Networked Learning and Learning Analytics: A Study on the Employment of Facebook in a Virtual Training Program

The growing popularity of Social Networking Sites (SNS) that are embedded with networked infrastructures serves as an ideal platform for developing a networked learning environment connecting geographically dispersed learners. Unlike the traditional learning systems which provide only limited sources of data, the learners engaged in virtual networked social environments tend to produce huge volumes of digital footprints that cannot be analyzed using conventional analytical techniques. Two new branches of analytical sciences – Educational Data Mining (EDM) and Learning Analytics (LA) – are being employed for processing digital data derived from online educational platforms in order to obtain meaningful inferences and data-driven insights.

Hence, the present experimental study involving a small group of geographically dispersed learners intend to examine the engagement level and interaction patterns that occur within a feminist networked learning environment created in Facebook using popular EDM and learning analytical techniques such as K-means Clustering, Social network analysis and correlation mining. Upon analysis, it was found that the peer network influence played a vital role in activating passive learners, eventually leading to the development of a closely bound networked learning community over time.     

A systematic review on the deployment and effectiveness of data analytics in higher education to improve student outcomes

Abstract

This is a systematic review conducted of primary research literature published between 2007 and 2018 on the deployment and effectiveness of data analytics in higher education to improve student outcomes. We took a methodological approach to searching databases; appraising and synthesising results against predefined criteria. We reviewed research on the effectiveness of three differentiated forms of data analytics: learning, academic and learner analytics. Student outcomes are defined as retention, academic performance and engagement. Our results find that three quarters of studies report the use of educational data analytics to be effective in improving student outcomes but their relationship with student outcomes requires further and more robust investigation and assessment. We argue that research must interpret and communicate effectiveness qualitatively, as well as quantitatively, by including the student voice in assessments of impact.     

Effective usage of learning analytics: what do practitioners want and where should distance learning institutions be going?

ABSTRACT

The implementation of learning analytics may empower distance learning institutions to provide real-time feedback to students and teachers. Given the leading role of the Open University UK (OU) in research and application of learning analytics, this study aims to share the lessons learned from the experiences of 42 participants from a range of faculty, academic and professional positions, and expertise with learning analytics. Furthermore, we explored where distance learning institutions should be going next in terms of learning analytics adoption. The findings from the Learning Analytics User Stories (LAUS) workshop indicated four key areas where more work is needed: communication, personalisation, integrated design, and development of an evidence-base. The workshop outputs signalled the aspiration for an integrated analytics system transcending the entire student experience, from initial student inquiry right through to qualification completion and into life-long learning. We hope that our study will spark discussion on whether (or not) distance learning institutions should pursue the dream of an integrated, personalised, and evidence-based learning analytics system that clearly communicates useful feedback to staff and students, or whether this will become an Orwellian nightmare.     

How do the existing fairness metrics and unfairness mitigation algorithms contribute to ethical learning analytics?

With the widespread use of learning analytics (LA), ethical concerns about fairness have been raised. Research shows that LA models may be biased against students of certain demographic subgroups. Although fairness has gained significant attention in the broader machine learning (ML) community in the last decade, it is only recently that attention has been paid to fairness in LA. Furthermore, the decision on which unfairness mitigation algorithm or metric to use in a particular context remains largely unknown. On this premise, we performed a comparative evaluation of some selected unfairness mitigation algorithms regarded in the fair ML community to have shown promising results. Using a 3-year program dropout data from an Australian university, we comparatively evaluated how the unfairness mitigation algorithms contribute to ethical LA by testing for some hypotheses across fairness and performance metrics. Interestingly, our results show how data bias does not always necessarily result in predictive bias. Perhaps not surprisingly, our test for fairness-utility tradeoff shows how ensuring fairness does not always lead to drop in utility. Indeed, our results show that ensuring fairness might lead to enhanced utility under specific circumstances. Our findings may to some extent, guide fairness algorithm and metric selection for a given context.

Practitioner notes

What is already known about this topic

• LA is increasingly being used to leverage actionable insights about students and drive student success.
• LA models have been found to make discriminatory decisions against certain student demographic subgroups—therefore, raising ethical concerns.
• Fairness in education is nascent. Only a few works have examined fairness in LA and consequently followed up with ensuring fair LA models.

What this paper adds

• A juxtaposition of unfairness mitigation algorithms across the entire LA pipeline showing how they compare and how each of them contributes to fair LA.
• Ensuring ethical LA does not always lead to a dip in performance. Sometimes, it actually improves performance as well.
• Fairness in LA has only focused on some form of outcome equality, however equality of outcome may be possible only when the playing field is levelled.

