Big news from LSE tonight (I think, anyway). I was in a meeting with colleagues in which I’m pretty sure I learned that we will be selling the campus in its entirety because we have no further use for the buildings.

I say “pretty sure” because my kid was on Fortnite and the WiFi kept dropping out.

But the gist of it was: everyone is so completely comfortable with working, teaching, learning, researching and so forth at home that we really don’t need this massive investment in bricks and mortar, so why not sell it?

Word is, Mike Ashley is interested in turning the Old Building into a Sports Direct and Tim Martin has made a very reasonable offer for the Three Tuns.

Someone with a cat avatar in MS Teams said:

LSE has a grand tradition, stretching back at least two weeks, of making bold decisions and acting on them swiftly. We are proud to be the first of the Russell Group universities to follow the Open University into the online space. Stop hitting your sister with that. Don’t make me come upstairs. One… TWOOOOO

The advantages are legion, and even I can think of at least one, despite having been woken by my children at 04:30 because someone thought it would be fun to put the clocks forward in the middle of a pandemic.

No you shut up.

As I was saying: the “academic hour” is at an end. No longer will I have to scoot across campus from Clement House to 32 Lincoln’s Inn Fields and show up late for a meeting. I can simply drop out of my eleven o’clock and spend the following ten minutes saying: “Can you hear me? No? What about now? Ugh! I hate this computer. Wait, I have a headset somewhere…”

The Eden Centre Learning Technology team are pleased to announce the launch of our new Moodle and lecture recording guides.

As part of the review and reorganisation of the existing LTI website the Learning Technology team decided to move our guides to a new system that results in an easier to read page design and structure. The new system also allows us to more easily edit the guides and keep them up-to-date, with another advantage being that we are able to more closely integrate the guides with LSE Moodle, including a new search box that is visible to all Moodle teachers, course editors and managers.

We have also reorganised the Moodle guides so that they more closely match the organisation of Moodle tools and activities. We have tested the pages with some of our users, but we will of course be grateful if you would like to let us know of any problems or errors on the pages. These guides will also be integrated into the main Eden Centre resources page ready for next term.

Also, if you would like us to produce a guide on something that isn’t included in the current catalogue, please do let us know by emailing eden.digital@lse.ac.uk and we will add it to our to-do list!

It’s this time of the year! Marking is in progress, and you will soon need to Release grades and feedback to students via Moodle!

Don’t forget Marking Workflow is enabled for all Moodle Assignments! You need to Release the grades and feedback for students!

Firstly, the Marking workflow in Moodle is a great feature. It enables teachers to add grades and feedback to student submissions and allow them to ‘Release’ (reveal the grades and feedback) at the same time for all students when they choose to do so.

How to ‘Release’ grades and feedback to students?

Considering Marking workflow is enabled for all newly created Assignments in Moodle (2019/20), you must ‘Release’ the grades and feedback to students. To do so, follow the steps below:

1. Click the Assignment link and View all assignments.

2. Scroll to the bottom of the page and under Options > Assignments per page select All to get all assignments to display on one page.

3. Select the submissions for which you want to use marking workflow using the Select box on the left of each submission – to select all submissions, click the Select all box under Select.

4. Scroll to the bottom of the page and from the drop-down menu With selected… select Set marking workflow state and click Go.

5. On the screen that appears, and from the drop-down menu Marking workflow state, select Released.  You can choose to Notify students by selecting Yes to generate an email. Click Save changes.

6. Students will be able to review their grades and feedback.

N.B. If blind marking is in use with marking workflow, the grades are released regardless of whether student identities have been revealed. However, you and other markers are likely to want to reveal identities at some point. You can find the option to Reveal student identities in the Grading action drop-down menu at the top of the submissions table. You need to have Teacher (Editor) or higher access to do this.

If you experience problems releasing grades and feedback to your students, get in touch with eden.digital@lse.ac.uk

This post is based on a presentation I gave at this afternoon’s M25 Learning Technology Group meeting at King’s College London.

