Omiyakita_Elementary_School
2023-11-05
#Import data
read.csv("R.csv") #All students N=65## ID username feedback ITS pre_test_type post_test_type class
## 1 1 gocola effort 12 B A 1
## 2 4 goniku effort 12 B A 1
## 3 5 gosoba effort 12 B A 1
## 4 7 gotamago effort 12 B A 1
## 5 8 gotofu effort 10 B A 1
## 6 28 yescola effort 12 A B 1
## 7 29 yesfugu effort 12 A B 1
## 8 30 yesmiso effort 12 A B 1
## 9 31 yesniku effort 12 A B 1
## 10 33 yessushi effort 12 A B 1
## 11 34 yestamago effort 12 A B 1
## 12 35 yestofu effort 12 A B 1
## 13 37 gogyoza effort 12 B A 2
## 14 38 goika effort 12 B A 2
## 15 39 gokaki effort 12 B A 2
## 16 40 goonigiri effort 12 B A 2
## 17 41 goramen effort 12 B A 2
## 18 42 gosakana effort 12 B A 2
## 19 43 gotako effort 12 B A 2
## 20 44 gounagi effort 12 B A 2
## 21 45 goyuba effort 12 B A 2
## 22 69 yessakana effort 12 A B 2
## 23 74 goimo effort 12 B A 3
## 24 75 gonabe effort 12 B A 3
## 25 76 gonigiri effort 12 B A 3
## 26 77 goocha effort 12 B A 3
## 27 79 gosomen effort 12 B A 3
## 28 100 yesice effort 12 A B 3
## 29 101 yesimo effort 12 A B 3
## 30 102 yesnabe effort 12 A B 3
## 31 103 yesnigiri effort 12 A B 3
## 32 105 yessake effort 12 A B 3
## 33 106 yessomen effort 12 A B 3
## 34 107 yestaiyaki effort 12 A B 3
## 35 10 nicecola performance 11 A B 1
## 36 12 nicemiso performance 12 A B 1
## 37 13 niceniku performance 12 A B 1
## 38 15 nicesushi performance 12 A B 1
## 39 16 nicetamago performance 12 A B 1
## 40 20 okfugu performance 12 B A 1
## 41 21 okmiso performance 12 B A 1
## 42 23 oksoba performance 12 B A 1
## 43 25 oktamago performance 12 B A 1
## 44 26 oktofu performance 12 B A 1
## 45 27 okudon performance 12 B A 1
## 46 46 nicegyoza performance 12 A B 2
## 47 48 nicekaki performance 12 A B 2
## 48 49 niceonigiri performance 12 A B 2
## 49 50 niceramen performance 12 A B 2
## 50 51 nicesakana performance 12 A B 2
## 51 52 nicetako performance 12 A B 2
## 52 53 niceunagi performance 12 A B 2
## 53 54 niceyuba performance 12 A B 2
## 54 56 okika performance 12 B A 2
## 55 57 okkaki performance 12 B A 2
## 56 58 okonigiri performance 12 B A 2
## 57 62 okunagi performance 12 B A 2
## 58 63 okyuba performance 12 B A 2
## 59 82 niceice performance 12 A B 3
## 60 83 niceimo performance 12 A B 3
## 61 87 nicesake performance 12 A B 3
## 62 91 okice performance 12 B A 3
## 63 93 oknabe performance 12 B A 3
## 64 96 oksake performance 12 B A 3
## 65 98 oktaiyaki performance 12 B A 3
## PreTest_Total_Score PostTest_Total_Score Post_Pre Intrinsic_Value
## 1 14 14 0 15
## 2 13 12 -1 10
## 3 4 6 2 6
## 4 3 9 6 10
## 5 10 8 -2 13
## 6 13 14 1 11
## 7 9 11 2 8
## 8 12 14 2 14
## 9 12 11 -1 8
## 10 11 11 0 15
## 11 4 3 -1 6
## 12 3 10 7 9
## 13 3 3 0 6
## 14 14 14 0 15
## 15 9 4 -5 15
## 16 11 12 1 13
## 17 2 1 -1 11
## 18 14 14 0 14
## 19 13 13 0 12
## 20 5 8 3 8
## 21 12 12 0 13
## 22 8 11 3 5
## 23 11 3 -8 5
## 24 8 6 -2 10
## 25 10 12 2 13
## 26 12 10 -2 15
## 27 5 10 5 8
## 28 9 6 -3 9
## 29 13 13 0 11
## 30 7 2 -5 12
## 31 13 13 0 13
## 32 12 14 2 15
## 33 1 0 -1 6
## 34 14 13 -1 12
## 35 9 11 2 13
## 36 9 9 0 11
## 37 1 14 13 14
## 38 8 8 0 11
## 39 3 3 0 9
## 40 13 13 0 13
## 41 11 14 3 12
## 42 5 9 4 14
## 43 3 4 1 7
## 44 11 14 3 7
## 45 10 12 2 12
## 46 5 2 -3 10
## 47 12 13 1 15
## 48 8 11 3 15
## 49 11 13 2 7
## 50 14 14 0 11
## 51 2 3 1 8
## 52 13 12 -1 13
## 53 3 12 9 7
## 54 11 13 2 14
## 55 13 14 1 13
## 56 11 10 -1 9
## 57 12 12 0 7
## 58 12 6 -6 11
## 59 3 1 -2 4
## 60 2 13 11 3
## 61 12 12 0 12
## 62 8 12 4 12
## 63 5 8 3 13
## 64 11 13 2 11
## 65 14 14 0 8
## Self.regulation Self.efficacy Test_anxiety Survey_Total_Score
## 1 19 20 14 68
## 2 16 13 8 47
## 3 11 12 7 36
## 4 15 12 9 46
## 5 16 17 15 61
## 6 16 11 4 42
## 7 11 9 15 