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  1. 10th anniversary
  2. Claude Steele
  3. Dean, Standford graduate school of education
  4. FMRI research neuroimaging research
  5. psychology research after imaging research
  6. before neuroimaging psychology was powerful science
  7. URestriction was based around the measurement of behavior
  8. ABaed on inferences
  9. Neuro imaging allowed measuring of neuroimaging itself
  10. Related to behavior and cognitive changes
  11. Changes did not apply immediately
  12. Tooktime
  13. But transformed the time
  14. Created a new language and
  15. insight into an age old science
  16. Technology development chan change these fields
  17. Education in particular can benefit from these studies
  18. civil life
  19. social issues
  20. technology can support the shifts in educational fields that can be improved
  21. Business and university communities need to be working together
  22. university recognizes this is critical
  23. MediaX as crucial part of the graduate school
  24. Goal to generate strong ideas
  25. Roy Pea
  26. Faculty director
  27. Sees relationship between grants and donations
  28. Students who come to study
  29. University students can go deeper than companies
  30. Physical Interaction Design
  31. Scott R. Klemner
  32. The influence of research direction and teaching methods
  33. cae from median exposure
  34. Hiroshi
  35. Collaboration with mediax resulted in user interfere with social intelligence
  36. Used for systems healthcare
  37. Name: Nakagima
  38. Creative Commons dude
  39. Virtual Jurisdiction
  40. Questions changed legal rights n digital realm
  41. Lessig
  42. Forbidden problems
  43. Corruption
  44. Opening new opportunities for democracy
  45. Catalyst
  46. Broker
  47. Idea collaboration
  48. Member companies work together to do what they could not do previously.
  49. Larry Leifer
  50. Mission
  51. create breakthrough media innovations
  52. Hypothesis
  53. shortest path to better media is a better design
  54. Value proposition
  55. 7 phd finding in 7 minutes
  56. pivot thinking and the differential sharing of information within new product development
  57. Mark Schar
  58. PhD 2011
  59. Interested in how decisions are made in the corporate world
  60. 2 teams of six students each
  61. All the same problem solving preference
  62. Converger
  63. Diverger
  64. Make design challenge in the group
  65. Choose a shoe: experiement
  67. Which group
  68. Divergers work fast in the beginning but
  69. Convergers move slow at first, then quickly and slow at end
  70. Convergers choose the worst but justified their choice after
  71. Key issue in team work is getting people to collaborate
  72. Alexander Lubbe
  73. Tangible business process modeling
  74. How to get people to be tangible
  75. Clince redesign
  76. Bring in expert
  77. Physical modeming
  78. Capturing consensus in big picture
  79. Capture the model into computer that runs the clinic
  80. The company responsible launch their company the next week
  81. A visual representation medium to characterize moment to memo net concept representation is important
  82. Creating a language to code concept generation activity
  83. Interaction-dynamics
  84. A lot being communicated that is not fact and figure
  85. Provided a method to code the exchanges in real time to characterize activity
  86. Improvisation priciples
  87. Learning how to overcome the block
  88. You need to get around the blocks
  89. Focus on animated behavior
  90. Affected interactions is emotion
  91. What is the role of emotions in engineering teamwork
  92. Can we adapt methods that were developed to predict forced divorce
  93. Couples at work
  94. is similar to engineering
  95. Performance of a team can be predicted based on a short time period
  96. The study state that the lab and field study have similar dynamics
  97. The teams had 99% similarity
  98. the performance can be tracked
  99. The coding can be done in real time
  100. The research created a new coding
  101. The power of hedonic balane
  102. Wow team
  103. thank you team
  104. Greg Kress
  105. intrinsic member difference as predictive media fro long term team performance
  106. What 17 different factors contribute to team performance
  107. Everything contributes
  108. But nothing contributes predictively
  109. Except 1 thing
  110. Possession of a extraverted feeling person is valuable
  111. They are open to the world and perceive from the world
  112. The performance
  113. The more extraverted feeling there was on the team the better they performed
  114. !!!!!! The extraverted feeling creates a better team and better design
  115. Intrinsict difference matters
  116. Extraverted feeling matters the most
  117. The Douglas while personality profile worked the best
  118. Johnathan Edelman
  119. Understanding how people influence media and how media influences people
  120. Looked at teams with radical ideas during design sessions
  121. Looked at the ones that were most productive in radical design session
  122. Correelated with media used and organized it
  123. Feeling acting and narrating
  124. Looking at innovation
  125. The low performance team is not as emotionally involved
  126. The engagement that are optimizing and breakers
  127. The ones that gesture more create more
  128. !!! People who gesture more are the ones that create more
  129. The activity and animation of the groutp creates more product envelopment and better understanding off the innovation process in radical ideas