The title of this post refers to an Adam Curtis documentary series from 2011, itself taken from a Richard Brautigan poem.  I’ve reproduced the last stanza:

I like to think
(it has to be!)
of a cybernetic ecology
where we are free of our labors
and joined back to nature,
returned to our mammal
brothers and sisters,
and all watched over
by machines of loving grace.

This is a lyric expression of something that’s come to be known as Technological Utopianism.  This isn’t merely the preserve of beatniks and hippies; Bertrand Russell wrote, in his 1932 essay In Praise of Idleness, “four hours’ work a day should entitle a man to the necessities and elementary comforts of life, and that the rest of his time should be his to use as he might see fit,” because:

Leisure is essential to civilization, and in former times leisure for the few was rendered possible only by the labors of the many. But their labors were valuable, not because work is good, but because leisure is good. And with modern technique it would be possible to distribute leisure justly without injury to civilization.

And John Maynard Keynes wrote, in his 1930 essay Economic Possibilities for our Grandchildren, that within 100 years the “economic problem” would be solved.  In 2030 we would all be working “three-hour shifts or a fifteen-hour week” and:

For the first time since his creation man will be faced with his real, his permanent problem-how to use his freedom from pressing economic cares, how to occupy the leisure, which science and compound interest will have won for him, to live wisely and agreeably and well.

Keynes’s grandchildren are eleven years from this horizon, and (needless to say) things haven’t quite worked out that way.  Why not?

Russell’s “Modern technique”

The Jacquard Loom in the Musée des Arts et Métiers in Paris [Moof (CC BY 2.0)]

Those punched cards may look familiar to computer users of a certain age; they are practically identical to those once used to program computers.  The comparison is not lost on the curators of the museum; the exhibition leads finally to a room containing a Cray 2 supercomputer. Nor was it lost on Charles Babbage, who understood punched cards could be used to program his Analytical Engine.

A fifteen hour week?

The textile industry gave Britain its first full-blown industrial relations crisis: the outbreak of machine breaking by the Luddites.  The Luddites were not, contrary to popular opinion, opposed to technology per se; textile workers had been using stocking frames since Tudor times, but in a highly-regulated industry.  The Luddites’ machine breaking was instead a response to the use of machinery in “a fraudulent and deceitful manner,” particularly by unskilled apprentices working without the supervision of master craftsmen.  In Eric Hobsbawm’s memorable phrase, the Luddites were conducting “collective bargaining by riot“.

The textile workers’ expertise, hitherto distributed among men and machines in a cottage industry, became concentrated in machines housed in factories owned by capitalists.

Did the textile workers share in the profits that followed? Did they reduce their hours to fifteen a week?  Of course not:

[Wages] could be compressed by direct wage-cutting, by the substitution of cheaper machine-tenders for dearer skilled workers, and by the competition of the machine.​  This last reduced the average weekly wage of the handloom weaver from 33s. in 1795 to 4s. 1½d. in 1829.​

Eric Hobsbawm, The Age of Revolution: Europe 1789-1848

In thirty four years, their wages were reduced to one eighth.

So much for history

This is not a phenomenon confined to the pages of history books.  A similar battle is playing out right now, on the streets of London, between black cab drivers and Uber.

In order to become a cabbie, you need “the knowledge,” earned by learning 80 runs across the city, getting at least 60% on two written exams, and passing three oral exams.  This can take between three and five years to accomplish.  By contrast, becoming an Uber driver in London requires that you have a TfL Private Hire license, and I estimate this to take a minimum of eight weeks. The process includes what TfL calls a “topographical skills assessment“, which (being brutally honest) ensures you are able to read a map.

It is very difficult to find out the earnings of black cab drivers or Uber drivers, because they are self-employed and not required to reveal their earnings to impertinent systems administrators.  But the New York Times estimates Uber fares to be about 30% cheaper than black cabs, and Uber extracts a fee upwards of 25% from its drivers.  As Daniel Markovits, author of The Meritocracy Trap, points out, a cabbie can earn enough to own a home, provide for his family, and go on holiday.  The precarious finances of the Uber driver, on the other hand, are legendary.

