Introduction to learning analytics

Editor's Notes

• #2: Title slide Introduction to learning analytics Rebecca Ferguson, The Open University, UK
• #3: Divider slide, introducing the question ‘Who?’ and asking ‘Who is in the learning analytics community?’
• #4: Picture taken at LASI 2019, held at the University of British Columbia. Used to introcduce the Learning Analytics Summer Institutes and also LASI locals that take place around the world
• #5: Two pictures taken at Learning Analytics and Knowledge 2019 (LAK19) in Arizona. The first is of an audience waiting for a keynote. The second was taken on International Women’s Day and brings together the women in the community who were attending the conference.
• #6: An overview of the current executive of the Society for Learning Analytics Research (SoLAR) with the current President, Maren Scheffel, at the top left.
• #7: The Journal of Learning Ananlytics Research with a list of recent special sections of the journal
• #8: Participants in the last LASI, which took place in Vancouver in 2019. Workshop participants are asked to introduce themselves, to say which time zone they are in, and to use emojis to represent how they feel about LASI
• #9: A divider slide introducing the question ‘What?’ and asking ‘What are learning analytics?’
• #10: Introducing academic analytics. These provide information for administrators, funders, marketing departments, education authoritieis and governments. They address the political challenge, ‘How can we substantially improve learning opportunities and educational results [at national or international levels]? ‘
• #11: Introducing educational data mining, which describes itself as ‘an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings in which they learn.. It addresses a technical challenge: How can we extract value from these big sets of learning-related data?
• #12: Enforcement (such as proctoring and TurnItIn) also makes use of data in an educational setting and the criticisms of enforcement are often extended to learning analytics. It deals with the question ‘How can we make sure our students turn up, engage, and don’t cheat?’. The two images relate to ways in which learners are forced to become data, rather than the data they generate during their activities being used to support them.
• #13: Learning analytics produces information for educators and learners. It addresses an educational challenge: How can we optimise opportunities for [online and blended] learning?
• #14: The SoLAR definition of learning analytics ‘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.’
• #15: Divider introducing the question ‘why?’ and asking ‘Why learning analytics?’
• #16: A rephrasing of the definition of learning analytics: ‘Learning analytics help us to identify and make sense of patterns in the data to improve our teaching, our learning and our learning environments.’
• #17: A definition of education taken from Twitter, where it was shared by Dr Kevin Smith: ‘the true purpose of education is to make minds, not careers’.
• #18: Another definition of education, provided by Ofsted, the UK’s Office for Standards in Education, Children's Services and Skills, and shared on Twitter by Paul Hopkins. It states that ‘The aim of education is to deliver a high-quality curriculum so that pupils know more and remember more.’. Paul draws attention to gaps in this definition, such as love of learning and application of learning.
• #19: A third definition of education from Twittter, this time from Dylan William. He identifies four broad puroses for education: personal empowerment, transmission of culture, preparation for democratic citizenship, and preparation for work.
• #20: Some of the Eureopean Union’s priority areas for education (taken from a document some years ago). Focus on learning outcomes for employability, innovation, active citizenship and well-being and inclusive education, equality, equity, non-discrimination and the promotion of civic competences.
• #21: Part of the educational vision of the University of Michigan, taken from its website. ‘We will strive to develop highly knowledgeable, well-skilled, flexible and globally-engaged students through the provision of excellent and relevant learning and teaching. This will be characterised by discovery, creativity and opportunity, and enabled by outstanding educators and the best in technology and learning spaces.'
• #22: Part of the educational vision of the University of British Columbia, taken from its website. It refers to the university’s values: excellence, integrity, and respect.
• #23: Guidance for the discussion activity that follows. Make a note of the questions before you go. Switch cameras on if you can (and you are comfortable doing this). Try to involve everyone in the group. Add thoughts in Slack.
• #24: Instructions for the breakout discussion. Which aspects of teaching and learning are you currently trying to optimize? Which aspects of teaching and learning would you like to optimize?
• #25: Divider slide introducing the question ‘Where?’ and asking ‘Where does my work fit in?’
• #26: The Learning Analytics Cycle, introduced by Doug Clow in a paper at LAK12. The cycle links learners, data, metrics and interventions. A URL gives a link to the paper.
