Learning at Scale: Using an Evidence Hub To Make Sense of What We Know

The large datasets produced by learning at scale, and the need for ways of dealing with high learner/educator ratios, mean that MOOCs and related environments are frequently used for the deployment and development of learning analytics. Despite the current proliferation of analytics, there is as yet relatively little hard evidence of their effectiveness. The Evidence Hub developed by the Learning Analytics Community Exchange (LACE) provides a way of collating and filtering the available evidence in order to support the use of analytics and to target future studies to fill the gaps in our knowledge

Knowledge management to support learning analytics in Higher Education

This paper argues based on evidence from the literature that learning analytics, when undertaken by higher education institutions, is not considered within a holistic knowledge management strategy, which could provide significant improvement to the outcomes of learning analytics. Particularly, a synthesis of knowledge extraction via learning analytics and appropriate handling of such knowledge via knowledge management is not typically implemented in higher education practices, but it constitutes a promising path to improving it, and eventually contributes to improving learning services. Essentially, knowledge management can support improvements and innovation in analytics tools, translate an organisation's strategic vision into action, and enable sharing of information among different actors. These are all necessary requirements for effective learning analytics

Socio-semantic Networks of Research Publications in the Learning Analytics Community

Fazeli, S., Drachsler, H., & Sloep, P. B. (2013). Socio-semantic Networks of Research Publications in the Learning Analytics Community. In M. d'Aquin, S. Dietze, H. Drachsler, E. Herder, & D. Taibi (Eds.), Linked data challenge, Learning Analytic and Knowledge (LAK13) (pp. 6-10). Vol. 974, Leuven, Belgium.In this paper, we present network visualizations and an analysis of publications data from the LAK (Learning Analytics and Knowledge) in 2011 and 2012, and the special edition on Learning and Knowledge Analytics in Journal of Educational Technology and Society (JETS) in 2012.NELLL, FP7 EU Open Discovery Space (ODS

Analytics for Knowledge Creation: Towards Epistemic Agency and Design-Mode Thinking

Innovation and knowledge creation call for high-level epistemic agency and design-mode thinking, two competencies beyond the traditional scopes of schooling. In this paper, we discuss the need for learning analytics to support these two competencies, and more broadly, the demand for education for innovation. We ground these arguments on a distinctive Knowledge Building pedagogy that treats education as a knowledge-creation enterprise. By critiquing current learning analytics for their focus on static-state knowledge and skills, we argue for agency-driven, choice-based analytics more attuned to higher order competencies in innovation. We further describe ongoing learning analytics initiatives that attend to these elements of design. Prospects and challenges are discussed, as well as broader issues regarding analytics for higher order competencies

Learning analytics to identify exploratory dialogue within synchronous text chat

While generic web analytics tend to focus on easily harvested quantitative data, Learning Analytics will often seek qualitative understanding of the context and meaning of this information. This is critical in the case of dialogue, which may be employed to share knowledge and jointly construct understandings, but which also involves many superficial exchanges. Previous studies have validated a particular pattern of “exploratory dialogue” in learning environments to signify sharing, challenge, evaluation and careful consideration by participants. This study investigates the use of sociocultural discourse analysis to analyse synchronous text chat during an online conference. Key words and phrases indicative of exploratory dialogue were identified in these exchanges, and peaks of exploratory dialogue were associated with periods set aside for discussion and keynote speakers. Fewer individuals posted at these times, but meaningful discussion outweighed trivial exchanges. If further analysis confirms the validity of these markers as learning analytics, they could be used by recommendation engines to support learners and teachers in locating dialogue exchanges where deeper learning appears to be taking place