43
## 8 20 19 16 69
## 9 13 12 8 41
## 10 16 17 13 61
## 11 12 11 7 36
## 12 13 14 14 50
## 13 15 6 4 31
## 14 20 20 6 61
## 15 18 14 12 59
## 16 19 17 5 54
## 17 16 16 4 47
## 18 18 20 17 69
## 19 11 16 10 49
## 20 11 12 8 39
## 21 13 20 20 66
## 22 15 11 7 38
## 23 10 6 6 27
## 24 11 9 14 44
## 25 16 15 12 56
## 26 15 13 7 50
## 27 14 7 7 36
## 28 8 7 12 36
## 29 11 16 14 52
## 30 18 17 15 62
## 31 16 16 15 60
## 32 14 18 18 65
## 33 9 9 9 33
## 34 16 18 17 63
## 35 18 16 15 62
## 36 15 15 16 57
## 37 17 17 15 63
## 38 15 15 4 45
## 39 13 10 8 40
## 40 18 19 17 67
## 41 15 12 12 51
## 42 13 11 5 43
## 43 14 13 9 43
## 44 14 11 13 45
## 45 14 11 12 49
## 46 16 11 4 41
## 47 16 18 15 64
## 48 17 16 17 65
## 49 14 8 7 36
## 50 16 17 13 57
## 51 7 5 5 25
## 52 14 18 16 61
## 53 13 12 13 45
## 54 18 20 14 66
## 55 19 18 10 60
## 56 14 12 9 44
## 57 14 12 7 40
## 58 13 12 17 53
## 59 17 11 4 36
## 60 11 4 5 23
## 61 19 15 8 54
## 62 14 17 14 57
## 63 17 15 16 61
## 64 11 13 11 46
## 65 7 5 8 28read.csv("effort.csv") #Only effort group N=34## ID username feedback ITS pre_test_type post_test_type class
## 1 1 gocola effort 12 B A 1
## 2 4 goniku effort 12 B A 1
## 3 5 gosoba effort 12 B A 1
## 4 7 gotamago effort 12 B A 1
## 5 8 gotofu effort 10 B A 1
## 6 28 yescola effort 12 A B 1
## 7 29 yesfugu effort 12 A B 1
## 8 30 yesmiso effort 12 A B 1
## 9 31 yesniku effort 12 A B 1
## 10 33 yessushi effort 12 A B 1
## 11 34 yestamago effort 12 A B 1
## 12 35 yestofu effort 12 A B 1
## 13 37 gogyoza effort 12 B A 2
## 14 38 goika effort 12 B A 2
## 15 39 gokaki effort 12 B A 2
## 16 40 goonigiri effort 12 B A 2
## 17 41 goramen effort 12 B A 2
## 18 42 gosakana effort 12 B A 2
## 19 43 gotako effort 12 B A 2
## 20 44 gounagi effort 12 B A 2
## 21 45 goyuba effort 12 B A 2
## 22 69 yessakana effort 12 A B 2
## 23 74 goimo effort 12 B A 3
## 24 75 gonabe effort 12 B A 3
## 25 76 gonigiri effort 12 B A 3
## 26 77 goocha effort 12 B A 3
## 27 79 gosomen effort 12 B A 3
## 28 100 yesice effort 12 A B 3
## 29 101 yesimo effort 12 A B 3
## 30 102 yesnabe effort 12 A B 3
## 31 103 yesnigiri effort 12 A B 3
## 32 105 yessake effort 12 A B 3
## 33 106 yessomen effort 12 A B 3
## 34 107 yestaiyaki effort 12 A B 3
## PreTest_Total_Score PostTest_Total_Score Post_Pre Intrinsic_Value
## 1 14 14 0 15
## 2 13 12 -1 10
## 3 4 6 2 6
## 4 3 9 6 10
## 5 10 8 -2 13
## 6 13 14 1 11
## 7 9 11 2 8
## 8 12 14 2 14
## 9 12 11 -1 8
## 10 11 11 0 15
## 11 4 3 -1 6
## 12 3 10 7 9
## 13 3 3 0 6
## 14 14 14 0 15
## 15 9 4 -5 15
## 16 11 12 1 13
## 17 2 1 -1 11
## 18 14 14 0 14
## 19 13 13 0 12
## 20 5 8 3 8
## 21 12 12 0 13
## 22 8 11 3 5
## 23 11 3 -8 5
## 24 8 6 -2 10
## 25 10 12 2 13
## 26 12 10 -2 15
## 27 5 10 5 8
## 28 9 6 -3 9
## 29 13 13 0 11
## 30 7 2 -5 12
## 31 13 13 0 13
## 32 12 14 2 15
## 33 1 0 -1 6
## 34 14 13 -1 12
## Self.regulation Self.efficacy Test_anxiety Survey_Total_Score
## 1 19 20 14 68
## 2 16 13 8 47
## 3 11 12 7 36
## 4 15 12 9 46
## 5 16 17 15 61
## 6 16 11 4 42
## 7 11 9 15 43
## 8 20 19 16 69
## 9 13 12 8 41
## 10 16 17 13 61
## 11 12 11 7 36
## 12 13 14 14 50
## 13 15 6 4 31
## 14 20 20 6 61
## 15 18 14 12 59
## 16 19 17 5 54
## 17 16 16 4 47
## 18 18 20 17 69
## 19 11 16 10 49
## 20 11 12 8 39
## 21 13 20 20 66
## 22 15 11 7 38
## 23 10 6 6 27
## 24 11 9 14 44
## 25 16 15 12 56
## 26 15 13 7 50
## 27 14 7 7 36
## 28 8 7 12 36
## 29 11 16 14 52
## 30 18 17 15 62
## 31 16 16 15 60
## 32 14 18 18 65
## 33 9 9 9 33
## 34 16 18 17 63read.csv("performance.csv") #Only performance group N=31## ID username feedback ITS pre_test_type post_test_type class
## 1 10 nicecola performance 11 A B 1
## 2 12 nicemiso performance 12 A B 1
## 3 13 niceniku performance 12 A B 1
## 4 15 nicesushi performance 