  130. statement about gestures being important
  131. Ramesh Johari
  132. Professor in management science and engineers
  133. /The engineer as economist
  134. The design and use of online market platforms
  135. El Roth
  136. Winner of noble prize in economics
  137. He said we can engineer markets like woe couldn't ever do before
  138. Look at markets
  139. Question: Who can I trade with
  140. Who are my competitors
  141. How much should I pay?
  142. These are the challenges
  143. The institutions have changed at a quick timescale
  144. ][The information available to you is relatively limited
  147. look at Etsy and Ebay
  148. Sponsored earch market
  149. Odesk
  150. task rabbit
  151. app store and google play
  152. Uber and kick starter
  153. How do online markets change the play of markets
  154. The two important
  155. Fine-graned matching of makert participants
  156. Fine-grained information about matches
  157. An unprecedented ability toeingeer the economic interaction
  158. these are the opportunities
  159. We cana not only find the information but we can mine the results
  160. How we can understand how happy people were after
  161. The serve and the rating
  162. In the past this was not something that we could access
  163. Economics change when you can engineer the market
  164. You choose what people see and how they make decisions
  165. Look at the market designer and the market participant
  166. Centralized or decentralized matching
  167. Opacity or transparency
  168. What is the matching process and how much should you intervene
  169. Market Designers
  170. The example is on uber
  171. The über people centralize the whole market place
  172. You never choose, you just say you need a ride
  173. Odesk is very decentralized
  174. The filter system i a n engineering of the intervention process
  175. There is a limit of how much you can see at once
  176. how do you choose
  177. Centralization has an advantage
  178. if you think you know what the matches should be, t
  179. then you can implement them
  180. The national resident matching program is a good example
  181. Single service
  182. Decentraliztion has its advantages
  183. If you don't know what the user wants,
  184. Then you can let the party express their desire efficiently
  185. Search engine
  186. Oopactiy and transparency
  187. You have the choice to show a lot of information or very little
  188. The theory says that more information should make more market inefficient
  189. More information makes a cognitive burden on the decision maker
  190. We moved from web 2.0 is too much information
  191. 3.0 is took uch information
  192. Not so obvious
  193. Not obvious that full transparency is the clear decision
  194. To much information ix not always good
  195. Market participants
  196. The match i a finer grain
  197. Finding the trading partner
  198. ]pricng goods to sell
  199. bidding on goos buy
  200. What is the optimal strategy for X?
  201. What are the optimal for individual to a group
  202. Algorithmic trader
  203. financial security
  204. Bid on ebay
  205. Pricing goods to sell
  206. Most compelling problem
  207. If you are a merchant entering the online market the pricing changes rapidly
  208. bidding gooods toi buy
  209. one example
  210. Content on the go: the economics of the market for mobile apps
  211. How should developers price their apps
  212. rapid experimentation
  213. stiff competition for visibility
  214. Project: a dart driven study of optimal strategy
  215. question: how do you form marketing strategies
  216. John Willinsky
  217. and Alex Garnett
  218. Left to their own devices: automating xml parsing and rendering for scholarly publishing
  219. Public knowledge project
  220. Benefits: Your document is your metadata
  221. The document itself contains al the content available in it
  222. There is not separate metadata
  223. Documents being marketed up in documents have been existent for a while
  224. is not wide spread with publishers
  225. Markup is expensive
  226. Someone has to tag manually
  227. There are tools to automate the process but the tools are not perfect
  228. The public knowledge product
  229. Developers of open journal systems and open monograph press
  231. Opensource software to support open access publishing
  232. Userbase happens to include many such small publishers who publish exclusively in PDF
  233. Goal: Kill PDF
  234. Beyond the PDf
  235. Things PDF don't have:
  236. well-structured text mining and indexing
  237. rendering in different formats-
  238. mobile
  239. embedding dyanmic contact
  240. XML publishing is difficult
  241. So its complicated
  242. Despite is having many good tools
  243. available at different stages at this workflows
  245. The demo
  246. External services
  247. Start with unformatted article drft
  248. Submission in PDF form
  249. Does fuzzy pdfx parsing
  250. XSL transformation from PDFX to XML to NLM XML
  251. can be turned into HTML
  252. Document text reflowing
  253. unformated article
  254. Submission to pdf
  255. turn to XSL
  256. Citation parsing
  257. Extract references and ParsCit
  258. ParsCit good at xml pdf
  259. find author and content
  260. Tool BibTex
  261. OJS plugin used soon
  262. PDFx
  263. OMP
  264. CrossRef
  265. mPach
  266. University of Michigan mach system
  267. support eBook
  268. HathiTrust
  269. CrossRef
  270. Automatically link in PDF
  271. Developers
  272. ]Damion Dooley
  273. Steve Pettifer
  274. Juan Alperin
  275. Alf Eaton
  276. NLM Spreadsheet
  277. Paulo Blilkstein
  278. Assistant Professor
  279. Reinventing Science and Engineering in K-12 Schools
  280. Assistant Professor
  281. School of Education
  282. reinventive science and engineering
  283. Problem in science and engineering
  284. STEM pipeline
  285. By 7th grade- 70% don't have any interest in science
  286. Self reported in science
  287. In 10 years people are not learning
  288. Trying to teach hands on science
  289. In a place with desks and white or black board
  290. If we care about swimming, we need to build a swimming pool
  291. You need to have an environment that fits the learning process
  292. Stanford created the first global project and making engineering and fabrication
  293. FabLab@School project
  294. Projects all around the world
  295. Renovate one big room in the school
  296. Create 3d printer, robotics, engineering equipment
  297. What can you do about science?
  298. People can actually make all the things that they learn about
  299. Don't just learn about leonardo divimci
  300. Do what divimci did
  301. Talk to phat: Actually go through thep recesses
  302. Students who love Bach, but no ability to play music
  303. Made a robot to play music
  304. Non-eingineer
  305. Students build their own microscope
  306. They decorate them their self
  307. They made their own instruments
  308. More engaged
  309. Brail: self rocking stroller
  310. creating solutions to problems that people have
  311. Young kids building their own tools
  312. Build new toolkits
  313. Bring to younger kids
  314. Start at a younger age
  315. FabLab@School
  317. Renate Fruchter
  318. Global Teamwork 3.0
  319. Question: how do we harvest knowledge and foster creativity on global/macro level
  320. Micro to macro
  321. How to capitalize on core competency in global corporate competences
  322. space, time, technology, sickle., culture
  323. disrupt work process
  324. How do you communicate
  325. How do you get feedback and share ideas
  326. How do you let recommendations known to team members and boss
  327. PBL Lab creating a collaborative eco system
  328. Developing collaboration
  329. people, Places, , Devices, Network Infrustructer
  330. M3R
  331. Remote collaboration in mixed medixa mixed reality
  332. fusion of physical, ritual, and mobile works
  333. i room physical work
  334. 3di collaboration team space virtual world
  335. Real time situation status and decision making
  336. Combine smart board collaboration and digital space
  337. Multiple Channel Presence
  338. Face to face is best
  339. But multi channel presence can work
  340. virtual, physical, robotic, world
  341. Collaboration
  342. Integrate sensor physical presence
  343. Wired for feedback
  344. How to make so many choices
  345. We build on our own data
  346. the power of feedback is not to control you, but to give you control
  347. We can self resulate when we are given control
  348. Six steps to engagement
  349. I know where am
  350. i know where you are
  351. we know where we are
  352. we know where we want to go
  353. we know where we can go
  354. we move
  355. Feedback nudge
  356. eMoC Prototype
  357. LPBL Lab
  358. 10 key characteristics for co-colab
  359. 1. foster cocreation, interaction and coaction
  360. transform the wAY participants express ideas and solutions
  361. enrich forma and informal interaction experiences
  362. increase awareness, attention, participation, and engagement
  363. sustain persisntect presence of content and models
  364. leverage knowledge in context
  365. facilitate tanspacrency
  366. maximize flexibility
  367. create emergent work practice and social dyanmics
  368. create and mange choice
  369. 10 more take aways
  370. product from knowledge to information
  371. from viewing to experiencing
  372. group dynamics to cohesion
  373. to sequential to agile sprints
  374. to ministral activity to result acuity
  375. to being a source to being a a network
  376. to broadcast to crowdsource
  377. to present to participant in large space
  378. from multitasking to engagement
  379. Anthony D Wagner
  380. Brain Patterns and the Mind
  381. Dept of Psychology and Neurosciences
  382. Cognitive neuro scienctist
  383. Codirect center for cognitive and neuro imaging
  384. Looking at imagine to address societal problems
  385. cross disciplinary
  386. Lab is focused on neuroscience and learning and memory
  387. Question
  388. How do we learn from experience and draw on memory to informal current thought decisions and action
  389. how can we optimize learning including through use of immersive learning environments
  390. Goal:
  391. Direct learning to solve issues
  392. Interst
  393. Memory and disease
  394. 'Neuroscience for society'
