This document summarizes a presentation on data science in education given by Dr. Liu Ming-chi from National Cheng Kung University. The presentation outlines include topics such as data science, data scientists, the PISA assessment, future skills needed in education, how AI could impact education, and cases studies. It also includes several links to videos and articles about topics relating to using data in education.

1. 雲科技職所 2016
教育中的資料科學：深又大
Data Science for Education: Deep & Big
劉明機 博士後研究員
國立成功大學 工程科學系
Email: liumingchi@gmail.com

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Outlines
Data science
Data is the New Everything
Data scientist
Process
PISA
Future education
Non-academic Skills Are Key To Success
What AI could mean for education?
Cases
Data science pitfalls
Cases
Upload knowledge to your brain
Appearance and performance
Class skips
Problem solving
Autonomy
Persistence
Feedback
Creativity

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https://www.youtube.com/watch?v=gjIXK13AmNE
是誰住在深海的大鳳梨裡

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Data is the New Everything

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https://www.flickr.com/photos/57312875@N03/8008798697/

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http://www.slideshare.net/condamoor/next-generation-analytics-architecture-for-business-advantage

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http://www.slideshare.net/AmandaMakulec/identifyi
ng-your-audience-40086476

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Data Lake vs Data Warehouse
http://martinfowler.co
m/bliki/DataLake.html

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Data Lake Benefits
Capture data from wide range of traditional (operational, transactional) and new
sources (structured and unstructured) as-is
Store all your data in one environment for cross-functional business analysis
Support the analytics and data science to uncover new customer, product, and
operational insights
Empower front-line employees and managers, and drive a more profitable customer
engagement leveraging customer, product and operational insights
Integrate analytic insights into operational (Finance, Manufacturing, Marketing, Sales
Force, Procurement, Logistics) and management systems (Business Intelligence
reports and dashboards)
http://www.kdnuggets.com/2016/02/data-lakes-plumbers-operationalizing.html

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Apple 收購 LearnSprout 的公
司，他們是專注於分析學生
數據。
例如出席率、升學準備和健
康資訊。
http://chinese.engadget.com/2016/01/28/ap
ple-learnsprout-acquisition/

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在用戶輸入筆記之時，自動彈出來自不同來
源及跟其有關的內容，例如自己的筆記、同
事分享出來的筆記以及來自華爾街日報、
Fast Company 和 TechCrunch 等網上新聞來源。
http://blog.evernote.com/blog/2014/12/19/context-makes-
informed-work-android-windows/

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Data Scientist: The Sexiest Job
Of the 21st Century.
Davenport, T. H., & Patil, D. J. (2012). Data scientist: The sexiest job of the 21st century. Harvard Business Review, 90(10), 70-+.

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cs109.org
What is the work flow of a data scientist?

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PISA 國際學生能力評量計劃
PISA (the Programme for International Student Assessment)
PISA 2012 共有 65 個國家或地區參與，超過 51 萬名15 歲學生進行兩小時的紙
筆式評量。
閱讀、數學和科學三大素養
PISA 2012 results in focus: What 15-year-olds know and what they can do with what
they know. (2014). Retrieved from www.oecd.org/pisa

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Excellence through Equity
Giving Every Student the Chance to Succeed

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Performance and equity

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What Makes Schools Successful?
Resources, Policies and Practice

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Teachers' salaries and mathematics performance

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Students' motivation and grouping of students

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Allocation of educational resourcesandmathematics performance

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Creative Problem Solving
Students’ Skills in Tackling Real-Life Problems

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Students' strengths and weaknesses in problem solving

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台灣學生優不優秀啊?
排名怎麼樣?
有沒有贏韓國?

