Human–robot collaboration and machine learning: A systematic review of recent research

Technological progress increasingly envisions the use of robots interacting with people in everyday life. Human–robot collaboration (HRC) is the approach that explores the interaction between a human and a robot, during the completion of a common objective, at the cognitive and physical level. In HRC works, a cognitive model is typically built, which collects inputs from the environment and from the user, elaborates and translates these into information that can be used by the robot itself. Machine learning is a recent approach to build the cognitive model and behavioural block, with high potential in HRC. Consequently, this paper proposes a thorough literature review of the use of machine learning techniques in the context of human–robot collaboration. 45 key papers were selected and analysed, and a clustering of works based on the type of collaborative tasks, evaluation metrics and cognitive variables modelled is proposed. Then, a deep analysis on different families of machine learning algorithms and their properties, along with the sensing modalities used, is carried out. Among the observations, it is outlined the importance of the machine learning algorithms to incorporate time dependencies. The salient features of these works are then cross-analysed to show trends in HRC and give guidelines for future works, comparing them with other aspects of HRC not appeared in the review.     

Managing workload in human–robot interaction: A review of empirical studies

Working with artificial agents is a challenging endeavor, often imposing high levels of workload on human operators who work within these socio-technical systems. We seek to understand these workload demands through examining the literature in major content areas of human–robot interaction. As research on HRI continues to explore a host of issues with operator workload, there is a need to synthesize the extant literature to determine its current state and to guide future research. Within HRI socio-technical systems, we reviewed the empirical literature on operator information processing and action execution. Using multiple resource theory (MRT; Wickens, 2002) as a guiding framework, we organized this review by the operator perceptual and responding demands which are routinely manipulated in HRI studies. We also reviewed the utility of different interventions for reducing the strain on the perceptual system (e.g., multimodal displays) and responses (e.g., automation). Our synthesis of the literature demonstrates that much is known about how to decrease operator workload, but there are specific gaps in knowledge due to study operations and methodology. This work furthers our understanding of workload in complex environments such as those found when working with robots. Principles and propositions are provided for those interested in decreasing operator workload in applied settings and also for future research.     

Augmented reality versus conventional interface: Is there any difference in effectiveness?

Multimedia Tools and Applications - The moment immediately before the “add to cart” decision is very critical in online shopping. Drawing on theories of transfer, spreading activation...     

Augmented reality in science laboratories: The effects of augmented reality on university students’ laboratory skills and attitudes toward science laboratories

This study investigated the effects of the use of augmented reality (AR) technologies in science laboratories on university students' laboratory skills and attitudes towards laboratories. A quasi-experimental pre-test/post-test control group design was employed. The participants were 76 first-year university students, aged 18–20 years old. They were assigned to either an experimental or a control group. Qualitative and quantitative data collection tools were used. The experimental results obtained following the 5-week application revealed that the AR technology significantly enhanced the development of the university students' laboratory skills. AR technology both improved the students’ laboratory skills and helped them to build positive attitudes towards physics laboratories. The statements of the students and the instructor regarding other effects of AR technology on science laboratories, both negative and positive, are also discussed.     

The use of machine learning and deep learning algorithms in functional magnetic resonance imaging—A systematic review

Functional Magnetic Resonance Imaging (fMRI) is presently one of the most popular techniques for analysing the dynamic states in brain images using various kinds of algorithms. From the last decade, there is an exponential rise in the use of the machine and deep learning algorithms of artificial intelligence for analysing fMRI data. However, it is a big challenge for every researcher to choose a suitable machine or deep learning algorithm for analysing fMRI data due to the availability of a large number of algorithms in the literature. It takes much time for each researcher to know about the various approaches and algorithms which are in use for fMRI data. This paper provides a review in a systematic manner for the present literature of fMRI data that makes use of the machine and deep learning algorithms. The major goals of this review paper are to (a) identify machine learning and deep learning research trends for the implementation of fMRI; (b) identify usage of Machine Learning Algorithms and deep learning in fMRI, and (c) help new researchers based on fMRI to put their new findings appropriately in existing domain of fMRI research. The results of this systematic review identified various fMRI studies and classified them based on fMRI types, mental diseases, use of machine learning and deep learning algorithms. The authors have provided the studies with the best performance of machine learning and deep learning algorithms used in fMRI. The authors believe that this systematic review will help incoming researchers on fMRI in their future works.     

Simulation-based learning: The learning–forgetting–relearning process and impact of learning history

The results of empirical experiments evaluating the effectiveness and efficiency of the learning–forgetting–relearning process in a dynamic project management simulation environment are reported. Sixty-six graduate engineering students performed repetitive simulation-runs with a break period of several weeks between the runs. The students used a teaching tool called the project management trainer (PMT) that simulates a generic dynamic, stochastic project management environment. In this research, we focused on the effect of history recording mechanism on the learning forgetting process. Manual or automatic history recording mechanisms were used by the experimental group, while the control group did not use any history recording mechanism. The findings indicate that for the initial learning phase, the manual mechanism is better than the automatic mechanism. However, for the relearning phase, the break period length influenced the performance after the break. When the break period is short, the manual history keeping mechanism is better, but for a long period break, there is no significant difference. A comparison between the experimental group and the control group revealed that using any history recording mechanism reduced forgetting. Based on the findings, some practical implications of using simulators to improve the learning–forgetting process are discussed.     