Naturally all this enrages the black cab drivers; as with the looms in the dark satanic mills of the 19th century, their previously distributed expertise is becoming concentrated in the machines of capitalists.

But this time, there are no looms to smash.  Uber has developed not one machine learning algorithm, but so many that their engineers have created a bespoke machine learning service, so the teams of engineers working on the myriad components of the Uber service can automate them more easily.

So they want to replace you with a machine. But is it any good?

Models used in forecasting have a property called “skill”, which measures how good they are at what they’re intended to do.  I’d like you to consider a specific example which, while detailed, is readable enough for a non-technical audience.  Amazon Web Services will rent you a machine learning service, which you can bend to your requirements.  In this example, Denis Batalov shows how you can use Amazon Machine Learning to predict customer churn from a mobile phone service.

For mobile providers, obtaining new customers is costly. Those special offers you see advertised are loss leaders designed to lure you into signing a contract. They will absorb the loss because they assume you are too lazy to switch providers at the end of your contract, at which point they can milk you for profit. Those customers who do leave are said to “churn”.

The set uses comparatively few data points, for example how long the customer has had the service, how much they use their phone, how much the service costs and how many times they’ve called Customer Services.  The goal of the exercise is to identify those customers most likely to churn, and to stage an automated intervention, buttering them up with free minutes, a new handset, etc.

The algorithm is trained first on data where it can see the outcome.  Customer x with the following attributes remained a customer, but customer y with these attributes decided to leave.  Then, to test its skill, it is shown the data without seeing the outcome.  More successful models are selected for evolution, and the remainder are culled.  This continues until the returns are diminished to the extent that there’s no more tweaking to be done.

As you can see, 14.5% of customers in the set “churned”. Can the machine identify those who will stay, and those who will leave?  Well, it can identify 86% of them.  But this is, in practical terms, the same as having no model at all, or (to put it another way) having a model which assumes all customers are loyal but is wrong about 14.5% of them.

However, since losing customers is expensive, and offering butter-ups is (comparatively) cheap, you can tweak the model so that it is more wrong than having no model at all, and yet saves the company $22.15 per customer.  Scaled up, this is big money (and a big bonus for the ML developer).

Less accurate than guessing, but much more profitable.

What has this to do with Learning Technology?  I’ve seen ML models not very different to the above, deployed in a VLE and using very few data points, making predictions about whether a student will pass or fail a particular module, or whether a student will drop out or remain enrolled.  The problems come in the “costs” we attach to the quadrants in the truth table, and in concentrating expertise in a machine at the expense of our distributed expertise as individual educators.

Seen it all before

Any forecasting discipline also suffers the problem of bias.  In human actors, we hope it is unconscious.  But in machine learning, it is built in, because we are training our AIs on historical data.

In 2017, Amazon announced it had shuttered an experimental programme to train an AI for recruitment. Scouring LinkedIn or sifting a pile of applications is time-consuming (and thus costly), repetitive, boring, and tiring.  These characteristics belong to tasks which IT professionals immediately select for automation.  But, just as when training a model to predict customer churn, historical data are required, with all the perils inherent therein.  Amazon’s AI was chucking women’s applications on the discard pile, because the company had, in the past, consistently favoured male applicants over female ones.

Again, what has this to do with learning technology, or even with IT in HE?  I’ve found institutions which were at least toying with the idea of using machine learning to sift admissions applications.  What will that do to our efforts to widen participation?  Instead of artificial intelligence, we will have automated ignorance.

When you combine bias-as-code with the kinds of de-skilling discussed in the cases of the textile workers and taxi drivers, you have a potent recipe for problematic decision making.  The most recent example is the “sexist AI” behind Apple Card, which assessed a married couple who, on the face of it, presented identical credit risks.  It offered the husband 20x the credit limit it offered his wife. Apple’s customer services people threw their hands up: “It’s just the algorithm”.  Even The Woz waded in, observing “It’s hard to get to a human for a correction though. It’s big tech in 2019.”  Once again, we see expertise, previously distributed among financial advisers, concentrated in a machine.