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數學、閱讀與科學頂尖學生的表現
Taiwan PISA 2012 Short Report. (2014). Retrieved from Tainan, Taiwan: http://pisa.nutn.edu.tw/

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PISA的成功，告訴我們什麼?
Telling countries what everybody else has been doing
Data can be more powerful than administrative control of financial subsidy through
https://www.ted.com/talks/andreas_schleicher_use_data_to_build_better_schools/

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學習投入、學生上學遲到、曠課
毅力：當遭遇問題時，他們會不會輕易放棄、碰到困難的作業，會不會拖延。
解題開放性：「我可以處 理許多的資訊」、「我可以很快理解事情」、「我尋
求事情的解釋」、「我可以輕易地 將各種事實連結起來」以及「我喜歡解決複
雜的問題」。
內在動機：由活動本身所獲得的快樂而從事該活動的驅力。學生認為數學對他
們以 及未來的學業和職業有所幫助

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Why You Shouldn't Trust All Self-reporters
http://bestreviews.com/how-reliable-are-amazon-ratings

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The Paid Review Bias

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二十年後的公民需要具備什麼核心能力？
http://sa.ylib.com/MagCont.aspx?Unit=featurearticles&id=2096

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人類沒有工作的那一天

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機器人，它可能讓全人類都沒有工作
(數位時代)
機器人終將統治人類 (經濟學人)
2020年後將有超過500萬份工作會被機器人取代(世界經濟論壇)
日本49%職業在20年後由機器人力取代(日本野村綜合研究所)

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Robots that can adapt like animals
Cully, Antoine, Clune, Jeff, Tarapore, Danesh, & Mouret, Jean-Baptiste. (2015). Robots that can
adapt like animals. Nature, 521(7553), 503-U476. doi:10.1038/nature14422
Using the Intelligent Trial
and Error algorithm, robots,
like animals, can quickly
adapt to recover from
damage.

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https://youtu.be/iQ_fSP3LGw8?t=30s

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https://www.youtube.com/watch?v=s2GzfoKDx-w

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Computershad mastered the artof telling sportsandfinancestories
(StatsMonkey)
http://singularityhub.com/2009/11/09/is-software-set-to-replace-sports-journalists/

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https://en.wikipedia.org/wiki/Infinite_monkey_theorem

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Eureqa：The Robot Scientist
http://www.wired.com/2009/04/newtonai/
http://www.wired.com/2009/12/download-robot-scientist/

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http://krishna.org/evolution-from-scientist-to-monkey/

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Non-academic Skills Are Key To Success
http://www.npr.org/sections/ed/2015/05/28/404684712/non-academic-skills-are-key-to-success-but-what-should-we-call-them

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未來教育：孩子不用變機器人，要做有「人性」思考的人
未來的世界將不再需要把人類訓練成機器了
所以就像人類無法取代電腦，人類不需要也無法叫電腦去做電腦做不了的事，
像是寫一首詩、作一首動人的歌、去解決人與人之間的摩擦、批判性思考、說
服他人、同理他人、或是判斷對錯。
http://topic.parenting.com.tw/issue/2016/coding/article-9.html

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Microsoft deletes 'teen girl' AI
Tay has a dirty mouth
http://www.businessinsider.com/microsoft-deletes-racist-genocidal-tweets-from-ai-chatbot-tay-2016-3
http://www.telegraph.co.uk/technology/2016/03/24/microsofts-teen-girl-ai-turns-into-a-hitler-loving-sex-robot-wit/
https://www.technologyreview.com/s/601111/why-microsoft-accidentally-unleashed-a-neo-nazi-sexbot/

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What Artificial Intelligence Could Mean For Education?
What could AI technologies do for human education?
How should human education respond to the challenges posed by AI?
http://www.npr.org/sections/ed/2016/03/16/470011574/what-artificial-intelligence-could-mean-for-education

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Work Smart, Not Hard

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Upload knowledge to your brain
穿顱直流電刺激tDCS
Working memory is linked primarily
with brain activity in the
dorsolateral prefrontal cortex
(DLPFC)
Skill acquisition and procedural
learning is linked primarily with
brain activity in primary motor
cortex (M1)
Choe, Jaehoon, Coffman, Brian A, Bergstedt, Dylan T, Ziegler, Matthias, & Phillips, Matthew E. (2016).
Transcranial direct current stimulation modulates neuronal activity and learning in pilot training. Frontiers in
Human Neuroscience, 10, 25. doi:10.3389/fnhum.2016.00034

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Flight path deviation between subjects and autopilot
Choe, Jaehoon, Coffman, Brian A, Bergstedt, Dylan T, Ziegler, Matthias, & Phillips, Matthew E. (2016). Transcranial direct current stimulation modulates neuronal
activity and learning in pilot training. Frontiers in Human Neuroscience, 10, 25. doi:10.3389/fnhum.2016.00034

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http://www.dispatch.com/content/stories/business/2015/06/15/company-creates-wearable-tech-to-alter-mind-body.html
Send electrical impulses to users' nerves to
make the person calmer or more energized.
Thync

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如何開學第一週就打好學期成績?

