A concept map-embedded educational computer game for improving students' learning performance in natural science courses

Many recent studies have reported the benefits of educational computer games in promoting students' learning motivations. On the other hand, however, the effect of digital game-based learning in improving students' learning performance has been questioned. Several previous studies have reported that without properly integrating learning strategies into gaming scenarios, the effectiveness of educational computer games could be limited, or may be even worse than that of the conventional technology-enhanced learning approach. In this study, a concept map-embedded gaming approach is proposed for developing educational computer games by integrating concept mapping as part of the gaming scenarios to help students organize what they have learned during the game-based learning process. Moreover, a role-playing game has been developed for an elementary school natural science course based on the proposed approach. From the experimental results, it is found that the concept map-embedded gaming approach can significantly improve the students' learning achievement and decrease their cognitive load. Moreover, the students who learned with the proposed approach revealed a significantly higher degree of perceived usefulness than those who learned with the conventional game-based learning approach.     

A contextual game-based learning approach to improving students' inquiry-based learning performance in social studies courses

Inquiry-based learning, an effective instructional strategy, can be in the form of a problem or task for triggering student engagement. However, how to situate students in meaningful inquiry activities remains to be settled, especially for social studies courses. In this study, a contextual educational computer game is developed to improve students' learning performance based on an inquiry-based learning strategy. An experiment has been conducted on an elementary school social studies course to evaluate the effects of the proposed approach on the inquiry-based learning performances of students with different learning styles. The experimental results indicate that the proposed approach effectively enhanced the students' learning effects in terms of their learning achievement, learning motivation, satisfaction degree and flow state. Furthermore, it is also found that the proposed approach benefited the “active” learning style students more than the “reflective” style students in terms of learning achievement. This suggests the need to provide additional supports to students with particular learning styles in the future.     

An interactive concept map approach to supporting mobile learning activities for natural science courses

Mobile and wireless communication technologies not only enable anytime and anywhere learning, but also provide the opportunity to develop learning environments that combine real-world and digital-world resources. Nevertheless, researchers have indicated that, without effective tools for helping students organize their observations in the field, the mobile learning performance could be disappointing. To cope with this problem, this study proposes an interactive concept map-oriented approach for supporting mobile learning activities. An experiment has been conducted on an elementary school natural science course to evaluate the effectiveness of the proposed method. The experimental results show that the proposed approach not only enhances learning attitudes, but also improves the learning achievements of the students.     

嵌入式习字教室系统

针对嵌入式平台局域网协议、师生可叠写作业原图交互等问题,根据局域网内部嵌入式设备实时通信链路状态相对较好,硬件资源要求不高等特征,设计嵌入式习字教室系统。嵌入式局域网协议（ELP）在以太网帧上扩展8字节字段的包头;使用本地和远程的端口号和MAC地址来标识一个连接;使用窗口和速率相结合的流控机制,提供面向连接的可靠传输服务;参考uip优化状态机制、重传队列机制、确认机制和拥塞控制算法。实验结果表明,该教室系统内文件传输速度方面相比嵌入式TCP/IP有较大提高,有效实现了实时性原图作业交互。     

Developing multi-dimensional evaluation criteria for English learning websites with university students and professors

Many English learning websites have been developed worldwide, but little research has been conducted concerning the development of comprehensive evaluation criteria. The main purpose of this study is thus to construct a multi-dimensional set of criteria to help learners and teachers evaluate the quality of English learning websites. These evaluation guidelines are based on web usability, learning materials, functionality of assisting language learning, technology integration, and learner preferences. In order to achieve this goal, the researchers used a rigorous four-phase procedure and utilized both qualitative and quantitative research methods with university students and professors in Design-based Research to construct and refine the evaluation criteria in 2008–2009. Fifty-eight evaluation criteria were refined and finalized. In addition, to cope with the difficulties in applying the derived criteria and explaining what the results indicate, this study presents a practical application by evaluating an English learning website.     

A tight bound on concept learning

A tight bound on the generalization performance of concept learning is shown by a novel approach. Unlike existing theories, the new approach uses no assumption on large sample size as in Bayesian approach and does not consider the uniform learnability as in the VC dimension analysis. We analyze the generalization performance of some particular learning algorithm that is not necessarily well behaved, in the hope that once learning curves or sample complexity of this algorithm is obtained, it is applicable to real learning situations. The result is expressed in a dimension called Boolean interpolation dimension, and is tight in the sense that it meets the lower bound requirement of Baum and Haussler (1989). The Boolean interpolation dimension is not greater than the number of modifiable system parameters, and definable for almost all the real-world networks such as backpropagation networks and linear threshold multilayer networks. It is shown that the generalization error follows from a beta distribution of parameters m, the number of training examples, and d, the Boolean interpolation dimension. This implies that for large d, the learning results tend to the average-case result, known as the self-averaging property of the learning. The bound is shown to be applicable to the practical learning algorithms that can be modeled by the Gibbs algorithm with a uniform prior. The result is also extended to the case of inconsistent learning.     

