Parental Aspirations and Computer Aided Learning in Rural India Joyojeet Pal Department of City and...

Parental Aspirations and Computer Aided Learning in Rural IndiaJoyojeet PalDepartment of City and Regional Planning&TIER Research GroupUniversity of California at Berkeley

MotivationEnvironment of Need / Discourse of Technology

ApproachResearch Group: Parents of children in villages allotted computers under the Computer Aided Learning (CAL) program

Questions: How do parents perceive computersWhere do they get information about computersWhat is the economic environment, expectationWhat perceived in village since coming of CALAny changes in the childs behaviour?Occupational expectations for the childAspiration: Computers v/s English

Study second-order impacts of computers in rural India

MethodologyBackground Study140 Interviews, 4 focus groups, 35 group observations Dates: 2005-2006Locations: Orissa, Karnataka, Maharashtra, Tamil NaduInitial stakeholder interviewsChild observationsParent focus groupInterviews of parents 203 Parent interviewsDates: 2007Locations: 14 locations in 4 rural districts of KarnatakaOpen ended thematic discussionsStructured questionnaires

SampleBELLARY sub-set 66 interviews (primarily factory/mine workers)BANGALORE RURAL sub-set 68 interviews (primarily small farmers)KODAGU sub-set 18 interviews (all estate workers)SHIMOGA sub-set 20 interviews (farmers and day laborers)

Environment: Occupational PushOnly 2 from 117 agricultural families desired continuing in agricultureOnly 13.7% agricultural families wanted their children to continue living in their villages, as compared to 28.6% non agricultural familiesMost desired occupation is government jobs specially teaching

Move to a city and get a government job. That is like a horse for a long race, small farmer, Bangalore RuralThe price of rice has multiplied twice since Vasantdada Patils government (1970s) here, but look at the price of living. Small farmers can become labourers, but if you have 5 acres, you may as well commit suicide because you wont be able to degrade yourself to digging holes and laying tar, mid-sized farmer (15 acres), Vidarbha

Demand: Computers, Teachers, or Meals? Ill feed my children at home. Anyway dont like them eating the food they make in the school, sitting next to dirty children, parent, UdupiIf the mid-day meal is stopped, I will withdraw my child from the school. What is the need for him to go to school then? parent ShimogaBUTIf the computers are not fixed for months, nobody cares. If the mid-day meal does not happen on time, well have a riot, headmaster, PondicherryResponse mid-day meal rises from poorest to richest districtMajority view teachers / state as primarily responsible for their childrens education, contrast with urban/rich parents.

Chart7

0.0190.0770.3080.596

0.080.0270.5470.347

0.2160.1620.3510.27

0.2860.190.3810.143

Meals

Notebooks

Teachers

Computers

Sheet1

Computers are of no use to us27.00%

Too busy to go for training21.00%

Conflict with e-Center19.00%

E-Center is too far9.00%

Other location issues2.00%

e-Center teachers are not good1.00%

Too expensive1.00%

Sheet1

0000000

Computers are of no use to us

Too busy to go for training

Conflict with e-Center

E-Center is too far

Other location issues

e-Center teachers are not good

Too expensive

Sheet2

Malappuram e-literacy recipient (n=338)Kozhikode computer literate (n=49)

Lost fear of computers95.60%72.20%

Understood "a computer" better57.40%70.60%

Gained respect in society30.50%54.40%

Decided to study further16.90%28.10%

Signed up relative for comp. course9.20%39.10%

Took subsequent comp. course6.50%32.80%

Communicating with relatives/friends0.90%19.60%

Using web for information1.80%18.50%

Job prospects increased3.30%26.90%

Considering working abroad1.80%9.70%

Responsibilities at work changed0.60%10.60%

Sheet2

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Malappuram e-literacy recipient (n=338)

Kozhikode computer literate (n=49)