Implications for practice and/or policy

• Based on desired notion of fairness and which segment of the LA pipeline is accessible, a fairness-minded decision maker may be able to decide which algorithm to use in order to achieve their ethical goals.
• LA practitioners can carefully aim for more ethical LA models without trading significant utility by selecting algorithms that find the right balance between the two objectives.
• Fairness enhancing technologies should be cautiously used as guides—not final decision makers. Human domain experts must be kept in the loop to handle the dynamics of transcending fair LA beyond equality to equitable LA.

     

Designing for impact: a conceptual framework for learning analytics as self-assessment tools

Abstract

Although it is frequently claimed that learning analytics can improve self-evaluation and self-regulated learning by students, most learning analytics tools appear to have been developed as a response to existing data rather than with a clear pedagogical model. As a result there is little evidence of impact on learning. Even fewer learning analytics tools seem to be informed by an understanding of the social context and social practices within which they would be used. As a result, there is very little evidence that learning analytics tools are actually impacting on practice. This paper draws on research in self-regulated learning and in the social practices of learning and assessment to clarify a series of design issues which should be considered by those seeking to develop learning analytics tools which are intended to improve student self-evaluation and self-regulation. It presents a case study of how these design issues influenced the development of a particular tool: the Learning Companion.     

论实施自主创新战略的历史必然性与现实可能性

自主创新国家战略的确立,体现了中国应对知识经济挑战、增强国家核心竞争力、实现科技跨越发展、进一步落实科学发展观的必然选择;自主创新国家战略的确立是对于当下现实的判定,中国以独具的“后发优势”作为动力、以国家的发展战略作为导向、以逐步完善的科技政策作为依靠、以日益增加的科技投入作为保障和以科技发展的局部优势作为基础,实施自主创新战略具有现实的可能性。     

Towards mapping competencies through learning analytics: real-time competency assessment for career direction through interactive simulation

Abstract

The selection of career paths and making of academic choices is a difficult and often confusing task for young people. The impact on their lives, however, is enormous as it can determine entire future career possibilities. In India, a general remedy to this stress is that instead of choosing a field of study tailored to individual preferences and strengths, topics are chosen that align with the choices of the students’ families or their friends. This can have the effect of entrenching patterns of intergenerational inequity. The aim of this research is to give students greater access to the knowledge capital which will help them make better choices. This is achieved by engaging students in the career planning process, in order to convey information in a likeable and credible way. The COMPCAT (Competency and Career Assessment Tool) game engine combines the use of learning analytics and real time, interactive computer simulations designed to gain insights into the students’ engagement in the making of these complex decisions. This paper presents the conceptual architecture of the game and demonstrates its role in enhancing the learning effectiveness of the students.     

基于大数据分析的旅游微博用户偏爱研究

为在巨量数据下挖掘旅游微博用户的偏爱链路,分析了传统偏爱链路算法在巨量数据背景下存在的挖掘能力不足等问题,通过重新评判用户浏览兴趣与点击量的真实关系,对已有的网络拓扑算法进行了改进,提出了一种基于大数据分析的旅游微博用户偏爱链路计算方法。研究结果表明:基于大数据分析的旅游微博用户偏爱链路算法在互联网巨量数据下较传统算法有更高的计算速度和更高的计算准确度。     

Using Big Data to Examine the Relationship Between Facebook Engagement and Local Newscast Ratings

ABSTRACT

Local TV news stations regularly post to social media, on Facebook in particular, in an effort to engage audiences, but little is known about the effect of this practice on newscast ratings. We examine the relationship between engagement on Facebook and newscast ratings using data from Shareablee and Nielsen respectively in regression analyses. We find that Facebook engagement does not cannibalize TV ratings, rather there is a clear positive relationship between the two. However, further analyses suggest that this relationship may be driven by interest in news events or other market-wide factors that result in both Facebook engagement and viewership increasing or decreasing simultaneously.     

Data entry: towards the critical study of digital data and education

The generation and processing of data through digital technologies is an integral element of contemporary society, as reflected in recent debates over online data privacy, ‘Big Data’ and the rise of data mining and analytics in business, science and government. This paper outlines the significance of digital data within education, arguing for increased interest in the topic from educational researchers. Building on themes from the emerging sub-field of ‘digital sociology’, the paper outlines a number of ways in which digital data in education could be questioned along social lines. These include issues of data inequalities, the role of data in managerialist modes of organisation and control, the rise of so-called ‘dataveillance' and the reductionist nature of data-based representation. The paper concludes with a set of suggestions for future research and discussion, thus outlining the beginnings of a framework for the future critical study of digital data and education.