Big Data: Bums on Seats Measures Wrong end of Learners Tracking Apps for Schoolchildren. A Deeper Look at why Big Data
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1 Big Data: Bums on Seats Measures Privacy When The Concerns Big Big Data Data for goes Privacy ClassDojo Bad: Problem 6 and Epic Other Fails Wrong end of Learners for Data Colleges Mining Your Children Tracking Apps for Schoolchildren A Deeper Look at why Big Data Good Why Data Big won t Data Guarantee (mostly) Can t Good Help Decisions We is Must Hurting Never Education Forget that Pupils are not Improve The The Impending Hidden Education Biases Student Data, and Teachers are not in Big Data Data Data Crisis Managers
2
3 Of Big Data & Little Data How Numbers Have (Almost) Ruined Everything Big Data People Little Data 3
4 1 Living by Numbers He does love his numbers. And they run, they run, they run him
5 Living by Numbers How quantity shouts louder than quality BIG DATA& the new EdTech discourse living by numbers 5
6 Living by Numbers How quantity shouts louder than quality living by numbers 6
7 2 God in the Numbers Business, numbers, money, people
8 God in the Numbers How we all got carried away BIG DATA& the new EdTech players god in the numbers 8
9 God in the Numbers How we all get carried away Facebook Twitter The Consultants-E s post Want to do an e is performing better than 80% of other posts on that Page. Promote it to get even better results. Blogs god in the numbers 9
10 God in the Numbers How we all get carried away Publishers Examiners apple With over 10 million users, we re now able to guarantee highspeed, high-volume translations in a matter of hours. By combining the effort of multiple students translating each phrase, our algorithms are able to produce crowdsourced translations as accurate as those from skilled professionals, meeting the quality standards of major publishers. App Developers god in the numbers 10
11 God in the Numbers $ How we all get carried away god in the numbers 11
12 God in the Numbers How we all get carried away video available here: god in the numbers 12
13 3 Drowning by Numbers When numbers get serious, you see their shape everywhere
14 Drowning by Numbers How numbers give EdTech a bad name BIG DATA& the people who question it drowning by numbers 14
15 Drowning by Numbers How numbers give EdTech a bad name Neil Selwyn > Academic Scott Thornbury > Writer Philip Kerr > Writer Monash Webpage A-Z of ELT Blog Adaptive Learning Blog drowning by numbers 15
16 Drowning by Numbers How numbers give EdTech a bad name BIG DATA& state education systems drowning by numbers 16
17 Drowning by Numbers How numbers give EdTech a bad name Neil Selwyn Interviewed by Philip Kerr as part of a joint event organised by the IATEFL LTSIG and GISIG. In the full interview Philip and Neil talk about EdTech, Sugata Mitra and much more. Event name Dates LTSIG GISIG : EdTech & Global Issues : November 1-30, 2014 : : Click here to listen to the whole interview drowning by numbers 17
18 Drowning by Numbers How numbers give EdTech a bad name In contrast to the generally learner-centred educational technology community is a set of powerful for-profit and/or conservative interests also involved in the promotion and advancement of the digital re-arrangement of education. Picciano and Spring (2013) refer to these interests as the education-industrial complex Click here to listen to the whole interview drowning by numbers 18
19 Drowning by Numbers How numbers give EdTech a bad name BIG DATA& english language teaching drowning by numbers 19
20 Drowning by Numbers How numbers give EdTech a bad name Philip Kerr Teacher trainer, lecturer and materials writer. Books include Inside Out and Straightforward (Macmillan), and Translation and Own- Language Activities (CUP) on Amazon Free ebook Website Facebook : : : : Click here to watch Philip introducing the concept of adaptive learning drowning by numbers 20
21 Drowning by Numbers How numbers give EdTech a bad name The drive towards adaptive learning is being fuelled less by individual learners or teachers than it is by commercial interests, large educational institutions and even larger agencies, including national governments. How one feels about adaptive learning is likely to be shaped by one s beliefs about how education should be managed. Click here to watch Philip introducing the concept of adaptive learning drowning by numbers 21
22 Drowning by Numbers How numbers give EdTech a bad name Scott Thornbury Teacher and teacher educator, with over 30 years experience in ELT, and an MA from the University of Reading. Currently Curriculum Coordinator of the MA TESOL program at The New School in New York. Twitter Website Blog Facebook : : : : Click here to visit Scott s blog drowning by numbers 22
23 Drowning by Numbers How numbers give EdTech a bad name The question then is, surely, what method of predicting failure (or of drop-out) is the most reliable, i.e. what method eliminates the most error? And/or which method can be most easily, quickly, cost-effectively improved? The data-driven method, or the (traditional) method, of teachers regularly making predictions (and communicating them to their students), based on observation of the students performance and/or formative tests? Click here to visit Scott s blog drowning by numbers 23
