Removing obstacles to the pedagogical changes required by Jonassen's vision of authentic technology-enabled learning

Educators have been striving to achieve meaningful technology use in our K-12 classrooms for over 30 years. Yet, despite significant investments of time and money in infrastructure, training, and support “we have few assurances that [educators] are able to use technology for teaching and learning” (NEA, 2008, p. 1). In this article, we call for a shift in focus from technology integration (and the tools used to achieve it), to technology-enabled learning (and the pedagogy used to support it). Building on the early work of Jonassen (1996) in which he proposed using technology as cognitive tools, we suggest that future technology integration efforts should focus on helping teachers engage students in authentic technology-enabled learning environments. As such, technology integration is no longer an isolated goal to be achieved separately from pedagogical goals, but simply the means by which students engage in relevant and meaningful interdisciplinary work. Implications for supporting teacher pedagogical change, from a system's perspective, are discussed.     

Highly-cited papers in software engineering: The top-100

ContextAccording to the search reported in this paper, as of this writing (May 2015), a very large number of papers (more than 70,000) have been published in the area of Software Engineering (SE) since its inception in 1968. Citations are crucial in any research area to position the work and to build on the work of others. Identification and characterization of highly-cited papers are common and are regularly reported in various disciplines.ObjectiveThe objective of this study is to identify the papers in the area of SE that have influenced others the most as measured by citation count. Studying highly-cited SE papers helps researchers to see the type of approaches and research methods presented and applied in such papers, so as to be able to learn from them to write higher quality papers which will likely receive high citations.MethodTo achieve the above objective, we conducted a study, comprised of five research questions, to identify and classify the top-100 highly-cited SE papers in terms of two metrics: total number of citations and average annual number of citations.ResultsBy total number of citations, the top paper is "A metrics suite for object-oriented design", cited 1817 times and published in 1994. By average annual number of citations, the top paper is "QoS-aware middleware for Web services composition", cited 154.2 times on average annually and published in 2004.ConclusionIt is concluded that it is important to identify the highly-cited SE papers and also to characterize the overall citation landscape in the SE field. We hope that this paper will encourage further discussions in the SE community towards further analysis and formal characterization of the highly-cited SE papers.     

中国医学影像人工智能20年回顾和展望

在过去20年里,医学影像技术、人工智能技术以及这两项技术相结合的临床应用都取得了长足发展。中国在该领域的研究也取得卓越成就,并且在全世界范围内的贡献比例仍在逐步提高。为了记录和总结国内同行的科研成果,本文对中国医学影像人工智能过去20年的发展历程进行回顾和展望。重点分析了国内同行在公认的医学影像人工智能领域的国际顶级刊物Medical Image Analysis(MedIA)和IEEE Transactions on Medical Imaging(TMI)以及顶级会议Medical Image Computing and Computer Assisted Intervention(MICCAI)发表的论文,定量统计了论文发表数量、作者身份、发表单位、作者合作链、关键词和被引次数等信息。同时总结了近20年中国医学影像人工智能发展进程中的重要事件,包括举办的医学影像人工智能知名国际和国内会议、《中国医学影像AI白皮书》的发布以及国内同行在COVID-19(corona virus disease 2019)期间的贡献,最后展望了中国医学影像人工智能领域未来的发展趋势。上述统计结果系统...     

Artificial neural networks and deep learning in urban geography: A systematic review and meta-analysis

Artificial neural networks (ANNs) and their latest advancement in deep learning are blooming in computer science. Geography has integrated these artificial intelligence techniques, but not with the same enthusiasm. The main reason for hesitation is that ANNs are still confronted as complex and black boxes. However, ANNs might be more solid methods than conventional approaches when dealing with complex geographical problems. This study considers the great potential of ANNs for research in urban geography. First, using the PRISMA protocol, it provides a statistical review of 140 papers on studies that employed ANNs in urban geography between 1997 and 2016. Second, it performs a quantitative meta-analysis using non-parametric bootstrapping. 45 (of the 140) papers were assessed regarding ANNs' overall accuracy (OA) achieved when used for urban growth prediction or urban land-use classification. Third, a new guideline for reporting ANNs is proposed. Statistical review indicated that ANNs performed better in 75.7% of case studies compared to conventional methods. Meta-analysis found that on bootstrapped averages, the median OA achieved when using, ANNs was higher than the median OA achieved by other techniques by 2.3% (p < .001). ANNs also performed better when used for classification compared to prediction. Analysis also identified inadequate presentation of ANNs and related results when used in urban studies. For this reason, a new guideline for reporting ANNs is suggested in this work to ensure consistency and easier dissemination of individual lessons learned. These findings aim to motivate further studies on ANNs and deep learning in urban geography.     