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外表重要嗎?

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French,M.T., Robins, P.K., Homer, J. F., &Tapsell, L. M.(2009). Effectsof
physicalattractiveness,personality,andgroomingonacademic
performanceinhighschool.Labour Economics, 16(4), 373-382.
Photographs of the student ID
Approval from the institution
Multiple raters (1-5)
The same 50 images for normalization
Google 雲科大正妹

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Grooming hasthe largest effect on GPAformale students,having a
very attractive personality is most importantfor female students.
Personal appearance measures
Models
Males (N = 2487) Females (N = 2878)
A B C D E F G H
Very physically attractive 0.055 − 0.122* 0.080** − 0.047
Below average physical attractiveness − 0.146** − 0.005 − 0.114 − 0.044
Very attractive personality 0.145*** 0.081 0.173*** 0.145***
Below average personality attractiveness − 0.182** − 0.114 − 0.128* − 0.091
Very well groomed 0.263*** 0.274*** 0.163*** 0.114**
Below average grooming − 0.492*** − 0.468*** − 0.197* − 0.155
R squared 0.219 0.222 0.235 0.237 0.29 0.294 0.293 0.297
linear regression results for the effects of personal appearance on overall GPA
French, M. T., Robins, P. K., Homer, J. F., & Tapsell, L. M. (2009). Effects of physical attractiveness, personality, and grooming on
academic performance in high school. Labour Economics, 16(4), 373-382. doi:10.1016/j.labeco.2009.01.001

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More
Attractive female students earn higher grades than unattractive ones (Hernández-
Julián & Peters, 2015)
Rating of charisma and the display of an attractive photograph were both positively
associated with Teaching effectiveness ratings (Rannelli et al., 2014)
Scholars‘ physical appearance is significantly correlated with their research
performance (Dilger, Lutkenhoner, & Muller, 2015)
Lombarts, Kmjmh. (2014). A (good) look at the rating of teaching effectiveness: Towards holistic and programmatic assessment. Medical Education, 48(8), 744-747. doi:10.1111/medu.12491
Rannelli, L., Coderre, S., Paget, M., Woloschuk, W., Wright, B., & McLaughlin, K. (2014). How do medical students form impressions of the effectiveness of classroom teachers? Medical
Education, 48(8), 831-837. doi:10.1111/medu.12420
Hernández-Julián, Rey, & Peters, Christina. (2015). Student appearance and academic performance. Metropolitan State University of Denver.

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But
Attractiveness can be a disadvantage in some situations (Lombarts, 2014)
Disadvantaged when applying for military
Less may be expected of them
Appearance is significantly smaller for both male and female students in online course
environments (Hernández-Julián & Peters, 2015)
Scholars' research performance is especially correlated with perceived
trustworthiness (Dilger, Lutkenhoner, & Muller, 2015)

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量翹課次數做研究?
沒穿高中制服翹過一次課，長大以後才會後悔。

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Asarta, C. J., & Schmidt, J. R. (2015). Thechoiceofreducedseat
timeinablendedcourse.Internet and Higher Education, 27,
24-31.
The flipped and flexible course V.S. Traditional course
Semi-weekly classes meeting
Final grade: Exams 90%; homework 10%
Measurement
reduced seat time = attendance rate (class skips)
Accesses of the online materials (consistency)
Grade point average
Prior knowledge
Credit hours

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Studentpreference is to reducetraditionalseat time by49% to 63%.
Characteristic Blended (n=87) Traditional (n=92) Difference t-statistic
Age 20.21 20.24 − .03 − .11
Total credit hours 53.58 54.60 − 1.02 − .30
Semester credit hours 13.41 13.68 − .27 − .92
Transfer credit hours 14.89 15.43 − .54 − .18
Grade point average 3.22 3.19 .03 .47
Math quiz score (of 16) 11.24 11.52 − .28 − .65
Calculus grade 8.93 8.49 .44 1.23
ACT math score 24.99 24.61 .37 .59
ACT composite score 25.10 24.28 .83 1.40
Course points (of 160) 117.09 115.48 1.61 .56
Attendance (percent) 38.2 86.0 − 47.8 − 13.99**

