2016/9/5 pudding@nccu.edu.tw

http://l.pulipuli.info/16/9/sa/slide

http://l.pulipuli.info/16/9/sa/slide

Part 1. Part 2. Part 3. Part 4. Part 5. Part 6. Part 7. #

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Experimental GroupControl Group#

()

http://www.clipartpanda.com/clipart_images/classroom-clip-art-teacher-at-26279702#

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http://www.clipartpanda.com/clipart_images/school-student-clipart-5593450#

Bakeman, R. & Gottman, J. M. (1997)#

Bakeman, R., & Gottman, J. M. (1997). Observing interaction: an introduction to sequential analysis. Cambridge: University Press.

Bakeman(1986)(coding scheme) (behavioral sequences) (observer agreement): 1 (event sequences) (time sequences) (cross-classified events)#Bakeman, R., & Gottman, J. M. (1986). Observing interaction: an introduction to sequential analysis. Cambridge: University Press.

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https://www.facebook.com/womaninthestriped/ ABCDEF

http://d35brb9zkkbdsd.cloudfront.net/wp-content/uploads/2013/03/Understanding-your-child1.jpg

https://www.facebook.com/womaninthestriped/ #

Part 1.#

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B. C.

A.

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A. (1/3)1949~1953(Bloom)19602001Anderson, L. W., & Krathwohl, D. R. (2001). A taxonomy for learning, teaching, and assessing: a revision of Blooms taxonomy of educational objectives. New York: Longman.#

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A. (2/3)

#2004Bloom 152247274

http://163.20.119.100/f2blog/attachments/201203/4168874042.pdf#

A. (3/3)B1Remember B2Understand B3Apply B4Analyze B5Evaluate B6Create B7Others

(Revised Blooms Taxonomy)#

B. (1/2)Gunawardena, LoweAnderson1997(Interaction Analysis Model)IAMIAMIAM1997IAMGunawardena, C. N., Lowe, C. A., & Anderson, T. (1997). Analysis of a Global Online Debate and the Development of an Interaction Analysis Model for Examining Social Construction of Knowledge in Computer Conferencing. Journal of Educational Computing Research, 17(4), 397-431.#

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B. (2/2)K1/K2K3/K4K5/K6

(Interaction Analysis ModelIAM)#

C. (1/2)Hou(2008)Mayer, 1992; D'Zurilla & Goldfried, 1971 #Hou, H.-T., Chang, K.-E., & Sung, Y.-T. (2008). Analysis of Problem-Solving-Based Online Asynchronous Discussion Pattern. Educational Technology & Society, 11(1), 17-28.

C. (2/2)#P1 P2/ P3P4 P5

Part 2.#

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Morea

8063888024#

TechSmith Morae

Morae Recorder

Morae Observer

Morae Manager#

xAPI (eXperience API)xAPI(LMS)()(LRS)LRSLRS

#LMS xAPILRS

LMS xAPILMS xAPI

LRSxAPI

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Anonymous: joined world: Example Scene:

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#Hou, H.-T. (2012). Exploring the behavioral patterns of learners in an educational massively multiple online role-playing game (MMORPG). Computers & Education, 58(4), 1225-1233. doi:10.1016/j.compedu.2011.11.015

CodeBehaviorFTFightPTPetsTLTools/ItemsTKTalkGPGroup-workTRTrading of items/toolsTSConducting tasksLFLearning in fightingLTLearning in tasksOTOthers

Talking Islandhttps://www.youtube.com/watch?v=7rcgjds-VEA

http://www.sciencedirect.com/science/article/pii/S0360131511003009#

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2016/9/6 10:05ok, Stu AStu B2016/9/6 10:07po2016/9/6 10:122016/9/6 10:2020160906 2016/9/6 10:22

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1StuC2StuB 3StuC4StuB 5StuC6StuB 7StuB~~8StuB(1)(2)(3)(4)9StuB10StuB11StuB 12StuB13StuC~~14StuB

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#http://l.pulipuli.info/160905

Part 3.#

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2. 1. 3.

4.

6.

5.

