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    Archive for the 'Samantha’s Scribbles' Category

    Learning Analytics talks, past and upcoming

    By Samantha Ahern, on 14 June 2017

    I was invited by Skills Matter to give a talk on the 8th March 2017 as part of the InfiniteConf Bytes series of meet-ups.

    InfiniteConf Bytes is a series of talks showcasing the speakers and topics for the upcoming InfiniteConf at Skills Matter in July (6th & 7th).  At which I will be giving a talk entitled: Ethical Conundrums of an Educational Data Scientist.

    My talk on the 8th March was a general overview of what learning analytics is and what some of the difficulties faced by the field are.

    A recording of the talk can be viewed at: https://skillsmatter.com/skillscasts/10055-learning-analytics-what-is-it-and-why-is-it-so-difficult-with-samantha-ahern and the slide deck from the talk is available on slide share (https://www.slideshare.net/SamanthaAhernMIET/learning-analytics-infiniteconfbytes).

    I am currently preparing a session on Learning Analytics for the HEA Conference on 5th July, and have had my session proposal for September’s ALT conference accepted.  A summer of literature reviews and writing for me it seems.

    In addition, I have found some interesting patterns in the laptop loans data that I am hoping to investigate further. Included below is a plot from Tableau showing the relative number of laptop loans by location from the 26th September 2016 until 24th April 2017. In the plot the larger the bubble, the greater the number of loaned laptops.

    BublePlot LaptopLoans

     

     

     

     

     

    A tale of two cities, or five

    By Samantha Ahern, on 7 April 2017

    March was a very busy month, 8 events over 31 days in 5 different cities, one of which was on a different continent. My poor suitcase did suffer a little, but I also got to do exciting things for engineers such as travel on different types of aircraft, cross a 150m suspension bridge and hear about pit stop optimisation in Formula 1 racing.  The events themselves were a combination of seminars, workshops and conferences; some academic, others more industry focused.

    The main event: LAK’17, 13-17 March, Simon Fraser University, Vancouver

    This was the eight meeting of the International Learning and Knowledge (LAK) Conference organised by the Society for Learning Analytics Research (SoLAR). Conference website: http://educ-lak17.educ.sfu.ca/

    On the 13th & 14th March I had the opportunity to be involved with the LAK17 Hackathon (GitHub: https://github.com/LAK-Hackathon/LAK17Hackathon). For the Hackathon I worked with colleagues from the University of British Columbia (UBC), University of Wollongong (UoW) Australia and JISC.  During the Hackathon we created Tableau dashboards to visualise staff and student interactions with courses in a VLE (Tableau workbook can be viewed and downloaded from: https://public.tableau.com/profile/alison.myers3113#!/vizhome/LAKHackathonv1/Student). The staff pages focus on identifying the contents and activities incorporated in a course, when it was created, who it was created/edited or added by and the usage of that content or activity.  The student pages focus on how students interact with and navigate through courses. For the hackathon we used dummy data, initial small files were hand crafted but larger files were generated by JISC’s Michael Webb (https://github.com/jiscdev/lakhak). I am hoping to use these dashboards to gain some initial understanding of the structure of UCL’s Moodle courses for taught modules and how students interact with them.

    The main conference ran from 15th to 17th March with inspirational keynotes from Dr. Sanna Järvelä (University of Oulu, Finalnd), Dr Timothy McKay (University of Michigan) and Dr. Sidney D’Mello (University of Notre Dame).  The overall conference theme was Understanding, Informing and Improving Learning with Data. Concurrent session talks were organised by sub-theme, these sub-themes included Modelling Student Behaviour, Understanding Discourse and LA Ethics, the talks on ethics were so popular that there was barely standing room only.  Many of the papers presented still focused on LA research but there is a growing number of implementations. During the conference SoLAR launched the Handbook of Learning Analytics, hard copies were available to preview. This will primarily be freely available as electronic download, for more information please see:https://solaresearch.org/hla-17/.

    Data Fest Data Summit #Data Changes Everything, 23rd & 24th March, Edinburgh and Big Data Innovation Summit, 30th & 31st March, London

    Of these two events, the one I probably enjoyed the most was the Edinburgh Data Summit which was part of Data Fest (http://www.datafest.global/), a week long series of activities organised by the Data Innovation Lab.  The Data Summit had a nice buzz about it and this was helped along by the hosts Phil Tetlow (Director and Chief Architect, Three Steps Left), Day 1, and Georgie Barratt (Presenter, The Gadget Show), Day 2.  Social good was a key theme of the event with talks from NHS Scotland and Transport for London, with humanitarian applications of data science discussed in talks from Nuria Oliver of Vodafone discussing Data-Pop Alliance and Natalia Adler from Unicef discussing DataCollaboratives. The Big Data Innovation Summit (https://theinnovationenterprise.com/summits/big-data-innovation-summit-london-2018) also featured a number of public sector talks from HMRC, Department for Work and Pensions and Camden Borough Council.  A highlight was Camden’s approach to open data.

