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

The Impact of Learning Analytics on the Dutch Education System

The article reports the findings of a Group Concept Mapping study that was conducted within the framework of the Learning Analytics Summer Institute (LASI) in the Netherlands. Learning Analytics are expected to be beneficial for students and teacher empowerment, personalization, research on learning design, and feedback for performance. The study depicted some management and economics issues and identified some possible treats. No differences were found between novices and experts on how important and feasible are changes in education triggered by Learning Analytics

An Exercise in Institutional Reflection: The Learning Analytics Readiness Instrument (LARI)

While the landscape of learning analytics is relatively well defined, the extent to which institutions are ready to embark on an analytics implementation is less known. Further, while work has been done on measuring the maturity of an institution’s implementation, this work fails to investigate how an institution that has not implemented analytics to date might become mature over time. To that end, the authors developed and piloted a survey, the Learning Analytics Readiness Instrument (LARI), in an attempt to help institutions successfully prepare themselves for a successfully analytics implementation. The LARI is comprised of 90 items encompassing five factors related to a learning analytics implementation: (1) Ability, (2) Data, (3) Culture and Process, (4) Governance and Infrastructure, and, (5) Overall Readiness Perception. Each of the five factors has a high internal consistency, as does the overall tool. This paper discusses the need for a survey such as the LARI, the tool’s psychometric properties, the authors’ broad interpretations of the findings, and next steps for the LARI and the research in this field.http://deepblue.lib.umich.edu/bitstream/2027.42/110781/1/An Exercise in Institutional Reflection_The Learning Analytics Readiness Instrument.pdfDescription of An Exercise in Institutional Reflection_The Learning Analytics Readiness Instrument.pdf : Proceeding pd

Learner Analytics and Student Success Interventions

The implementation of analytics in support of student success requires effective use of feedback and interventions, as well as a system by which the use of feedback and institutional supports can be tracked and evaluated

Learning analytics in higher education: a review of impact scientific literature

Las analíticas de aprendizaje pueden definirse como una serie de técnicas para recopilar, analizar y otorgar datos procesables y generados por parte de los estudiantes con el objetivo de elaborar estrategias adecuadas para mejorar los procesos de aprendizaje, el rendimiento de los alumnos o el de la propia institución. Este tipo de técnicas son especialmente útiles para establecer patrones de acción que guíen y orienten el proceso educativo en educación superior. Bajo estas premisas, la presente investigación tiene por objetivo analizar la producción científica de mayor impacto sobre el empleo de analíticas de aprendizaje en educación superior. Para ello se ha seguido una metodología cuantitativa atendiendo diez variables: año de publicación, publicaciones periódicas, autores, instituciones, países, tipo de documento, formato de publicación, área de publicación, idioma y artículos más citados. Los resultados proyectan una tendencia de investigación que se encuentra totalmente en auge, especialmente por la mayor producción científica ocurrida en los últimos años (2015-2018) destacando países como Australia, Estados Unidos y Reino Unido. Las publicaciones, en su gran mayoría, proceden de conferencias y se encuentran publicadas en inglés. Destacan las áreas de conocimiento de Ciencias Computacionales y Ciencias Sociales.Learning analytics can be defined as a series of techniques for collecting, analyzing and granting processable data generated by students. Their objective is to develop appropriate strategies to improve the learning processes, the performance of the students or that of the institution itself. These types of techniques are especially useful to establish patterns of action that guide and guide the educational process in higher education. Under these premises, the present research aims to analyze the scientific production with the greatest impact on the use of learning analytics in higher education. For this purpose, a quantitative methodology has been followed, based on ten variables: year of publication, periodicals, authors, institutions, countries, type of document, publication format, publication area, language and most cited articles. The results project a research trend that is fully on the rise, especially due to the greater scientific production that has occurred in recent years (2015- 2018), highlighting countries such as Australia, the United States and the United Kingdom. The publications, in their great majority, come from conferences and are published in English. The knowledge areas of Computational Sciences and Social Sciences stand out.Ministry of Education, Culture and Sport through the Aid of the University Teaching Staff Training Program FPU14/0462