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P3P2

P2?P3?

#120.720.080.800.180.020.200.900.101.00

2015Kappa : 2712015531http://www.shanghaiarchivesofpsychiatry.org/cn/assets/215010cn.pdf

Cohen's KappaCohen's KappaCohenKappa-1~1 #Cohen, J. (1960). A coefficient of agreement for nominal scale. Educational and Psychological Measurement, 20(1), 3746. doi:10.1177/001316446002000104Kappa< 0.40.4 ~ 0.60.6 ~ 0.8> 0.8

120.72 (p11)0.08 (p10)0.80 (p1+)0.18 (p01)0.02 (p00)0.20 (p0+)0.90 (p+1)0.10 (p+0)1.00

Cohen's Kappa#2015Kappa : 2712015531http://www.shanghaiarchivesofpsychiatry.org/cn/assets/215010cn.pdf

Cohen's Kappa#http://j.mp/2015-kappa

#1. 2.

P2!

#121ABA2BBB3BBB4CBC5ABB6BBB7ACC8AAA9AAA10CCC

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1A2B3B4C5B6B7C8A9A10C

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ABBCBBCAAChttp://j.mp/2015-code-to-seq

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Part 4.#

Sackett,G.P19741997BakmanGottmanObserving interaction#

N (10)Ns (9)AB BB BC CB BB BC CA AA AC

ABBCBBCAAC#

(Target Code), lag 1(Given Code)lag 0ABClag 0A1 (A->A)1 (A->B)1 (A->C)33B02244C11023lag 1 243910

AB#ABBCBBCAAC

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B->C

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B->C (Target Code), lag 1(Given Code)lag 0ABClag 0A11 1 33B02244C11023lag 1 243910

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f(g,t)(g->t)f(g)gp(t)tp(g)z1.95(p < 0.05)

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B->CB->CH0: x NPB->CH1: x > NPB->C

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Allison & Liker (1982) zxf(g,t)(g->t)(frequency)Nf(g)(given code, g)Pp(t)(target code, t) (probability)Q[1 - p(t)]p(g)z1.95(p < 0.05)#

(Target Code), lag 1(Given Code)lag 0ABClag 0A11 1 33B022=f(g,t)4=f(g)4C11023lag 1 243=f(t)9=Ns10

#f(g,t)=2 f(g)=4 p(t)=? p(g)=?

f(g,t)(g->t)f(g)gp(t)tp(g)z1.95(p < 0.05)

p(g)f(g)lag 0f(g)= f(B) = 4Ns Ns=9

p(g) = 4 / 9 = 0.44#

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p(t)#f(t)lag 1f(t) = f(C) = 3Ns Ns=9

p(t) = 3 / 9 = 0.33

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z#

f(g,t)=2 f(g)=4 p(t)=0.33 p(g)=0.44 z1.95H0B->C

4-(2*0.44)

0.12

2*0.44(1-0.44)(1-0.22)0.38

sqrt(0.38) = 0.62

0.12/0.62=

(2-(4*0.33)) / Math.sqrt((4 * 0.33 * (1-0.33) * (1-0.44)))-0.4547069558667864#

B->C

#

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B->C (Target Code), lag 1(Given Code)lag 0ABClag 0A11 1 33B02244C11023lag 1 243910

#

f(g,t)(g->t)f(g)gp(t)tp(g)z1.95(p < 0.05)

#, lag 1lag 0ABCA1 1 1 3B0224C11022439

, lag 1lag 0ABCA0.57-0.470B-1.430.30.95C1.070.18-1.13

#

Bakeman

#, lag 1lag 0USPTGU0625215S5076523P270111030T561201033G249110261423303327127

, lag 1lag 0USPTGU-1.452.34-1.000.69-0.80S1.81-2.490.850.010.06P-0.870.85-3.491.531.85T0.880.012.00-3.961.48G-0.61-0.401.482.13-2.97

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Behavioral transfer diagram #

, lag 1lag 0USPTGU-1.452.34-1.000.69-0.80S1.81-2.490.850.010.06P-0.870.85-3.491.531.85T0.880.012.00-3.961.48G-0.61-0.401.482.13-2.97