Strategic information systems research: An archival analysis

The Journal of Strategic Information Systems (JSIS) has been an international outlet for Information Systems research that focuses on strategic issues since 1991. This paper reports on an analysis of the research published in JSIS to date. The paper presents a preliminary classification system for research topics related to Strategic Information Systems into which all 316 JSIS research papers as at end 2009 are classified. Discussion on changing emphases in topics over time is provided, in the context of the editorial philosophy of the journal. The paper seeks to stimulate discussion on future directions for research in Strategic Information Systems.     

Research intelligence involving information retrieval – An example of conferences and journals

This paper reports a work that was intended to reveal the connection between topics investigated by conference papers and journal papers. This work selected hundreds of papers in data mining and information retrieval from well-known databases and showed that the topics covered by conference papers in a year often leads to similar topics covered by journal papers in the subsequent year and vice versa. This study used some existing algorithms and combination of these algorithms to proposed a new detective procedure for the researchers to detect the new trend and get the academic intelligence from conferences and journals.The goal of this research is fourfold: First, the research investigates if the conference papers’ themes lead the journal papers’. Second, the research examines how the new research themes can be identified from the conference papers. Third, the research looks at a specific area such as information retrieval and data mining as an illustration. Fourth, the research studies any inconsistencies of the correlation between the conference papers and the journal papers.This study explores the connections between the academic publications. The methodologies of information retrieval and data mining can be exploited to discover the relationships between published papers among all topics. By discovering the connections between conference papers and journal papers, researchers can improve the effectiveness of their research by identifying academic intelligence.This study discusses how conference papers and journal papers are related. The topics of conference papers are identified to determine whether they represent new trend discussed in journal papers. An automatic examination procedure based on information retrieval and data mining is also proposed to minimize the time and human resources required to predict further research developments. This study develops a new procedure and collects a dataset to verify those problems. Analytical results demonstrate that the conference papers submitted to journals papers are similar each year. Conference papers certainly affect the journal papers published over three years. About 87.23% of data points from papers published in 1991–2007 support our assumption. The research is intended to help researchers identify new trend in their research fields, and focus on the urgent topics. This is particularly valuable for new researchers in their field, or those who wish to perform cross-domain studies.     

Preface

The International Conference on Knowledge Science, Engineering and Management (KSEM), which was held in Belfast, Northern Ireland in 2010, was the fourth success in the series of such conferences. The conference focuses on the three themes of knowledge science, engineering and management, covering a wider range of research topics in the KSEM-related areas. This event offered an invaluable opportunity to bring together researchers, engineers and practitioners to present original work, the latest advances on knowledge representation, pioneered knowledge engineering, knowledge related systems, as well as discuss and debate practical challenges in deploying knowledge-based systems and research opportunities in the research community. To highlight research activities drawn by this event and provide an insight into the latest developments in the related areas, we present this special issue which is dedicated to all the delegates and researchers in the community who made the conference a success. We selected more than 10 papers that were presented in the conference and asked the authors to extend these papers. After careful review, we finally select 6 papers to be included in this special issue. This collection of extended versions of papers consists of the KSEM2010''s finest papers and covers the prominent topics of knowledge representation and reasoning, ontology engineering and applications, data mining and knowledge discovery. They represent state of the art of research in KSEM-related research areas.     

The development of Bridger: A methodological study of research on the use of machine learning in design

Research is a design activity whose decisions involve the ways in which research is carried out and its results interpreted. These activities comprise what is referred to as research methodology. This paper brings the concepts of capturing design rationale and machine learning to bear on the design of research itself. Therefore, design decisions concerning research must be recorded to allow for understanding feedback and updating of research strategies. In addition, successes as well as failures of research decisions must be reported to facilitate learning about research. This requires a shift in the way research is carried out and reported. This paper illustrates this shift in the context of a specific project on machine learning in design.     