A Bibliometric Study on Learning Analytics

Learning analytics tools and techniques are continually developed and published in scholarly discourse. This study aims at examining the intellectual structure of the Learning Analytics domain by collecting and analyzing empirical articles on Learning Analytics for the period of 2004-2018. First, bibliometric analysis and citation analyses of 2730 documents from Scopus identified the top authors, key research affiliations, leading publication sources (journals and conferences), and research themes of the learning analytics domain. Second, Domain Analysis (DA) techniques were used to investigate the intellectual structures of learning analytics research, publication, organization, and communication (Hjørland & Bourdieu 2014). The software of VOSviewer is used to analyze the relationship by publication: historical and institutional; author and institutional relationships and the dissemination of Learning Analytics knowledge. The results of this study showed that Learning Analytics had captured the attention of the global community. The United States, Spain, and the United Kingdom are among the leading countries contributing to the dissemination of learning analytics knowledge. The leading publication sources are ACM International Conference Proceeding Series, and Lecture Notes in Computer Science. The intellectual structures of the learning analytics domain are presented in this study the LA research taxonomy can be re-used by teachers, administrators, and other stakeholders to support the teaching and learning environments in a higher education institution

From Theory to Action: Developing and Evaluating Learning Analyticsfor Learning Design

Producción CientíficaThe effectiveness of using learning analytics for learning design primarily depends upon two concepts: grounding and alignment. This is the primary conjecture for the study described in this paper. In our design-based research study, we design, test, and evaluate teacher-facing learning analytics for an online inquiry science unit on global climate change. We design our learning analytics in accordance with a socioconstructivism-based pedagogical framework, called Knowledge Integration, and the principles of learning analytics Implementation Design. Our methodology for the design process draws upon the principle of the Orchestrating for Learning Analytics framework to engage stakeholders (i.e. teachers, researchers, and developers). The resulting learning analytics were aligned to unit activities that engaged students in key aspects of the knowledge integration process. They provided teachers with actionable insight into their students' understanding at critical junctures in the learning process. We demonstrate the efficacy of the learning analytics in supporting the optimization of the unit's learning design. We conclude by synthesizing the principles that guided our design process into a framework for developing and evaluating learning analytics for learning design.Ministerio de Ciencia, Innovación y Universidades (Project TIN2017-85179-C3-2-R)Junta de Castilla y León (project VA257P18) by the European Commission under project grant 588438-EPP-1-2017-1-EL-EPPKA2-KA

Student Attitudes toward Learning Analytics in Higher Education: "The Fitbit Version of the Learning World"

Increasingly, higher education institutions are exploring the potential of learning analytics to predict student retention, understand learning behaviors, and improve student learning through providing personalized feedback and support. The technical development of learning analytics has outpaced consideration of ethical issues surrounding their use. Of particular concern is the absence of the student voice in decision-making about learning analytics. We explored higher education students' knowledge, attitudes, and concerns about big data and learning analytics through four focus groups (N = 41). Thematic analysis of the focus group transcripts identified six key themes. The first theme, “Uninformed and Uncertain,” represents students' lack of knowledge about learning analytics prior to the focus groups. Following the provision of information, viewing of videos and discussion of learning analytics scenarios three further themes; “Help or Hindrance to Learning,” “More than a Number,” and “Impeding Independence”; represented students' perceptions of the likely impact of learning analytics on their learning. “Driving Inequality” and “Where Will it Stop?” represent ethical concerns raised by the students about the potential for inequity, bias and invasion of privacy and the need for informed consent. A key tension to emerge was how “personal” vs. “collective” purposes or principles can intersect with “uniform” vs. “autonomous” activity. The findings highlight the need the need to engage students in the decision making process about learning analytics

Using Linked Data in Learning Analytics

Learning Analytics has a lot to do with data, and the way to make sense of raw data in terms of the learner’s experience, behaviour and knowledge. In this article, we argue about the need for a closer relationship between the field of Learning Analytics and the one of Linked Data, which in our view constitutes an ideal data management layer for Learning Analytics. Based on our experience with organising the “Using Linked Data in Learning Analytics” tutorial at the Learning Analytics and Knowledge conference, we discuss the existing trends in the use of linked data and semantic web technologies, in general in education and in learning analytics specifically. We find that the emerging connections between the two fields are still, at the time of writing, much less prominent than one would expect considering the complementary nature of the considered technologies and practices. We therefore argue that specific efforts, somehow materialised through the tutorial and the work in the LinkedUp support action, are needed to ensure the realisation of the potential cross-benefits that combining Learning Analytics and Linked Data research could bring.LinkedU