在线开放课程下混合式教学学习效果的研究--以“数据库基础”课程为例

本文以"数据库基础"课程为例,以学习者的学习过程和教学者的教学活动为研究对象,经过两个周期的在线开放课程建设后,根据教学数据进行统计分析,研究结果表明,采用在线开放课程进行混合式教学的学生成绩与没有参加过的学生成绩具有显著性差异。在线学习活动的完成率和正确率与学习者最后的考试成绩有较强的相关性。     

Analyzing youth digital participation: Aims,actors, contexts and intensities

Participation is often used as a blanket term that is uncritically celebrated; this is particularly true in the case of youth digital participation. In this article, we propose a youth-focused analytical framework, applicable to a wide variety of youth digital participation projects, which can help facilitate a more nuanced understanding of these participatory practices. This framework analyzes the aims envisioned for youth participation, the actors and contexts of these activities, and the variable levels of participatory intensity, in order to more accurately assess the forms and outcomes of youth digital participation. We demonstrate the value of this framework by applying it to two contemporary cases of digital youth participation: an informal online community (Nerdfighters) and a formalized educational initiative (CyberPatriot). Such analyses facilitate normative assessments of youth digital participation, which enable us to better assess what participation is good for, and for whom.     

A Contrastive learning-based Task Adaptation model for few-shot intent recognition

Few-shot intent recognition aims to identify user’s intent from the utterance with limited training data. A considerable number of existing methods mainly rely on the generic knowledge acquired on the base classes to identify the novel classes. Such methods typically ignore the characteristics of each meta task itself, resulting in the inability to make full use of limited given samples when classifying unseen classes. To deal with such issues, we propose a Contrastive learning-based Task Adaptation model (CTA) for few-shot intent recognition. In detail, we leverage contrastive learning to help achieve task adaptation and make full use of the limited samples of novel classes. First, a self-attention layer is employed in the task adaptation module, which aims to establish interactions between samples of different categories so that new representations are task-specific rather than relying entirely on the base classes. Then, the contrastive-based loss functions and the semantics of the label name are respectively used for reducing the similarity between sample representations in different categories while increasing it in the same categories. Experimental results on a public dataset OOS verify the effectiveness of our proposal by beating the competitive baselines in terms of accuracy. Besides, we conduct the cross-domain experiments on three datasets, i.e., OOS, SNIPS as well as ATIS. We find that CTA gains obvious improvements in terms of accuracy in all cross-domain experiments, indicating that it has a better generalization ability than other competitive baselines in both cross-domain and single-domain settings.     

Gender Scripts and the Internet: The Design and Use of Amsterdam's Digital City,by Els Rommes. Enschede,The Netherlands: Twente University Press, 2002. 304 pp. 24.00. ISBN 9-0365-1774-5

This article seeks to address how religion fits into the larger domain of Internet studies and why studies of religion within computer-mediated communication (CMC) need to be given more attention. An argument is made for the need to take religion online more seriously, not just because it is an interesting phenomenon or a popular use of the Internet, but also because religion continues to be an important part of contemporary life for many people. A summary of the growth and development of religion online is presented along with an overview of how religion has been approached and studied on the Internet. This review shows what CMC studies of religion might offer in approaching research questions related to authority, identity construction, and community online. It calls for recognition of the contribution, and possibilities that underrepresented areas within interdisciplinary research, like religion, might offer Internet studies as a whole.     

广度学习研究进展:基于情报学的视角

[目的/意义]总结了基于在线社交媒体数据的广度学习工作研究进展,从情报学的视角分析了广度学习的应用展望及未来发展趋势。[方法/过程]利用文献统计分析方法,重点分析了广度学习技术在网络嵌入、链路预测、社区检测等在线社交网络分析领域的应用现状。[结果/结论]广度学习可以将多个不同种类的大型异构数据源融合在一起,设计并使用一套统一的分析方法来跨越这些融合的数据源执行协同数据挖掘任务。广度学习在异构社交网络分析中的这些成功应用为其在情报学领域中的研究奠定了理论基础和技术支持,将会有更广泛更深远的研究成果出现。     