  395. technology use and nerocognitive function
  396. law and neuroscience
  397. Look at bain imagines technology with
  398. Machine learning
  399. How to use interaction of technology to address important questions about mind and status of real world
  400. Functional brain imaginegs changed the field of psychology
  401. Memory change
  402. Look at brain data to inform questions about psychological function state
  403. At the second phase of a field
  404. First phase,
  405. "where in the brain are there changes in jeural activty
  406. Look at the faces scenes sounds works
  407. Look at patterns of activation
  408. See where the pattern is the change when certain events were active
  409. This data is being used to analyze patterns
  410. individual neurons can not be analyzed
  411. Machine learning can help differentiate the pattern
  412. Mutlvariate
  413. pattern
  414. classifier
  415. Decode what people are looking at
  416. Look for test patterns
  417. Can you decode from neural activity whether someone has seen a face or not
  418. Under some conditions you can be near perfect
  419. This can look at brain pattern to see if someone has seen a face before
  420. Forenscs implication
  421. You can look if ads are being successful
  422. Look at what is being remembered
  423. Neural evidence to see if things are being remembered
  424. Decoding the contents of the memory and cortical reinstatement
  425. Interesting that we don't need to know specifics
  426. But we are looking at the examples of patterns
  427. We don't need to know the specifics if we can analyze the change
  428. Hippocampus is the first to be affected with alzheimers
  429. if you don't have a hipocampus, you will not be able to remember
  430. The cue allows you to create pattern
  431. Scientific fiction
  432. judicial can look at brain acidity to know if you have seen people
  433. criminals create brain disease after crimes are kept
  434. people want to live in bliss so they are not a threat
  435. sincere is not perfect but has results with high possibility
  436. Story about cyber murders
  437. Coding and password remembrance
  438. knowledge ofspeicifc planted information
  439. not just faces but experiences
  440. Looking at what is remembered and recallable and how is can be masked
  441. [ep[;e willingly forget
  442. Training patterns
  443. Words and scene event
  444. words and person events
  445. Look at the result based on encoding data
  446. Training patterns
  447. Lookg for the reinstated cure
  448. Understanding retreival patterns
  449. By looking at activation
  450. people can decode the kind of memory content they are bringing back to life
  451. Look at the pattern
  452. See based on the stjdys
  453. look at the memory performance
  454. Can people be put in multiple virtual worlds
  455. S[atial coding in brain patterns: decoding virtual worlds
  456. scan them while mentally in virtual worlds
  457. Learn from their neural disturbed pattern to see if people are in the world
  458. brain imaging can show if people think they are in virtual world one or two
  459. The wedding of digital media science technology can look at legal restrictions
  460. People can analyze the possibilities of analyzing what world people think they are in
  461. Neural devices can understand expression
  462. People could understand mental illness
  463. Reasons of feeling alone or together
  465. Lessig
  466. Schools are poor
  467. graduation rates is low
  468. Bill and Malinda gates foundation has goal for 17 year goal of having 70% graduation rate
  469. US dropout factory
  470. 7/10 do not graduate from college
  471. learning prospects today
  472. k-12
  473. What can we design differently
  474. The k-12 school is 1 million minutes
  475. Hour long classes
  476. with seat time requirements
  477. students grouped by age
  478. lecture based teaching
  479. paper textbook is primary learning
  480. very little data
  481. reports midterm and final
  482. Have the [people give reports to students at every class
  484. national education plan
  485. technology plan
  486. very different vision
  487. Learning in always on network world
  488. The figure depicts a mobile powered by technology
  489. Single teacher transmits all students
  490. Students should be centered We have centered topics that should be taught
  491. We have
  492. We can have student centered learning
  494. :earning communities
  495. lmpw;edge bio;going tools
  496. peers with common interests
  497. onine tutoring and guided courses
  498. mentors and coaches
  499. peers
  500. expertise and authoritative goals
  501. lifelong and life wide learning
  502. We can have ongoing access to learning tools throughput life
  503. Possible that people can have connected learning
  504. We can bridge in and out of school learning experiences
  505. We can have in and out of school learning.
  506. We can extend the learning time because people spend less time in the school
  508. The national plan can have changed school structures
  509. Teachers can give visual learning
  511. The math and science quality drop rates for low income students is specially declining over the hsummer
  512. People can use new services to conner students to school through non class rime through games
  513. Conntecting merica: the national broadband plan
  514. Transforming american education learning powered by technology
  515. LEAD COMISSION: Leading education by advancing digital
  516. Look at the LEAD commission