24 Drowning by Numbers How numbers give EdTech a bad name Philip Kerr Pit Corder Scott Thornbury Neil Postman Hugh Dellar Nicholas Carr Neil Selwyn Diane Ravitch drowning by numbers 24
25 Drowning by Numbers How numbers give EdTech a bad name BIG DATA& what happens when it goes bad drowning by numbers 25
26 Drowning by Numbers How numbers give EdTech a bad name John Hattie Professor of Education and Director of the Melbourne Education Research Institute at the University of Melbourne, Australia. He is author of Visible Learning, and Visible Learning for Teachers. Twitter Website TEDx Facebook : : : : Click here to read Hattie on feedback drowning by numbers 26
27 Drowning by Numbers How numbers give EdTech a bad name We should focus on the greatest source of variance that can make the difference the teacher. We need to ensure that this greatest influence is optimised to have powerful and sensationally positive effects, but they must be exceptional effects. We need to direct attention at higher quality teaching Click here to read Hattie on feedback drowning by numbers 27
28 Drowning by Numbers How numbers give EdTech a bad name Visible Learning was based on more than 800 meta-analyses of 50,000 research articles, about 150,000 effect sizes, and about 240 million students. A further 100+ meta-analyses completed since Visible Learning was first published have subsequently been added. Click here to read Hattie on feedback drowning by numbers 28
29 Drowning by Numbers How numbers give EdTech a bad name Simulations / Games Formative Feedback Quality of Teaching Small Group Learning Web-Based Learning Distance Education Classroom Discussion Computer-Assisted Instruction Problem Solving Teaching Teacher Credibility
30 Drowning by Numbers How numbers give EdTech a bad name Teacher Credibility Classroom Discussion Formative Feedback Problem-Solving Teaching Small Group Learning Quality of Teaching Computer Assisted Instruction Simulations / Games Web-Based Learning Distance education
31 Drowning by Numbers How numbers give EdTech a bad name Much of the data on technology was gathered starting in 1987 (pre- Windows, pre-internet...). The earliest meta-analysis cited goes back to 1977, 25 are pre-1990 and the next 25 pre-2000 (more than half of the 114 total) Indeed, in the latest Visible Learning for Teachers, neither computer, nor technology feature in the index at the back of the book. Click here to read Hattie on feedback drowning by numbers 31
32 Drowning by Numbers How numbers give EdTech a bad name John Hattie admits that half of the Statistics in Visible Learning are wrong ( ) Hattie reluctantly acknowledges that the CLE has in fact been calculated incorrectly throughout the book People who don t know that Probability can t be negative, shouldn t write books on Statistics. Click here to read Hattie on feedback drowning by numbers 32
33 Drowning by Numbers How numbers give EdTech a bad name Hattie declares that the use of computers (sic!) is most effective: with a diversity of teaching strategies after teacher training in appropriate use when there are multiple opportunities for learning when the student is in "control" of learning when peer learning is optimised when feedback is optimised Click here to read Hattie on feedback drowning by numbers 33
34 4 Away from the Numbers We re still waiting, on a number from the modern man
35 Away from the Numbers From big data to little data - EdTech reclaimed DATA& LITTLE the old EdTech away from the numbers 35
36 Away from the Numbers From big data to little data - EdTech reclaimed Teacher Credibility Classroom Discussion Formative Feedback Problem-Solving Teaching Small Group Learning Quality of Teaching Computer Assisted Instruction Simulations / Games Web-Based Learning Distance education away from the numbers 36
37 Away from the Numbers From big data to little data - EdTech reclaimed Teacher Credibility Classroom Discussion Formative Feedback Problem-Solving Teaching Small Group Learning Quality of Teaching Computer Assisted Instruction Simulations / Games Web-Based Learning Distance education away from the numbers 37
38 Away from the Numbers From big data to little data - EdTech reclaimed Classroom Discussion: Nicky Hockly Click here to visit Nicky s blog away from the numbers 38
39 Away from the Numbers From big data to little data - EdTech reclaimed Problem-Solving Teaching: Koenraad et al video unavailable online Click here to explore webquests further away from the numbers 39
40 Away from the Numbers From big data to little data - EdTech reclaimed Small-Group Learning: Carol Rainbow video unavailable online Click here for more on mobiles in class away from the numbers 40
41 Away from the Numbers From big data to little data - EdTech reclaimed Formative Feedback: Russell Stannard Click here to visit Russell s site away from the numbers 41
42 Away from the Numbers From big data to little data - EdTech reclaimed Russell Stannard Ayat Tawel Graham Stanley Joy Egbert Burcu Akyol Neil Ballantyne Carla Arena Christian Newby away from the numbers 42
43 Away from the Numbers From big data to little data - EdTech reclaimed Christine Bauer your name here Vance Stevens Nicky Hockly Teresa Almeida Rita Zeinstejer Elizabeth Anne Nina Liakos away from the numbers 43
44 Of Big Data & Little Data How Numbers Have (Almost) Ruined Everything Get the Slides 44
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