Sheet6

MealsNotebooksTeachersComputers

BLR (n=68)1.90%7.70%30.80%59.60%

Bellary (n=66)8.00%2.70%54.70%34.70%

Kodagu (n=18)21.60%16.20%35.10%27.00%

Shimoga (n=20)28.60%19.00%38.10%14.30%

Sheet6

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Meals

Notebooks

Teachers

Computers

Sheet7

Sheet8

Sheet5

Sheet4

No ResponsePrefers MovePrefers Stay

Agriculturist (n=70)7.10%80.00%12.90%

Agriculture labor (n=47)85.10%14.90%

All agriculture (n=117)4.30%82.10%13.70%

All non-agriculture (n=56)3.60%67.90%28.60%

Total (n=173)4.00%77.50%18.50%

Sheet4

000

000

000

000

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No Response

Prefers Move

Prefers Stay

Sheet3

Survey Respondents FiguresEntrepreneurs Figures

e-Center Codee-literacy awarenesse-literacy takene-literacy takenData discrepancy

2205 (40)47.50%45.00%68.30%23.30%e-literacy taken 1e-literacy taken 2

2204 (40)62.50%40.00%66.70%26.70%2205 (40)45.00%68.30%

1105 (40)70.00%22.50%65.20%42.70%2204 (40)40.00%66.70%

3218 (40)27.50%15.00%58.90%43.90%1105 (40)22.50%65.20%

2102 (52)53.80%28.90%74.20%45.30%3218 (40)15.00%58.90%

3104 (28)78.60%42.90%88.70%45.80%2102 (52)28.90%74.20%

2203 (40)57.50%27.50%74.30%46.80%3104 (28)42.90%88.70%

1103 (41)41.50%12.20%58.80%46.60%2203 (40)27.50%74.30%

2103 (40)65.00%50.00%100.00%50.00%1103 (41)12.20%58.80%

2202 (40)47.50%25.00%76.00%51.00%2103 (40)50.00%100.00%

3105 (41)51.20%31.70%83.50%51.80%2202 (40)25.00%76.00%

1207 (40)60.00%27.50%83.30%55.80%3105 (41)31.70%83.50%

1204 (40)50.00%42.50%100.00%57.50%1207 (40)27.50%83.30%

2206 (40)42.50%15.00%76.00%61.00%1204 (40)42.50%100.00%

1206 (39)20.50%20.50%89.60%69.10%2206 (40)15.00%76.00%

3207 (26)26.90%7.70%80.00%72.30%1206 (39)20.50%89.60%

3224 (40)82.50%27.50%100.00%72.50%3207 (26)7.70%80.00%

2207 (33)30.30%18.20%100.00%81.80%3224 (40)27.50%100.00%

2207 (33)18.20%100.00%

Sheet3

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e-literacy taken 1

e-literacy taken 2

Perceived importance: Computers v/s EnglishI have seen my son working on the computer, making designs. He knows how to use it in less than one year. You see all these boys in the 7th standard, after three years of learning English if you ask them for a glass of water in English they will run away. Even the English teacher will not talk to you in English, marginal farmer, Bangalore Rural

Children become intelligent when they use computers. If you know computers, you can learn English through a computer, marginal farmer, BRDemand: Computers or English? I have seen the security guards using the computers. Even coolies can use computers nowadays, factory worker, Bellary

Choice: Kannada medium with computersChoice: English-medium without computersBLR (n=68)96.2%3.8%Bellary (n=66)59.7%40.3%Kodagu (n=18)70.6%29.4%Shimoga (n=20)65.0%35.0%Total(n=172)73.5%26.5%

My children have become more active, they seem more interested in things and have even started directing their parents (referring to herself) in many things. They want to go to school everyday, even during the holidays to play with the computers. The whole village respects the school now, seamstress in Bangalore Rural

"This is all a waste. Children in the 7th (grade) dont even know how to read. Computers are never running, casual labourer, Bellary

Key differentiators b/w BLR and BellarySummer programSchool selectionPerceptions of ChangeParents perception of changes in school since CAL

