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.     

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.     

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...     

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.     

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.     

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.     

A new learning algorithm with logarithmic performance index for complex-valued neural networks

In a fully complex-valued feed-forward network, the convergence of the Complex-valued Back Propagation (CBP) learning algorithm depends on the choice of the activation function, learning sample distribution, minimization criterion, initial weights and the learning rate. The minimization criteria used in the existing versions of CBP learning algorithm in the literature do not approximate the phase of complex-valued output well in function approximation problems. The phase of a complex-valued output is critical in telecommunication and reconstruction and source localization problems in medical imaging applications. In this paper, the issues related to the convergence of complex-valued neural networks are clearly enumerated using a systematic sensitivity study on existing complex-valued neural networks. In addition, we also compare the performance of different types of split complex-valued neural networks. From the observations in the sensitivity analysis, we propose a new CBP learning algorithm with logarithmic performance index for a complex-valued neural network with exponential activation function. The proposed CBP learning algorithm directly minimizes both the magnitude and phase errors and also provides better convergence characteristics. Performance of the proposed scheme is evaluated using two synthetic complex-valued function approximation problems, the complex XOR problem, and a non-minimum phase equalization problem. Also, a comparative analysis on the convergence of the existing fully complex and split complex networks is presented.     

BenchMetrics: a systematic benchmarking method for binary classification performance metrics

Neural Computing and Applications - This paper proposes a systematic benchmarking method called BenchMetrics to analyze and compare the robustness of binary classification performance metrics based...     

A quasi-experimental study of three online learning courses in computing

This paper presents the results of a 3-year quasi-experimental study of three Masters courses in computing. The purpose of this study was to investigate the extent to which the teaching and learning of these courses were enhanced by being developed and delivered in an online format as compared to face-to-face full-time and part-time delivery. Key methodological questions and issues to be examined were: (a) any observable difference in student performance as measured by end-of-module grades/marks; (b) any observable difference between coursework and exam performance in the technically-oriented modules; (c) any observable difference in dropout rates, student satisfaction and faculty satisfaction. The study examined the results from 4684 module enrolments, consisting of 269 online students, 796 part-time students, and 3619 full-time students distributed across 7 modules and 79 module instantiations. The data was gathered using a variety of different means which included interviews, end of module questionnaires, student records and enrolment data. The results show that the online students consistently perform better then the face-to-face students and the paper reflects on this finding.     

Personalized E-learning system with self-regulated learning assisted mechanisms for promoting learning performance

With the rapid development of Internet technologies, the conventional computer-assisted learning (CAL) is gradually moving toward to web-based learning. Additionally, instructors typically base their teaching methods to simultaneously interact with all learners in a class based on their professional disciplines in the traditional classroom learning. However, the requirements of individual learners are frequently ignored in the traditional classroom learning. Compared to the conventional classroom learning, individual learners are the focus in web-based learning environments and many web-based learning systems provide personalized learning mechanisms for individual learners. One key problem is that learners have to frequently interact with web-based learning systems even though they lack instructors to monitor their learning attitudes and behavior during learning processes. Hence, a learner’s ability to self-regulated learning is clearly an important factor affecting learning performance in a web-based learning environment. Self-regulated learning is a goal-oriented learning strategy that is very suited to self-managed learning to promote learning performance of individual learners in a web-based learning environment. However, how to assist learners in cultivating self-regulated learning abilities efficiently is an important research issue in the self-regulated learning field. This study presents a novel personalized e-learning system with self-regulated learning assisted mechanisms that help learners enhance their self-regulated learning abilities. The proposed self-regulated learning mechanisms assist learners in becoming lifelong learners who have autonomous self-regulated learning abilities. Additionally, four self-regulated learning types, based on a self-regulated learning competence index and self-regulated learning performance index, are also proposed. Experimental results demonstrate that the proposed self-regulated learning assisted mechanisms aid learners by speeding up their acquisition of self-regulated learning abilities in a personalized e-learning system, and help their learning performance.     

An easy-to-use evaluation framework for benchmarking entity recognition and disambiguation systems

Entity recognition and disambiguation (ERD) is a crucial technique for knowledge base population and information extraction. In recent years, numerous papers have been published on this subject, and various ERD systems have been developed. However, there are still some confusions over the ERD field for a fair and complete comparison of these systems. Therefore, it is of emerging interest to develop a unified evaluation framework. In this paper, we present an easy-to-use evaluation framework (EUEF), which aims at facilitating the evaluation process and giving a fair comparison of ERD systems. EUEF is well designed and released to the public as an open source, and thus could be easily extended with novel ERD systems, datasets, and evaluation metrics. It is easy to discover the advantages and disadvantages of a specific ERD system and its components based on EUEF. We perform a comparison of several popular and publicly available ERD systems by using EUEF, and draw some interesting conclusions after a detailed analysis.     

Development,evaluation and benchmarking of simulation software for biomolecule-based computing

Simulators for biomolecular computing, (both in vitro and in silico), have come to play an important role in experimentation, analysis, and evaluation of the efficiency and scalability of DNA and biomolecule based computing. Simulation in silico of DNA computing is useful to support DNA-computing algorithm design and to reduce the cost and effort of lab experiments. Although many simulations have now been developed, there exists no standard for simulation software in this area. Reliability, performance benchmarks, user interfaces, and accessibility are arguably the most important criteria for development and wide spread use of simulation software for BMC. The requirements and evaluation of such software packages for DNA computing software are discussed, particularly questions about software development, appropriate user environments, standardization of benchmark data sets, and centrally available common repositories for software and/or data.