Data driven #education session #OEB18 @oebconference #data @m_a_s_c

From the session on data driven education, with great EU links and projects.

Carlos Delgado Kloos: using analytics in education

Opportunities

Khan academy system is a proven system, with one of the best visualisations of how the students are advancing. With a lot of stats and graphs. Carlos used this approach for their 0 courses (courses on basic knowledge that students must know before moving on in higher ed).

Based on the Khan stats, they built a high level analytics system.

Predictions in MOOCs (see paper of Kloos), focusing on drop-out.

Monitoring in SPOCs (small private online courses)

Measurement of Real Workload of the students, the tool adapts the workload to the reality.

FlipApp (to gamify flipped classroom), remember and to notify the students that they need to see the videos before class, or they will not be able to follow. (Inge: sent to Barbara).

Creation of Educational Material using Google classroom. Google classroom sometimes knows what the answer of a quiz will be, which can save time for the teacher.

Learning analytics to improve teacher content delivery.

Use of IRT (Item Response Theory) to see which quizzes are more useful and effective, interesting to select quizzes.

Coursera define skills, match it to the jobs and based on that recommend courses.

Industry 4.0 (big data, AI…) for industry, can be transferred to Education 4.0 (learning analytics based on machine learning). (Education3.0 is using the cloud, where both learners and teachers go to).

Machine learning infers the rules from getting answers which are data analysed (in comparison to computer learning, which is just the opposite, based on rules, giving answers).

Dangers:

Correlations: correlations are not necessary correct conclusions. (see spurious correlations for fun links).

Bias: e.g. decisions for giving credit based on redlining and weblining.

Decisions for recruitment: eg. Amazon recruits that the automation of their recruiting system resulted in a biase leading to recruiting more men than women.

Decisions in trials: eg. Compas is used by judges to calculate repeat offenders, but color of skin was a clear bias in this program.

Decisions in academia: Cathy O’Neil: weapons of Math Destruction – how big data increases inequalityand threatens democracy”.

Chinese social credit system which gives minor points if you do something that is seen as not being ‘proper’. Also combined with facial recognition, and monitoring attention in class (Hangzhou number 11 high school).

Monitoring (gaggle, …)

Challenges

Luca challenge: responsible use of AI.

GDPR Art 22: automated individual decision-making, including profiling.

Sheilaproject.eu : identifying policies to adopt learning analytics. Bit.ly/sheilaMOOC is the course on the project.

Atoms and bits comparison. As with atoms you can use it for the better, or for the worse (like atomic bombs).

Maren Scheffel on Getting the trust into trusted learning analytics @m_a_s_c

(Welten Institute of Open University, Netherlands)

Learning analytics: Siemens (2011) definition still the norm. But nowadays it is a lot about analytics, but only little about learning.

Trust: currently we believe that something is reliable, the truth, or ability. Multiple definitions of trust, it is multidimensional and multidisciplinary construct. Luhmanndefined trust as a way to cope with risk, complexity, and a lack of system understanding. For Luhmann the concept of trust compensates for insufficient capabilities for fully understanding the complexity of the world (Luhmann, 1979, trust and …)

 For these reasons we must be transparent, reliable, and be integer to attract the trust of learners. There should not be a black box, but it should be a transparent box with algorithms (transparent indicators, open algorithms, full access to data, knowing who accesses your data).

Policies: see https://sheilaproject.eu   

User involvement and co-creation: see the competen-SEA project see http://competen-sea.eu capacity building projects for remote areas or sensitive learner groups. One of the outcomes was to co-design to create MOOCs (and trust) getting all the stakeholders together in order to come to an end product. MOOCs for people, by people.  Twitter #competenSEA

@oebconference workshop notes and documents #instructionalDesign #learningTools

After being physically out of the learning circuit for about a year and a half, it is really nice to get active again. And what better venue to rekindle professional interests than at Online Educa Berlin.

Yesterday I lead a workshop on using an ID instrument I call the Instructional Design Variation matrix (IDVmatrix). It is an instrument to reflect on the learning architecture (including tools and approaches) that you are currently using, to see whether these tools enable you to build a more contextualized or standardized type of learning (the list organises learning tools according to 5 parameters: informal - formal, simple - complex, free - expensive, standardized to contextualized, and more aimed at individual learning - social learning). The documents of the workshop can be seen here.

The workshop started of with an activity called 'winning a workshop survival bag', where the attendees could win a bag with cookies, nuts, and of course the template and lists of the IDVmatrix.
We then proceeded to give a bit of background on the activity, and how it related to the IDVmatrix.
Afterwards focusing on learning cases, and particularly challenges that the participants of the workshop were facing.
And we ended up trying to find solutions for these cases, sharing information, connections, ideas (have a look at this engaging crowd - movie recorded during the session).
The workshop was using elements from location-based learning, networking, mobile learning, machine learning, just-in-time learning, social learning, social media, multimedia, note taking, and a bit of gamification.

It was a wonderful crowd, so everyone went away with ideas. The networking part went very well also due to the icebreaker activity at the beginning. This was the icebreaker:

The WorkShop survival bag challenge!
Four actions, 1 bag for each team!

Action 1
Which person of your group has the longest first name?
Write down that name in the first box below.

Action 2

• Choose two person prior to this challenge: a person who will record a short (approx. 6 seconds)
• video with their phone and tweet it, and a person/s who will talk in that video.
• Record a 6 second video which includes a booth at the OEB exhibition (shown in the
• background) and during which a person gives a short reason why this particular learning solution
• (the one represented by the booth) would be of use to that persons learning environment
• (either personal or professional).
• Once you have recorded the video, share it on twitter using the following hashtags: #OEB #M5
• #teamX (with X being the number of your team, e.g. #team1) . This share is necessary to get the
• next word of your WS survival bag challenge.
• Once you upload the movie, you will get a response tweet on #OEB #M5 #teamX (again with the
• number of your team).

Write down the word you received in response to your video in the second box below.

Action 3

• Go to the room which is shown in the 360° picture in twitter (see #M5 #OEBAllTeams).
• Find the spot where 5 pages are lined up, each of them with another language sign written on
• them.
• Each team has to ‘translate’ the sign assigned to their team. You can use the Google Translateapp for this (see google play, the app is free!).

Write down the translation in the third box below.

Action 4
Say the following words into the Google Home device which is located in the WS room

“OK Google 'say word box 1', say word box 2, say word box 3“

If Google answers, you will get your WS survival bag!

And although the names were not always very English, with a bit of tweaking using the IFTTT app, all the teams were able to get Google home mini to congratulate them for getting all the challenges right.