Chart3

0.2350.4560.1320.1030.074

0.1360.2120.0760.1060.47

Children Learning Better

Children more interested in school

Parents more interested in school

School has improved overall

No change

Sheet1

Sheet2

Children Learning BetterChildren more interested in schoolParents more interested in schoolSchool has improved overallNo change

BLR (n=68)23.50%45.60%13.20%10.30%7.40%

Bellary (n=66)13.60%21.20%7.60%10.60%47.00%

Sheet2





No change

Sheet3

MBD00009BF9.xls

Chart8

0.2350.4560.1320.1030.074

0.1360.2120.0760.1060.47

0.50.3890.05600.056

0.10.30.20.250.15





No change

Sheet1

Computers are of no use to us27.00%

Too busy to go for training21.00%

Conflict with e-Center19.00%

E-Center is too far9.00%

Other location issues2.00%

e-Center teachers are not good1.00%

Too expensive1.00%

Sheet1

0000000

Computers are of no use to us

Too busy to go for training

Conflict with e-Center

E-Center is too far

Other location issues

e-Center teachers are not good

Too expensive

Sheet2

Malappuram e-literacy recipient (n=338)Kozhikode computer literate (n=49)

Lost fear of computers95.60%72.20%

Understood "a computer" better57.40%70.60%

Gained respect in society30.50%54.40%

Decided to study further16.90%28.10%

Signed up relative for comp. course9.20%39.10%

Took subsequent comp. course6.50%32.80%

Communicating with relatives/friends0.90%19.60%

Using web for information1.80%18.50%

Job prospects increased3.30%26.90%

Considering working abroad1.80%9.70%

Responsibilities at work changed0.60%10.60%

Sheet2

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Malappuram e-literacy recipient (n=338)

Kozhikode computer literate (n=49)

Sheet6

MealsNotebooksTeachersComputers

BLR (n=68)1.90%7.70%30.80%59.60%

Bellary (n=66)8.00%2.70%54.70%34.70%

Kodagu (n=18)21.60%16.20%35.10%27.00%

Shimoga (n=20)28.60%19.00%38.10%14.30%

Sheet6

0000

0000

0000

0000

0000

0000

0000

0000

Meals

Notebooks

Teachers

Computers

Sheet7

Children Learning BetterChildren more interested in schoolParents more interested in schoolSchool has improved overallNo change

BLR (n=68)23.50%45.60%13.20%10.30%7.40%

Bellary (n=66)13.60%21.20%7.60%10.60%47.00%

Kodagu (n=18)50.00%38.90%5.60%0.00%5.60%

Shimoga (n=20)10.00%30.00%20.00%25.00%15.00%

Sheet7

00000

00000

00000

00000

00000

00000

00000

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No change

Sheet8

Sheet5

Sheet4

No ResponsePrefers MovePrefers Stay

Agriculturist (n=70)7.10%80.00%12.90%

Agriculture labor (n=47)85.10%14.90%

All agriculture (n=117)4.30%82.10%13.70%

All non-agriculture (n=56)3.60%67.90%28.60%

Total (n=173)4.00%77.50%18.50%

Sheet4

000

000

000

000

000

No Response

Prefers Move

Prefers Stay

Sheet3

Survey Respondents FiguresEntrepreneurs Figures

e-Center Codee-literacy awarenesse-literacy takene-literacy takenData discrepancy