    The key message from both events was that Data Science is not magic, there is no alchemy. Exploratory data analysis is great and has its place, but the main function is to support the decision making process, and in order to this you need to understand the business questions you are trying to answer.  This is echoed in Step 4 of Jisc’s Effective Learning Analytics On-boarding, Institutional Aims (https://analytics.jiscinvolve.org/wp/on-boarding/step-4/).

    And everything else

    Other events attended focused on collaborative working in the sciences (http://sciencetogether.online/), testing and validation of computational science (https://camfort.github.io/tvcs2017/) and some more theoretical probability days (http://www.lancaster.ac.uk/maths/probability_days_2017/).

    In summary it was a slightly exhausting but very informative month. Many of the ideas percolated at these events will find their way into the work I am undertaking in Digital Education over the next few months.

    AI and society – the Asilomar Principles and beyond

    By Samantha Ahern, on 2 February 2017

    This has been quite an enlightening week. I have added my name in support of the Asilomar AI Principles (https://futureoflife.org/ai-principles/) and attended a British Academy / The Royal Society big debate ‘Do we need robot law?’ (http://www.britac.ac.uk/events/do-we-need-robot-law).

    This is against the backdrop of the European Parliament’s Legal Affairs Committee EU rules for the fast-evolving field of robotics to be put forward by the EU Commission (Robots: Legal Affairs Committee calls for EU-wide rules) and reports of big data psychometric profiling being used in the US Presidential election and EU referendum (https://motherboard.vice.com/en_us/article/how-our-likes-helped-trump-win).

    This raises a number of questions around ethics, liability and criminal responsibility.

    A sub-set of AI, machine learning, is ubiquitous in its use in everyday tools that we use such as social media and online shopping recommendations, but only 8% of the UK population are aware of the term, as noted at The Royal Society panel discussion Science Matters – Machine Learning and Artificial intelligence. Recent high profile advances in machine learning, for example AlphaGo, have utilised a technique known as deep learning which predominantly uses deep neural networks, an evolution of artificial neural networks that were first introduced in the 1950s (for more about the history of AI please see: https://skillsmatter.com/skillscasts/5704-lightning-talk).  To all intents and purposes these are black box algorithms as these systems teach themselves and the exact nature of what is learned is unknown, this is known issue where these systems are used for automated decision making.  In addition to this being ethically dubious when these decisions relate to living beings, they may also fall foul of the upcoming General Data Protection Regulations (Rights related to automated decision making and profiling).

    Professor Noel Sharkey has stated that we are on the cusp of a robot revolution.  Robots are shifting from the production line, to our homes and service industries. There has been a lot of development of care robots, particularly in Japan, and is an active area of research (e.g. Socially Assistive Robots in Elderly Care: A Systematic Review into Effects and Effectiveness).  The introduction of robots into shared spaces has an impact on society.

    A lot has already been said and written about decision making processes of autonomous vehicles and the ethics of the decisions made, including myself (https://blog.swiftkey.com/slam-self-driving-cars-ai-and-mapping-on-the-move/), but there are still a level of uncertainty especially with regards to the law as to who is responsible for these vehicles’ actions.  What is less discussed is the potential impact of the roll-out of these vehicles on wider society; for example 3m jobs in the USA alone may be lost due to autonomous vehicles.

    Unlike human beings, AI systems, include robots, do not have a sense of agency (What Is the Sense of Agency and Why Does it Matter?), this can cause difficulties within society as behaviours of robots or autonomous vehicles may be different from expected societal norms, causing a disruption to society and values. It also introduces ambiguity around liability and criminal responsibility.  If an AI does not have an understanding of how its actions are having a negative impact on society, how can it be helped accountable? Alternatively, are the developers or manufacturers then accountable for an AI’s behaviour? This is known as liability diffusion.

    If an AI is capable of learning a sense of agency, what is an acceptable time-frame for this learning to take place in and what will be an acceptable level of behaviour and/or error until sufficient learning has taken place?

    The one thing that is clear, is that these emerging technologies will be disruptive to all areas of society, and could be considered a Pandora’s box but also has the potential to bring huge benefits to the whole of society. As a result there are a number of organisations that are considering the potential impacts of these technologies; these include the Machine Intelligence Research Institute (https://intelligence.org/) and Centre for the Study of Existential Risk (http://cser.org/), plus organisations such as OpenAI (https://openai.com/about/) that are making these technologies available to all.

    For further reading on where we are now and where we are heading I recommend Murray Shanahan’s book ‘The Technological Singularity’ , and Danny Wallace’s documentary  ‘Where’s my robot?‘ .

     

     

    Walking in a data wonderland

    By Samantha Ahern, on 9 January 2017

    So where do we begin? Straight down the rabbit hole or some contextual rambling?

    The contextual rambling.

