浅析密码学技术

本通过密码学的起源、发展、概念、分类、特性及其研究现状来介绍了密码学技术。     

The effectiveness of learning analytics for identifying at-risk students in higher education

Abstract

In this article we investigate the effectiveness of learning analytics for identifying at-risk students in higher education institutions using data output from an in-situ learning analytics platform. Amongst other things, the platform generates ‘no-engagement’ alerts if students have not engaged with any of the data sources measured for 14 consecutive days. We tested the relationship between these alerts and student outcomes for two cohorts of first-year undergraduate students. We also compared the efficiency of using these alerts to identify students at risk of poorer outcomes with the efficiency of using demographic data, using widening participation status as a case study example. The no-engagement alerts were found to be more efficient at spotting students not progressing and not attaining than demographic data. In order to investigate the efficacy of learning analytics for addressing differential student outcomes for disadvantaged groups, the team also analysed the likelihood of students with widening participation status generating alerts compared with their non-widening participation counterparts. The odds of students with widening participation status generating an alert were on average 43% higher, demonstrating the potential of such a system to preferentially target support at disadvantaged groups without needing to target directly based on immutable factors such as their socio-economic background.     

Exploring the influences of MOOC design features on student performance and persistence

Massive open online courses (MOOCs) face persistent challenges related to student performance, including high rates of attrition and low student achievement scores. Previous studies that have examined the performance of students in MOOCs have done so using qualitative analysis and the quantitative analysis of small samples. This study is the first to examine general course features of MOOCs on a large scale and to quantify the influences of these course features on student performance. Informed by the theory of web-based online instruction, this study used two-stage K-means clustering to analyze more than 200 MOOCs that had enrolled about 300,000 students, identifying three patterns of course features among the MOOCs. A MANOVA test and follow-up statistical tests revealed that these patterns of course features influenced the MOOCs’ dropout rates and student achievement scores to statistically different degrees. The implications of these findings are discussed.     

Thinking critically about learning analytics,student outcomes,and equity of attainment

Abstract

This is the introduction to the special edition on learning analytics.     

Assessment,technology and democratic education in the age of data

This paper contends that powerful techniques to manipulate data, enabled by technological and economic developments, can be easily co-opted to serve the restrictive frameworks of hyper-controlling, managerial accountability that characterise current cultures of summative assessment in education. In response to these challenges, research is urgently needed to increase our understanding of the impact that assessments have on individuals and society. The paper concludes that social research ought to contribute to the identification of responses – educational, technological and political – that can minimise inequalities and potential abuses through the encouragement of data literacy across society.     

Assessment of Digitized Library and Archives Materials: A Literature Review

Professional literature about the assessment of digital libraries reflects a growing interest in both improving the user experience and in justifying the creation of digital collections to multiple stakeholders. This article explores some of the key themes in digital library assessment literature through a review of current literature (2004–14) gathered from both scholarly and popular resources online. The majority of scholarship about digital library assessment utilizes usability testing and Web statistics for data collection, while studies about altmetrics, the reuse of digital library materials, cost benefit analysis, and the holistic evaluation of digital libraries are also present in the literature. Exploring the literature about digital library assessment allows libraries to create effective and sustainable evaluation models based on the successes and shortcomings of previously completed projects.     

Where to go: Computational and visual what-if analyses in soccer

ABSTRACT

To prepare their teams for upcoming matches, analysts in professional soccer watch and manually annotate up to three matches a day. When annotating matches, domain experts try to identify and improve suboptimal movements based on intuition and professional experience. The high amount of matches needing to be analysed manually result in a tedious and time-consuming process, and results may be subjective. We propose an automatic approach for the realisation of effective region-based what-if analyses in soccer. Our system covers the automatic detection of region-based faulty movement behaviour, as well as the automatic suggestion of possible improved alternative movements. As we show, our approach effectively supports analysts and coaches investigating matches by speeding up previously time-consuming work. We enable domain experts to include their domain knowledge in the analysis process by allowing to interactively adjust suggested improved movement, as well as its implications on region control. We demonstrate the usefulness of our proposed approach via an expert study with three invited domain experts, one being head coach from the first Austrian soccer league. As our results show that experts most often agree with the suggested player movement (83%), our proposed approach enhances the analytical capabilities in soccer and supports a more efficient analysis.     

Learning design in diverse institutional and cultural contexts: suggestions from a participatory workshop with higher education professionals in Africa

Learning design approaches, such as those adopted by the Open University, provide a set of tools and resources for purposefully-designing modules with a focus on student experiences. However, many of the current learning design strategies have been situated within specific institutions in Europe and North America. This means that there are several issues worth considering around if and how established learning design approaches make sense in diverse institutional and cultural contexts. To critically assess the relevance and appropriateness of learning design strategies in new contexts, this article describes an in-depth participatory workshop with 34 education professionals from five African countries. Altogether, 10 suggestions for learning design practices were derived from the consensus of workshop participants, which provide a foundation for the development of learning design practices moving forward.     

Towards a big data framework for analyzing social media content

Modern companies generate value by digitalizing their services and products. Knowing what customers are saying about the firm through reviews in social media content constitutes a key factor to succeed in the big data era. However, social media data analysis is a complex discipline due to the subjectivity in text review and the additional features in raw data. Some frameworks proposed in the existing literature involve many steps that thereby increase their complexity. A two-stage framework to tackle this problem is proposed: the first stage is focused on data preparation and finding an optimal machine learning model for this data; the second stage relies on established layers of big data architectures focused on getting an outcome of data by taking most of the machine learning model of stage one. Thus, a first stage is proposed to analyze big and small datasets in a non-big data environment, whereas the second stage analyzes big datasets by applying the first stage machine learning model of. Then, a study case is presented for the first stage of the framework to analyze reviews of hotel-related businesses. Several machine learning algorithms were trained for two, three and five classes, with the best results being found for binary classification.     

中文搜索引擎的搜索结果重合率研究

本文的研究目的是测试主流中文搜索引擎搜索结果之间的重合程度和差异程度.利用一个具有11 171条来自真实用户的提问样本集对百度、谷歌和中国雅虎进行实际测试,发现中文搜索引擎搜索结果之间的差异很大,重合率很低.在全部的第一页搜索结果中,三个引擎中任何一个引擎独有的搜索结果总数占89.34%,任何两个引擎之间重合的搜索结果总数占8.11%,三个引擎重合的搜索结果数量占2.54%.三个引擎前两页搜索结果的重合比例更低.通过和已有的英文搜索引擎重合率测试数据相比较,发现中英文搜索引擎的搜索结果重合率都很低,且很相近.