is dissected into two approaches, namely machine learning and deep learning, to present a better understanding and a clear objective. Next, the authors present a viewpoint on which approach is better and future research trends, issues and challenges for researchers, given the relevance and urgency of a detailed and thorough analysis of existing models. This paper also delves into fake new detection during COVID-19, and it can be inferred that research and modeling are shifting toward the use of ensemble approaches.

The study also identifies several novel automated web-based approaches used by researchers to assess the validity of pandemic news that have proven to be successful, although currently reported accuracy has not yet reached consistent levels in the real world.