基于关联挖掘的在线学习行为规律实证分析研究

当前,在线教育已经成为现代教育的重要组成部分,为教育形式的创新开拓了无限的可能。但是,以慕课为代表的在线教育的课程注册人数通常远远高于最终完成课程的人数,课程完成率较低。该文探索影响在线学习效果特别是课程完成率的因素,并据此提出改进建议。具体采取了实证研究的方法,运用了关联规则挖掘技术和WEKA数据挖掘开源工具,对学堂在线平台上39门课程的学习记录数据进行分析,得出了一系列基于大数据的、有指导意义的在线学习行为方面的关联规则,为进一步开展后续研究提供了参考。     

Letter from Rob Kling,Editor-in-Chief and Guest Editors Kenneth L. Kraemer and Jason Dedrick

We find that the presence of village Internet facilities, offering government to citizen services, is positively associated with the rate at which the villagers obtain some of these services. In a study of a rural Internet project in India, we identify a positive correlation for two such Internet services: obtaining birth certificates for children and applications for old age pensions. Both these government services are of considerable social and economic value to the citizens. Villagers report that the Internet based services saved them time, money, and effort compared with obtaining the services directly from the government office. We also find that these services can reduce corruption in the delivery of these services. After over one year of successful operation, however, the e-government program was not able to maintain the necessary level of local political and administrative support to remain institutionally viable. As government officers shifted from the region, or grew to find the program a threat, the e-government services faltered. We argue that this failure was due to a variety of Critical Failure Factors. We end with a simple sustainability failure model. In summary, we propose that the e-government program failed to be politically and institutionally sustainable due to people, management, cultural, and structural factors     

Narrative research in climate change adaptation—Exploring a complementary paradigm for research and governance

Climate adaptation research increasingly focuses on the socio-cultural dimensions of change. In this context, narrative research is often seen as a qualitative social science method used to frame adaptation communication. However, this perspective neglects an important insight provided by narrative theory as applied in the cognitive sciences and other practical fields: human cognition is organized around specific narrative structures. In adaptation, this means that how we ‘story’ the environment determines how we understand and practice adaptation, how risks are defined, who is authorized as actors in the change debate, and the range of policy options considered. Furthermore, relating an experience through story-telling is already doing ‘knowledge work’, or learning. In taking narrative beyond its use as an extractive social research methodology, we argue that narrative research offers an innovative, holistic approach to a better understanding of socio-ecological systems and the improved, participatory design of local adaptation policies. Beyond producing data on local knowledge(s) and socio-cultural and affective-emotive factors influencing adaptive capacity, it can significantly inform public engagement, deliberation and learning strategies–features of systemic adaptive governance. We critically discuss narrative as both a self-reflective methodology and as a paradigmatic shift in future adaptation research and practice. We explore the narrative approach as a basis for participatory learning in the governance of socio-ecological systems. Finally, we assemble arguments for investing in alternative governance approaches consistent with a shift to a ‘narrative paradigm’.     

A machine learning based framework to identify unseen classes in open-world text classification

Classical supervised machine learning (ML) follows the assumptions of closed-world learning. However, this assumption does not work in an open-world dynamic environment. Therefore, the automated systems must be able to discover and identify unseen instances. Open-world ML can deal with unseen instances and classes through a two-step process: (1) discover and classify unseen instances and (2) identify novel classes discovered in step (1). Most existing research on open-world machine learning (OWML) only focuses on step 1. However, performing step 2 is required to build intelligent systems. The proposed framework comprises three different but interconnected modules that discover and identify unseen classes. Our in-depth performance evaluation establishes that the proposed framework improves open accuracy by up to 8% compared to the state-of-the-art models.     

Transcending conventional credentialing and assessment paradigms with information-rich digital badges

ABSTRACT

Open digital badges are Web-enabled tokens of learning and accomplishment. They operate in an environment of explicit (rather than tacit) trust; open badges provide issuers the ability to include specific claims and associate those claims with detailed supporting evidence. Earners are encouraged to share their badges over social networks, e-mail, and websites, and the information they contain is expected to circulate readily in these spaces. Building upon current concepts and theories from the Information Sciences and Learning Sciences, this article shows how the informational affordances of digital badges are transforming education and learning more generally, and more particularly by transcending conventional paradigms of academic credentialing and educational assessment.     