    I have recently been thinking about the logic puzzles, syllogisms, of Charles Dodgson and the literary work of his alter-ego Lewis Carroll – Alice’s Adventures in Wonderland.  This and discussions with my colleague Dr Steve Rowett lead me to explore Anastasia Salter’s project Alice in Dataland (http://aliceindataland.net/).  Alice in Dataland is an experiment in critical making, an exploration guided by the question: “Why does Alice in Wonderland endure as a metaphor for experiencing media?”

    Down the rabbit hole.

    Exploring Anastasia’s project has generated some questions of my own; What if data is Alice and data analysis is Wonderland?

    It has been noted that each new representation of Alice has showed her in a new and different way, it has been argued that these changes have added to our interpretation.  Is this also true of our analysis of data, or do we see “different truths” through different lenses of our analysis? In other words, do our analysis of data add to understanding by providing insight or do we alter the narrative told by data by how we choose to analyse or visualise it.

    In August 2016 theNode (http://thenode.biologists.com/barbarplots/photo/) reported on the kickstarter campaign #BarBarPlots! with the focus of the campaign being how to avoid misleading representations of statistical data.  This follows on from a 2015 ban on null hypothesis significance testing procedures by the journal Basic and Applied Social Psychology, which was discussed in an article by the Royal Statistical Society (https://www.statslife.org.uk/features/2114-journal-s-ban-on-null-hypothesis-significance-testing-reactions-from-the-statistical-arena).

    Do these analyses constitute re-imaginings of the data and like the use of Photoshop and other media tools described by Salter re-imagine Wonderland or data analysis as a remediation of reality through a different lens?

    When data is collected over time to create user profiles, and potential in learning analytics creating identities through narratives (data analysis and visualisation), again noted by Salter: “it is through narrative that we create and re-create selfhood” (Bruner, Jerome. Making stories: Law, literature, life. Harvard University Press, 2003.).  Are these generated identities subject to “defamiliarization of perception”; threatened by time as new data received alters our models and the story told? I am not sure, but it is an interesting thought.

    white rabbit

     

    Data dialogues – new data, old data and respect

    By Samantha Ahern, on 19 December 2016

    Somewhat unsurprisingly, some would say, over the last two weeks I have been preoccupied with data.

    More specifically, the notion of data having a life of its own.

    This is was the key theme of Prof. Charlotte Roueché’s talk at the Science & Engineering South event The Data Dialogue – At War with Data at King’s College on the 7th December. Citing a number of examples of data reuse such as archaeological maps by British Armed Forces and aerial photographs of Aleppo taken in 1916 for military used now being used as archaeological record, she argued that data develop a life of their own.  This means that we need to make sure that the data we collect is of the best possible quality and well curated. It should meet the FAIR principles: Findable, Accessible, Interoperable and Re-usable. However, once we have released our data into the wild, we will never truly know how it will be used and by whom. Unfortunately, history has shown that not all re-use is benign.

    This then begs the question: how open, should our open data be? Is there a case for not disclosing some data if you know it could do harm. e.g. In the current political climate, the exact location of archaeological sites of religious significance.

    This ties in to the two main themes of the National Statistician John Pullinger’s talk at The Turing Institute event Stats, Decision Making and Privacy on 5th December of respect and value.

    The key thing about respect is that data is about people and entities, this should never be forgotten.  People’s relationships with and perceptions of organisations who collect and process their data varies, as data analysts/scientists we should understand and respect this.  This means being alive to what privacy means to individuals and entities, and the context of how it is being discussed. Caring about the security of the data and demonstrating this through good practices. Additionally, thinking about what we should do, not just what we could do with data available to us. This is very pertinent with the rise in the use of machine learning tools and techniques within data science.

    This last point links into the second theme of value.  Data is valuable. It enables us to make better, more informed decisions and is a critical resource.  However, a balance needs to be drawn between extracting value from the data and respect.  So, is there a need to change the way in which we think about our data analysis processes?

    Dr Cynthia Dwork in her talk on Privacy-Preserving Data Analysis (The Turing Institute event Stats, Decision Making and Privacy) noted that statistics are inherently not private, with aggregate statistics destroying privacy.  Echoing the talk of John Pullinger, Dr Dwork raised the question ‘What is the nature of the protection we wish to provide?’. It is also important to understand who is a threat to our data and why. A move towards differential privacy (https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf) was proposed. When an analysis is undertaken in this way the outcome of the analysis is essentially equally likely, irrespective of whether any individuals join, or refrain from joining, the data set. However, this would require a completely different way of working.

    We’ve all heard the old adage of ‘lies, damned lies and statistics’; a key factor in making sure this is not the case is the presentation of the data.  We need to ensure that the data is correctly understood and correctly interpreted.  Start from where your audience is, and think carefully about your choice of words and visualisations. We also need to help our audiences to be more data literate.  But to undermine good analysis and communication we need to invest in skills and develop a good data infrastructure.

    Support the Royal Statistical Society’s Data Manifesto: http://www.rss.org.uk/Images/PDF/influencing-change/2016/RSS_Data%20Manifesto_2016_Online.pdf

    and in the words of John Pullinger ‘step up, step forward and step on the gas’!