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Class attendance is positively related to the use of online
materials.
Estimates of the models for attendance in the blended course.
Variables Linear probability (proportions) model Logit (log odds) model
Grade point average 13.843 (4.81)** 17.199 (6.18)**
Math quiz score .832 (2.28)* .183 (.49)
Semester credit hours − .089 (− .18) − .136 (− .26)
Transfer credit hours .373 (6.54)** .371 (6.88)**
Total accesses − .223 (− 4.43)** − .305 (− 6.62)**
Consistency 2.427 (9.82)** 3.225 (13.68)**
Constant − 41.326 (− 4.66)** –
R
2
.456 .470

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如何培養問題解決能力?
Teaching a man how to fish is better than giving him a fish.
With good information-seeking skills, students are more likely
to be able to solve problems on their own that they face in an
online, hypermedia context (Bulu & Pedersen, 2010; Hwang,
Chen, Tsai, & Tsai, 2011).

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Liu,Ming-Chi,Huang, Yueh-Min,Kinshuk,&Wen, Dunwei.(2013).
Fosteringlearners'metacognitiveskillsofkeywordreformulationin
imageseekingbylocation-basedhierarchicalnavigation.Educational
Technology Research and Development, 61(2), 233-254.
The process for students to monitor what they know and to refine subsequent
queries based on this is classified as a metacognitive strategy (She, Cheng, Li, Wang,
Chiu, Lee, et al., 2012).
For image retrieval in particular, students need a structural overview of the
information required to navigate the system when they do not have sufficient prior
knowledge if they are to make useful search queries (Gong, Muyeba, & Guo, 2010).

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Location-based hierarchical navigation support (LHINS)

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Based on the structural analysis of
semantic relatedness between terms, to
represent the students’ keyword
reformulation skills.
LHINS group tended to develop better
metacognitive skills related to keyword
reformulation as compared to the NKBS
group.

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Huang,Yueh-Min,Liu, Ming-Chi,Chen,Nian-Shing,Kinshuk,&Wen,
Dunwei.(2014). Facilitatinglearners'Web-basedinformationproblem
solvingbyqueryexpansion-basedconceptmapping.Australasian
Journal of Educational Technology, 30(5), 517-532.
Research has also pointed out the influence of perceived information overload on
students' cognitive processes (C. Y. Chen, Pedersen, & Murphy, 2012)
The problems faced by learners with insufficient prior knowledge by presenting them
all the related concepts and linking words (Hay et al., 2008; Koc, 2012).

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Query Expansion for constructing Concept Maps (QECM)

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It is demonstrated that the proposed approach can provide
sound results that are close to expert skeleton concept maps.

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如何培養自主學習?
An autonomous language learner can independently set goals; appropriately search
and organize learning resources; continually perform the learning tasks; and critically
reflect and evaluate their own progress by chose criteria (Collentine, 2011).
Learners work in isolation can develop the greater autonomous ability to take charge
of their own learning than work in cooperation (Lee, 2011; Sanprasert, 2010)

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Foreign language anxiety can have a debilitating effect
on students’ autonomous behavior.
Social interaction plays a key role in learner autonomy, as
learners needed such authentic context to let them fully
and critically participate in learning tasks through
interacting with the interlocutor.

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Microsoft Emotion Recognition API
https://www.projectoxford.ai/demo/Emotion

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蕭博駿到底喜不喜歡當工具人？

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Videos

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毅力的重要性!
「當你遭遇失敗後，才會找到解決失敗的課題。
把失敗付諸一笑，然後採取新的行動，這才是
最重要的事。」
－日本太陽Parts公司社長城岡陽志
「人的一生為什麼要努力？因為最痛苦的事，
不是失敗，是我本來可以。」 － 網絡名言

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怎麼培養毅力?
乙武洋匡雖然沒有四肢，
但是他卻擁有超越一般
人的毅力。

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Flappy Bird game
FLAPPY BIRD DEATH MONTAGE (iOS Gameplay Video)
https://www.youtube.com/watch?v=FxKb_zx9cUg

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「Flappy Bird 誤我一生」
https://www.youtube.com/watch?v=RLYINpY-zLw