12 A B 1
## 5 16 nicetamago performance 12 A B 1
## 6 20 okfugu performance 12 B A 1
## 7 21 okmiso performance 12 B A 1
## 8 23 oksoba performance 12 B A 1
## 9 25 oktamago performance 12 B A 1
## 10 26 oktofu performance 12 B A 1
## 11 27 okudon performance 12 B A 1
## 12 46 nicegyoza performance 12 A B 2
## 13 48 nicekaki performance 12 A B 2
## 14 49 niceonigiri performance 12 A B 2
## 15 50 niceramen performance 12 A B 2
## 16 51 nicesakana performance 12 A B 2
## 17 52 nicetako performance 12 A B 2
## 18 53 niceunagi performance 12 A B 2
## 19 54 niceyuba performance 12 A B 2
## 20 56 okika performance 12 B A 2
## 21 57 okkaki performance 12 B A 2
## 22 58 okonigiri performance 12 B A 2
## 23 62 okunagi performance 12 B A 2
## 24 63 okyuba performance 12 B A 2
## 25 82 niceice performance 12 A B 3
## 26 83 niceimo performance 12 A B 3
## 27 87 nicesake performance 12 A B 3
## 28 91 okice performance 12 B A 3
## 29 93 oknabe performance 12 B A 3
## 30 96 oksake performance 12 B A 3
## 31 98 oktaiyaki performance 12 B A 3
## PreTest_Total_Score PostTest_Total_Score Post_Pre Intrinsic_Value
## 1 9 11 2 13
## 2 9 9 0 11
## 3 1 14 13 14
## 4 8 8 0 11
## 5 3 3 0 9
## 6 13 13 0 13
## 7 11 14 3 12
## 8 5 9 4 14
## 9 3 4 1 7
## 10 11 14 3 7
## 11 10 12 2 12
## 12 5 2 -3 10
## 13 12 13 1 15
## 14 8 11 3 15
## 15 11 13 2 7
## 16 14 14 0 11
## 17 2 3 1 8
## 18 13 12 -1 13
## 19 3 12 9 7
## 20 11 13 2 14
## 21 13 14 1 13
## 22 11 10 -1 9
## 23 12 12 0 7
## 24 12 6 -6 11
## 25 3 1 -2 4
## 26 2 13 11 3
## 27 12 12 0 12
## 28 8 12 4 12
## 29 5 8 3 13
## 30 11 13 2 11
## 31 14 14 0 8
## Self.regulation Self.efficacy Test_anxiety Survey_Total_Score
## 1 18 16 15 62
## 2 15 15 16 57
## 3 17 17 15 63
## 4 15 15 4 45
## 5 13 10 8 40
## 6 18 19 17 67
## 7 15 12 12 51
## 8 13 11 5 43
## 9 14 13 9 43
## 10 14 11 13 45
## 11 14 11 12 49
## 12 16 11 4 41
## 13 16 18 15 64
## 14 17 16 17 65
## 15 14 8 7 36
## 16 16 17 13 57
## 17 7 5 5 25
## 18 14 18 16 61
## 19 13 12 13 45
## 20 18 20 14 66
## 21 19 18 10 60
## 22 14 12 9 44
## 23 14 12 7 40
## 24 13 12 17 53
## 25 17 11 4 36
## 26 11 4 5 23
## 27 19 15 8 54
## 28 14 17 14 57
## 29 17 15 16 61
## 30 11 13 11 46
## 31 7 5 8 28read.csv("R_value.csv") #Feedback(effort=1,performance=2)## ID feedback ITS class PreTest_Total_Score PostTest_Total_Score Post_Pre
## 1 1 1 12 1 14 14 0
## 2 4 1 12 1 13 12 -1
## 3 5 1 12 1 4 6 2
## 4 7 1 12 1 3 9 6
## 5 8 1 10 1 10 8 -2
## 6 28 1 12 1 13 14 1
## 7 29 1 12 1 9 11 2
## 8 30 1 12 1 12 14 2
## 9 31 1 12 1 12 11 -1
## 10 33 1 12 1 11 11 0
## 11 34 1 12 1 4 3 -1
## 12 35 1 12 1 3 10 7
## 13 37 1 12 2 3 3 0
## 14 38 1 12 2 14 14 0
## 15 39 1 12 2 9 4 -5
## 16 40 1 12 2 11 12 1
## 17 41 1 12 2 2 1 -1
## 18 42 1 12 2 14 14 0
## 19 43 1 12 2 13 13 0
## 20 44 1 12 2 5 8 3
## 21 45 1 12 2 12 12 0
## 22 69 1 12 2 8 11 3
## 23 74 1 12 3 11 3 -8
## 24 75 1 12 3 8 6 -2
## 25 76 1 12 3 10 12 2
## 26 77 1 12 3 12 10 -2
## 27 79 1 12 3 5 10 5
## 28 100 1 12 3 9 6 -3
## 29 101 1 12 3 13 13 0
## 30 102 1 12 3 7 2 -5
## 31 103 1 12 3 13 13 0
## 32 105 1 12 3 12 14 2
## 33 106 1 12 3 1 0 -1
## 34 107 1 12 3 14 13 -1
## 35 10 2 11 1 9 11 2
## 36 12 2 12 1 9 9 0
## 37 13 2 12 1 1 14 13
## 38 15 2 12 1 8 8 0
## 39 16 2 12 1 3 3 0
## 40 20 2 12 1 13 13 0
## 41 21 2 12 1 11 14 3
## 42 23 2 12 1 5 9 4
## 43 25 2 12 1 3 4 1
## 44 26 2 12 1 11 14 3
## 45 27 2 12 1 10 12 2
## 46 46 2 12 2 5 2 -3
## 47 48 2 12 2 12 13 1
## 48 49 2 12 2 8 11 3
## 49 50 2 12 2 11 13 2
## 50 51 2 12 2 14 14 0
## 51 52 2 12 2 2 3 1
## 52 53 2 12 2 13 12 -1
## 53 54 2 12 2 3 12 9
## 54 56 2 12 2 11 13 2
## 55 57 2 12 2 13 14 1
## 56 58 2 12 2 11 10 -1
## 57 62 2 12 2 12 12 0
## 58 63 2 12 2 12 6 -6
## 59 82 2 12 3 3 1 -2
## 60 83 2 12 3 2 13 11
## 61 87 2 12 