Weird, inscrutable logic

Even if Apple had retained the human capability to consider an appeal against the AI’s decision, it wouldn’t have been able to explain that decision, because ML algorithms do not admit of human scrutiny: software that is evolved is unreadable.

As a systems administrator, I like code that can be audited.  When something aberrant happens, I like to be able to see if there’s a logic problem.  But in discussions with non-systems (i.e. normal) people, I’ve come to agree that it’s acceptable, in some circumstances, to audit a system only knowing its inputs and its outputs.  An example is the pocket calculator.  You can ask it to solve 5 x 5, 10 – 8, etc, and compare it with your own working.  Eventually you come to trust the system and ask it to solve the square root of pi, and because it’s been right about everything until now, you believe that it’s right about this.

But as we’ve seen, ML is being asked to solve more complex problems than the root of pi.  It’s being asked to make predictions and decisions, with multiple inputs that it may or may not be using to draw its inferences, some of which could be wildly inappropriate.  There are, after all, a lot of spurious correlations in the world.

So I finish on an appeal: if your institution is ever considering the use of AI to admit applicants, or mark students’ work, or predict their likely success, press as hard as you can for the institution to retain a human in the process.  Because if the past is any guide — and it surely is, because that’s the basis on which we’re training our machines — if you don’t, there won’t be anyone left to hear an appeal.

LTI hosted this year’s Moodle Recharge event on 12th June 2019. LTI highlighted new Moodle features on Moodle 3.5, and also reminded attendees of some underused ones, as well as aspects of Moodle design and interactivity. The event was attended by 70 attendees.

We received 60 feedback forms, and the overall feeling is that the event was much appreciated and useful. If you missed the event and would like to find out about the Moodle 3.5 and/or receive our presentation slides, please feel free to email LTI. If you wish you can watch the Moodle Recharge recording that is is available for all LSE staff to watch the video.

LTI training courses are advertised on LSE’s Training and Development system and on our Events & Workshops page. We also deliver bespoke courses to groups and departments, around your needs. Contact lti.support@lse.ac.uk to discuss your needs and how we can help.

New Features in Moodle 3.5

Collapsible comments in assignments: when grading and adding comments from the annotation tools, comments are automatically collapsed and displayed as a small dialogue icon next to the commented text line.

Switch role: switching role is now available through each user’s Profile. Access your Profile by clicking on the icon next to your name. “Switch role to…” can be found in the drop-down menu, as below.

Improved navigation: navigation is enhanced with a few changes.

• Gear icon opens a settings menu for Course, Activity and User pages;
• Jump to menu to navigate between course activities at the bottom of each activity page;
• Enrol, view, search for, filter, edit and delete course users from the Participants page;
• Change course image button.

Discussion locking: enables forums to be automatically locked after a certain period of inactivity. Discussion locking is available through the Forum settings.

Override assignments: you can now override assignments for individual students or groups of students.

New course overview block: students can view their course progress and upcoming deadlines and quickly access upcoming activities.

Activity completion: allows you to set completion criteria for resources and activities in bulk.

Scheduler booking form: optionally set a form to be presented to students before booking a slot, see below:

Choice: choice results are shown to students, it displays whether the results are anonymous.

Stealth activities: display a long list of activities or resources more neatly inside other activities on your course page. Activities on stealth mode are at first sight hidden on the course page but remain accessible to students through the pages they are contained in.

Activity navigation menu: move from one activity or resource to another through a simple click to the left (to view the previous activity) or to the right (for the next course activity), or select other course activities from the Jump to menu.

On 17^th June 2019 we are upgrading Moodle from 3.1 to 3.5 and therefore Moodle will be unavailable during the day. The new version will include improvements and new features.

Here’s a quick summary:

Collapsible comments in assignments: when grading and adding comments from the annotation tools, comments are automatically collapsed and displayed as a small dialogue icon next to the commented text line.

Locking a discussion: enables forums to be automatically locked after a certain period of inactivity.

Override assignments: override assignments for individual students or groups of students.

New course overview block: students can view their course progress and upcoming deadlines and quickly access upcoming activities.

Activity completion: allows you to set completion criteria for resources and activities in bulk.