The role of “craft language” in learning “Waza”

The role of craft language in the process of teaching (learning) Waza (skill) will be discussed from the perspective of human intelligence.It may be said that the ultimate goal of learning Waza in any Japanese traditional performance is not the perfect reproduction of the teaching (learning) process of Waza. In fact, a special metaphorical language (craft language) is used, which has the effect of encouraging the learner to activate his creative imagination. It is through this activity that the he learns his own habitus (Kata).It is suggested that, in considering the difference of function between natural human intelligence and artificial intelligence, attention should be paid to the imaginative activity of the learner as being an essential factor for mastering Kata.This article is a modified English version of Chapter 5 of my bookWaza kara shiru (Learning from Skill), Tokyo University Press, 1987, pp. 93–105.     

Does peer assessment in on-line learning environments work? A systematic review of the literature

There is an increase use of Peer Assessment (PA) approaches in On-line Learning Environments to support students and teachers on educational process. However, the community still lacks a comprehensive understanding of how Peer Assessment behaves within these educational environments, since there are no studies that make an analysis of the researches which has been conducted in this context, analysing the benefits and disadvantages of the use of Peer Assessment. Thus, the main goal of this work is to investigate and better understand how PA contributes in student learning as well as can bring benefits to the teachers involved. In order to meet our goal, we conducted a systematic literature review (SLR). In summary, the main findings are: (1) there are empirical evidences of the benefits of using PA in both industry and academy, and in several educational levels; (2) PA is most addressed to the correction of written activities on on-line courses; (3) About 60% of the articles showed PA improvement in student performance; (4) About 1/3 of the studies showed PA brings some benefits to teachers; (5) In some studies the use of the PA had difficulties, especially issues related to student motivation, which impairs the peer review process.     

A review of Nyström methods for large-scale machine learning

Generating a low-rank matrix approximation is very important in large-scale machine learning applications. The standard Nyström method is one of the state-of-the-art techniques to generate such an approximation. It has got rapid developments since being applied to Gaussian process regression. Several enhanced Nyström methods such as ensemble Nyström, modified Nyström and SS-Nyström have been proposed. In addition, many sampling methods have been developed. In this paper, we review the Nyström methods for large-scale machine learning. First, we introduce various Nyström methods. Second, we review different sampling methods for the Nyström methods and summarize them from the perspectives of both theoretical analysis and practical performance. Then, we list several typical machine learning applications that utilize the Nyström methods. Finally, we make our conclusions after discussing some open machine learning problems related to Nyström methods.     

Synthetic speech in foreign language learning: an evaluation by learners

Can synthetic speech be utilized in foreign language learning as natural speech? In this paper, we evaluated synthetic speech from the viewpoint of learners in order to find out an answer. The results pointed out that learners do not recognize remarkable differences between synthetic voices and natural voices for the words with short vowels and long vowels when they try to understand the meanings of the sounds. The data explicates that synthetic voice utterances of sentences are easier to understand and more acceptable by learners compared to synthetic voice utterances of words. In addition, the ratings on both synthetic voices and natural voices strongly depend upon the learners’ listening comprehension abilities. We conclude that some synthetic speech with specific pronunciations of vowels may be suitable for listening materials and suggest that evaluating TTS systems by comparing synthetic speech with natural speech and building a lexical database of synthetic speech that closely approximates natural speech will be helpful for teachers to readily use many existing CALL tools.     

Player–video game interaction: A systematic review of current concepts

Video game design requires a user-centered approach to ensure that the experience enjoyed by players is as good as possible. However, the nature of player–video game interactions has not as yet been clearly defined in the scientific literature. The purpose of the present study was to provide a systematic review of empirical evidences of the current concepts of player–video game interactions in entertainment situations. A total of 72 articles published in scientific journals that deal with human–computer interaction met the criteria for inclusion in the present review. Major findings of these articles were presented in a narrative synthesis. Results showed that player–video game interactions could be defined with multiple concepts that are closely linked and intertwined. These concepts concern player aspects of player–video game interactions, namely engagement and enjoyment, and video game aspects, namely information input/output techniques, game contents and multiplayer games. Global approaches, such as playability, also exist to qualify player–video game interactions. Limitations of these findings are discussed to help researchers to plan future advances of the field and provide supplementary effort to better know the role of less-studied aspects. Practical implications are also discussed to help game designers to optimize the design of player–video game interactions.     