3 12 12 0
## 62 91 2 12 3 8 12 4
## 63 93 2 12 3 5 8 3
## 64 96 2 12 3 11 13 2
## 65 98 2 12 3 14 14 0
## Intrinsic_Value Self.regulation Self.efficacy Test_anxiety
## 1 15 19 20 14
## 2 10 16 13 8
## 3 6 11 12 7
## 4 10 15 12 9
## 5 13 16 17 15
## 6 11 16 11 4
## 7 8 11 9 15
## 8 14 20 19 16
## 9 8 13 12 8
## 10 15 16 17 13
## 11 6 12 11 7
## 12 9 13 14 14
## 13 6 15 6 4
## 14 15 20 20 6
## 15 15 18 14 12
## 16 13 19 17 5
## 17 11 16 16 4
## 18 14 18 20 17
## 19 12 11 16 10
## 20 8 11 12 8
## 21 13 13 20 20
## 22 5 15 11 7
## 23 5 10 6 6
## 24 10 11 9 14
## 25 13 16 15 12
## 26 15 15 13 7
## 27 8 14 7 7
## 28 9 8 7 12
## 29 11 11 16 14
## 30 12 18 17 15
## 31 13 16 16 15
## 32 15 14 18 18
## 33 6 9 9 9
## 34 12 16 18 17
## 35 13 18 16 15
## 36 11 15 15 16
## 37 14 17 17 15
## 38 11 15 15 4
## 39 9 13 10 8
## 40 13 18 19 17
## 41 12 15 12 12
## 42 14 13 11 5
## 43 7 14 13 9
## 44 7 14 11 13
## 45 12 14 11 12
## 46 10 16 11 4
## 47 15 16 18 15
## 48 15 17 16 17
## 49 7 14 8 7
## 50 11 16 17 13
## 51 8 7 5 5
## 52 13 14 18 16
## 53 7 13 12 13
## 54 14 18 20 14
## 55 13 19 18 10
## 56 9 14 12 9
## 57 7 14 12 7
## 58 11 13 12 17
## 59 4 17 11 4
## 60 3 11 4 5
## 61 12 19 15 8
## 62 12 14 17 14
## 63 13 17 15 16
## 64 11 11 13 11
## 65 8 7 5 8
## Survey_Total_Score
## 1 68
## 2 47
## 3 36
## 4 46
## 5 61
## 6 42
## 7 43
## 8 69
## 9 41
## 10 61
## 11 36
## 12 50
## 13 31
## 14 61
## 15 59
## 16 54
## 17 47
## 18 69
## 19 49
## 20 39
## 21 66
## 22 38
## 23 27
## 24 44
## 25 56
## 26 50
## 27 36
## 28 36
## 29 52
## 30 62
## 31 60
## 32 65
## 33 33
## 34 63
## 35 62
## 36 57
## 37 63
## 38 45
## 39 40
## 40 67
## 41 51
## 42 43
## 43 43
## 44 45
## 45 49
## 46 41
## 47 64
## 48 65
## 49 36
## 50 57
## 51 25
## 52 61
## 53 45
## 54 66
## 55 60
## 56 44
## 57 40
## 58 53
## 59 36
## 60 23
## 61 54
## 62 57
## 63 61
## 64 46
## 65 28setwd("/Users/satoushintarou/Desktop/Master thesis/大宮北小/Data") #Directory
data<-read.csv("R.csv")
datae<-read.csv("effort.csv")
datap<-read.csv("performance.csv")
datav<-read.csv("R_value.csv")#Demographic
#All Students (N=65)
table(data$feedback)##
## effort performance
## 34 31#Pre-test/All Students (N=65)
mean(data$PreTest_Total_Score)## [1] 8.907692sd(data$PreTest_Total_Score)## [1] 4.076315median(data$PreTest_Total_Score)## [1] 10hist(datae$PreTest_Total_Score,main="PreTest_Total_Score/All students (N=65)",ylim=c(0,15))
#Post-test/All Students (N=65)
mean(data$PostTest_Total_Score)## [1] 9.784615sd(data$PostTest_Total_Score)## [1] 4.173923median(data$PostTest_Total_Score)## [1] 11hist(data$PostTest_Total_Score,main="PostTest_Total_Score/All students (N=65)",ylim=c(0,15))
#Pre-test/effort group (N=34)
mean(datae$PreTest_Total_Score)## [1] 9.235294sd(datae$PreTest_Total_Score)## [1] 4.068049median(datae$PreTest_Total_Score)## [1] 10.5hist(datae$PreTest_Total_Score,main="PreTest_Total_Score/Effort group (N=34)",ylim=c(0,15))
#Post-test/effort group (N=34)
mean(datae$PostTest_Total_Score)## [1] 9.323529sd(datae$PostTest_Total_Score)## [1] 4.339533median(datae$PostTest_Total_Score)## [1] 11hist(datae$PostTest_Total_Score,main="PostTest_Total_Score/Effort group (N=34)",ylim=c(0,15))
#Pre-test/performance group (N=31)
mean(datap$PreTest_Total_Score)## [1] 8.548387sd(datap$PreTest_Total_Score)## [1] 4.121801median(datap$PreTest_Total_Score)## [1] 10hist(datap$PreTest_Total_Score,main="PreTest_Total_Score/Performance group (N=31)",ylim=c(0,15))
#Post-test/performance group (N=31)
mean(datap$PostTest_Total_Score)## [1] 10.29032sd(datap$PostTest_Total_Score)## [1] 3.993274median(datap$PostTest_Total_Score)## [1] 12hist(datap$PostTest_Total_Score,main="PostTest_Total_Score/Performance group (N=31)",ylim=c(0,15))
#Analysis 1 #Compare Pre- vs. Post-Test #All Students (N=65)