Automated monitoring of online news accuracy with change classification models

In the past decade, news consumption has shifted from printed news media to online alternatives. Although these come with advantages, online news poses challenges as well. Notable here is the increased competition between online newspapers and other online news providers to attract readers. Hereby, speed is often favored over quality. As a consequence, the need for new tools to monitor online news accuracy has grown. In this work, a fundamentally new and automated procedure for the monitoring of online news accuracy is proposed. The approach relies on the fact that online news articles are often updated after initial publication, thereby also correcting errors. Automated observation of the changes being made to online articles and detection of the errors that are corrected may offer useful insights concerning news accuracy. The potential of the presented automated error correction detection model is illustrated by building supervised classification models for the detection of objective, subjective and linguistic errors in online news updates respectively. The models are built using a large news update data set being collected during two consecutive years for six different Flemish online newspapers. A subset of 21,129 changes is then annotated using a combination of automated and human annotation via an online annotation platform. Finally, manually crafted features and text embeddings obtained by four different language models (TF-IDF, word2vec, BERTje and SBERT) are fed to three supervised machine learning algorithms (logistic regression, support vector machines and decision trees) and performance of the obtained models is subsequently evaluated. Results indicate that small differences in performance exist between the different learning algorithms and language models. Using the best-performing models, $F_2$-scores of 0.45, 0.25 and 0.80 are obtained for the classification of objective, subjective and linguistic errors respectively.     

Multimodal data as a means to understand the learning experience

Most work in the design of learning technology uses click-streams as their primary data source for modelling & predicting learning behaviour. In this paper we set out to quantify what, if any, advantages do physiological sensing techniques provide for the design of learning technologies. We conducted a lab study with 251 game sessions and 17 users focusing on skill development (i.e., user's ability to master complex tasks). We collected click-stream data, as well as eye-tracking, electroencephalography (EEG), video, and wristband data during the experiment. Our analysis shows that traditional click-stream models achieve 39% error rate in predicting learning performance (and 18% when we perform feature selection), while for fused multimodal the error drops up to 6%. Our work highlights the limitations of standalone click-stream models, and quantifies the expected benefits of using a variety of multimodal data coming from physiological sensing. Our findings help shape the future of learning technology research by pointing out the substantial benefits of physiological sensing.     

The Role of Learning-by-Using in the Design of Health Care Technologies: A Case Study

New technologies typically go through significant improvements during their early diffusion. Literature suggests that these modifications follow from learning-by-using. However, the micro-level processes by which learning-by-using is actually achieved remain understudied. This article examines these processes through an in-depth case study of the design and use of a new health-care device. It identifies several learning processes and preconditions for learning that constituted learning-by-using. The results question the dominant image of learning-by-using as a harmonious flow of user feedback.     

Irony detection via sentiment-based transfer learning

Irony as a literary technique is widely used in online texts such as Twitter posts. Accurate irony detection is crucial for tasks such as effective sentiment analysis. A text’s ironic intent is defined by its context incongruity. For example in the phrase “I love being ignored”, the irony is defined by the incongruity between the positive word “love” and the negative context of “being ignored”. Existing studies mostly formulate irony detection as a standard supervised learning text categorization task, relying on explicit expressions for detecting context incongruity. In this paper we formulate irony detection instead as a transfer learning task where supervised learning on irony labeled text is enriched with knowledge transferred from external sentiment analysis resources. Importantly, we focus on identifying the hidden, implicit incongruity without relying on explicit incongruity expressions, as in “I like to think of myself as a broken down Justin Bieber – my philosophy professor.” We propose three transfer learning-based approaches to using sentiment knowledge to improve the attention mechanism of recurrent neural models for capturing hidden patterns for incongruity. Our main findings are: (1) Using sentiment knowledge from external resources is a very effective approach to improving irony detection; (2) For detecting implicit incongruity, transferring deep sentiment features seems to be the most effective way. Experiments show that our proposed models outperform state-of-the-art neural models for irony detection.     