  517. business and academic leaders who are reporting to the FCC
  518. Looking at a whole raft of state polices, finding better ways to aggregate their markets and gdo smart purchasing
  519. Whatr are learning analytics
  520. The simple definition
  521. The learning analytics is about collecintg traces to hat learners leave behind to cuimprive learning
  522. To improve teaching
  523. To develop online learning systems
  524. To better improve peoples learning experiences
  525. Personalized adaptive learning pathways through online learning systems that can dbettere support learning for everything
  526. Eric Duval, U. Leuven, Belgium LAK 2012
  527. Learning analytics is about collecting traces that learning leave behind and using those traves to improve learning
  528. Recommended learning resources
  529. More engaging and inspiring 23/7 learning: games projects and badgers for competencies
  530. Can we identify students difficulties early and provide the kinds of sppport needed for success
  531. Continulosly improvable curricula: learning networks getting smarting with every click
  532. Comparitve pdagogdy
  533. ]
  534. We cn A/B test th e success of certain teaching examples and solutions and difficulty
  535. We currently have a one-size fits all system
  536. Persoinalization means that sutdet get tho have a choice in what they learn and how they are taught
  538. Personalization, differentiation, individualization
  540. US DEPARTMENT of EDCUATION, National education technology Plan (2010)
  541. Adaptive pacing
  542. Adaptive peda gogy,
  543. personalizd learning goals.
  544. Grand challenge problem: Personal Learning
  545. The grand clang defenses a commitment by a sciienfitifc community to work together towards a common goal- valuable and ahie3vabe within a predicted
  546. Jim Bray: Director microsoftJim Grey: What Next? 2003) A dozen information-technology research goals: Journal of the ACM 50(1)
  547. Grand Challenge Problem #1
  548. Design and validate an integrate system that iprovides realtime access to learning experiences tutnded to the levels of difficulty and assistance that optimize learning for all learnings, and that incorporates self-improiving features that enable it to become increasingly effective thtourgh interaction learning.
  549. [such integration systems should
  550. Discover the appropriate learning reousrc4es
  551. Configure them with representation that is appropriate for age and background
  552. And select appropriate paths and scaffolds for moving the learn through there right resources with optimal level of challenge and support
  553. WE have learning management system
  554. And sequences of materials
  555. We don't hasave systems that perform these functions dynamically
  556. It would be essential to have some nonexistent systems
  557. What are in student information systems today?
  558. basic demographic
  559. very little grade
  560. Background (some judicial)
  561. economic
  562. Information and medical diagnostic information
  563. With digital learning
  564. We should have Student Information Systems
  565. With digital learning we would have deluge of data - 5 orders of magnitude or greater than our slim data today.
  566. Massive dataset e-sciece explorations have led to breakthroughs in: biology health, environmental science, astronomy, physics
  567. Attract these big data in education to have big data education scientists
  568. Need people in the education domain
  569. SLC
  570. Shared Learning Collaborative
  571. Alliane of states, foundations, educators, content providers, developers, and vendors.
  572. Project: Agile/scrum for class
  573. Ways for people to learn outside of school
  574. A cloud for learning about things in real world
  575. Bring outside world into the classroom
  576. Bring and share
  577. Show and tell for learning
  578. diverse learners
  579. Technology and learning maps
  580. Provide graphical representations of the representation for application programming interface
  581. Plug the curricula
  582. The data Deluge
  583. Enhaving teaching and learning through educational data mining and learning analytics
  584. And Issue Brief
  585. ]Us. Departement of educatuob
  586. !READ THIS'
  587. look at analysis o the benefits and real outcomes from the EDM (Leanring analytics) ago improve learning process
  588. What are the problems with big data in education
  589. There is a difference
  590. Computer interactive learning analytics
  591. vs
  592. ultimo dal learning analtycis:
  593. CILA: Caputer fine grained interact6ion: keystrokes, clickstream
  594. Look at relations between large systems an relation between nation and departments
  595. MLA
  596. Drings up sensor and emotion
  597. Have privacy issues
  599. What are the scientific problems?
  600. A lot of these are modeling chanllgnges
  601. learning what the use knows based on aaho wt h eysintercaft
  602. With the systems
  603. We need to model the user behavior
  604. User emotion
  605. D o they like it? About to quit?
  606. user profiling ]
  607. Beign asble to
  608. Domain odeling
  609. User knowledge, user beahvirior, anuser ex[erience molding, user profiling, domain modeling, learning com[ponenet analysis
  610. We need edicatopm data scoemtosts tp make progress on those issues
  611. ` The complex reasons
  612. challenges of personalization
  613. Pacing, tayloring, recommendation
  614. Interactive data visualization systems for
  615. look not just at retrospective guidance, but runtime guidance to see how their efforts are improve the success