#Comment:Overall, the test score increased significantly before and after the ITS learning activity.
#T-test
#Pre-test and Post-test
#p-value = 0.04363
t.test(data$PreTest_Total_Score, data$PostTest_Total_Score, paired=TRUE)##
## Paired t-test
##
## data: data$PreTest_Total_Score and data$PostTest_Total_Score
## t = -2.0584, df = 64, p-value = 0.04363
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -1.72799831 -0.02584785
## sample estimates:
## mean difference
## -0.8769231#Analysis 2 #Compare Pre-Test / Effort vs. Performance Group #Compare Post-Test / Effort vs. Performance Group
#Comment:No significant difference in the pre- and post-test between the effort and performance group.
#T-test
#Pre-test between the effort and performance group
#p-value = 0.502
var.test(datae$PreTest_Total_Score,datap$PreTest_Total_Score)#等分散##
## F test to compare two variances
##
## data: datae$PreTest_Total_Score and datap$PreTest_Total_Score
## F = 0.97409, num df = 33, denom df = 30, p-value = 0.9373
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.4750086 1.9733731
## sample estimates:
## ratio of variances
## 0.9740879t.test(PreTest_Total_Score ~ feedback, var.equal=T,data = data)##
## Two Sample t-test
##
## data: PreTest_Total_Score by feedback
## t = 0.67568, df = 63, p-value = 0.5017
## alternative hypothesis: true difference in means between group effort and group performance is not equal to 0
## 95 percent confidence interval:
## -1.344633 2.718447
## sample estimates:
## mean in group effort mean in group performance
## 9.235294 8.548387#T-test
#Post-test between the effort and performance group
#p-value = 0.355
var.test(datae$PostTest_Total_Score,datap$PostTest_Total_Score)#等分散##
## F test to compare two variances
##
## data: datae$PostTest_Total_Score and datap$PostTest_Total_Score
## F = 1.1809, num df = 33, denom df = 30, p-value = 0.6484
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.5758789 2.3924284
## sample estimates:
## ratio of variances
## 1.18094t.test(PostTest_Total_Score ~ feedback, var.equal=T,data = data)##
## Two Sample t-test
##
## data: PostTest_Total_Score by feedback
## t = -0.93176, df = 63, p-value = 0.355
## alternative hypothesis: true difference in means between group effort and group performance is not equal to 0
## 95 percent confidence interval:
## -3.040265 1.106678
## sample estimates:
## mean in group effort mean in group performance
## 9.323529 10.290323#Analysis 3 #Compare Test Score Increase (増加量) / Effort vs. Performance Group
#Comment:There is a trend (p=0.051) that greater test score increase [(Post_Test_Total_Score) - (Pre_Test_Total_Score)] among kids who received performance feedback compared to those who received effort-based feedback.
#T-test
#Score Increase [Post_pre = (Post_Test_Total_Score)ー(Pre_Test_Total_Score)] between the effort and performance group
#p-value = 0.05177
var.test(datae$Post_Pre,datap$Post_Pre)#等分散##
## F test to compare two variances
##
## data: datae$Post_Pre and datap$Post_Pre
## F = 0.62939, num df = 33, denom df = 30, p-value = 0.1963
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3069204 1.2750686
## sample estimates:
## ratio of variances
## 0.6293939t.test(Post_Pre ~ feedback,var.equal=T,data = data)##
## Two Sample t-test
##
## data: Post_Pre by feedback
## t = -1.9827, df = 63, p-value = 0.05177
## alternative hypothesis: true difference in means between group effort and group performance is not equal to 0
## 95 percent confidence interval:
## -3.32044774 0.01304737
## sample estimates:
## mean in group effort mean in group performance
## 0.08823529 1.74193548#Analysis 4 #Regression Model #All Students (N=65)
#Comment:"Feedback" and "Pre-Test" has a trend to predict "Post-Test".
