(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.     

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.     

GoogleAnalytics在校园网站数据分析中的应用

文章详细介绍了GoogleAnalytics数据统计分析工具,并对如何应用进行了探讨。结合实际的应用情况,举例说明了在校园网站中使用GoogleAnalytics的结果。通过“受众群体”分析、“流量来源”分析和“内容”分析,可以有针对性地对校园网站进行调整、布局,对师生或访问者关心的内容加以充实。     

Tracking the Elusive Student: Opportunities for Connection and Assessment

ABSTRACT

At Eastern Michigan University, information about library resources and services for Extended Programs (off-campus and online) students was provided in a number of online locations and was sometimes inconsistent and difficult to manage. The library formed an internal task force to evaluate all of the library information and instructional materials provided to Extended Programs students. The task force consolidated key information in one location on the library Web site and collaborated with departments within the library and around campus to provide links from the relevant online locations. This case study describes how Google Analytics was used to assess the use of the revised library Web site and online instructional materials by Extended Programs students. The researchers describe examples of techniques for using Google Analytics and explain how the data collected was used to identify further enhancements to the information provided to Extended Programs students.     

Undercounting File Downloads from Institutional Repositories

ABSTRACT

A primary impact metric for institutional repositories (IR) is the number of file downloads, which are commonly measured through third-party Web analytics software. Google Analytics, a free service used by most academic libraries, relies on HTML page tagging to log visitor activity on Google's servers. However, Web aggregators such as Google Scholar link directly to high value content (usually PDF files), bypassing the HTML page and failing to register these direct access events. This article presents evidence of a study of four institutions demonstrating that the majority of IR activity is not counted by page tagging Web analytics software, and proposes a practical solution for significantly improving the reporting relevancy and accuracy of IR performance metrics using Google Analytics.     

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.     

Team and seasonal performance indicator evolution in the ANZ Championship netball league

ABSTRACT

Analysis of netball has received scant attention in the literature and there is little understanding of the dynamics of netball game-play. This study aimed to analyse team and seasonal performance indicator (dis)similarity in the ANZ Championship (netball). Total season values for nine performance indicators were analysed for the ten ANZ Championship teams from 2009 to 2016. The data were analysed using a multivariate, distance-based, approach. Specifically, non-metric multidimensional scaling was used to examine seasonal and team (dis)similarity. After declining from 2009, shooting percentage, goal assists, centre pass receives, penalties and turnovers began to rise from 2011. Both penalties and turnovers declined from 2015, in addition to attempts at goal. The two-dimensional multivariate ordination plot showed relative similarity between each team and season over the observational period, suggesting stagnant game-play dynamics. Further, despite idiosyncratic seasonal profiles, teams generally followed similar directional progression across the ordination surface. Despite being observed in other team invasion sports, league-wide synchronous evolutionary tendencies were not observed within the ANZ Championships between the 2006 to 2016 seasons. However, certain teams did display seasonal fluctuation in their observed multivariate profile, with these seasonal idiosyncrasies being discussed relative to coaching and playing roster changes specific to the analysed team.     

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.