How virtual reality affects perceived learning effectiveness: a task–technology fit perspective

The application of virtual reality (VR) in improving users’ learning outcomes, especially in perceived learning effectiveness, is a new area. VR provides visualisation and interaction within a virtual world that closely resembles a real world, bringing an immersive study experience. It also has two special features: representational fidelity and immediacy of control. However, only when the technology fits the tasks that users are performing will it be adopted. In addition, technology itself cannot improve learning outcomes; certain learning behaviours, such as reflective thinking, should be prompted first so that learning outcomes can be improved. The research hypotheses derived from this model have empirically been validated using the responses to a survey among 180 users. These responses have been examined through SmartPLS 2.0. Surprisingly, task–technology fit does not moderate the relationship between VR and technology quality and the relationship between VR and technology accessibility. From this study, we can conclude that VR will influence reflective thinking and further indirectly improve perceived learning effectiveness.     

Particle swarm optimisation in designing parameters of manufacturing processes: A review (2008–2018)

The evolutionary optimisation algorithms appeared as an effective alternative to conventional statistical methods that have certain limitations in optimising complex manufacturing processes. Considering works published in the last decade, this paper presents an analysis of the particle swarm optimisation (PSO) implementation in designing parameters of heterogeneous manufacturing processes, both conventional and emerging, new processes. The literature review and analysis was structured according to the complexity of the optimisation problem (single response and multiresponse problems), and the development of an objective function for PSO. The tuning of the PSO algorithm-specific parameters was analysed in detail. The PSO algorithm performance was benchmarked with the results of other methods, including evolutionary algorithms, in designing process parameters. The concerns in applying PSO for multiresponse manufacturing problems were highlighted, and recommendations for future research were drawn. Such a comprehensive review on the PSO application in optimising manufacturing processes, including the detailed discussion on the algorithm characteristics and benchmark with other optimisation procedures, has not been pursued so far. Therefore, this review analysis provides hands on information for researchers and engineers at one place, and it is believed that the findings could serve as a basis for the future research and implementation directions.     

Detection of hydrocephalus using machine learning in medical science – a review

Multimedia Tools and Applications - Machine learning (ML) is the study of computer algorithms that expand spontaneously by knowledge. ML algorithms construct an analytical model centred on sample...     

Learning game innovations in immersive game environments: a factor analytic study of students’ learning inventory in virtual reality

Virtual Reality - The research is designed to trigger students’ game innovations and facilitate their creative learning process by providing them with a “stimulated” immersive...     

Expert system applications in business: A review and analysis of the literature (1977–1993)

A survey of expert system (ES) business application papers published between 1977 and 1993 indicates that an increasing amount of ES research is being conducted for a diverse range of business activities. The classification of literature by (1) year of publication, (2) application area, (3) generic problem area addressed, (4) problem domain, (5) level of management, (6) level of task interdependence, (7) means of development, (8) corporate/academic interaction in development, and (9) technology integration provides some insights in the trend. Implications to ES developers are discussed.     

A framework for process–solution analysis in collaborative learning environments

One of the most challenging aspects of computer-supported collaborative learning (CSCL) research is automation of collaboration and interaction analysis in order to understand and improve the learning processes. It is particularly necessary to look in more depth at the joint analysis of the collaborative process and its resulting product. In this article, we present a framework for comprehensive analysis in CSCL synchronous environments supporting a problem-solving approach to learning. This framework is based on an observation–abstraction–intervention analysis life-cycle and consists of a suite of analysis indicators, procedures for calculating indicators and a model of intervention based on indicators. Analysis indicators are used to represent the collaboration and knowledge building process at different levels of abstraction, and to characterize the solution built using models of the application domain, the problems to solve and their solutions. The analysis procedures combine analysis of actions and dialogue with analysis of the solution. In this way, the process and the solution are studied independently as well as together, enabling the detection of correlations between them. In order to exemplify and test the framework, the methodological process underlying the framework was followed to guide the implementation of the analysis subsystems of two existing CSCL environments. In addition, a number of studies have been conducted to evaluate the framework's approach, demonstrating that certain modes of collaborating and working imply particular types of solutions and vice versa.     

A task–technology fit view of learning management system impact

Learning management systems (LMSs) are very widely used in higher education. However, much of the research on LMSs has had a technology focus or has been limited to studies of adoption. In order to take advantage of the potential associated with LMSs, research that addresses the role of LMSs in learning success is needed. Task–technology fit is one factor that has been shown to influence both the use of information systems and their performance impacts. The study described in this paper used the technology-to-performance chain as a framework to address the question of how task–technology fit influences the performance impacts of LMSs. The results provide strong support for the importance of task–technology fit, which influenced perceived impact on learning both directly and indirectly via level of utilization. Whilst task–technology fit had a strong influence on perceived impact of the LMS on learning it only had a weak impact on outcomes in terms of student grades. Contrary to expectations, facilitating conditions and common social norms did not play a role in the performance impacts of LMSs. However, instructor norms had a significant effect on perceived impact on learning via LMS utilization.