Thursday 6 December 2018

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
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).
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.
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, …)
Luca challenge: responsible use of AI.
GDPR Art 22: automated individual decision-making, including profiling. : identifying policies to adopt learning analytics. 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   

User involvement and co-creation: see the competen-SEA project see 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

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