学习分析:正在浮现中的数据技术

随着教育信息化的普及与逐渐深入,学习管理系统已经获取并存储了大量的有关学生复杂学习行为的数据,从这些数据中挖掘出改进教学系统、提升学习效果的信息,在教育信息化领域一直有着巨大的吸引力。因此,有必要从分析数据以改进学习的角度,对日益受到关注的学习分析技术进行解读。首先,学习分析技术是测量、收集、分析和报告有关学生的学习行为以及学习环境的数据,用以理解和优化学习及其产生的环境的技术。接着,综述学习分析技术的发展,指出其在教育中有着广泛的应用前景和巨大的发展潜力:学习分析技术可作为教师教学决策、优化教学的有效支持工具,也可为学生的自我导向学习、学习危机预警和自我评估提供有效数据支持,还可为教育研究者的个性化学习设计和增进研究效益提供数据参考。最后,提出学习分析技术也存在隐私、准确性和兼容性等诸多挑战和问题。     

A Study of Detecting Student Engagement Modes in an Online Learning Platform

In this paper, we used the platform log data to extract three features (proportion of passive video time, proportion of active video time, and proportion of assignment time) aligning with different learning activities in the Interactive- Constructive-Active-Passive (ICAP) framework, and applied hierarchical clustering to detect student engagement modes. A total of 840 learning rounds were clustered into four categories of engagement: passive (n = 80), active (n = 366),constructive (n = 75) and resting (n = 319). The results showed that there were differences in the performance of the four engagement modes, and three types of learning status were identified based on the sequences of student engagement modes: difficult, balanced and easy. This study indicated that based on the ICAP framework, the online learning platform log data could be used to automatically detect different engagement modes of students, which could provide useful references for online learning analysis and personalized learning.     

CSCL情境中的群体感知工具:设计类型与效果评估

为了帮助学习者建立在CSCL情境中的群体感知意识,研究者往往会设计群体感知工具,为学习者呈现协作过程中的情境化信息,以此触发高质量的协作学习生成。通过对近15年30篇国外采用群体感知工具开展的实证研究进行系统分析,探讨了不同类型(认知型、行为型和社会型)的群体感知工具如何支持学习者的协作学习过程,具体从信息来源、信息可比性、细粒度、表征方法等维度对这些工具进行了较为详细的对比分析,并介绍了工具对协作过程、群体绩效和个人绩效三方面的影响。最后基于文献分析的结果,从信息可比性和人机协同两方面提出了未来潜在的研究方向。     

新技术与电子书包融合构建智慧学习环境的研究

随着信息技术的飞速发展,教育信息化正在迈入智慧化教育的新阶段,构建智慧学习环境就成为信息化进程中的首要问题。文章在分析了智慧学习环境特征的基础上,提出了融合新技术和电子书包的智慧学习环境的架构,并分析了智慧学习环境支持的学习模式。     

The logic of data-sense: thinking through Learning Personalisation

Big Data and Learning Analytics’ promise to revolutionise educational institutions, endeavours, and actions through more and better data is now compelling. Multiple, and continually updating, data sets produce a new sense of ‘personalised learning’. A crucial attribute of the datafication, and subsequent profiling, of learner behaviour and engagement is the continual modification of the learning environment to induce greater levels of investment on the parts of each learner. The assumption is that more and better data, gathered faster and fed into ever-updating algorithms, provide more complete tools to understand, and therefore improve, learning experiences through adaptive personalisation. The argument in this paper is that Learning Personalisation names a new logistics of investment as the common ‘sense’ of the school, in which disciplinary education is ‘both disappearing and giving way to frightful continual training, to continual monitoring'.     

Speaking the unspoken in learning analytics: troubling the defaults

Abstract

Assessment and learning analytics both collect, analyse and use student data, albeit different types of data and to some extent, for various purposes. Based on the data collected and analysed, learning analytics allow for decisions to be made not only with regard to evaluating progress in achieving learning outcomes but also evaluative judgments about the quality of learning. Learning analytics fall in the nexus between assessment of and for learning. As such it has the potential to deliver value in the form of (1) understanding student learning, (2) analysing learning behaviour (looking to identify not only factors that may indicate risk of failing, but for opportunities to deepen learning), (3) predicting students-at-risk (or identifying where students have specific learning needs), and (4) prescribing elements to be included to ensure not only the effectiveness of teaching, but also of learning. Learning analytics have underlying default positions that may not only skew their impact but also impact negatively on students in realising their potential. We examine a selection of default positions and point to how these positions/assumptions may adversely affect students’ chances of success, deepening the understanding of learning.     

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.     

(Re)centring students in learning analytics: in conversation with Paulo Freire

Abstract

Student data, whether in the form of engagement data, assignments or examinations, form the foundation for assessment and evaluation in higher education. As higher education institutions progressively move to blended and online environments, we have access to, not only more data than before, but also a greater variety of demographic and behavioural data. While the notion of ‘student-centred’ is well-established in the discourses and practices surrounding assessment and evaluation, the concept of student-centred learning analytics is yet to be fully realised by the sector. This article explores and extends this debate by introducing the teachings of Freire as a framework to examine the potential to include students as partners in the collection, analysis and use of their data. The exclusion of students in much of current learning analytics practices, as well as defining categories of analysis and making sense of (their) learning, not only impoverishes our (and their) understanding of the complexities of learning and assessment, but may actually increase vulnerabilities and perpetuate bias and stereotypes. In acknowledging the voice and agency of students, and recentring them as data owners, rather than data objects, learning analytics can realise its transformative potential – for students and institutions alike.