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合作學習
https://www.youtube.com/watch?v=gPBnJO1Niww

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Ventura, Matthew, & Shute, Valerie. (2013). The validity of a
game-basedassessmentofpersistence.Computers in Human
Behavior, 29(6), 2568-2572.
Example problem in a physics video game
NP session logs
Performance measure of persistence (PMP)
Game-based assessment of persistence (GAP)
Physics test
Self-report measure of persistence
Enjoyment question
Video game experience

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The GAP, silver, and unsolved times significantly relate to the
PMP and the physics posttest scores for low performers
PMP GAP Unsolved Silver Gold Enjoy Self-p
GAP .51**
Unsolved .47** .96**
Silver .42** .81** .62**
Gold .00 .22 .14 .32**
Enjoy .23 .08 .02 .18 .06
Self P .10 −.01 −.01 .00 .03 −.05
Physics post test .30* .33** .31* .29* .08 .18 .15
Correlations for 70 low performers in Newton’s Playground.
GAP = unsolved and silver times; unsolved = unsolved time; silver = silver time; gold = gold time;
enjoy = I enjoyed playing NP; self P = self-report measure of persistence.

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The GAP relates much lower to the PMP for high performers
versus low performers
PMP GAP Unsolved Silver Gold Enjoy Self-p
GAP .22
*
Unsolved .12 .94
**
Silver .31
**
.84
**
.60
**
Gold .12 .30
**
.36
**
.11
Enjoy .10 .11 .11 .09 −.03
Self P .09 −.06 −.05 −.05 −.13 .07
Physics post
test
.16 .02 .01 .04 −.21 .15 .15
Correlations for 84 high performers in Newton’s Playground.
GAP = unsolved and silver times; unsolved = unsolved time; silver = silver time; gold = gold time;
enjoy = I enjoyed playing NP; self P = self-report measure of persistence.

80. 雲科技職所 2016
Liu,Chia-Ju,Huang,Chin-Fei,Liu,Ming-Chi,Chien,Yu-Cheng,Lai,Chia-Hung,&Huang,Yueh-Min*.
(2015).Doesgenderinfluenceemotionsresultingfrompositiveapplausefeedbackinself-
assessmenttesting?Evidencefromneuroscience.EducationalTechnology&Society,18(1),337-
350.
Rewarding with applause sound during computerized test
掌聲鼓勵

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Two tests
the controlled task the experimental task
30 students (15 males, 15 females; mean age ± S.D. = 19.2 ± 2.0 years) participated in this experiment.

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Gender differences in anxiety
The controlled task The experimental task

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Applause decrease male’s anxiety
Male Female
Topographical map of the brain

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如何培養創造力?
美國重視專題導向學習(Project-based learning)中，要求學生團體合作，以真實
世界問題為出發點，進而構思、設計與創新。
德國認為創意設計教育應該要與產業密切結合。

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Muldner, K., & Burleson, W. (2015). Utilizing sensor data to
model students' creativity in a digital environment. Computers
in Human Behavior, 42, 127-137.
Input data
EEG
Eye
movement
Physiological
signals
Dialogue
情緒角度
個體處於正向情感將有助於產生更多元化的資訊、更多樣性的聯
想，因而增強個體的思考彈性 (Alice M Isen, 1990; Murray, Sujan,
Hirt, & Sujan, 1990)
引起個體負向的情感(如:焦慮)，會導致傾向使用僵硬的策略，而
且厭煩的情緒會使個體降低注意力，造成淺層的知識處理。

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Target problem
為什麼用這個task?
1.過去用過
2.不需要領域知識
3.很好評分

87. 雲科技職所 2016
Reliable differences in sensor features
characterizing low vs. high creativity students
The low creativity students had
– marginally fewer total fixations
– significantly shorter total saccade path length
– significantly lower average saccade speed
– marginally lower saccade speed standard deviation
– marginally more short term excitement
– marginally greater standard deviation of frustration
Classifier TPR FPR F-measure
Rule Nnge 85.7 21.4 85.4
Naive bayes – simple 76.2 33.3 75.7
True Positive Rate, higher is better.
False Positive Rate, lower is better.

