Using data (analytics:analysis) to guide (e)teaching and (e)learning

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Using data from learning analytics and assessments, educators can better understand student engagement, milestones, and learning outcomes. This allows them to evaluate and improve learning design and ensure it leads to the intended learning outcomes. Several studies have explored how learning analytics can inform pedagogical actions by aligning with learning design elements like assessments, in order to guide and drive student learning.

Education

Using data (analytics/analysis) to guide (e)Teaching and (e)Learning
Analytics
Information on learning pathway

engagement, milestones
Assessment
Information on learning outcomes
Analysis and Evaluation
Guided by …. Informed by
Learning design Learning outcomes
Frank JR, Snell LS, Cate OT, Holmboe ES, Carraccio C, Swing SR, Harris P,
Glasgow NJ, Campbell C, Dath D, Harden RM, Iobst W, Long DM, Mungroo R,
Richardson DL, Sherbino J, Silver I, Taber S, Talbot M, Harris KA.
Competency-based medical education: theory to practice. Med Teach.
2010;32(8):638-45. doi: 10.3109/0142159X.2010.501190.
 
Lori Lockyer, Elizabeth Heathcote, Shane Dawson.
Informing Pedagogical Action - Aligning Learning
Analytics with Learning Design. American Behavioral
Scientist Vol 57, Issue 10, pp. 1439 - 1459
“Assessment Drives Student Learning”

George E Miller
Poh-Sun Goh

1st draft on 23 October 2017 @ 0727am

Across the globe many institutions and organisations have high hopes that learning analytics can play a major role in helping their organisations remain fit-for-purpose, flexible, and innovative. A broad goal of learning analytics is to apply the outcomes of analysing data gathered by monitoring and measuring the learning process. Learning analytics applications in education are expected to provide institutions with opportunities to support learner progression, but more importantly provide personalised, rich learning on a large scale. Substantial progress in learning analytics research has been made in the last few years. Researchers in learning analytics use a range of advanced computational techniques (e.g., Bayesian modelling, cluster analysis, natural language processing, machine learning) for predicting which learners are likely to fail or succeed, and how to provide appropriate support in a flexible and adaptive manner. In this keynote, I will argue that unless educational researchers at EARLI embrace some of the key principles, methods, and approaches of learning analytics, educational researchers may be left behind. In particular, a main merit of learning analytics is linking large datasets of actual learning processes and outcomes with learning dispositions and learner characteristics. Using evidence-based approaches rapid insights and advancements are developed how learning designs and learning processes can be optimised to maximise the potential of each learner. For example, our recent research with 151 modules and 133K students at the Open University UK indicates that learning design has a strong impact on student behaviour, satisfaction, and performance. Learning analytics can also drive learning in more “traditional”, face-to-face contexts. For example, by measuring emotions, epistemological expressions, and cross-cultural dialogue, social interactions can be effectively supported by innovative dashboards and adaptive approaches. I aim to unpack the advantages and limitations of learning analytics and how EARLI researchers can embrace such data-driven research approaches More info at www.bartrienties.nl

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Bart Rienties

Date and time: Wednesday 20 September 2017 at 5pm AEST Abstract: The Open University UK (OU) has been one of few institutions that have explicitly and systematically captured the designs for learning at a large scale. By applying advanced analytical techniques on large and fine-grained datasets, we have been unpacking the complexity of instructional practices, as well as providing empirical evidence of how learning designs influence student behaviour, satisfaction, and performance. This seminar will discuss the implementation of learning design at the OU in the last 5 years, and reviews empirical evidence from several studies that have linked learning design with learning analytics. Recommendations are put forward to support future adoptions of the learning design approach, and potential research trajectories. https://ascilite.org/get-involved/sigs/learning-analytics-sig/ www.bartrienties.nl

Learning Design and Analytics in Learning and Teaching: The Role of Big Data

Bart Rienties

Keynote presentation OOFHEC2016: Bart Rienties

EADTU

Across the globe many educational institutions are collecting vast amounts of small and big data about students and their learning behaviour, such as their class attendance, online activities, or assessment scores. As a result, the emerging field of Learning Analytics is exploring how data can be used to empower teachers and institutions to effectively support learners. The way teachers design learning and teaching practices have a substantial impact how our students are engaging in- and outside class. Recent research within the Institute of Educational Technology has found that 69% of how students learn on a weekly basis is determined by what we as teachers design and teach. Furthermore, how teachers are using learning analytics data significantly can help to support students, but what if teachers do not want or are able to embrace big data? In this APT2018 keynote, based upon 6 years of experience with LA data and large-scale implementations amongst 450000+ students and 400+ teachers at a range of contexts, I will use an interactive format to discuss and debate three major questions: 1) To what extent is learning analytics the new holy grail of learning and teaching? 2) How can learning design be optimised using the principles of learning analytics?; 3) How should institutions provide academic development opportunities to learn to embrace the affordances and limitations of learning analytics?

Towards Strengthening Links between Learning Analytics and Assessment

Dragan Gasevic

. The emergence of learning analytics afforded for the analysis of digital traces of user interaction with technology. This analysis offers many opportunities to advance understanding and enhance learning and the environments in which learning occurs. Existing research has shown how learning analytics can provide contributions to different areas of education such as prediction of student success, uncovering learning strategies, understanding affective states, and unpacking the role social networks in learning. While these results have shown much promise, one critical challenge remains unclear – how learning analytics can help track learning progression and inform assessment especially from the perspective of the 21st century skills. This talk will explore opportunities and challenges for the integration of methods commonly used in learning analytics to analyze different digital traces with methods commonly used in assessment and psychometric research. The paper particularly focuses on open learning environments where analytics-based assessment is rather underexplored in contrast to assessment in specialized (intelligent tutoring) systems where the combined use of data mining and psychometric techniques has been established for some time now.

