Effects of real‐time visual feedback on pre‐service teachers' singing

This pilot study focuses on the use real‐time visual feedback technology (VFT) in vocal training. The empirical research has two aims: to ascertain the effectiveness of the real‐time visual feedback software ‘Sing & See’ in the vocal training of pre‐service music teachers and the teachers' perspective on their experience with VFT. Forty participants from an undergraduate music teacher education programme were randomly assigned to three groups. The first two groups were assigned the teacher and student versions of Sing & See, respectively, and the third group served as a control group without access to the software. The experimental groups were given 12 weeks to use the software for vocal training on a self‐regulated basis. The technique of complex selections (TCS) was designed to assess the pitch accuracy and richness of vocal timbre. Pre‐ and post‐test singing tasks showed that the participants from the experimental groups significantly improved their vocal timbre. A questionnaire survey conducted after the training period found that most of the participants in the experimental s were positive about the effectiveness of VFT software in vocal training. As future leaders in the implementation of music education technology, pre‐service music teachers expressed a positive attitude towards the use of VFT as a technology‐enhanced learning (TEL) component in music education.     

Assessing the learning potential and students' perception of rich lecture captures

There is a large variety of lecture capture forms being used today when considering their content and ways in which that content is being reproduced. The objective of this study was therefore to investigate the students' perception and the learning potential of lecture captures enriched using additional learning contents compared to lecture captures containing only the video and audio recording of the lecturer and his slideshow. In an experiment in which students were learning using each of those two types of learning materials, a slight yet unsignificant difference was observed in favour of learning from rich lecture captures. Students' survey responses indicated they prefer rich lecture captures for learning and that their availability would not necessarily have a negative impact on students' live lecture attendance rates. These results suggest that even though students prefer rich lecture capture materials for learning, they did not prove to be more efficient in achieving the desired learning outcomes. Additional research would be needed to verify this conclusion on more complex subjects.     

Multitask bilateral learning for real‐time image enhancement

Nowadays, deep neural networks (DNNs) for image processing are becoming more complex; thus, reducing computational cost is increasingly important. This study highlights the construction of a DNN for real‐time image processing, training various image processing operators efficiently through multitask learning. For real‐time image processing, the proposed algorithm takes a joint upsampling approach through bilateral guided upsampling. For multitask learning, the overall network is based on an encoder‐decoder architecture, which consists of encoding, processing, and decoding components, in which the encoding and decoding components are shared by all the image processing operators. In the processing component, a semantic guidance map, which contains processing information for each image processing operator, is estimated using simple linear shifts of the shared deep features. Through these components, the proposed algorithm requires an increase of only 5% in the number of parameters to add another image processing operator and achieves faster and higher performance than that of deep‐learning‐based joint upsampling methods in local image processing as well as global image processing.     

Multi-task MIML learning for pre-course student performance prediction

In higher education, the initial studying period of each course plays a crucial role for students, and seriously influences the subsequent learning activities. However, given the large size of a course’s students at universities, it has become impossible for teachers to keep track of the performance of individual students. In this circumstance, an academic early warning system is desirable, which automatically detects students with difficulties in learning (i.e., at-risk students) prior to a course starting. However, previous studies are not well suited to this purpose for two reasons: 1) they have mainly concentrated on e-learning platforms, e.g., massive open online courses (MOOCs), and relied on the data about students’ online activities, which is hardly accessed in traditional teaching scenarios; and 2) they have only made performance prediction when a course is in progress or even close to the end. In this paper, for traditional classroomteaching scenarios, we investigate the task of pre-course student performance prediction, which refers to detecting at-risk students for each course before its commencement. To better represent a student sample and utilize the correlations among courses, we cast the problem as a multi-instance multi-label (MIML) problem. Besides, given the problem of data scarcity, we propose a novel multi-task learning method, i.e., MIML-Circle, to predict the performance of students from different specialties in a unified framework. Extensive experiments are conducted on five real-world datasets, and the results demonstrate the superiority of our approach over the state-of-the-art methods.     

