“电工学”国家级一流课程建设与实践

根据新时代新工科人才需求特点和电工学课程特点,分析非电专业学生的电类实践需求,提出构建电工学课程为核心的电工电子实践创新能力培养新体系。通过优化教学内容、开展教学模式和考核模式改革、构建教学资源体系和进行教师队伍建设,使我校电工学课程教学质量提升、学生工程实践和创新能力提高、学生满意度增加、一流课程建设成效显著。     

Tactile colour pictogram to improve artwork appreciation of people with visual impairments

A recent development in tactile technology enables an improvement in the appreciation of the visual arts for people with visual impairment (PVI). The tactile sense, in conjunction with, or a possibly as an alternative to, the auditory sense, would allow PVIs to approach artwork in a more self‐driven and engaging way that would be difficult to achieve with just an auditory stimulus. Tactile colour pictograms (TCPs), which are raised geometric patterns, are ideographic characters that are designed to enable PVIs to identify colours and interpret information by touch. In this article, three TCPs are introduced to code colours in the Munsell colour system. Each colour pattern consists of a basic cell size of 10 mm × 10 mm to represent the patterns consistently in terms of regular shape. Each TCP consists of basic geometric patterns that are combined to create primary, secondary, and tertiary colour pictograms of shapes indicating colour hue, intensity and lightness. Each TCP represents 29 colours including six hues; they were then further expanded to represent 53 colours. Two of them did not increase the cell size, the other increased the cell size 1.5 times for some colours, such as yellow‐orange, yellow, blue, and blue‐purple. Our proposed TCPs use a slightly larger cell size compared to most tactile patterns currently used to indicate colour, but code for more colours. With user experience and identification tests, conducted with 23 visually impaired adults, the effectiveness of the TCPs suggests that they were helpful for the participants.     

Quantifying co-creative writing experiences

Collaboration with artificial intelligence (AI) is a growing trend even in the field of creativity. This paper examines which quantitative metrics can be used to comparatively analyse human-computer co-creativity with children. To study this question, 24 schoolchildren of age 10–11 wrote a poem with three co-creative poetry writing processes: a human-computer, a human-human, and a human-human-computer process. The computational participant in the processes was an AI-based application called the Poetry Machine. The children were asked to evaluate their user experience with a 5-point Likert-type questionnaire after each writing process and a comparative questionnaire after finishing all processes. The metrics used in the evaluation were immediate fun, long-term enjoyment, creativity, self-expression, outcome satisfaction, ease of starting and finishing writing, quality of ideas and support from others, and ownership.

Significant differences were found in fun, long-term enjoyment, quality of ideas, support, and ownership. The high number of statistically relevant results was enabled by exposing all participants to all writing processes, and the comparative questionnaire. The human-human-computer process was evaluated the best in long-term enjoyment and the human-computer process the weakest in support and idea quality. Creativity and ease of finishing writing turned out to be outlining metrics for the co-creative processes.     

Relationships among boredom proneness,sensation seeking and smartphone addiction among Chinese college students: Mediating roles of pastime,flow experience and self-regulation

Smartphones are indispensable for many young people, which in turn increases the risk of smartphone addiction. Previous studies have discovered significant and positive influences of boredom proneness, sensation seeking and smartphone addiction. This study further explored these influences, their underlying mechanisms, and the mediating effects of pastime, flow experience and self-regulation on smartphone addiction. A total of 442 Chinese college students completed a survey focusing on boredom proneness, sensation seeking, pastime, flow experience, self-regulation and smartphone addiction. The results indicated that boredom proneness positively affects smartphone addiction. Contrary to expectations, the results showed that there is no significant relationship between sensation seeking and smartphone addiction. Moreover, pastime and self-regulation have partial mediation effects on the relationship between boredom proneness and smartphone addiction, while the effect of sensation seeking on smartphone addiction is completely transmitted with the help of flow experience and self-regulation. The findings and their implications are discussed.     

Reality-based interaction affecting mental workload in virtual reality mental arithmetic training

ABSTRACT

The concept of digital game-based learning (DGBL) evolves rapidly together with technological enhancements of virtual reality (VR) and smart phones. However, the mental workload (MWL) that VR-training applications demand and motivational qualities originating from user experience (UX) should be identified in order to create effective and enjoyable training/learning challenges that fit with individual users’ capabilities. This study examined the effects of reality-based interaction (RBI) and VR on measures of student motivation and MWL, in a mental arithmetic game for secondary school pupils. In a randomised controlled trial with sixty school children, a mental arithmetic game was tested with three different interaction and two different presentation methods – VR RBI, VR head-mounted-display tapping and tablet flick-gesture. Results found a significant effect of RBI on MWL but no differences in enjoyment of training were found between VR-experience and tablet training-experience. In fact, adding the gaming-context to the mental arithmetic task created an enjoyable, motivating experience regardless of presentation or interaction-style.     

Epos – an instrument for the assessment of the ethical position in software development

Digital technology becomes more powerful, intelligent, pervasive and ubiquitous. Ethical aspects of this development have not yet drawn the appropriate attention of researchers and engineers. This paper presents an instrument that aims at measuring the individual ethical position with regard to the design and development of computer software. The development of the Epos tool was based on two data collections. The data of the first survey (n₁ = 147 participants) were used to select items and to determine the factorial structure of the questionnaire. Results show that the Epos instrument reliably assesses peoples’ ethical opinion with respect to five central components: (1) regulation, (2) data privacy, (3) domain specific knowledge, (4) societal responsibility and (5) company responsibility. In the second survey, we determined the stability of the instruments factor structure by assessing a sample of n₂?=?196 participants. A confirmatory factor analysis (CFA) supported the initial factor structure. Next steps and further implications are discussed regarding the final version of the questionnaire.     

Usability of a cloud-based collaborative learning framework to improve learners’ experience

Computer-Supported Collaborative Learning (CSCL) is concerned with how Information and Communication Technology (ICT) might facilitate learning in groups which can be co-located or distributed over a network of computers such as Internet. CSCL supports effective learning by means of communication of ideas and information among learners, collaborative access of essential documents, and feedback from instructors and peers on learning activities. As the cloud technologies are increasingly becoming popular and collaborative learning is evolving, new directions for development of collaborative learning tools deployed on cloud are proposed. Development of such learning tools requires access to substantial data stored in the cloud. Ensuring efficient access to such data is hindered by the high latencies of wide-area networks underlying the cloud infrastructures. To improve learners’ experience by accelerating data access, important files can be replicated so a group of learners can access data from nearby locations. Since a cloud environment is highly dynamic, resource availability, network latency, and learner requests may change. In this paper, we present the advantages of collaborative learning and focus on the importance of data replication in the design of such a dynamic cloud-based system that a collaborative learning portal uses. To this end, we introduce a highly distributed replication technique that determines optimal data locations to improve access performance by minimizing replication overhead (access and update). The problem is formulated using dynamic programming. Experimental results demonstrate the usefulness of the proposed collaborative learning system used by institutions in geographically distributed locations.     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.