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Christian Bokhove

Keynote SEC2019 Leeds: The power of learning analytics to impact learning and...

Bart Rienties

The second keynote will be delivered by Professor Bart Rienties of the Open University who will discuss how the power of learning and teaching can be unharnessed by using learning analytics on Friday, January 11 . The theme – Learning Spaces – will examine the many arenas in which students can learn and develop, create and collaborate, forge partnerships with communities, cross thresholds or take risks. Over the course of both days, plenaries, breakout sessions and a panel will also consider sub-themes, such as informal learning spaces and architecture, digital platforms and technology enhanced learning environments. http://teachingexcellence.leeds.ac.uk/events/keynoted-announced-and-bookings-now-open-for-sec2019/

Enhancing (in)formal learning ties in interdisciplinary management courses: a...

Bart Rienties

While interdisciplinary courses are regarded as a promising method for students to learn and apply knowledge from other disciplines, there is limited empirical evidence available whether interdisciplinary courses can effectively “create” interdisciplinary students. In this innovative quasi-experimental study amongst 377 Master’s students, in the control condition students were randomised by the teacher into groups, while in the experimental condition students were “balanced” by the teacher into groups based upon their initial social network. Using Social Network Analysis, learning ties after eleven weeks were significantly predicted by the friendship and learning ties established at the beginning of the course, as well as (same) discipline and group allocation. The effects were generally greater than group divisions, irrespective of the two conditions, but substantially smaller than initial social networks. These results indicate that interdisciplinary learning does not occur “automatically” in an interdisciplinary module. This study contributes to effective learning in interdisciplinary learning environments. Rienties, B., & Héliot, Y. (2016). Enhancing (in)formal learning ties in interdisciplinary management courses: a quasi-experimental social network study. Studies in Higher Education. DOI: 10.1080/03075079.2016.1174986. Impact factor: 1.037. Full version is available at: http://www.tandfonline.com/doi/full/10.1080/03075079.2016.1174986

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The emergence of learning analytics afforded for the analysis of digital traces of user interaction with technology. This analysis offers many opportunities to advance understanding and enhance learning and the environments in which learning occurs. Existing research has shown how learning analytics can provide contributions to different areas of education such as prediction of student success, uncovering learning strategies, understanding affective states, and unpacking the role social networks in learning. While these results have shown much promise, one critical challenge remains unclear – how learning analytics can help track learning progression and inform assessment especially from the perspective of the 21st century skills. This talk will explore opportunities and challenges for the integration of methods commonly used in learning analytics to analyze different digital traces with methods commonly used in assessment. The talk particularly focuses on open learning environments where analytics-based assessment is rather underexplored in contrast to assessment in specialized (intelligent tutoring) systems where the combined use of data mining and assessment has been established for some time now.

Resume Jeremy Kyle Jennings

Jeremy Kyle Jennings

22 January 2018 HEFCE open event “Using data to increase learning gains and t...

Bart Rienties

With the Teaching Excellence Framework being implemented across England, a lot of higher education institutions have started to ask questions about what it means to be “excellent” in teaching. In particular, with the rich and complex data that all educational institutions gather that could potentially capture learning gains, what do we actually know about our students’ learning journeys? What kinds of data could be used to infer whether our students are actually making affective (e.g., motivation), behavioural (e.g., engagement), and/or cognitive learning gains? Please join us on 22 January 2018 in lovely Milton Keynes at a free OU- and HEFCE-supported event on Using data to increase learning gains and teaching excellence. 14.00-15.00 Measuring learning gains with (psychometric) questionnaires Dr Sonia Ilie, Prof Jan Vermunt, Prof Anna Vignoles (University of Cambridge, UK): Learning gain: from concept to measurement Dr Fabio Arico (University of East Anglia): Learning Gain and Confidence Gain Through Peer-instruction: the role of pedagogical design Dr Paul Mcdermott & Dr Robert Jenkins (University of East Anglia): A Methodology that Makes Self-Assessment an Implicit Part of the Answering Process 15.00-15.45 Measuring employability learning gains Dr Heike Behle (University of Warwick): Measuring employability gain in Higher Education. A case study using R2 Strengths Fiona Cobb, Dr Bob Gilworth, David Winter (University of London): Careers Registration Learning Gain project

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Competency-based education has been a concept in medical education since the 1970s, though has only gained traction and application in programs in the last 15-20 years. Multiple competency models exist (e.g. CANMeds, ACGME), though ACGME is prevalent in the US and is the focus of this presentation. The most common tensions in the competency-based education movement exist around: the deconstruction of clinical practice over respect for the complexity of the tasks; the challenge of appropriate assessments; and when to know to trust a resident with increasing responsibilities. The benefits and challenges are discussed; the session closes with an exploration of three case studies, drawing from different geographical regions (US, Canada, Australia), as a way to help participants appreciate the issues in implementating competency-based education in residency programs. Prepared for and presented to Teaching Scholars Program, University of Colorado School of Medicine, Dec 18, 2012. Available under Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. References used within the presentation available upon request - email author please.

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