Overcoming the novelty effect in online gamified learning systems: An empirical evaluation of student engagement and performance

Learners in the higher education context who engage with computer-based gamified learning systems often experience the novelty effect: a pattern of high activity during the gamified system's introduction followed by a drop in activity a few weeks later, once its novelty has worn off. We applied a two-tiered motivational, online gamified learning system over 2 years to a total number of 333 students. In a mixed methods research design, we used 3-year worth of longitudinal data (333 students for the treatment group and 175 in the control group) to assess students' engagement and performance in that period. Quantitative results established that students engaged and performed better in the gamified condition vis-à-vis the nongamified. Furthermore, students exhibited higher levels of engagement in the second year compared with the first year of the gamified condition. Our qualitative data suggest that students in the second year of the gamified delivery exhibited sustained engagement, overcoming the novelty effect. Thus, our main contribution is in suggesting ways of making the engagement meaningful and useful for the students, thus sustaining their engagement with computer-based gamified learning systems and overcoming the novelty effect.     

应用型本科离散数学的课堂教学

应用型本科离散数学教学具有教学内容繁多与教学时数相对较少的矛盾,教学过程具有一定挑战性。该文结合教学实际探讨离散数学的课堂教学,通过具体实例说明如何运用教学理论和方法,启发学生自觉概括,以及运用多媒体技术,从而提高教学质量和效率,并阐述在理论教学中如何联系应用,融入算法教育,进而培养学生计算机学科的基本思维方式。     

Enhancing OSGi with real‐time Java support

OSGi was designed with embedded systems in mind, its current support is insufficient for coping with one main characteristic of many embedded systems: real‐time performance. This article analyzes different key issues in providing OSGi with real‐time Java performance covering motivational issues, and different integration ways and challenges stemming from the integration. It also contributes a general framework for introducing real‐time performance in OSGi, which is called the real‐time for OSGi framework. The framework uses real‐time Java virtual machines and the real‐time specification for Java. The adoption of this framework allows cyber‐physical systems to experience real‐time Java performance in their applications. The framework introduces several integration levels for OSGi and real‐time specification for Java, and specific real‐time OSGi services. An empirical implementation was carried out using standard software, which was extended with the new defined services. Copyright © 2012 John Wiley & Sons, Ltd.     

Development and performance analysis of real‐world applications for distributed and parallel architectures

Several large real‐world applications have been developed for distributed and parallel architectures. We examine two different program development approaches. First, the usage of a high‐level programming paradigm which reduces the time to create a parallel program dramatically but sometimes at the cost of a reduced performance; a source‐to‐source compiler, has been employed to automatically compile programs—written in a high‐level programming paradigm—into message passing codes. Second, a manual program development by using a low‐level programming paradigm—such as message passing—enables the programmer to fully exploit a given architecture at the cost of a time‐consuming and error‐prone effort. Performance tools play a central role in supporting the performance‐oriented development of applications for distributed and parallel architectures. SCALA—a portable instrumentation, measurement, and post‐execution performance analysis system for distributed and parallel programs—has been used to analyze and to guide the application development, by selectively instrumenting and measuring the code versions, by comparing performance information of several program executions, by computing a variety of important performance metrics, by detecting performance bottlenecks, and by relating performance information back to the input program. We show several experiments of SCALA when applied to real‐world applications. These experiments are conducted for a NEC Cenju‐4 distributed‐memory machine and a cluster of heterogeneous workstations and networks. Copyright © 2001 John Wiley & Sons, Ltd.     

Effects of anxiety levels on learning performance and gaming performance in digital game‐based learning

Anxiety plays an influential role in foreign language learning. However, a lack of attention was paid to examining the effects of anxiety levels on learning performance and gaming performance in digital game‐based learning. To this end, this study developed a game‐based English learning system and investigated how different levels of anxiety affected learners' learning performance and gaming performance. A quasi‐experiment was conducted in an elementary school. The results showed that high‐anxiety learners performed worse than low‐anxiety learners in speaking, word/sentence match, and overall learning performance. However, they performed similarly in listening performance. Moreover, the results showed that high‐ and low‐anxiety learners demonstrated a similar level of gaming performance. A subsequent analysis showed that significant correlations existed between learning performance and gaming performance for learners with high anxiety whereas such positive correlations were rarely found for learners with low anxiety, indicating that high‐anxiety learners' learning performance could be fostered by their gaming performance. The findings suggested that digital game‐based learning was particularly beneficial to high‐anxiety learners, whose gaming performance was a facilitative factor of their learning performance.     