#y="Post-test", x="Feedback type" and "Pre-test"
model1 <- lm(PostTest_Total_Score ~ feedback*PreTest_Total_Score, data = data)
summary(model1)##
## Call:
## lm(formula = PostTest_Total_Score ~ feedback * PreTest_Total_Score,
## data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.7402 -1.6567 0.6542 1.3433 7.9167
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.9096 1.3393 1.426 0.1590
## feedbackperformance 3.6163 1.8685 1.935 0.0576
## PreTest_Total_Score 0.8028 0.1330 6.034 1.02e-07
## feedbackperformance:PreTest_Total_Score -0.2454 0.1915 -1.282 0.2047
##
## (Intercept)
## feedbackperformance .
## PreTest_Total_Score ***
## feedbackperformance:PreTest_Total_Score
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.109 on 61 degrees of freedom
## Multiple R-squared: 0.4712, Adjusted R-squared: 0.4452
## F-statistic: 18.12 on 3 and 61 DF, p-value: 1.598e-08model2 <- lm(PostTest_Total_Score ~ feedback+PreTest_Total_Score, data = data)
summary(model2)##
## Call:
## lm(formula = PostTest_Total_Score ~ feedback + PreTest_Total_Score,
## data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.531 -1.915 0.416 1.469 8.875
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.00386 1.03734 2.896 0.00522 **
## feedbackperformance 1.43684 0.77883 1.845 0.06983 .
## PreTest_Total_Score 0.68430 0.09617 7.115 1.36e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.125 on 62 degrees of freedom
## Multiple R-squared: 0.457, Adjusted R-squared: 0.4395
## F-statistic: 26.09 on 2 and 62 DF, p-value: 6.01e-09library(ggplot2)
ggplot(data, aes(x = PreTest_Total_Score, y = PostTest_Total_Score, col = feedback)) + geom_point() +geom_smooth(method = "lm",se=FALSE)## `geom_smooth()` using formula = 'y ~ x'
#Comment: "Feedback" and "Pre-Test" has a trend to predict "Score Increase（増加量）".
#y="Score Increase(増加量)", x="Feedback type" and "Pre-test"
model3 <- lm(Post_Pre ~ feedback*PreTest_Total_Score, data = data)
summary(model3)##
## Call:
## lm(formula = Post_Pre ~ feedback * PreTest_Total_Score, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.7402 -1.6567 0.6542 1.3433 7.9167
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.9096 1.3393 1.426 0.1590
## feedbackperformance 3.6163 1.8685 1.935 0.0576 .
## PreTest_Total_Score -0.1972 0.1330 -1.483 0.1434
## feedbackperformance:PreTest_Total_Score -0.2454 0.1915 -1.282 0.2047
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.109 on 61 degrees of freedom
## Multiple R-squared: 0.2191, Adjusted R-squared: 0.1807
## F-statistic: 5.706 on 3 and 61 DF, p-value: 0.001646model4 <- lm(Post_Pre ~ feedback+PreTest_Total_Score, data = data)
summary(model4)##
## Call:
## lm(formula = Post_Pre ~ feedback + PreTest_Total_Score, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.531 -1.915 0.416 1.469 8.875
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.00386 1.03734 2.896 0.00522 **
## feedbackperformance 1.43684 0.77883 1.845 0.06983 .
## PreTest_Total_Score -0.31570 0.09617 -3.283 0.00169 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.125 on 62 degrees of freedom
## Multiple R-squared: 0.1981, Adjusted R-squared: 0.1722
## F-statistic: 7.659 on 2 and 62 DF, p-value: 0.001066library(ggplot2)
ggplot(data, aes(x = PreTest_Total_Score, y = Post_Pre, col = feedback)) + geom_point() +geom_smooth(method = "lm",se=FALSE)## `geom_smooth()` using formula = 'y ~ x'
#Analysis 5 #Compare Math Motivation Score (算数学習への意欲) / Effort vs. Performance Group
#Comment:In terms of the Math motivation score, no significant difference between the two groups.
