MOOC & SLN. Mung Chiang Princeton University

Transcription

1 MOOC & SLN Mung Chiang Princeton University

2

3 I. A Personal Journey of Learning

4 An Inter- disciplinary Course

5 Two Just- in- Time Textbooks

6 Going MOOC One of six Princeton pilots in Sept Non- exclusive arrangement with Coursera No credits at all Tons of s complaining about that Khan Academy recording style Tremendous TA help Kudos, VOH, GCH 100,000+ students enrolled so far That many people who know eigenvector?

7 Flipping at Princeton Why pay tuiuon? Class Ume is for interacuon One- way lecturing stays on YouTube. I don t know what I m talking about Be[er teacher on campus. Same 3- hand Be[er student on campus? Did you actually watch the video? Where is the new spine of synchronous learning?

8 Policy What counts as teaching outside? What counts as publishing? Who owns IP? What counts as class Ume credit? How about teacher- signed ceruficate? Why are we even in MOOC? PCAST, Trustee/President, Alumni, Faculty, Staff, Students, PotenUal students

9 II. Many Dimensions of MOOC

10 There s More Than 1 MOOC Content provider vs. Plaaorm provider Content aggregauon vs. Content creauon Open source plaaorm vs. closed Nonprofit vs. For profit Degree, credits, ceruficate, nothing

11 Many Types of MOOC Consumer/Producer Ins/tu/ons Individual Teachers Individual Non- teachers InsUtuUons Needs accreditauon Georgia Tech/ Udacity Difficult to achieve Individuals Coursera Udacity Fancy publishing Goals/Ages K12 College Graduate & Professional Accelerate degree Get/switch jobs General educauon Fun * * * * Lifelong Learning * * * * *

12 1. Broad Access and Reduce Cost Thousands of Dollars in Private Nonprofit Four- Year Public Four- Year

13 2. Roles of University/Faculty What s the physical campus of a university for? A. Drinking at party B. Social club iniuauon C. Sanity check before branded stamp D. Face to face learning experience So, how much can you charge for the service? What about students who didn t dare to apply?

14 3. Economics Revenue (not working out yet) Eyeballs (e.g., adverusing) Content (e.g., freemium package) CerUficate (e.g., proctored exams) Data (e.g., employment matching) Cost ProducUon HosUng Labor by teaching staff: one- Ume and recurrent Opportunity cost

15 4. Policy AuthenUcaUon Are you who you say you are? Assessment Self- grade Peer- grade Machine- grade Expert- grade AccreditaUon Who approves? Who cares?

16 5. (Most Importantly) Pedagogy New science of learning Distance Asynchronous Heterogeneous Massive Low (average) engagement

17 III. Scale

18 This Isn t the First A[empt Year Name Technology Descrip/on Correspondence Learning EducaUonal Radio Licenses 1963 IFTS Coastline Community College NaUonal Technological University Jones InternaUonal University MIT s OpenCourseWare Postal mail Radio TV Satellite University of Chicago created first college- level distance learning program FCC began granung educauonal radio licenses to colleges, allowing educauon delivery through live radio shows FCC created InstrucUonal Television Fixed Service (ITFS), allowing broadcast of courses over TV First college without physical campus, courses mainly broadcasted on TV Online degree courses via satellite transmission; students could call in and parucipate in discussions First accredited, fully online university Free, open, web- based publicauon of MIT course materials 2005 Blackboard Internet Blackboard and WebCT merge to become a leading LMS 2007 Kahn Academy itunes U Non- profit educauonal site offering video lectures to anyone 2008 MOOC Canadian universiues K MOOC Udacity, edx, Coursera

19 One Core Challenge Is 2013 the year for teaching and learning to become a scalable human acuvity? Is technology ready? (Pre[y much) Is pedagogy ready? (Not yet) Are business models ready? (Not yet) Are teachers and students ready? (Not yet)

20 Scale Efficacy Tradeoff Is this possible? Is this fronuer possible? Data from h[p://

21 Scale Up: Social Learning Only 30% of Coursera courses have forum- acuve students

22 Scale Down: IndividualizaUon IndividualizaUon shows promise over One- Size- Fits- All (OSFA)

23 Data about Learning (massive amount of) Data is Common bridge across disciplines EssenUal foundauon to analyucs Major (potenual) revenue source Open access to data presents: Legal issues Business issues A key uncertainty today

24 What is Open? Open content consumpuon Open content creauon? Open content packaging? Open policy- seung? Open plaaorm? Open data?

25 Science of Learning Research Metrics of efficacy Design of experiment Personalize IncenUvize Social learning networks Model of learning Taxonomy/structure of knowledge (MOOE)

26 Social Learning Networks (SLN) InformaUon Technology Science of learning Data Science

27 IV. Scaling Up

28 ObservaUons About Forum Sharp decline rate Impact on social learning InformaUon overload Possibility of automauc recommendauon Not the same as forums like Stackoverflow Focused around one course Both social and tech. discussions

29 Data Summer courses on Coursera 8 vocauonal courses 29 quanutauve (non- vocauonal) courses 36 other courses 115,922 students 171,197 threads 830,000 posts

30 Examples Posts Posts Drug Discoveries Development Functional Programming posts Posts audiomusicengpart Sustainability of Food Systems

31 Student AcUvity & Thread Length Log log Plot Log log Plot number of users count number of posts length of the threads

32 1. Regression Analysis QuanUtaUve: smaller iniual volume, but slower decline IniUal popularity: light impact on decline rate Teacher parucipauon: increased volume but similar decline rate Peer- reviewed homework: much increased volume but slightly increased decline rate More threads at the same Ume reduces a[enuon received by each thread

33 2. GeneraUve Model SVM- based classifier Topic extracuon Ranking and recommendauon Fast converging: 10 days of training suffices Accurate keyword extracuon Twice as accurate as a- idf

34 V. Scaling Down

35 1. IndividualizaUon Scale

36 Mobile MIIC Meet the challenge of seamless dynamic content modificauon Integrated First system integraung book + lecture + assessment + social learning Individualized One course transparently and intelligently turns into parallel universes

37 ImplementaUon ios/android mobile app Webkit- based rendering PDF/ePub to HTML Video hosung Assessment database Social learning features Machine learning engine AdaptaUon logic

38 Student Trial

39 2. CollaboraUve Filter 3196 students and 69 quizzes, relauvely sparse Train neighborhood method 81% score- predicuon accuracy so far

40 VI. This Experiment We Call MOOC

41 Long Timescale Will be a long Ume before we have sufficient data to validate the many hypotheses today. Tiered models to emerge: 1. (free) TED talks 2. $49.99 freshman courses with on- campus tutors 3. online professional degree

42 Diverse ExpectaUons Extremely diverse set of consutuents, with vastly different expectauons Flipping people is hard

43 Advancing Pedagogy Pedagogical advances need to catch up with business discussions Are these millions of people actually learning? What data informs us?

44 Much More To Learn About Learning

45 Thank You