U N I V E R S I T Y O F B E R G E N Barbara Wasson, Director NIFU Workshop on Learning Analytics 25 May 2016
OVERVIEW SLATE Mandate Learning Analysis? SLATE Organisation & Collaboration Partners Research 3 Clusters Year 1: Foundations 2
SLATE INVITATION TO APPLY 3
SLATE 4 Pedagogical competence Educational research Digital and flexible learning Informatics / Information Science Psychology Statistics
SLATE MANDATE Fagmiljøet skal utføre forskning av høy kvalitet om læringsanalyse Fagmiljøet skal være en FoU-enhet som bidrar til nasjonal kompetanse- og kunnskapsutvikling innen læringsanalyse Fagmiljøet skal kartlegge og være en sentral ressurs for hvilken muligheter og utfordringer som ligger i bruk av og forskning på læringsanalyse i Norge Fagmiljøet skal være internasjonalt orientert og søke å inngå relevante samarbeid med forskningsmiljøer innen læringsanalyse Fagmiljøet skal gjennom sin FoU-aktivitet utvikle og formidle kunnskap til relevante aktører i utdanningssektoren Fagmiljøet skal gjennom å søke samarbeid medvirke til kompetanseoppbygging i disiplinen læringsanalyse ved andre miljøer i UH-sektoren 5
SLATE MANDATE Fagmiljøet skal utføre forskning av høy kvalitet om læringsanalyse Fagmiljøet skal være en FoU-enhet som bidrar til nasjonal kompetanse- og kunnskapsutvikling innen læringsanalyse Fagmiljøet skal kartlegge og være en sentral ressurs for hvilken muligheter og utfordringer som ligger i bruk av og forskning på læringsanalyse i Norge Fagmiljøet skal være internasjonalt orientert og søke å inngå relevante samarbeid med forskningsmiljøer innen læringsanalyse Fagmiljøet skal gjennom sin FoU-aktivitet utvikle og formidle kunnskap til relevante aktører i utdanningssektoren Fagmiljøet skal gjennom å søke samarbeid medvirke til kompetanseoppbygging i disiplinen læringsanalyse ved andre miljøer i UH-sektoren 6
MANDATE The long-term goal is that the scientific community (i.e., SLATE) will develop into a broad environment for learning science by involving a wide range of relevant disciplines such as cognitive psychology, developmental psychology, and neuroscience. 7
SLATE AMBITION SLATE will generate empirical knowledge with relevance for educational practice in the broadest sense, at the intersection of learning, learners, technology, and pedagogical practice AND advance the frontiers of all the sciences of learning and technology through integrated research. 8
SLATE BREADTH SLATE research and development will address scientific, technological, educational, workforce, and leisure time challenges related to learning and technology in all facets of human learning throughout a lifetime. 9
SLATE LEARNING ANALYSIS FOCUS decisions about the use of data approaches in Education must be informed by a scientific understanding of the impact of learning analysis on learners, teachers, institutions, and society. 10
SLATE Learning Analysis OR Learning Analytics? Learning Analysis the role of data and data analysis for learning, teaching, and education 3 distinct, but overlapping fields that address the role of data and data analysis Educational data mining (EDM) Learning analytics and knowledge (LAK) Big Data 11
SLATE EDM, LAK & BIG DATA Educational Data Mining (EDM) Intelligent data mining roots in Artificial Intelligence in Education & Intelligent Tutoring Systems research, as far back as the 1970s applies computational approaches such as data mining, machine learning classification, clustering, Bayesian modelling, relationship mining, discovery with models, statistics, and visualisation to information generated in educational settings to better understand students and the settings in which they learn 12
SLATE EDM, LAK & BIG DATA Learning Analytics and Knowledge (LAK) Emerging research field and design discipline LA 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. (LAK 12) LAK facilitates a clear theoretical understanding of what is learning, how we assess it, how we foster it, and how we operationalise it in productive educational practices, teaching and learning environments 13
SLATE EDM, LAK & BIG DATA Big Data in Education Refers to large amounts of data produced very quickly by a high number of diverse sources Data generated by people (e.g., computer logs, an essay) or generated by technology (e.g., sensor readings, photos, videos, GPS signals, etc.) The analysis of large data sets generated in educational context could identify and validate patterns cross institutions, regions and countries, which benefit the different levels of stakeholders in education systems (predictive analytics) à Is there big data, or rather just small data in the educational sector??? 14
A current problem is that the information provided by learning analytics tools is not generally aligned with teachers needs for the management of learning activities. Mor, Y., Ferguson, R. & Wasson, B. (2015). Editorial: Learning design, teacher inquiry into student learning and learning analytics: A call for action. British Journal of Educational Technology, 46(2), 221-229 Data literacy and use for teaching Wasson, B. & Hansen, C. (2016). Data Literacy and Use for Teaching. In P. Reimann, S. Bull, R. Lukin, B. Wasson (Eds.) Measuring and visualising competence development in the information-rich classroom, 56-74. New York: Routledge Challenges for learning Wasson, B., Hansen, C. & Netteland, G. (2016). Data Literacy and Use for Learning when using Learning Analytics for Learners. Workshop on Learning Analytics for Learners LAK 16. 15
SLATE 16
SLATE COLLABORATION 17
SLATE COLLABORATION 18
STRATEGIC GOALS SLATE will advance knowledge by leveraging ongoing and new research studies on learners and the learning process within Cluster Areas: Assessment Innovation, Big and Small Data, and Innovative Research Futures 19
Year 1: Foundations national dugnad 20
SLATE Communication slate.uib.no 21
SLATE Communication Twitter @SlateResearch #slateresearch #slateguestlecture Facebook - Group (closed) - Page (open) 22
SLATE Opening 23