2205 (40)47.50%45.00%68.30%23.30%e-literacy taken 1e-literacy taken 2

2204 (40)62.50%40.00%66.70%26.70%2205 (40)45.00%68.30%

1105 (40)70.00%22.50%65.20%42.70%2204 (40)40.00%66.70%

3218 (40)27.50%15.00%58.90%43.90%1105 (40)22.50%65.20%

2102 (52)53.80%28.90%74.20%45.30%3218 (40)15.00%58.90%

3104 (28)78.60%42.90%88.70%45.80%2102 (52)28.90%74.20%

2203 (40)57.50%27.50%74.30%46.80%3104 (28)42.90%88.70%

1103 (41)41.50%12.20%58.80%46.60%2203 (40)27.50%74.30%

2103 (40)65.00%50.00%100.00%50.00%1103 (41)12.20%58.80%

2202 (40)47.50%25.00%76.00%51.00%2103 (40)50.00%100.00%

3105 (41)51.20%31.70%83.50%51.80%2202 (40)25.00%76.00%

1207 (40)60.00%27.50%83.30%55.80%3105 (41)31.70%83.50%

1204 (40)50.00%42.50%100.00%57.50%1207 (40)27.50%83.30%

2206 (40)42.50%15.00%76.00%61.00%1204 (40)42.50%100.00%

1206 (39)20.50%20.50%89.60%69.10%2206 (40)15.00%76.00%

3207 (26)26.90%7.70%80.00%72.30%1206 (39)20.50%89.60%

3224 (40)82.50%27.50%100.00%72.50%3207 (26)7.70%80.00%

2207 (33)30.30%18.20%100.00%81.80%3224 (40)27.50%100.00%

2207 (33)18.20%100.00%

Sheet3

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e-literacy taken 1

e-literacy taken 2

Technological ExpressionsNecessity: Computers are needed for everything v/s Computers can do anythingTangibility: Short term gratification of My child can use computers no levels of proficiency Mastery of machine possible, English impossibleSystemic Empowerment: Interface with the non-human: neutrality of computerPlaces where seen computers in use (n=166)The Symbolic Value of Computing.

Bank 36.1%Taluk (Administrative) Office31.9%Bus Stand19.9%Hospital16.9%Factories16.3%Electricity Bill Office11.4%Market Place / Shops 8.4%Never actually seen a computer myself20.5%

Computers contextualized sociallyShared resourceSense of communal gain our village has computersMy child uses it (with / as well as) the richGenerational changeFamilial PrideIncreasing generational schism GenderDowry concepts of farmers vs. labourers (more savings for weddings than education)Teacher as class symbolLocal computer teacher as class breaker v/s Traditional state teacher as class vestige

ImplicationsShort termChild attendanceHousehold propensity to invest (Rs. 10 - Rs. 50 per month for computers)Parent involvement (this may be the clincher research unable to show other investments make significant differences)

Long termRaised graduation rates?State interest in continued investmentRisks of expectation

[email protected]

Strong suggestion that seating patterns reinforce social and classroom inequalitiesUsing the ANOVA test for Statistical Significance we find: The correlation between the position occupied by the student during the computer class and the students familys economic position is statistically significant to over 95.1%and to a students performance in class is statistically significant to over 99.8%Seating Patterns

Seating Position (n=102)L2L1TR1R2Class Performance1.502.002.681.951.50Economic Affluence2.002.362.682.241.00

Position :: Family AffluenceLEFTSD=0.66CENTERSD=0.48RIGHTSD=0.83

Chart5

10

10

2

interviews

Interviews

LanguageLandscapeChild LabourPovertyAPF involvementCALCOccupationSchoolsHead TeachersOther TeachersComp.InstructorStudentsParentsSDMC/VECCommunityGovernmentTotalObservations

KogaduMultilingualHillyMinimalModerateI & CDevelopedEstatesAreacaud2221522141

Dakshin K.MultilingualCoastalMinorModerateI & CDevelopedSF, ALMadanthiyar22210115

UdupiMultilingualCoastalModerateModerateI & CDevelopedAL, Fishing2121225131

BellaryMonolingualArid plainsModerateDireI & CDevelopedAL, Livestock, Mines111431212

RaichurMultilingualArid plainsSeriousDireI & CDevelopedAL11323312

GadagMonolingualAgriculturalModerateDireI & CDevelopedHandlooms, ALShigli11511311