Verification of real‐time systems design

The main objective of this paper is to present an approach to accomplish verification in the early design phases of a system, which allows us to make the system verification easier, specifically for those systems with timing restrictions. For this purpose we use RT‐UML sequence diagrams in the design phase and we translate these diagrams into timed automata for performing the verification by using model checking techniques. Specifically, we use the Object Management Group's UML Profile for Schedulability, Performance, and Time and from the specifications written using this profile we obtain the corresponding timed automata. The ‘RT‐UML Profile’ is used in conjunction with a very well‐known tool to perform validation and verification of the timing needs, namely, the UPPAAL tool, which is used to simulate and analyze the behaviour of real‐time dynamic systems described by timed automata. Copyright © 2009 John Wiley & Sons, Ltd.     

Effects of an intelligent web‐based English instruction system on students' academic performance

This research conducted quasi‐experiments in four middle schools to evaluate the long‐term effects of an intelligent web‐based English instruction system, Computer Simulation in Educational Communication (CSIEC), on students' academic attainment. The analysis of regular examination scores and vocabulary test validates the positive impact of CSIEC, and in most cases, the positive impact is statistically significant. The reliability is ensured by the spectrum of the students from Grade 1 to Grade 3 in three junior high schools and from Grade 1 to Grade 2 in one senior high school, and of the teachers with or without blended learning experience, as well as by the various school locations from rural to urban areas in four provinces of China. The learning content‐oriented design and the instant feedback feature of the web‐based system, as well as its regular integration into the English class, contributed to its reliable positive effect on students' learning performance in ordinary examinations. This is the research's implication for instructional design. The conclusion of this paper could serve as a reference to the technical feasibility and pedagogical benefit of regular usage of appropriate Computer Assisted Learning system in the mainstream subject of middle schools. The multiple quasi‐experiments in divergent school settings to ensure the results' reliability distinguish this research from previous ones that just implemented one single experiment in one school.     

Using Fourier coefficients in time series analysis for student performance prediction in blended learning environments

In this work, it is shown that student access time series generated from Moodle log files contain information sufficient for successful prediction of student final results in blended learning courses. It is also shown that if time series is transformed into frequency domain, using discrete Fourier transforms (DFT), the information contained in it will be preserved. Hence, resulting periodogram and its DFT coefficients can be used for generating student performance models with the algorithms commonly used for that purposes. The amount of data extracted from log files, especially for lengthy courses, can be huge. Nevertheless, by using DFT, drastic compression of data is possible. It is experimentally shown, by means of several commonly used modelling algorithms, that if in average all but 5–10% of most intensive and most frequently used DFT coefficients are removed from datasets, the modelling with the remained data will result with the increase of the model accuracy. Resulting accuracy of the calculated models is in accordance with results for student performance models calculated for different dataset types reported in literature. The advantage of this approach is its applicability because the data are automatically collected in Moodle logs.     

Failure‐resilient real‐time processing of health streams

The ability to analyze streaming data in real time is vital in systems that process data from health sensors. These systems need to build and maintain state, as well as preserve this state during system failures. In this work, we introduce a fault‐tolerance scheme designed for the Granules stream processing system. We work with two distinct health stream datasets: thorax extension and electroencephalogram (EEG) signal analysis. We have developed a monitoring program to track trends in the thorax extension dataset and a classification system for the EEG dataset, which allows us to determine user intent from EEG signals. Using these two motivating applications, we have explored several approaches to fault tolerance through replication, developing a hybrid approach that is particularly suited to health streams. Copyright © 2014 John Wiley & Sons, Ltd.     