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GTTP

GTP

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Part 5. #

GSEQGSEQ(Generalized Sequential Querier)Roger Bakeman & Vicen Quera1992GSEQ5.1varietyfrequenciesratedurationsproportion(percentages)kappa#http://www2.gsu.edu/~psyrab/gseq/index.htm

MEPAhttp://edugate.fss.uu.nl/mepa/ MEPAGijsbert ErkensMEPA Lag Sequential Analysishttp://l.pulipuli.info/160905-mepa #

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Cohen's Kappa http://j.mp/2015-kappa http://j.mp/2015-code-to-seq PHP http://l.pulipuli.info/160905-sa-php-temp (http://l.pulipuli.info/16/9/sa/php-source )#

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PHP (1/2)#http://l.pulipuli.info/160905-sa-php-temp

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PHP (2/2)#

z > 1.95

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> > http://j.mp/2015-code-to-seq PHP http://l.pulipuli.info/160905-sa-php-temp

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Part 6. #

=

B->BA,B,C -> U,S,P,T,Glag 2 -> lag 3#

ABBCBBCAAC#

(Given Code)lag 0ABClag 0A11 1 3B0224C1102lag 1 243

N = 10, Ns = 9

ABBC

#

#

ABCABBCBBCAAC

(Given Code)lag 0ABClag 0A01 1 2B0022C1102lag 1 123

N = 7, Ns = 6

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p(t)#f(t)lag 1f(t) = f(C) = 3Ns Ns=9

p(t) = 3 / 9 = 0.33

Ns Ns=6f(g)lag 0f(B) = 2 p(t) = 3 / (6-2) = 0.43

1-(4*0.43) / Math.sqrt(4*0.43*(1-0.43)*(1-0.44))#

z#, lag 1lag 0ABCA0.57-0.470B-1.430.30.95C1.070.18-1.13

, lag 1lag 0ABCA-10-1B-1-1.731C0.61-0.610

ABBCBBCAAC, Ns=9ABCBCAC, Ns=6

-1.32#

#

ABC

D

ABC

C

#

#

ABC

D

AB

D

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(1/2)#

ABBCBBCAAABCCBBCAB, lag 1lag 0ABCA0.940-0.94B-1.6301.63C0.940-0.94

, lag 1lag 0ABCA-0.621.63-1.26B-0.83-0.731.32C1.38-0.73-0.19

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(2/2)#

ABBCBBCAAABCCBBCAB, lag 1lag 0ABCA0.371.15-1.57B-1.69-0.501.97C1.47-0.54-0.65

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(Break)N = 18 breaks = 2 Ns = 16 (18-2)AB BB BC CB BB BC CA AAAB BC CC CB BB BC CA ABA->A#

ABBCBBCAAABCCBBCAB

Part 7. #

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2011 2015531http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi /ccd=X5LsV5/record?r1=1&h1=1

(1/2)#

ABBCBBCAAABCCBBCABABCBCBCCB...

ACACBCABAABBCABCACACCBBCACB...

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(2/2)#

, lag 1lag 0ABCA0.571.51-1.95B-1.56-1.662.78C1.120.42-1.20

, lag 1lag 0ABCA-20.311.46B-0.78-0.320.99C2.680-2.35

0.31-0.320

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#2011 2015531http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi /ccd=X5LsV5/record?r1=1&h1=1

(1/2)#BC13.58BC8.93

0.53

(2/2)#ZPTTRTKPT25.4713.3429.97TR13.57523.031.49TK27.110.6282.56

ZGenderPTTRTKPTMale17.1811.8623.61Female18.775.8718.34TRMale12.21363.472.28Female5.68370.494.31TKMale20.863.48200.14Female17.181.92193.63

Hou, H.-T. (2012). Exploring the behavioral patterns of learners in an educational massively multiple online role-playing game (MMORPG). Computers & Education, 58(4), 1225-1233. doi:10.1016/j.compedu.2011.11.015

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BLOG: http://blog.pulipuli.info/

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