  616. Look at realtime ability to see how people are learning
  617. The relationships of being alb to the creft the economic markets
  618. Look at how learning markets
  619. We ail be cable to decide that people are able to control the learning makreta
  620. The statistics and study about learning can directly be related yo the schools.
  621. look at the figures of learning urges
  622. infomrnuing curriculum designs, based on discover of learning curves t]
  623. Visualizations can visualize the students error rates and understand how students are progressing or making mistake to make sure that the state of information being distributed in best fit for the students
  624. The study can be given for knowing where the error rates are
  625. This would show that we could remove time away from the unnecessary areas and focus time on the difficult
  626. rescue wasted tacking if you know people are learning from the beginning
  627. Focus on parts that re more difficult and how people can be
  628. Look at how SLC shares the data that it creates to collect this information and present in ucha away that allows users to start and maximize learning
  629. provide immediate feedback to the teahcers to maximize the effort beings spent
  630. Look at variables: Teachers effort, ttiem spent to teacher,
  631. look at the law s around teaching
  632. Legal issues are that styes have different lawaw werlated to sharing data4e
  633. so you could bnot work across borers or states because they data can not be use
  634. Labeling issues are difficult]
  635. Labeling can be unproductive sometimes if they were not labeled
  636. So there are issues associated with labeling
  637. earner profiles would create many secondary issue associated with what roles an who would have access to the stereotype ricks
  638. student profiles associated with stereo type threat
  639. people in administrative situstioanrs care not alrwa as
  640. National Science Foundation is getting more active in the learning
  641. "Dear ColleAGUE ;ether = data =tenseie edicatoipn= related research
  642. Society for learning analytics focusing on analytics research
  643. Learning analytics work group
  644. Working together to increase arguments for spending more money on the learning technology development fields
  645. There are no degree programs in the subjects
  646. ie. learning analytics, learning analytics data science, education data science
  647. Need new prosodic programs
  648. New data sincere fields
  649. Graduate training promra
  650. Gates Foundation
  651. Would follow a given on focused for human capital development plans
  652. Scoping priorities in research tropics and tools for in airy
  655. Pamela J. Hinds
  656. Identity and performance
  657. Reativity and feedback
  658. Cultural context
  659. Talk with spady about differences in culture
  660. Look at Distributed Work
  661. Request slides and reason at school
  663. Iterative prototyping
  664. Learning quickly about their flaws and constraints
  665. Hope to result in better builders
  666. Look at focus and culture
  667. Look at the creative process and iteration
  668. Look without feedback the second time
  669. what happens when you have iteration with feebacj and without feedback
  670. Looking if national background made a difference in innovation
  671. Look at the conditions
  672. Feedback in middle and end
  673. Objective feedback
  674. Statistics
  675. Numbers
  676. Look at
  677. No differences contrary to popular belief that there is no difference in creativity
  678. Westerners did better with iteration rather than not iterating
  679. Eastern countries did worse with iteration
  680. Study: Culture and Iteration
  681. West was more creative when they didn't iterate
  682. East became less
  683. IterationxCulture
  684. Feedback is good for westerns when iterating
  685. Westerners increase in engagement with feedback
  686. But feedback doesn't not increase creativity
  687. Stereotype Threat
  688. Influences the creativity and engagements are
  689. Team of westerners benefit from iteration with feedback, but are less creative when there is no feedback:
  690. it may suggest that iteration is undirected without feedback
  691. Teams of easterners creative performance is harmed by iteration with feedback, but creativity is increased without feedback.
  692. What is the role of creative self-efficacy ( Tierney and Farmer, 2002, 2012) and tolerance for struggle
  693. Tolerance for struggle:
  694. We learn in the West that when we struggle, that we are not smart and its not good
  695. But in other countries, tolerance for struggle is valued more than the right answer
  696. The creativity may be influenced by the values in different countries
  697. Look at the cultural values and context comes into play
  698. How does a task come into play:
  699. Was a redesign of the student union less inspiring of novelty?
  700. Look at other relations of culture, interation, creative, focus, internal, external, mixed cultural groups, feedback, feedback
  701. Question: look at the groups of people who changed over their culture and returned home.
  702. The culture of people who were in oneCenter for Work Technology Organization
  704. Clifford Nass
  705. Four most important things to know about media and their implications
  706. just got off sabbatical
  707. Psycology of media
  708. CHIMe Lab: communication between humans and interative media
  709. Five important questions
  710. Why is media use continuing to grow
  711. How has media changed human relationships
  712. What is the most important locus of media growth
  713. How has emotional life changed?
  714. 1. Growing media use
  715. Media use is growing in all age groups
  716. Teens
  717. Music is consumed in enormous quantities
  718. Tweens
  719. average 12 year old has a cell phone 3 years ago
  720. average 9 year old have a cell phone now
  721. Adults
  722. Respond to emails
  723. have open multiple chat windows
  724. Kits
  725. More than cellphone
  726. Legos moved online
  727. What lessons teachers in legos
  728. when missing a part
  729. When you can't take them apart
  730. Systems are perfect
  731. Babyes
  732. When parents are brest feeding
  733. they watch tv that parents are watching
  734. What is driving the chang?
  735. Partial Media Displacement
  736. New information product and service appears
  737. It steals time fro other information activities
  738. Doesn't steal all the time
  739. And steals it from non-media activities
  740. We don't have a fixed media time budget
  741. We have an increasing lessen time available media
  742. Media stealing time from sociall rich interaction
  743. simultaneous talking and device usage
  744. parallel play
  745. before people can play together, they play side by side
  746. Adults have parallel play
  747. people on their own laptop or tablets
  748. Non-social interactions
  749. Just proximate
  750. Inflection point
  751. Media displacement time
  752. Parallel media use
  753. Double and triple booking
  754. Multitasking
  755. Drive to increase media use
  756. Multitasking is an outgrowth of media time displacement
  757. Students use 4 media at one time
  758. Horizontalization of media use
  759. aka multitasking
  760. Implications to multitasking
  761. Continued growth of multitasking
  762. with concomitant cognitive and social deficits
  763. its bad for people
  764. Selling attention becomes challenging and implications
  765. Measuring attention becomes challenging
  766. Because attention is nota single investment
  767. Decoupling makes it difficult to know where people are paying attention
  768. Immersion becomes devalued
  769. And there are conditions set for people to allow themselves
  770. Multitasking is bad for the brain
  771. it requires more mental power
  772. people follow to feel better about thinking
  773. Analyzing barney
  774. Ilove you
  775. you love me
  776. we're a happy family
  777. great big hug and kiss
  778. won't you say you love me too?