Utilizing commodity hardware and software to distribute a real‐world application: maximizing reuse while improving performance

Commodity computing hardware continues to increase performance while decreasing price. This combination is driving a renewed interest in parallel and distributed computing. In this study, we examine the performance of an existing application in a ten‐node computing cluster using commodity off‐the‐shelf components. The application is a statistical analysis software package that processes categorical data used by state public safety programs. The study examines various network topologies and focuses on minimizing the software modifications required to distribute the application. We conclude that parallel computing using commodity components is an effective mechanism to increase the performance of real‐world applications especially when the underlying application architectures have the flexibility to support efficient reuse of the existing code. Copyright © 2005 John Wiley & Sons, Ltd.     

Design and analysis of target‐sensitive real‐time systems

A significant number of real‐time control applications include computational activities where the results have to be delivered at precise instants, rather than within a deadline. The performance of such systems significantly degrades if outputs are generated before or after the desired target time. This work presents a general methodology that can be used to design and analyze target‐sensitive applications in which the timing parameters of the computational activities are tightly coupled with the physical characteristics of the system to be controlled. For the sake of clarity, the proposed methodology is illustrated through a sample case study used to show how to derive and verify real‐time constraints from the mission requirements. Software implementation issues necessary to map the computational activities into tasks running on a real‐time kernel are also discussed to identify the kernel mechanisms necessary to enforce timing constraints and analyze the feasibility of the application. A set of experiments are finally presented with the purpose of validating the proposed methodology. Copyright © 2015 John Wiley & Sons, Ltd.     

Learning through inquiry: student difficulties with online course-based Material

Abstract This study investigates the case-based learning experience of 133 undergraduate veterinarian science students. Using qualitative methodologies from relational Student Learning Research, variation in the quality of the learning experience was identified, ranging from coherent, deep, quality experiences of the cases, to experiences that separated significant aspects, such as the online case histories, laboratory test results, and annotated images emphasizing symptoms, from the meaning of the experience. A key outcome of this study was that a significant percentage of the students surveyed adopted a poor approach to learning with online resources in a blended experience even when their overall learning experience was related to cohesive conceptions of veterinary science, and that the difference was even more marked for less successful students. The outcomes from the study suggest that many students are unsure of how to approach the use of online resources in ways that are likely to maximise benefits for learning in blended experiences, and that the benefits from case-based learning such as authenticity and active learning can be threatened if issues closely associated with qualitative variation arising from incoherence in the experience are not addressed.     

On‐line real‐time physics‐based predictive motion control with balance recovery

In this paper, we present an on‐line real‐time physics‐based approach to motion control with contact repositioning based on a low‐dimensional dynamics model using example motion data. Our approach first generates a reference motion in run time according to an on‐line user request by transforming an example motion extracted from a motion library. Guided by the reference motion, it repeatedly generates an optimal control policy for a small time window one at a time for a sequence of partially overlapping windows, each covering a couple of footsteps of the reference motion, which supports an on‐line performance. On top of this, our system dynamics and problem formulation allow to derive closed‐form derivative functions by exploiting the low‐dimensional dynamics model together with example motion data. These derivative functions and their sparse structures facilitate a real‐time performance. Our approach also allows contact foot repositioning so as to robustly respond to an external perturbation or an environmental change as well as to perform locomotion tasks such as stepping on stones effectively.     

A simplification of a real‐time verification problem

We revisit the problem of real‐time verification with dense‐time dynamics using timeout and calendar‐based models and simplify this to a finite state verification problem. We introduce a specification formalism for these models and capture their behaviour in terms of semantics of timed transition systems. We discuss a technique, which reduces the problem of verification of qualitative temporal properties on infinite state space of a large fragment of these timeout and calender‐based transition systems into that on clock‐less finite state models through a two‐step process comprising of digitization and finitary reduction. This technique enables us to verify safety invariants for real‐time systems using finite state model checking avoiding the complexity of infinite state (bounded) model checking and scale up models without applying techniques from induction‐based proof methodology. In the same manner, we verify timeliness properties. Moreover, we can verify liveness for real‐time systems, which are not possible by using induction with infinite state model checkers. Copyright © 2016 John Wiley & Sons, Ltd.