PondiMonolingualCoastalMinimalModerateI & CNascentALAbishekapakkam1131322121

MumbaiMonolingualUrbanMinimalMinimalNoneDevelopedCLChembur113151

CuttackMonolingualUrbanMinimalModerateContentNascentCL1121151

MayurbhanjMonolingualForestMinorDireContentNascentTribal, AL21132111102

PuriMonolingualCoastalMinimalModerateContentNascentAL, SF21231

GanjamMultilingualCoastalModerateDireContentNascentAL22512101

15287271542151229

positions

L2L1CR1R2

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Sheet2

Anova: Single Factor

SUMMARY

GroupsCountSumAverageVariance

Column 1224420.6666666667

Column 222592.68181818180.2272727273

Column 322411.86363636360.8852813853

ANOVA

Source of VariationSSdfMSFP-valueF crit

Between Groups8.454545454524.22727272737.12773722630.0016197783.1428085171

Within Groups37.3636363636630.5930735931

Total45.818181818265

FOO


SUMMARY


Column 1224420.6666666667

Column 222592.68181818180.2272727273

Column 322411.86363636360.8852813853

ANOVA


Between Groups8.454545454524.22727272737.12773722630.0016197783.1428085171

Within Groups37.3636363636630.5930735931

Total45.818181818265

BAR


SUMMARY


Column 122522.36363636360.4329004329

Column 222592.68181818180.2272727273

Column 322472.13636363640.8852813853

ANOVA


Between Groups3.30303030321.65151515153.20588235290.04721555523.1428085171

Within Groups32.4545454545630.5151515152

Total35.757575757665

clean-data

L2L1TR1R2

C12331

C212321

C3132

C41321

T1331

T2333

T322

T4131

P1132

P222313STDEVL1TR1

P33330.81649658090.47673129460.8646496676

P4232GoodAveragePoor

P5321Left Seat7870.82

P6221Center16600.48

P7323Right Seat7680.86

P8232

G1121

G2131GoodAveragePoor

G3223Center1660

G4321


G6333Right Seat768

1.522.68181818181.95238095241.5

L2L1TR1R2

2331STDEVL1TR1

123110.6579516950.47673129460.8309489698

222L110102

1311T1570

331R11065

222

33

222

P1133

P233331STDEVL1TR1

P3333RichAveragePoorest

P4323Left Seat101020.66

P5333Center15700.48


P7323

P8331

G1222

G2221

G3233

G4232

G5232RichAveragePoorest

G6333Center15700.48

22.36363636362.68181818182.23809523811

RichAveragePoorest

Right Seat10650.83

L2L1TR1R2

1.502.002.681.951.50

2.002.362.682.241.00

clean-data

0

0

0

Sheet3

L2L1TR1R2

C1233133

C21232133

C313232

C4132133

T133133

T233332

T322023

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P333333

P423232

P532123

P622123

P732322

P823233

G112122

G213132

G322323

G432123

G513333

G633333

1.522.68181818181.86363636361.522

22

12

11

33

32

L2L1TR1R223

233112

1231111

22223

131133

33123

22233

33023

22233

P113323

P23333112

P333312

P432322

P533332

P633312

P732333

P833133

G122221

G222122

G323321

G423211

G523232

G633312

22.36363636362.68181818182.1363636364123

13

33

23

13

13

33

21

12

11

33

12

32

33

0.4273407541

Chart6

15

7

0

interviews

Interviews














15287271542151229

positions

L2L1CR1R2

C1BBAAAACC

C2CCBBAABCCC

C3CBABBB

C4CCAABCCC

T1AAAACC

T2ABABAB

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P1CCAABA

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Sheet2


SUMMARY


Column 1224420.6666666667

Column 222592.68181818180.2272727273

Column 322411.86363636360.8852813853

ANOVA


Between Groups8.454545454524.22727272737.12773722630.0016197783.1428085171

Within Groups37.3636363636630.5930735931

Total45.818181818265