A transductive multi-label learning approach for video concept detection

In this paper, we address two important issues in the video concept detection problem: the insufficiency of labeled videos and the multiple labeling issue. Most existing solutions merely handle the two issues separately. We propose an integrated approach to handle them together, by presenting an effective transductive multi-label classification approach that simultaneously models the labeling consistency between the visually similar videos and the multi-label interdependence for each video. We compare the performance between the proposed approach and several representative transductive and supervised multi-label classification approaches for the video concept detection task over the widely used TRECVID data set. The comparative results demonstrate the superiority of the proposed approach.     

A benchmarking system for MBS simulation software: Problem standardization and performance measurement

Despite the importance given to the computational efficiency of multibody system (MBS) simulation tools, there is a lack of standard benchmarks to measure the performance of these kinds of software applications. Benchmarking is done on an individual basis: different sets of problems are used, and the procedures and conditions considered to measure computational efficiency are also different. In this scenario, it becomes almost impossible to compare the performance of the different available simulation methods in an objective and quantitative way.This work proposes a benchmarking system for MBS simulation tools. The structure of the benchmark problem collection is defined, and a group of five problems involving rigid bodies is proposed. For these problems, documentation and validated reference solutions in standard formats have been generated, and a procedure to measure the computational efficiency of a given simulation software is described.Finally, the benchmarking system has been applied to evaluate the performance of two different simulation tools: ADAMS/Solver, a popular general-purpose commercial MBS simulation tool, and a custom Fortran code implementation of an Index-3 augmented Lagrangian formulation with projections combined with the implicit single-step trapezoidal rule as integration scheme. Results show that the proposed problems are able to reach the limits of the tested simulation methods, and therefore they can be considered good benchmark problems.     

Comprehensive evaluation criteria for English learning websites using expert validity surveys

This study aimed to develop a set of evaluation criteria for English learning websites. These criteria can assist English teachers/web designers in designing effective websites for their English courses and can also guide English learners in screening for appropriate and reliable websites to use in increasing their English ability. To fulfill our objective, we employed a three-phase research procedure: (a) establishing a preliminary set of criteria from a thorough review of the literature, (b) evaluating and refining the preliminary criteria by conducting interviews with in-service teachers and learners, and (c) validating and finalizing the criteria according to expert validity surveys. The established criteria have 46 items, classified into 6 categories (the number of items within the category) – general information (12), integrated English learning (13), listening (4), speaking (6), reading (5), and writing (6). The general information evaluates the authority, accuracy, and format of the learning websites. The integrated English learning evaluates the overall information relevant to English learning materials as well as the common features of the four language skills. The criteria for listening, speaking, reading, and writing, for example, examine the suitable intonation, skills of discourse, classification of reading articles by their attributes, and the proper use of discussion boards for students when practicing their writing skills. Based on qualitative and quantitative analysis of the interviews and expert validity surveys, we confirmed the effectiveness of the developed evaluation criteria with satisfactory indexes of inter-rater reliability, content validity, and factorial validity.     

A function-based computational method for design concept evaluation

Concept generation is an indispensable step of innovation design. However, the limited knowledge and design thinking fixation of designers often impede the generation of novel design concepts. Computational tools can be a necessary supplement for designers. They can generate a big number of design concepts based on an existing knowledge base. For filtering these design concepts, this work presents a computational measurement of novelty, feasibility and diversity based on 500,000 granted patents. First, about 1700 functional terms (terminologies) are mapped to high dimensional vectors (100 dimensional space) by word embedding technique. The resulted database is knowledge base-I (KB-I). Then, we adopt circular convolution to convert patents into high dimensional vectors. The resulted database is KB-II. Based on the two knowledge bases, the computational definitions of novelty, feasibility and diversity are developed. We conduct six experiments based on KB-II, a random dataset and a real product dataset, and the results show that these metrics can be used to roughly filter a big number of design concepts, and then expert-based method can be further used. This work provides a computational framework for measuring the novelty, feasibility and diversity of design concept.     

Economic performance assessment of advanced process control with LQG benchmarking

Economic performance assessment of advanced process control is conducted to investigate performance potentials that can be obtained by control system improvement. An optimization-based approach for economic performance assessment of the constrained process control is integrated with the LQG benchmark in this paper. By explicitly incorporating uncertainty into the performance assessment problem, economic performance evaluation can be formulated as a stochastic optimization problem, which helps to identify the opportunity to improve profitability of the process by taking appropriate risk levels. Using the LQG benchmark to estimate achievable variability reduction through control system improvement, the proposed method provides an estimate of both the performance that can be expected from the improved control system and the operating condition that delivers the improved performance. The results obtained can serve as a tool for control engineers to make decisions on control system tuning and/or upgrading. The proposed algorithm is illustrated via simulation examples as well as an industrial example.     