Comments on “Robust optimal design and convergence properties analysis of iterative learning control approaches” and “On the P-type and Newton-type ILC schemes for dynamic systems with non-affine input factors”

The papers by Xu and Tan [Robust optimal design and convergence properties analysis of iterative learning control approaches, Automatica 38 (2002) 1867-1880], and Xu and Tan [On the P-type and Newton-type ILC schemes for dynamic systems with non-affine input factors, Automatica 38 (2002) 1237-1242], give a convergence analysis for several iterative learning control approaches. Unfortunately, these papers contains several mathematical errors that render the proofs of the claimed results invalid. As there are no obvious ways to correct these errors, the results presented in these papers are questionable.     

An online learning algorithm for adaptable topologies of neural networks

Many real scenarios in machine learning are of dynamic nature. Learning in these types of environments represents an important challenge for learning systems. In this context, the model used for learning should work in real time and have the ability to act and react by itself, adjusting its controlling parameters, even its structures, depending on the requirements of the process. In a previous work, the authors presented an online learning algorithm for two-layer feedforward neural networks that includes a factor that weights the errors committed in each of the samples. This method is effective in dynamic environments as well as in stationary contexts. As regards this method’s incremental feature, we raise the possibility that the network topology is adapted according to the learning needs. In this paper, we demonstrate and justify the suitability of the online learning algorithm to work with adaptive structures without significantly degrading its performance. The theoretical basis for the method is given and its performance is illustrated by means of its application to different system identification problems. The results confirm that the proposed method is able to incorporate units to its hidden layer, during the learning process, without high performance degradation.     

Process-mining enabled feedback: “Tell me what I did wrong” vs. “tell me how to do it right”

Fast advancement of technology has led to an increased interest for using information technology to provide feedback based on learning behavior observations. This work outlines a novel approach for analyzing behavioral learner data through the application of process mining techniques specifically targeting a complex problem solving process. We realize this in the context of one particular learning case, namely, domain modeling. This work extends our previous research on process-mining analysis of domain modeling behavior of novices by elaborating with new insights from a replication study enhanced with an extra observation on how novices verify/validate models. The findings include a set of typical modeling and validation patterns that can be used to improve teaching guidance for domain modeling courses. From a scientific viewpoint, the results contribute to improving our knowledge on the cognitive aspects of problem-solving behavior of novices in the area of domain modeling, specifically regarding process-oriented feedback as opposed to traditional outcome feedback (is a solution correct? Why (not)?) usually applied in this type of courses. Ultimately, the outcomes of the work can be inspirational outside of the area of domain modeling as learning event data is becoming readily available through virtual learning environments and other information systems.     

Learning from class-imbalanced data: Review of methods and applications

Rare events, especially those that could potentially negatively impact society, often require humans’ decision-making responses. Detecting rare events can be viewed as a prediction task in data mining and machine learning communities. As these events are rarely observed in daily life, the prediction task suffers from a lack of balanced data. In this paper, we provide an in depth review of rare event detection from an imbalanced learning perspective. Five hundred and seventeen related papers that have been published in the past decade were collected for the study. The initial statistics suggested that rare events detection and imbalanced learning are concerned across a wide range of research areas from management science to engineering. We reviewed all collected papers from both a technical and a practical point of view. Modeling methods discussed include techniques such as data preprocessing, classification algorithms and model evaluation. For applications, we first provide a comprehensive taxonomy of the existing application domains of imbalanced learning, and then we detail the applications for each category. Finally, some suggestions from the reviewed papers are incorporated with our experiences and judgments to offer further research directions for the imbalanced learning and rare event detection fields.     

Intelligent distributed production control

This editorial introduces the special issue of the Springer journal, Journal of Intelligent Manufacturing, on intelligent distributed production control. This special issue contains selected papers presented at the 13th IFAC Symposium on Information Control Problems in Manufacturing??INCOM??2009 (Bakhtadze and Dolgui 2009). The papers in this special issue were selected because of their high quality and their specific way of addressing the variety of issues dealing with intelligent distributed production control. Previous global discussions about the state of the art in intelligent distributed production control are provided, as well as exploratory guidelines for future research in this area.     