Student privacy in learning analytics: An information ethics perspective

ABSTRACT

Higher education institutions have started using big data analytics tools. By gathering information about students as they navigate information systems, learning analytics employs techniques to understand student behaviors and to improve instructional, curricular, and support resources and learning environments. However, learning analytics presents important moral and policy issues surrounding student privacy. We argue that there are five crucial questions about student privacy that we must address in order to ensure that whatever the laudable goals and gains of learning analytics, they are commensurate with respecting students' privacy and associated rights, including (but not limited to) autonomy interests. We address information access concerns, the intrusive nature of information-gathering practices, whether or not learning analytics is justified given the potential distribution of consequences and benefits, and issues related to student autonomy. Finally, we question whether learning analytics advances the aims of higher education or runs counter to those goals.     

The three facets of knowledge: A critique of the practice-based learning theory

The community of practice learning theory (Lave and Wenger, 1991) can be credited with establishing the social basis of learning, viewing it as occurring through participation in social practices and activities. However, it remains silent on the cognitive content of what is learned by participants in a community of practice. Nor does it address explicitly the role of individuals in the knowing process. Individuals are merely depicted in terms of a desire to belong to a community, progressing from a peripheral participation position to a more central one. The aim of the present paper is to assess the contribution of Lave and Wenger's (1991) practice theory for educational and other learning social settings. In a schooling context, for instance, Lave and Wenger's (1991) account of learning would imply that what students learn at school is how to relate and belong to the school community. By reducing learning and knowing to participation, and by displacing cognition from individuals to anonymous practices, the practice-based epistemology ignores the significant corpus of content knowledge (such as curriculums) and ascribes too passive a role to individuals and glosses over their differentiated access to resources for changing practices and their differentiated efforts in their social and cognitive development. The present paper suggests an approach to knowledge that takes into account the various facets of knowledge, that is, knowledge as knowledge-productive practices, as content, and as relation to a knowing subject, at the same time preserving the sociality of knowledge and learning.     

新创企业员工间高质量关系影响失败学习的机理研究

本文以浙江省成立八年内新创企业的员工为调查对象,收集了212份样本数据,探究了员工间高质量关系与失败学习的关系以及心理资本和工作自主性在这个过程中的作用机制。数据分析结果表明：（1）员工间高质量关系显著正向影响心理资本与失败学习;（2）员工心理资本显著正向影响失败学习,且在高质量关系与失败学习之间起部分中介作用;（3）员工工作自主性在心理资本与失败学习的正向关系中起负向调节作用。本研究理论上丰富了关系协调理论和员工失败学习领域的研究,实践上为新创企业如何营造良好的失败学习氛围提供了有益参考。     

探索性学习、挖掘性学习和创新绩效

 本研究基于March提出的探索性学习和挖掘性学习及其平衡理论,提出了在开放式创新模式下,两类学习的协同及其它们对创新绩效的共同促进作用,并进而分析了环境动荡性在其中的调控效应。最后,基于大样本问卷调查对假设进行了实证研究。     

Live and learn from mistakes: A lightweight system for document classification

We present a Life-Long Learning from Mistakes (3LM) algorithm for document classification, which could be used in various scenarios such as spam filtering, blog classification, and web resource categorization. We extend the ideas of online clustering and batch-mode centroid-based classification to online learning with negative feedback. The 3LM is a competitive learning algorithm, which avoids over-smoothing, characteristic of the centroid-based classifiers, by using a different class representative, which we call clusterhead. The clusterheads competing for vector-space dominance are drawn toward misclassified documents, eventually bringing the model to a “balanced state” for a fixed distribution of documents. Subsequently, the clusterheads oscillate between the misclassified documents, heuristically minimizing the rate of misclassifications, an NP-complete problem. Further, the 3LM algorithm prevents over-fitting by “leashing” the clusterheads to their respective centroids. A clusterhead provably converges if its class can be separated by a hyper-plane from all other classes. Lifelong learning with fixed learning rate allows 3LM to adapt to possibly changing distribution of the data and continually learn and unlearn document classes. We report on our experiments, which demonstrate high accuracy of document classification on Reuters21578, OHSUMED, and TREC07p-spam datasets. The 3LM algorithm did not show over-fitting, while consistently outperforming centroid-based, Naïve Bayes, C4.5, AdaBoost, kNN, and SVM whose accuracy had been reported on the same three corpora.