• #27: Divider slide introducing the question ‘How” and asking ‘How do we move forward?’
• #28: An explanation of how the six challenges on the following slides were developed. Ideas were gathered from a series of workshops on Ethics and Privacy in Learning Ananlytics (EP4LA), which were held in many countries from 2014 onwards. Other ideas came from a piece of research carried out by the Learning Analytics Community Exchange (LACE) which used a set of ten provations to present participants with views of how learning analytics might develop in the coming decade, and asked for thoughts on the feasibility and desirability of these various outcomes. These ideas were brought together in an issue of the Journal of Learning Analytics in 2016, which identified a set of challenges for the field that related to ethical issues. A keynote by Neil Selwyn at LAK 2019 introduced new ideas, which were incorporated within a revised set of challenges published in the Journal of Learning Analaytics in 2019/
• #29: Challenge on is the duty to act. ‘Use data and analytics whenever they can contribute to learner success, ensuring that the analytics take into account all that is known about learning and teaching.’. This is associated with user-centred design, and with an approach to this that was published in the Journal of Learning Analytics. Researchers asked educators ‘If you could have any superpowers you wanted, to help you do your job, what would they be?. A URL is provided for the paper.
• #30: Challenge 2 is informed consent. Equip learners and educators with data literacy skills, so they are sufficiently informed to give or withhold consent to the use of data and analytics. A link is made to a recent quote from Sara Nelson, published in Wired magazine: ‘[Amazon] is in a position to collect huge amounts of data – through its shopping platform, but also through its Ring cameras, Alexa voice assistants, web services, delivery services, streaming services, and its many other business streams.’ A link is provided to the article.
• #31: Instructions for a breakout discussion. What are data literacy skills? What do people need to know to read data, work with data, analyse data, argue with data, and give informed consent? Participants are asked to add thoughts in Slack.
• #32: Challenge three: safeguarding. Take a proactive approach to safeguarding in an increasingly data-driven society, identifying potential risks, and taking action to limit them. Links are made here to two aspects of safeguarding. One is a news story in which the University of Maastricht states it paid a large ransom to hackers. The other is a tweet by Karen Triquet which refers to biased algorithms, biased data, and the human bias that underpins these. A list of potential problems with data notes it may be inaccurate, mislabelled, mistyped, misused, incomplete, porrly chosen biased sample, out of date, poorly protected, subject to attack, or ignored.
• #33: Challenge four: equality and justice, Work towards increased equality and justice, expanding awareness of ways in which analytics have the potential to increase or decrease these. The image here is from a screenshot of FaceBook taken by Lauren F Klein. It shows a series of options for the category ‘gender’ including Gender Fluid, Gender Variant, Genderqueet, Gender Questioning, Gender Nonconforming, Agender, Bigender, Cisgender, Cisgender Female, and Cisgender Male.
• #34: Challenge five: data ownership. Increase understanding of the value, ownership, and control of data. The image is taken from a cominc-book representation of Apples’ terms and conditions for iTunes. There is a link to the book.
• #35: Instricutsions for a breakout discussion. Which data are Zoom, Slack, and GatherTown collecting about you? Who can access that data? How would you feel about it being used (or sold) to support your learning? Add thoughts in Slack.
• #36: Challenge six: integrity of self. Increase the agency of learners and educators in relation to the use and understanding of educational data. This is related to a CBS news story shared on Twitter by John Wilander, referring to a lawuite that alleges Google secretly monitors millions of school children.
• #37: All six challenges are given in full. Use data and analytics whenever they can contribute to learner success, ensuring that the analytics take into account all that is known about learning and teaching Equip learners and educators with data literacy skills, so they are sufficiently informed to give or withhold consent to the use of data and analytics. Take a proactive approach to safeguarding in an increasingly data-driven society, identifying potential risks, and taking action to limit them. Work towards increased equality and justice, expanding awareness of ways in which analytics have the potential to increase or decrease these. Increase understanding of the value, ownership, and control of data. Increase the agency of learners and educators in relation to the use and understanding of educational data
• #38: Contact details for Rebecca Ferguson, including Slideshare, Twitter and her blog.