Building an automated English sentence evaluation system for students learning English as a second language

This paper presents an automated scoring system which grades students’ English writing tests. The system provides a score and diagnostic feedback to students without human’s efforts. Target users are Korean students in junior high schools who learn English as a second language. The system takes a single English sentence as its input. Dealing with a single sentence as an input has some advantages on comparing the input with the answers given by human teachers and giving detailed feedback to the students. The system was developed and tested with the real test data collected through English tests given to third grade students in junior high school. Scoring requires two steps of the process. The first process is analyzing the input sentence in order to detect possible errors, such as spelling errors and syntactic errors. The second process is comparing the input sentence with given answers to identify the differences as errors. To evaluate the performance of the system, the output produced by the system is compared with the result provided by human raters. The score agreement value between a human rater and the system is quite close to the value between two human raters.     

Multi-agent learning neural network and Bayesian model for real-time IoT skin detectors: a new evaluation and benchmarking methodology

Neural Computing and Applications - This study aimed to develop a new methodology for evaluating and benchmarking a multi-agent learning neural network and Bayesian model for real-time skin...     

The impact of general and specific performance and self-efficacy on learning with computer-based concept mapping

When students learn a new and challenging task, for which they have very limited corresponding personal or vicarious experiences to refer, it is important to understand how their self-efficacy beliefs evolve during the course of sequential lessons; how they differ in the way their general learning performance and self-efficacy influence their ratings of task-specific self-efficacy and performance; and, how such differences may result in different learning outcomes and motivations for learning. By examining a group of 66 students engaged in learning to construct good concept maps with computer software, this study revealed several important findings on these questions. Students generally tended to initially overestimate their ability to successfully carry out the required task. They also varied in the way their general self-efficacy and performance were associated with their task-specific self-efficacy and performance. And, these differences did appear to be associated with different learning outcomes and motivations for learning. These results indicate that investigating individual differences in students’ patterns of association between general and specific performance and their self-efficacy may lead to a better understanding of how students differ in their levels of motivation and outcomes when learning a new and challenging task.     

A process for post-implementation IT benchmarking

Post-implementation learning involves continuing improvements in how effectively information technology (IT) is utilized. This kind of IT learning is a core competency that may determine the competitiveness of firms in information intensive industries. While many chief information officers (CIOs) may have some experience with infrastructure, user satisfaction, or business process benchmarking, few firms benchmark how effectively IT is utilized or its impact on the user’s work.This paper proposes a web-enabled process for benchmarking IT outcomes (effective use and impacts) and diagnosing problems with the user’s learning. This process is based on an explicit causal model of how induced and autonomous learning factors drive IT usage and impacts. The proposed process enables internal versus “best-in-class” causal analysis. We discuss the common considerations and key issues involved in implementing this IT benchmarking process.     

A method for evaluation of learning components

Today, it is common to include machine learning components in software products. These components offer specific functionalities such as image recognition, time series analysis, and forecasting but may not satisfy the non-functional constraints of the software products. It is difficult to identify suitable learning algorithms for a particular task and software product because the non-functional requirements of the product affect algorithm suitability. A particular suitability evaluation may thus require the assessment of multiple criteria to analyse trade-offs between functional and non-functional requirements. For this purpose, we present a method for APPlication-Oriented Validation and Evaluation (APPrOVE). This method comprises four sequential steps that address the stated evaluation problem. The method provides a common ground for different stakeholders and enables a multi-expert and multi-criteria evaluation of machine learning algorithms prior to inclusion in software products. Essentially, the problem addressed in this article concerns how to choose the appropriate machine learning component for a particular software product.     

Texture feature benchmarking and evaluation for historical document image analysis

The use of different texture-based methods is pervasive in different subfields and tasks of document image analysis (DIA) and particularly in historical DIA (HDIA). Nevertheless, faced with a large diversity of texture-based methods used for HDIA, few questions arise. Which texture methods are firstly well suited for segmenting graphical contents from textual ones, discriminating various text fonts and scales, and separating different types of graphics? Then, which texture-based method represents a constructive compromise between the performance and the computational cost? Thus, in this article a benchmarking of the most classical and widely used texture-based feature sets has been conducted using a classical texture-based pixel-labeling scheme on a large corpus of historical documents to have satisfactory and clear answers to the above questions. We focus on determining the performance of each texture-based feature set according to the document content. The results reported in this study provide firstly a qualitative measure of which texture-based feature sets are the most appropriate and secondly a useful benchmark in terms of performance and computational cost for current and future research efforts in HDIA.