机器学习赋能的软件自适应性综述

软件系统自适应机制提供了应对动态变化的环境和不确定的需求的技术方案.在已有的软件系统自适应性的相关研究中，有一类工作将软件系统自适应性转换为回归、分类、聚类、决策等问题，并利用强化学习、神经网络/深度学习、贝叶斯决策理论和概率图模型、规则学习等进行问题建模，并以此构造软件系统自适应机制.本文通过系统化的文献调研，综述了机器学习赋能的软件系统自适应性的工作.首先介绍基本概念，然后从不同视角对当前工作进行分类；按被控系统、监测和控制过程、学习算法、学习赋能方式等方面进行分析，并讨论不同机器学习方法赋能的软件系统自适应性的切入点及其优势和不足；最后对未来研究进行展望.     

Analytic review of usability evaluation in ISMAR

There has been a rapid increase in research evaluating usability of Augmented Reality (AR) systems in recent years. Although many different styles of evaluation are used, there is no clear consensus on the most relevant approaches. We report a review of papers published in International Symposium of Mixed and Augmented Reality (ISMAR) proceedings in the past decade, building on the previous work of Swan and Gabbard (2005). Firstly, we investigate the evaluation goal, measurement and method of ISMAR papers according to their usability research in four categories: performance, perception and cognition, collaboration and User Experience (UX). Secondly, we consider the balance of evaluation approaches with regard to empirical–analytical, quantitative–qualitative and participant demographics. Finally we identify potential emphases for usability study of AR systems in the future. These analyses provide a reference point for current evaluation techniques, trends and challenges, which benefit researchers intending to design, conduct and interpret usability evaluations for future AR systems.     

The impact of hierarchical positions on the type of communication within online Communities of Learning

Communities of Learning (CoL) have been suggested to facilitate the learning process among participants of online trainings. Yet, previous studies often detached participants from the social context in which learning took place. The present study addresses this shortcoming by providing empirical evidence from 25 CoL of a global organization, where 249 staff members from different hierarchical positions engaged into collaborative learning via asynchronous discussion forums. We conduct a longitudinal study on the type of communication within these CoL, as well as participants' network positions, in order to investigate the research question: What is the impact of individual's hierarchical positions on the type of communication within CoL? Our results indicate that the higher participants' hierarchical position, the higher their amount of social and cognitive communication, which in turn was also positively related to their network position within CoL. We also identified a sub-group of Stars that outperformed their colleagues and who were at the center of CoL, irrespective of their hierarchical positions. Consequently, we propose design and facilitation strategies to practitioners and organizers of future CoL that can foster the learning processes and outcomes of all participants. Additionally, we consider future research avenues that could be explored further.     

Learning from positive and negative examples: New proof for binary alphabets

One of the most fundamental problems in computational learning theory is the problem of learning a finite automaton A consistent with a finite set P of positive examples and with a finite set N of negative examples. By consistency, we mean that A accepts all strings in P and rejects all strings in N. It is well known that this problem is NP-complete. In the literature, it is stated that NP-hardness holds even in the case of a binary alphabet. As a standard reference for this theorem, the work of Gold from 1978 is either cited or adapted. Nevertheless, the results in Gold's work are stated in terms of Mealy machines, and not in terms of deterministic finite automata (DFAs) as most commonly defined. As Mealy machines are equipped with an output function, they can be more compact than DFAs which accept the same language. We show that the adaptations of Gold's construction for Mealy machines stated in the literature have some issues, and provide a correct proof for the fact that the DFA-consistency problem for binary alphabets is NP-complete.     

通过试题难度控制试卷难度方法的探讨

命题是考试工作中非常重要的一个环节。一份试卷中各个试题的难度以及各级难度试题所占的比例将最终决定一份试卷的难度。文章通过分析试题难度,对试题难度进行量化后,根据试卷中各级难度试题所占的比例计算整份试卷的难度。考试结束后根据实测试卷难度和预估的试卷难度进行比较,为以后的工作提供依据。