  779. (even thought no one can hear but its important)
  780. we are friends as friends should be
  781. (faceeeeebook)
  782. You don't see each other, but because you can contact through media, you define friends
  784. Implications
  785. devaluing of face-to-face communication
  786. attenuation of trust
  787. fame as the #1 value in pre-adolescents
  788. It used to be compassion and community
  789. Not personal and about people (social)
  790. Social media algorithms create definition of self
  791. classic sociology talks about we learn through interacting with others
  792. Social media gives us content thats defines who we are
  793. Creation of self is not done through person to person
  794. its done with person through algorithm
  795. 3. Most important locus of media growth
  796. automobile
  797. increasing automation means more attention to media
  798. limitations of external distractions means more multitasking
  799. implications
  800. Models for expelling partial intelligence
  801. SIRI doesn't explain why you are getting the wrong answer
  802. It doesn't give feedback how % its correct
  803. Playground for varied screen sizes
  804. Car windshields are a big screen
  805. Playground for new interface paradigms
  806. Innovation is coming out of car companies
  807. Manifesting brand
  808. People want SIRI to control car
  809. Interface companies are owning the car experience
  810. SIRI aka Apple owns your car experience
  811. 4. How has emotional life changed?
  812. Social media was the place to discuss hard-to-discuss feelings
  813. Facebook is the happiest place on earth
  814. supplanting disneyland
  815. Faces
  816. All the faces are happy
  817. People don't see the sad
  818. Algorithim circulates the happy ]
  819. Because people don't like the sad
  820. Then people create happy
  821. Because they want likes
  822. Sadness vanishes from social media
  823. Negative feelings are the hard one
  824. Tolstoy
  825. "All happy families are alike, but all unhappy families are unhappy in a different way"
  826. Implications
  827. Young people do not get to practice negative feelings
  828. Never see negative feelings so people don't know how to manage negative feel ings
  829. People don't know how to manage their negative feelings os they become sadder
  830. Young people and increasingly everyone have less skills in
  831. Emotion regulation
  832. emotion reading
  833. working with other's emotions
  834. high multitasks are not as good at knowing peoples emotions
  835. People think that stye are less happy than the average person
  836. depression
  837. social anxiety
  838. Problems associated with cognitive development
  840. Biron
  841. Where do you work?
  842. Where do you play?
  843. What do people think about where they play?
  844. They love it
  845. Feedback
  846. Never question
  847. Always know about contribution to the team
  848. Visually rich environments
  849. Money and time evidence
  850. How do get world together
  851. Harvard Business Review
  853. About Games
  854. What are the intersection
  855. games are big
  856. new generation
  857. different tolerance for risk
  858. failure is not a problem
  859. try again
  860. there are recipes for great games
  861. there is a science about the recipe
  862. gamers already do work
  863. Looking at similarities to work
  864. making judgements
  865. categorizing content
  866. working in teams
  867. play is NOT the opposite of work
  868. engagement is a good business
  869. Demographic
  870. Games are played by
  871. mid-30's
  872. involvement in games
  873. are lower BMI
  874. Higher social network
  875. Better grades
  876. The ingredients for successful games
  877. self presentation
  878. narrative
  879. feedback
  880. transparency
  881. teams
  882. economies
  883. ranks and levels
  884. rules
  885. communication
  886. time pressure
  887. Company problem
  888. Engagement is important
  889. Why did people quit?
  890. Didn't know if they were making a contribution
  891. Didn't feel fun
  892. Didn't feel any control
  893. Engaged people are:
  894. Passionate connection to work
  895. Believe they can impact quality, customer satisfaction, costs
  896. They are less than 30% of the workforce
  897. Games and
  898. entertainment, sales team competitions, safe driving, diabetes bolo tests, home energy use, brushing teeth, carrier landings, delivery truck loading + routes, chip manufacturing estimates, taking medication, drive time lotteries, consumer help on forums, software debugging, tank driving, filling out forms, physical activity, surgical competence, folding RNA molecules, public transit contests, software testing, retail scanning accuracy, palette loading, call center speed + quality, AGIE computer teams, security video footage