FOO


SUMMARY


Column 1224420.6666666667

Column 222592.68181818180.2272727273

Column 322411.86363636360.8852813853

ANOVA


Between Groups8.454545454524.22727272737.12773722630.0016197783.1428085171

Within Groups37.3636363636630.5930735931

Total45.818181818265

BAR


SUMMARY


Column 122522.36363636360.4329004329

Column 222592.68181818180.2272727273

Column 322472.13636363640.8852813853

ANOVA


Between Groups3.30303030321.65151515153.20588235290.04721555523.1428085171

Within Groups32.4545454545630.5151515152

Total35.757575757665

clean-data

L2L1TR1R2

C12331

C212321

C3132

C41321

T1331

T2333

T322

T4131

P1132

P222313STDEVL1TR1

P33330.81649658090.47673129460.8646496676



P6221Center16600.48


P8232

G1121


G3223Center1660

G4321


G6333Right Seat768

1.522.68181818181.95238095241.5

L2L1TR1R2

2331STDEVL1TR1

123110.6579516950.47673129460.8309489698

222L110102

1311T1570

331R11065

222

33

222

P1133

P233331STDEVL1TR1


P4323Left Seat101020.66

P5333Center15700.48


P7323

P8331

G1222

G2221

G3233

G4232


G6333Center15700.48

22.36363636362.68181818182.23809523811

RichAveragePoorest

Right Seat10650.83

L2L1TR1R2

1.502.002.681.951.50

2.002.362.682.241.00

clean-data

0

0

0

Sheet3

L2L1TR1R2

C1233133

C21232133

C313232

C4132133

T133133

T233332

T322023

T413132

P113233

P22231333

P333333

P423232

P532123

P622123

P732322

P823233

G112122

G213132

G322323

G432123

G513333

G633333

1.522.68181818181.86363636361.522

22

12

11

33

32

L2L1TR1R223

233112

1231111

22223

131133

33123

22233

33023

22233

P113323

P23333112

P333312

P432322

P533332

P633312

P732333

P833133

G122221

G222122

G323321

G423211

G523232

G633312

22.36363636362.68181818182.1363636364123

13

33

23

13

13

33

21

12

11

33

12

32

33

0.4273407541

Chart7

10

6

5

interviews

Interviews














15287271542151229

positions

L2L1CR1R2

C1BBAAAACC

C2CCBBAABCCC

C3CBABBB

C4CCAABCCC

T1AAAACC

T2ABABAB

T3BABA

T4CBABCB

P1CCAABA

P2BABAAACAAC

P3AAAAAA

P4BAABBA

P5AABACA

P6BABACA

P7AABBAA

P8BAAABC

G1CBBBCB

G2CBABCC

G3BBBAAA

G4ABBACB

G5CBAAAB

G6AAAAAA

L2L1CR1R2

C1BAAC

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C4CABC

T1AAC

T2AAA

T3BB

T4CAC

P1CAB

P2BBACA

P3AAA

P4BAB

P5ABC

P6BBC

P7ABA

P8BAB

G1CBC

G2CAC

G3BBA

G4ABC

G5CAA

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L2L1CR1R2

BAAC

CBACC

BBB

CACC

AAC

BBB

AA

BBB

P1CAA

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P5AAA

P6AAA

P7ABA

P8AAC

G1BBB

G2BBC

G3BAA

G4BAB

G5BAB

G6AAA

Sheet2


SUMMARY


Column 1224420.6666666667

Column 222592.68181818180.2272727273

Column 322411.86363636360.8852813853

ANOVA


Between Groups8.454545454524.22727272737.12773722630.0016197783.1428085171

Within Groups37.3636363636630.5930735931

Total45.818181818265

FOO


SUMMARY


Column 1224420.6666666667

Column 222592.68181818180.2272727273

Column 322411.86363636360.8852813853

ANOVA


Between Groups8.454545454524.22727272737.12773722630.0016197783.1428085171

Within Groups37.3636363636630.5930735931

Total45.818181818265

BAR


SUMMARY


Column 122522.36363636360.4329004329

Column 222592.68181818180.2272727273

Column 322472.13636363640.8852813853

ANOVA


Between Groups3.30303030321.65151515153.20588235290.04721555523.1428085171

Within Groups32.4545454545630.5151515152