Learning Analytics to Inform Teaching and Learning Approaches

Learning analytics is an evolving discipline with capability for educational data analysis to enable better understanding of learning processes. This paper reports on learning analytics research at Institute of Technology Blanchardstown, Ireland, that indicated measureable factors can identify first year students at risk of failing based on data available prior to commencement of first year of study. The study was conducted over three years, 2010 to 2012, on a student population from a range of academic disciplines (n=1,207). Data was gathered from both student enrolment data maintained by college administration, and an online, self-reporting tool administered during induction sessions for students enrolling into the first year of study. Factors considered included prior academic performance, personality, motivation, self- regulation, learning approaches, learner modality, age and gender. A k-NN classification model trained on data from the 2010 and 2011 student cohort, and tested on data from the 2012 student cohort correctly identified 74% of students at risk of failing. Some factors predictive of at-risk students are malleable, and relate to an effective learning disposition; specifically, factors relating to self-regulation and motivation. This paper discusses potential benefits of measurement of learner disposition

D3.1 Framework of Quality Indicators

D3.1 Framework of Quality Indicators. LACE Projec

Special issue on: Learning Analytics (Editorial)

With the general technological advances of the recent years, current learning environments amass an abundance of data. Albeit such data offer the chance of better understand the learning process, stakeholders – learners, teachers and institutions – often need additional support to make sense of it (Dyckhoff et al., 2013; Macfadyen and Dawson, 2012). The acknowledgement of these needs is at the heart of the recent emergence of Learning Analytics (LA), a research area that draws from multiple disciplines such as educational science, information and computer science, sociology, psychology, statistics and educational data mining (Buckingham Shum and Ferguson, 2012). This multidisciplinarity in LA has motivated the work done by Ferguson (2012), which provides a first review of the drivers, development and challenges of this novel and young research area. Our understanding of learning analytics is based on the definition from the Society for Learning Analytics (SoLAR – Society for Learning Analytics1) which specifies that “Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs”. Since 2011, the Horizon reports list Learning Analytics as a hot topic in higher education and indicate the importance of data for this field (Johnson et al., 2011). Learning analytics are able to provide a fresh view on understanding of teaching and learning by observing patterns of complex data (Johnson et al., 2012). Furthermore, it will influence the evolution of higher education in a great measure. Nowadays, learners have access to a huge amount of online information having themselves the possibility of being content creators and information sharers. Therefore the quantity of available information grows in an exponential way, once that each and every citizen can access and produce information. For these purposes, learners have at their disposal many online resources, including LMSs, VLEs, MOOCs and many other online tools that facilitate the learning process and the development of competences. Taking into account these online learning facilities and therefore the learners’ acquisition of knowledge, it is also easier to measure and analyse their experiences by using learning analytics tools. Different online courses and institutions provide dashboards with information about student experiences, flaws and successes. Although the investigation of behavioural specific data makes learning analytics complex, the time comes to utilise personalised learning environments adapted to students learning paths, skills, previous knowledge, competences and motivation

Student privacy self-management: implications for learning analytics

Optimizing the harvesting and analysis of student data promises to clear the fog surrounding the key drivers of student success and retention, and provide potential for improved student success. At the same time, concerns are increasingly voiced around the extent to which individuals are routinely and progressively tracked as they engage online. The Internet, the very thing that promised to open up possibilities and to break down communication barriers, now threatens to narrow it again through the panopticon of mass surveillance. Within higher education, our assumptions and understanding of issues surrounding student attitudes to privacy are influenced both by the apparent ease with which the public appear to share the detail of their lives and our paternalistic institutional cultures. As such, it can be easy to allow our enthusiasm for the possibilities offered by learning analytics to outweigh consideration of issues of privacy. This paper explores issues around consent and the seemingly simple choice to allow students to opt-in or opt-out of having their data tracked. We consider how 3 providers of massive open online courses (MOOCs) inform users of how their data is used, and discuss how higher education institutions can work toward an approach which engages and more fully informs students of the implications of learning analytics on their personal data