  899. Gamification is not
  900. pointsification
  901. but it can include it
  902. Using leader boards
  903. Making interface and commercialize games
  904. Imagine:
  905. A security job thats like a game
  906. You "can" find a bad guy
  907. Add in virtual issues
  908. What are the most important ingredients
  909. Its more arousing to be apart of a narrative
  910. Applying game
  911. Dangerous
  912. Powerful also means dangerous
  913. There will be disruptions
  914. discouraged losers
  915. jealous bosses
  916. employment and hr issues
  917. abridgment of privacy
  918. avatar mistakes
  919. anti-social narratives
  920. repetitive stress
  922. StartX
  923. Community
  924. Trust
  925. Mentorship
  926. Customized Education
  927. Organized access to opportunity
  928. Resources
  931. founding guy
  933. Reid
  934. Cloudfleaps
  935. dashboard for information
  936. Didn't come to start a company
  937. Felt optimasm
  938. Anything is possible
  939. Generosity to provide answers to all questions
  940. Access to other contacts
  941. To build a new foundation
  943. Coursera
  944. Look at the pedagogy of the Coursera about section
  945. Standard lecture
  946. Mastery
  947. Personalizede
  949. Nemo
  950. Question: how do you design calm?
  951. Or relive the memory of calm
  952. When are you most productive
  953. when are you most creative
  954. when are you the most innovative
  956. Boris Deroiter
  957. Ambient Intelligence
  958. many individble distributed devices throughout the environment that are integrated into our lives
  959. System intelligence
  960. that know about their situational state
  961. that can be tailored toward our needs
  962. that can change inrespoend to your environment
  963. social intelligence
  964. experience Research
  965. Context studies
  966. collect user insights and requirements
  967. lab studies
  968. test usability and user acceptance
  969. field studieds
  970. validate and study longer term effects
  972. Crowd-powered systems
  973. Articles
  974. data collection, machine learning training, user studies, social science experiments,
  975. games with a purpose
  976. collective action
  977. historical roots: distribute
  978. crowd-poewred systems
  979. Challenge: Quality
  980. 1000 participants on amazon mechanial turk flip a coin and report 'h' or 't'
  981. Interactive systems that embed crowd intelligence
  982. computational techniques that product high-quality, fast results
  983. Paid crowd sourcing
  984. Amazon mechanical turk
  985. Large number of tasks for not so much money
  986. pay small amounts of money for short tasks
  987. amazon mechanical turk: roughly five million tasks completed per year at 1-5 cent each
  988. population: 40% us, 40% indeia
  989. Soylent: word processor
  990. Wordprocessor that recruits crowds to aid complex writing tasks
  991. embeds crowds as first-order building blocks in a software system
  992. decomposes open-ended tasks
  993. Shortn
  994. Crowdproof
  995. Using people to get perspectives
  996. human macro
  997. people will go over and bib text
  998. Two personas - an example
  999. lazy
  1000. proofread and giveback
  1001. does as little work as necessary to get paid
  1002. overdone
  1003. Result can be low-quality work
  1004. programming with crowds today is haphazard: we lack design patterns
  1005. Solution: Find-fix-verify
  1006. find-fi-verify is a design pattern
  1007. find:
  1008. find an area that can be shortened
  1009. collect a lot and look for independent agreement
  1010. Fix
  1011. have it fixed
  1012. verify
  1013. send it back to the application
  1014. Does this work?
  1015. how high is the quality
  1016. how long does it take?
  1017. how much does it take?
  1018. Adrenaline: real time crowd sourcing
  1019. Crowds in two sections
  1020. votes in five seconds
  1021. Votes across 100 frames
  1022. Synchronous crowds
  1023. Crowds can be faster than any individual member
  1024. Rapid refinement
  1026. Genreate and vote
  1027. Genrate one
  1028. Integrate social and crowd intelligence as core parts of interaction, software, and computation
  1029. crowd powered systems enable experiences that neither crowd nor machine intelligence can support alone.
  1030. computation ail be critical to the wisdom of the crowd
  1033. Jeremy Balenson
  1034. digital footprints
  1035. a decade of media-x research on using nonverbal behavior to predict behavior
  1037. Can we predict a person's future behavior?
  1038. honest signals
  1039. alex pentland
  1040. unconcious nonverbal ocial signals
  1041. evolved from animal signaling mechanism
  1042. unmatched window into our intentions
  1043. Historical attempts: small amounts of data
  1044. Sigmund Freud
  1045. Hours and hours of repeated therapy
  1046. Historical Attempts: obtrusive
  1047. trying to measure non-verbal behavior or MRI is clunky and difficult
  1048. When you measure it, you change it
  1049. Historical Attempts: Laborious
  1050. Paul Eckman
  1051. Trying to code visual coding
  1052. Trying to infer emotion
  1053. Takes hours of time to analyze
  1054. Historical attempts: biased by theory
  1055. You are biased by theory
  1056. Need to use your theory
  1057. Need to nod and smile
  1058. Limited by what your brain/theory can fathom
  1059. If you have massive non-verbal data that you don't have to comb by hand, it frees you from your paradigm
  1060. Digital footprints today
  1061. Kinetic Explorer using for non-verbal data
  1062. State of computer vision that emits sources of energy
  1063. Micro "Big Data"
  1064. Our approach
  1065. 10 years ago
  1066. do a test:
  1067. capture non verbal features
  1068. calculate statistics
  1069. time
  1070. frequency
  1071. train learning algorithms
  1072. Train-test paradigm
  1073. test new cases
  1074. Question: can you see facial expression to predict a car accident
  1075. Yes
  1076. Question: can you prevent operator fatigue
  1077. Moniter movement while subject participate in a simulated work line
  1078. predict performance
  1079. Errors can be tracked based on face
  1080. Question: can you manage shopping and know when someone is buy/wants to buy?
  1081. Question: Personality/demographic
  1082. Moniter 80 students for 40 hours over six weeks in second life
  1083. collect all input data every second
  1084. Based on movement
  1085. calculate race, gender, gap, weight,
  1086. Question: can you predict if a person is Learning
  1087. Look at two people do see who is learning or not
  1088. non-verbal pattern with student and teacher
  1089. Ethics;
  1090. What kind of experiences bring out the best in people
  1091. What is the role of technology in our lives
  1092. What questions can we explore with us to add value to the work we do?
  1094. Keynote
  1095. Larry Lessig
  1096. The forbidden problems
  1097. Talking about something old, new, borrowed, and new
  1098. Talking about something old
  1100. 1 Public and private goods
  1101. Public goods: Defence
  1102. private goods: underwear
  1103. Defence
  1104. publicly provided
  1105. underwear
  1106. privately provided
  1107. generalizations: we should publicly or privately provide respectively
  1108. Fable of the bees or private vines
  1109. We talk about Adam Smith
  1110. Wealth of nations
  1111. Peoples motivated by perfectly private moties, can not provide public goods
  1112. He intends only his own gain, but is led by an invisible hand which is not his own intention"
  1113. Sometimes we can get the public good in purely private motive
  1114. Anarchists as wrong as
  1115. sovialists
  1116. We don't need an extreme. We can have an in between.
  1117. 3 Dan Bricklan
  1118. Partnered to create visical
  1119. "The cornucopia of the Commons: HJow to get volunteer labor
  1120. Talked about napster
  1121. suggested we came to a social sharing
  1122. [soviet msusic here]
  1123. Explained sharing as a by-product
  1124. Byproduct of what people wanted
  1125. Default code: your music is shared
  1126. Default produced the "public good"
  1127. No altruistic sharing
  1128. It was just the default
  1129. Same could be said of CDDB Server
  1130. Acquired by grace note
  1131. You could put a CD in a and the data would be collected
  1132. "design the database so people use the data they enter, thus increasing the potential for them to use it"
  1133. "Public good is the by product of a private good.