#T-test
#Math motivation score between the effort and performance group
#p-value = 0.8305
var.test(datae$Survey_Total_Score,datap$Survey_Total_Score)#等分散##
## F test to compare two variances
##
## data: datae$Survey_Total_Score and datap$Survey_Total_Score
## F = 0.99344, num df = 33, denom df = 30, p-value = 0.9808
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.4844434 2.0125693
## sample estimates:
## ratio of variances
## 0.9934357t.test(Survey_Total_Score ~ feedback,var.equal=T,data = data)##
## Two Sample t-test
##
## data: Survey_Total_Score by feedback
## t = 0.21497, df = 63, p-value = 0.8305
## alternative hypothesis: true difference in means between group effort and group performance is not equal to 0
## 95 percent confidence interval:
## -5.423100 6.730501
## sample estimates:
## mean in group effort mean in group performance
## 49.91176 49.25806#T-test
#Intrinsic value between the effort and performance group
#p-value = 0.7563
var.test(datae$Intrinsic_Value,datap$Intrinsic_Value)#等分散##
## F test to compare two variances
##
## data: datae$Intrinsic_Value and datap$Intrinsic_Value
## F = 1.0885, num df = 33, denom df = 30, p-value = 0.8181
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.5308176 2.2052257
## sample estimates:
## ratio of variances
## 1.088534t.test(Intrinsic_Value ~ feedback,var.equal=T,data = data)##
## Two Sample t-test
##
## data: Intrinsic_Value by feedback
## t = 0.31166, df = 63, p-value = 0.7563
## alternative hypothesis: true difference in means between group effort and group performance is not equal to 0
## 95 percent confidence interval:
## -1.345265 1.842419
## sample estimates:
## mean in group effort mean in group performance
## 10.76471 10.51613#T-test
#Self efficacy between the effort and performance group
#p-value = 0.5474
var.test(datae$Self.efficacy,datap$Self.efficacy)#等分散##
## F test to compare two variances
##
## data: datae$Self.efficacy and datap$Self.efficacy
## F = 1.0701, num df = 33, denom df = 30, p-value = 0.8552
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.5218127 2.1678160
## sample estimates:
## ratio of variances
## 1.070068t.test(Self.efficacy ~ feedback,var.equal=T,data = data)##
## Two Sample t-test
##
## data: Self.efficacy by feedback
## t = 0.60497, df = 63, p-value = 0.5474
## alternative hypothesis: true difference in means between group effort and group performance is not equal to 0
## 95 percent confidence interval:
## -1.450972 2.710934
## sample estimates:
## mean in group effort mean in group performance
## 13.82353 13.19355#T-test
#Self regulation between the effort and performance group
#p-value = 0.8525
var.test(datae$Self.regulation,datap$Self.regulation)#等分散##
## F test to compare two variances
##
## data: datae$Self.regulation and datap$Self.regulation
## F = 1.1886, num df = 33, denom df = 30, p-value = 0.6357
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.579596 2.407871
## sample estimates:
## ratio of variances
## 1.188563t.test(Self.regulation ~ feedback,var.equal=T,data = data)##
## Two Sample t-test
##
## data: Self.regulation by feedback
## t = -0.18666, df = 63, p-value = 0.8525
## alternative hypothesis: true difference in means between group effort and group performance is not equal to 0
## 95 percent confidence interval:
## -1.665894 1.381264
## sample estimates:
## mean in group effort mean in group performance
## 14.47059 14.61290#T-test
#Anxiety between the effort and performance group
#p-value = 0.9412
var.test(datae$Test_anxiety,datap$Test_anxiety)#等分散##
## F test to compare two variances
##
## data: datae$Test_anxiety and datap$Test_anxiety
## F = 1.0587, num df = 33, denom df = 30, p-value = 0.8786
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.5162518 2.1447136
## sample estimates:
## ratio of variances
## 1.058664t.test(Test_anxiety ~ feedback,var.equal=T,data = data)##
## Two Sample t-test
##
## data: Test_anxiety by feedback
## t = -0.074085, df = 63, p-value = 0.9412
## alternative hypothesis: true difference in means between group effort and group performance is not equal to 0
## 95 percent confidence interval:
## -2.309021 2.143936
## sample estimates:
## mean in group effort mean in group performance
## 10.85294 10.93548#Analysis 6 #Correlation
library(corrplot)## corrplot 0.92 loadedround(cor(datav),2)## ID feedback ITS class PreTest_Total_Score
## ID 1.00 -0.05 0.24 0.94 0.09
## feedback -0.05 1.00 0.05 -0.08 -0.08
## ITS 0.24 0.05 1.00 0.20 -0.03
## class 0.94 -0.08 0.20 1.00 0.07
## PreTest_Total_Score 0.09 -0.08 -0.03 0.07 1.00
## PostTest_Total_Score -0.05 0.12 0.03 -0.09 0.65
## Post_Pre -0.17 0.24 0.08 -0.19 -0.39
## Intrinsic_Value -0.11 -0.04 -0.13 -0.08 0.48
## Self.regulation -0.26 0.02 -0.12 -0.20 0.27
## Self.efficacy -0.15 -0.08 -0.13 -0.15 0.45
## Test_anxiety 0.08 0.01 -0.16 0.00 0.35
## Survey_Total_Score -0.12 -0.03 -0.16 -0.12 0.48
## PostTest_Total_Score Post_Pre Intrinsic_Value
## ID -0.05 -0.17 -0.11
## feedback 0.12 0.24 -0.04
## ITS 0.03 0.08 -0.13
## class -0.09 -0.19 -0.08
## PreTest_Total_Score 0.65 -0.39 0.48
## PostTest_Total_Score 1.00 0.44 0.42
## Post_Pre 0.44 1.00 -0.07
## Intrinsic_Value 0.42 -0.07 1.00
## Self.regulation 0.27 0.00 0.58
## Self.efficacy 0.42 -0.03 0.77
## Test_anxiety 0.38 0.04 0.53
## Survey_Total_Score 0.46 -0.01 0.87
## Self.regulation Self.efficacy Test_anxiety
## ID -0.26 -0.15 0.08
## feedback 0.02 -0.08 0.01
## ITS -0.12 -0.13 -0.16
## class -0.20 -0.15 0.00
## PreTest_Total_Score 0.27 0.45 0.35
## PostTest_Total_Score 0.27 0.42 0.38
## Post_Pre 0.00 -0.03 0.04
## Intrinsic_Value 0.58 0.77 0.53
## Self.regulation 1.00 0.70 0.19
## Self.efficacy 0.70 1.00 0.57
## Test_anxiety 0.19 0.57 1.00
## Survey_Total_Score 0.71 0.93 0.75
## Survey_Total_Score
## ID -0.12
## feedback -0.03
## ITS -0.16
## class -0.12
## PreTest_Total_Score 0.48
## PostTest_Total_Score 0.46
## Post_Pre -0.01
## Intrinsic_Value 0.87
## Self.regulation 0.71
## Self.efficacy 0.93
## Test_anxiety 0.75
## Survey_Total_Score 1.00cor_matrix<-round(cor(datav),2)
corrplot(corr=cor_matrix)
#Comment:There is a correlation between the Math motivation score and the test scores.