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Data Science Pitfalls

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A correlation of 0.85 was obtained
A comparison of model estimates for the mid-Atlantic region (black) against Disease Control and Prevention (CDC)-
reported influenza-like illness ILI percentages (red), including points over which the model was fit and validated.
Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009). Detecting
influenza epidemics using search engine query data. Nature, 457(7232), 1012-U1014.

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Google Flu Trends 的成功
1. Data 分析能夠創造神奇般準確的結果。
2. 每一個個 Data 都能不被遺漏，使得舊有的統計抽樣方法過時。
3. 不用再煩惱 Data 間的因果關係，因為統計的相關性會告訴我們我們想要的資
訊，科學的或是統計的模型不再需要，因為套一句 2008 年在 Wired 發表的論文
《 The End of Theory 》裡的話：「有了足夠的資料，數字會自己說話」。
Big data- are we making a big mistake
http://buzzorange.com/techorange/2014/06/04/big-data-making-big-mistake/

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相關不等於因果
新聞中充斥著流感可怕的故事，而這些故事可能會引起健康的人們在網路上搜
尋相關資訊
Google 自身擁有的搜尋演算法可說是「朝夕令改」，是不斷的在轉變的，當人
們進入醫療症狀時它開始自動地建議診斷情形。

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大數據已死?
「大數據」是個非常差勁的命名，因為它讓人們直接聯想到「大」，但數據的大小其實是
最無趣的部分，最重要的其實是那些你從來沒有想過可以用的數據以及那些非傳統的資料，
我認為這才是人們對大數據應有的認知。
[專訪]美國Top 4 技術長寶立明：大數據即將在五年內消失
http://www.bnext.com.tw/article/view/id/35404

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Data規模大小稱不上一回事
1936年，美國總統選舉，預測選戰結果。
The Literary Digest，郵寄1,000 萬名民眾問卷。收到 240 萬個回覆，花了超過兩
個月的時間， Literary Digest 宣布它的結果：Landon 55% > 41% Franklin。
實際選舉的結果 Franklin 61% > 37% Landon
George Gallup 的小樣本調查反而更接近最終選戰的結果。
Harford, Tim. (2014). Big data: A big mistake? Significance, 11(5), 14-19. doi:10.1111/j.1740-9713.2014.00778.x

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壞樣本的毛病
1.對調查母體沒有定義清楚
2.母體裡面的個體有些永遠不可能被抽中

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龐大的資料量底下，不要犯相同傳統的統計錯誤。
The Literary Digest所寄出的郵件名單是同時從車輛登記及電話簿中編纂而成的
名單，至少在 1936 年的那時，是非常不成比例的
在所有郵寄回復的 240 萬個問卷結果中，Landon 的支持者更樂於將他們的結果
交還給 The Literary Digest。

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Battling bad science (實驗法)
不要安慰劑，跟最佳的方法比，才能幫助做決定。
不能將舊方法效用調低，或是調高。
不能隱藏資料。
Battling bad science
https://www.ted.com/talks/ben_goldacre_battling_bad_science

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P value 錯了嗎?
Statisticians issue warning over misuse of P values
http://www.nature.com/news/statisticians-issue-warning-over-misuse-of-p-values-1.19503
After 150 years, the ASA says no to p-values
https://matloff.wordpress.com/2016/03/07/after-150-years-the-asa-says-no-to-p-values/
Statisticians found one thing they can agree on: It’s time to stop misusing p-values
http://fivethirtyeight.com/features/statisticians-found-one-thing-they-can-agree-on-its-time-to-stop-misusing-p-values/
An unhealthy obsession with p-values is ruining science
http://www.vox.com/2016/3/15/11225162/p-value-simple-definition-hacking

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ASA released guidance on p-value
principles
P-values can indicate how incompatible the data are with a specified statistical model.
P-values do not measure the probability that the studied hypothesis is true, or the probability
that the data were produced by random chance alone.
Scientific conclusions and business or policy decisions should not be based only on whether a
p-value passes a specific threshold.
Proper inference requires full reporting and transparency.
A p-value, or statistical significance, does not measure the size of an effect or the importance
of a result.
By itself, a p-value does not provide a good measure of evidence regarding a model or
hypothesis.

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Writing up results
The effect size 正規化
The uncertainty around effect size
Confidence intervals
Limitations

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劉明機