Total35.757575757665

clean-data

L2L1TR1R2

C12331

C212321

C3132

C41321

T1331

T2333

T322

T4131

P1132

P222313STDEVL1TR1

P33330.81649658090.47673129460.8646496676



P6221Center16600.48


P8232

G1121


G3223Center1660

G4321


G6333Right Seat768

1.522.68181818181.95238095241.5

L2L1TR1R2

2331STDEVL1TR1

123110.6579516950.47673129460.8309489698

222L110102

1311T1570

331R11065

222

33

222

P1133

P233331STDEVL1TR1


P4323Left Seat101020.66

P5333Center15700.48


P7323

P8331

G1222

G2221

G3233

G4232


G6333Center15700.48

22.36363636362.68181818182.23809523811

RichAveragePoorest

Right Seat10650.83

L2L1TR1R2

1.502.002.681.951.50

2.002.362.682.241.00

clean-data

0

0

0

Sheet3

L2L1TR1R2

C1233133

C21232133

C313232

C4132133

T133133

T233332

T322023

T413132

P113233

P22231333

P333333

P423232

P532123

P622123

P732322

P823233

G112122

G213132

G322323

G432123

G513333

G633333

1.522.68181818181.86363636361.522

22

12

11

33

32

L2L1TR1R223

233112

1231111

22223

131133

33123

22233

33023

22233

P113323

P23333112

P333312

P432322

P533332

P633312

P732333

P833133

G122221

G222122

G323321

G423211

G523232

G633312

22.36363636362.68181818182.1363636364123

13

33

23

13

13

33

21

12

11

33

12

32

33

0.4273407541

Computer control patternsNarrative modules less popularCenter scrolls w/o much collaborationEye contact with screen poor for R1Sense of computer pride hurts scroll paceChoice of CAL module usually on center userOver time, the mouse controller gains automatic default position in usage

Seat Shuffle

COMPUTER SCREEN

L1

C

R1

R2

COMPUTER SCREEN

R1

R2

L1

C

CASE 1ORIGINAL SEATING

CASE 2: GROUP GETS SMALLER AS FORMER MOUSE CONTROLLERS MOVE CLOSER TO COMPUTER SCREEN

CASE 2 REARRANGED SEATING

Design InterventionSeat shuffle found effective only in short run, thus we concluded that two factors were critical to make CAL more effective:Modular design for short seating lengthMulti-user system designPedagogical Design needing children to talkPhysical Design shared input/interaction

First Design Iteration: Multiple MiceMSR-India wrote driver and application for MultiMouseFinding: Children learn basic retention tasks better in shared/collaborative scenarios

Words LearntEngagementDecision-makingResponse errorConflict (Boys)Conflict (Girls)Intra-group Dominance by a childCompetitivenessSU4.11High, tails offIndividualLown/an/an/an/aSS3.77LowCollaborativeVery LowHighLowMediumVariedMMR3.6Very HighIndividualMed-HighLowLowVery HighNoneMMV4.3HighCollaborativeVery LowMediumLowLowVariedTable 1: Findings Matrix for qualitative observations from experiments E1 and E2, N=238 (Words Learnt from E2)

CHI 2007 - Pawar, Pal, Gupta, Toyama

Second Design Iteration: Split ScreensBased on finding that both collaboration and competition are needed

Split screenPlaying in teamsTurn takingCollaborationCompetitionScoring

Second Iteration FindingsSplit screen interface very easy to understandChildren prefer playing in small teams than individuallyInactive mouse users help with partners with visual cuesWithout design intervention, sharing is highly unequal

Scoring:Individualized feedbackEncourages care when answeringClear goal

Parental Aspirations and Computer Aided Learning in Rural India Joyojeet Pal Department of City and...

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