  1134. Not quite public.
  1135. Corporate good to be eact.
  1136. Forexample.
  1137. Google:
  1138. They use it to produce a better research engine.
  1139. Like Apple,
  1140. Wchich takes the data we give it and produces a better corporate good
  1141. Like MAazon, Like net flicks.
  1142. And Facebook
  1143. And all the other people.
  1145. There are two economies.
  1146. The commercial, sharing
  1147. Whoops, theres a third.: The hybrid.
  1148. Sharing entity leverages a commercial economy.
  1149. Its hybrid because it used both.
  1150. Not unroblematic.
  1151. What are the issues with having relations between the two?
  1152. Does this culture of participation build business on our collective backs?
  1153. Social justice in the "Facebook" state
  1154. What is the relationship between the corporate value and the private groups that produced it.
  1155. Sharecroper relations?
  1156. Like free production???
  1157. slavery?
  1158. Back in the past
  1159. Center story was
  1160. "Dear Facebook: without the commons, we lose the sharing web.
  1161. Terms of service around instagram
  1162. The salience of this corporation produced on top of the labor of individuals
  1163. When you frame it as exploitation
  1164. The obvious answer for this question is...
  1165. Lok at the software layer
  1166. Open source guy
  1167. "Would commercial entities be allowed to profit from this production?"
  1168. in the GNU software, he said
  1169. Theres a basic social contract
  1170. Copyleft
  1171. Commercialize Galore.
  1172. Make as much money as you can.
  1173. So long as you leave as free what you took before and contribute back the improvements you made
  1174. Redhat's profit
  1175. Thats the social contract that free software had
  1176. And wikipedia.
  1177. Modifications must be licensed back freely
  1178. The software layer seems to be solved
  1179. ?But the con tent layer is not solve
  1180. "Bushified: Internets"
  1181. Internets were combined, but
  1182. Now they are islands of innovation
  1183. Rules to own
  1184. Permissions to particiapte
  1185. Only one that shares:
  1186. Yahoo: Flicker
  1187. The only one that expresses no control of its innovation
  1188. Microsoft Exec denies trying to harm netscape: LA times 1999.
  1189. Microsoft's decision to control who could develop on their platform
  1190. Today air supplies are cut all the time.
  1191. The industry takes the right to decide who gets to innovate and how
  1192. We see pushback from that control being exercised
  1193. APP .net
  1194. Twitter inspires a developer's revoke
  1195. "Are tired of being betrayed by so-called open platforms that suddenly change their terms of use to blithely destroy young and growing businesses
  1196. Dalton Caldwell Punches Facebook
  1197. Years ago, this was a federal government problem
  1198. Now its a normal.
  1199. 3. The hybrid economy is learning something.
  1200. It its learning about the limits in which it lives.
  1201. earning about what it can do.
  1202. Learning that sharecropping can not be understood as the way it does
  1203. Its not sustainable
  1204. its notThe
  1205. These companies are questioned
  1206. 3. Corporate goods produced as a by product of public goods.
  1207. Are corporate goods going to be produced only as byproducts of private goods?
  1208. There is a good from what I want
  1209. Is there also a good from what I ought to do
  1210. Not just what I want, but also what I ought to do
  1211. Good not just from choice
  1212. Good also from coercion
  1213. Coersion?
  1214. Not state based coercion
  1215. The possible public goods that come from architecture based coercion
  1216. Code based constraints
  1217. Getting us to do the things we need to do
  1218. 4. New v2
  1219. focus on a specific problem.
  1220. Once upon a time.
  1221. Lester-land
  1222. Politicans: Lester election and general election
  1223. Lesters are the only one that vote
  1224. Citizens and vote
  1225. To be allow to run in the general election
  1226. You must do well in the Lester election
  1227. What can we say about congress?
  1228. Supreme courts of the united States
  1229. The people have ultimate influence over general elections
  1230. They only have their say after they have their say in the gnereal elections.
  1231. To keep the pesters happy
  1232. Any reform that angers the pesters in lester land is highly unlikely
  1233. $$$ Election
  1234. Funders vote
  1235. To run in general election you need to do extremely well
  1236. There are just as few relevant funders in the united states
  1237. As there are pesters
  1238. .3% of america gave more than 200$ to election
  1239. .05 gave maximum amount
  1240. .01% gave 10,000 or more
  1241. .0003, gave 100,000 or more
  1242. .000042 (132 americans) gave more than 60% of the super pac amount
  1243. The relevant amount of americans is .05
  1244. The funders are our Lesters
  1246. The funders vote
  1247. dependence upon the funders produces an subtle bending to keep these funders happy
  1248. Members of congress spend 30-70% of time raising money to get their party back in power
  1249. They develop a sixth sense to raise money
  1250. Shape shifters to raise money
  1251. Westly
  1252. Always lean to the green "He was not an environmentalist Reform that would anger the funders is highly unlikely United states is worse than Lesterland
  1254. Its at least possible for pesters to be helpful
  1255. The pesters act for the pesters
  1256. Shifting coalition that drives the .05%
  1257. Lesters are the decision makers
  1258. iLesters are not drivers in public interest
  1259. In our land
  1260. Conflicting dependence on funders and pepolpl
  1261. This is corruption
  1262. This is not corruption
  1263. rob legoivitch
  1264. This is not illegal activity
  1265. All is completely legal
  1266. Instead: Legal corruption
  1267. Corruption relative to the baseline
  1268. A republic: representative democracy
  1269. Feberalist 52
  1270. A brand h of government dependent on the people alone
  1271. The people: Problem
  1272. Congress has evolved a different dependance
  1273. Dependance on the funders too
  1274. A dependance TOO
  1275. Its legally not the problem as long as the funders are the people too
  1276. Amsericans are right to believe
  1277. Americans believe money buys votes in congress
  1278. 75% of maericans believe money buys votes
  1279. 81% in republications
  1280. 71 in democreates
  1281. Americans believe
  1282. That believe erodes trust
  1283. 9% of americans believe in congress
  1284. At the time of the american revolution
  1285. a higher percentage believed in the royal crowd
  1286. Rock the vote
  1287. highest
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