##Significant Correlation##
#Pre-Test × Post-Test
#p-value = 3.582e-09, cor 0.6535961
cor.test(data$PreTest_Total_Score,data$PostTest_Total_Score)##
## Pearson's product-moment correlation
##
## data: data$PreTest_Total_Score and data$PostTest_Total_Score
## t = 6.8545, df = 63, p-value = 3.582e-09
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4873931 0.7740955
## sample estimates:
## cor
## 0.6535961#Math Motivation Score × Pre-test
#p-value = 5.368e-05, cor 0.4792619
cor.test(data$Survey_Total_Score,data$PreTest_Total_Score)##
## Pearson's product-moment correlation
##
## data: data$Survey_Total_Score and data$PreTest_Total_Score
## t = 4.3342, df = 63, p-value = 5.368e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2665163 0.6474765
## sample estimates:
## cor
## 0.4792619#Math Motivation Score × Post-test
#p-value = 0.0001171, cor 0.4596764
cor.test(data$Survey_Total_Score,data$PostTest_Total_Score)##
## Pearson's product-moment correlation
##
## data: data$Survey_Total_Score and data$PostTest_Total_Score
## t = 4.1083, df = 63, p-value = 0.0001171
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2430238 0.6326465
## sample estimates:
## cor
## 0.4596764#Intrinsic value × Post-test
#p-value = 0.0005316, cor 0.4179531
cor.test(data$Intrinsic_Value,data$PostTest_Total_Score)##
## Pearson's product-moment correlation
##
## data: data$Intrinsic_Value and data$PostTest_Total_Score
## t = 3.6516, df = 63, p-value = 0.0005316
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1938108 0.6006254
## sample estimates:
## cor
## 0.4179531#Self efficacy × Post-test
#p-value = 0.0005402, cor 0.4174799
cor.test(data$Self.efficacy,data$PostTest_Total_Score)##
## Pearson's product-moment correlation
##
## data: data$Self.efficacy and data$PostTest_Total_Score
## t = 3.6466, df = 63, p-value = 0.0005402
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1932590 0.6002589
## sample estimates:
## cor
## 0.4174799#Self regulation × Post-test
#p-value = 0.02902, cor 0.270957
cor.test(data$Self.regulation,data$PostTest_Total_Score)##
## Pearson's product-moment correlation
##
## data: data$Self.regulation and data$PostTest_Total_Score
## t = 2.2342, df = 63, p-value = 0.02902
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.02897262 0.48294046
## sample estimates:
## cor
## 0.270957#Test anxiety × Post-test
#p-value = 0.001859, cor 0.3788359
cor.test(data$Test_anxiety,data$PostTest_Total_Score)##
## Pearson's product-moment correlation
##
## data: data$Test_anxiety and data$PostTest_Total_Score
## t = 3.2491, df = 63, p-value = 0.001859
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1486740 0.5700625
## sample estimates:
## cor
## 0.3788359#Comment:There is not significant correlation between the Math motivation score and the Score increase.
##No significant##
#Math motivation score × Score increase（増加量）
cor.test(data$Survey_Total_Score,data$Post_Pre)##
## Pearson's product-moment correlation
##
## data: data$Survey_Total_Score and data$Post_Pre
## t = -0.080814, df = 63, p-value = 0.9358
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2534508 0.2342999
## sample estimates:
## cor
## -0.01018105#Intrinsic value × Score increase
cor.test(data$Intrinsic_Value,data$Post_Pre)##
## Pearson's product-moment correlation
##
## data: data$Intrinsic_Value and data$Post_Pre
## t = -0.52004, df = 63, p-value = 0.6049
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3044233 0.1814134
## sample estimates:
## cor
## -0.0653785#Self efficacy × Score increase
cor.test(data$Self.efficacy,data$Post_Pre)##
## Pearson's product-moment correlation
##
## data: data$Self.efficacy and data$Post_Pre
## t = -0.20616, df = 63, p-value = 0.8373
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2681662 0.2193227
## sample estimates:
## cor
## -0.02596541#Self regulation × Score increase
cor.test(data$Self.regulation,data$Post_Pre)##
## Pearson's product-moment correlation
##
## data: data$Self.regulation and data$Post_Pre
## t = 0.039199, df = 63, p-value = 0.9689
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2392488 0.2485383
## sample estimates:
## cor
## 0.004938531#Test anxiety × Score increase
cor.test(data$Test_anxiety,data$Post_Pre)##
## Pearson's product-moment correlation
##
## data: data$Test_anxiety and data$Post_Pre
## t = 0.31772, df = 63, p-value = 0.7517
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2059111 0.2811531
## sample estimates:
## cor
## 0.03999669