Scheduler booking form: optionally set a form to be presented to students before booking a slot.

Choice: choice results are shown to students, it displays whether the results are anonymous.

Question tagging: easily filter questions in the question bank using tags.

Stealth activities: display a long list of activities or resources more neatly inside other activities on your course page. Activities on stealth mode are at first sight hidden on the course page but remain accessible to students through the pages they are contained in.

Activity navigation menu: move from one activity or resource to another through a simple click to the left (to view the previous activity) or to the right (for the next course activity), or select other course activities from the Jump to menu.

And there are more…

Please contact LTI if you have any questions or further details.

Moodle will be upgrading to version 3.5 this summer, and therefore we will be holding an event for all LSE staff to provide an overview of the new features of Moodle.

The event will be held on Wednesday 12 June 2019 from 11:00 to 12:30 in the Old Building (OLD 4.10).

During the event, we will be providing everyone with a sneak peak of the changes that will be taking place this Summer in time for the new academic year and we will give attendees a chance to learn about the potentialities of Moodle as an educational tool to enhance teaching and learning.

To attend, please sign up on the LSE website as soon as possible.

If you need more details about the event, please contact LTI.

LSE is using Turnitin to check similarity of students’ work against submitted work of other students and various web sources. Papers submitted by students are added onto Turnitin’s repository, that allows teachers of one institution to find matches of student work that has been submitted at other institutions that use Turnitin. Moreover, teachers can request to view papers submitted to other institutions, if they think necessary.

What is a paper view request?

“Turnitin enables academics to find matches of students’ submitted work to other students’ work. When a match is found the LSE representative can only view part of the source. To view the full source, the LSE representative can make a paper view request to the institution where that source was originally submitted. These requests are made through Turnitin who pass the request on to the relevant institution.”

LSE has recently developed a Guidance for dealing with requests to view student papers. The guidance (approved by Academic Board) provides information on what is a paper view request, when and how to make a paper review request, and answers important questions, such as: on what basis you should decline or accept a paper view request from another institution, what is the procedure for declining/accepting paper view requests, how does a paper view request email looks like etc.

For further advice and questions about the paper view requests, please contact Martin Johnson, Assessment Regulations Manager.

Paper view requests to another institution should normally be made when the suspicion of plagiarism is high enough that the assessment in question needs to be investigated in accordance with the School’s Regulations on Assessment Offences: Plagiarism.

Also, don’t forget to visit our Staff and Students support page on using Turnitin, that provides useful information about interpreting similarity reports, FAQs for staff and students, etc. For general advice using Turnitin, contact LTI.

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CALL FOR APPLICATIONS! If you have a project in mind to use technology to enhance your teaching practices and improve the students’ experience, this is your second chance to get your project funded. LTI has extended the deadline for applications to Tuesday 30 April 2019.

For further information visit our website: http://lti.lse.ac.uk/lti-grants/

Check the SPARK! or IGNITE! winners of previous years on our website to get some inspiration. If you need more details about the schemes, or how to apply contact LTI.

Moodle Refresh for 2019

Each year, we refresh Moodle courses, by removing all student data and students’ contributions to activities (i.e. forum posts, assignment submissions etc). This way courses are ready for the new cohort of students. Course materials uploaded by editors are not deleted. Please make a note of this year’s Moodle refresh dates.

Refresh dates:

Thursday 29 August and Friday 30 August 2019 for the majority of courses. Moodle will be unavailable for all users on these dates (including those with 499 course codes that are refreshed on  later date).

Tuesday 10th September 2019 for courses used to collect dissertation submissions (primarily those with 499 course codes). These courses only will be unavailable on Moodle all day.

If you would like your course/s to be exempt from the 2019 refresh please email lti.support@lse.ac.uk

What do you need to do?

If you have any concerns about the above refresh dates please contact LTI on lti.support@lse.ac.uk

We will send further reminders as we approach the refresh dates, but if you have any questions at this point, please do not hesitate to contact us. Also, visit our Moodle end of year arrangement (Moodle refresh) page which provides detailed information and FAQs of the process.