Omiyakita_Elementary_School_Exclude_Pre-test_Score_14/14
2023-11-12
#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 28read.csv("Latency.csv") #Latency N=65-3 (3:Too long Latency seconds in Q1)## ID username Sum_of_Latency feedback ITS pre_test_type post_test_type
## 1 1 gocola 492 effort 12 B A
## 2 37 gogyoza 1367 effort 12 B A
## 3 38 goika 936 effort 12 B A
## 4 74 goimo 1762 effort 12 B A
## 5 39 gokaki 767 effort 12 B A
## 6 75 gonabe 863 effort 12 B A
## 7 76 gonigiri 653 effort 12 B A
## 8 4 goniku 1158 effort 12 B A
## 9 77 goocha 2456 effort 12 B A
## 10 40 goonigiri 1325 effort 12 B A
## 11 41 goramen 607 effort 12 B A
## 12 42 gosakana 729 effort 12 B A
## 13 5 gosoba 1619 effort 12 B A
## 14 79 gosomen 2242 effort 12 B A
## 15 43 gotako 1622 effort 12 B A
## 16 7 gotamago 1026 effort 12 B A
## 17 8 gotofu 1250 effort 10 B A
## 18 44 gounagi 2932 effort 12 B A
## 19 45 goyuba 923 effort 12 B A
## 20 10 nicecola 553 performance 11 A B
## 21 46 nicegyoza 1232 performance 12 A B
## 22 82 niceice 735 performance 12 A B
## 23 83 niceimo 1190 performance 12 A B
## 24 48 nicekaki 817 performance 12 A B
## 25 12 nicemiso 892 performance 12 A B
## 26 13 niceniku 941 performance 12 A B
## 27 49 niceonigiri 646 performance 12 A B
## 28 50 niceramen 766 performance 12 A B
## 29 51 nicesakana 1382 performance 12 A B
## 30 15 nicesushi 1618 performance 12 A B
## 31 52 nicetako 1826 performance 12 A B
## 32 16 nicetamago 1434 performance 12 A B
## 33 53 niceunagi 790 performance 12 A B
## 34 54 niceyuba 990 performance 12 A B
## 35 20 okfugu 859 performance 12 B A
## 36 91 okice 2485 performance 12 B A
## 37 56 okika 1355 performance 12 B A
## 38 57 okkaki 931 performance 12 B A
## 39 21 okmiso 1144 performance 12 B A
## 40 58 okonigiri 913 performance 12 B A
## 41 23 oksoba 691 performance 12 B A
## 42 98 oktaiyaki 957 performance 12 B A
## 43 25 oktamago 1428 performance 12 B A
## 44 26 oktofu 1473 performance 12 B A
## 45 27 okudon 1407 performance 12 B A
## 46 62 okunagi 1037 performance 12 B A
## 47 63 okyuba 665 performance 12 B A
## 48 28 yescola 1413 effort 12 A B
## 49 29 yesfugu 1157 effort 12 A B
## 50 100 yesice 713 effort 12 A B
## 51 101 yesimo 826 effort 12 A B
## 52 30 yesmiso 1322 effort 12 A B
## 53 102 yesnabe 701 effort 12 A B
## 54 87 nicesake NA performance 12 A B
## 55 103 yesnigiri 876 effort 12 A B
## 56 93 oknabe NA performance 12 B A
## 57 96 oksake NA performance 12 B A
## 58 31 yesniku 925 effort 12 A B
## 59 69 yessakana 1061 effort 12 A B
## 60 105 yessake 911 effort 12 A B
## 61 106 yessomen 742 effort 12 A B
## 62 33 yessushi 1532 effort 12 A B
## 63 107 yestaiyaki 714 effort 12 A B
## 64 34 yestamago 665 effort 12 A B
## 65 35 yestofu 842 effort 12 A B
## class PreTest_Total_Score PostTest_Total_Score Post_Pre Intrinsic_Value
## 1 1 14 14 0 15
## 2 2 3 3 0 6
## 3 2 14 14 0 15
## 4 3 11 3 -8 5
## 5 2 9 4 -5 15
## 6 3 8 6 -2 10
## 7 3 10 12 2 13
## 8 1 13 12 -1 10
## 9 3 12 10 -2 15
## 10 2 11 12 1 13
## 11 2 2 1 -1 11
## 12 2 14 14 0 14
## 13 1 4 6 2 6
## 14 3 5 10 5 8
## 15 2 13 13 0 12
## 16 1 3 9 6 10
## 17 1 10 8 -2 13
## 18 2 5 8 3 8
## 19 2 12 12 0 13
## 20 1 9 11 2 13
## 21 2 5 2 -3 10
## 22 3 3 1 -2 4
## 23 3 2 13 11 3
## 24 2 12 13 1 15
## 25 1 9 9 0 11
## 26 1 1 14 13 14
## 27 2 8 11 3 15
## 28 2 11 13 2 7
## 29 2 14 14 0 11
## 30 1 8 8 0 11
## 31 2 2 3 1 8
## 32 1 3 3 0 9
## 33 2 13 12 -1 13
## 34 2 3 12 9 7
## 35 1 13 13 0 13
## 36 3 8 12 4 12
## 37 2 11 13 2 14
## 38 2 13 14 1 13
## 39 1 11 14 3 12
## 40 2 11 10 -1 9
## 41 1 5 9 4 14
## 42 3 14 14 0 8
## 43 1 3 4 1 7
## 44 1 11 14 3 7
## 45 1 10 12 2 12
## 46 2 12 12 0 7
## 47 2 12 6 -6 11
## 48 1 13 14 1 11
## 49 1 9 11 2 8
## 50 3 9 6 -3 9
## 51 3 13 13 0 11
## 52 1 12 14 2 14
## 53 3 7 2 -5 12
## 54 3 12 12 0 12
## 55 3 13 13 0 13
## 56 3 5 8 3 13
## 57 3 11 13 2 11
## 58 1 12 11 -1 8
## 59 2 8 11 3 5
## 60 3 12 14 2 15
## 61 3 1 0 -1 6
## 62 1 11 11 0 15
## 63 3 14 13 -1 12
## 64 1 4 3 -1 6
## 65 1 3 10 7 9
## Self.regulation Self.efficacy Test_anxiety Survey_Total_Score
## 1 19 20 14 68
## 2 15 6 4 31
## 3 20 20 6 61
## 4 10 6 6 27
## 5 18 14 12 59
## 6 11 9 14 44
## 7 16 15 12 56
## 8 16 13 8 47
## 9 15 13 7 50
## 10 19 17 5 54
## 11 16 16 4 47
## 12 18 20 17 69
## 13 11 12 7 36
## 14 14 7 7 36
## 15 11 16 10 49
## 16 15 12 9 46
## 17 16 17 15 61
## 18 11 12 8 39
## 19 13 20 20 66
## 20 18 16 15 62
## 21 16 11 4 41
## 22 17 11 4 36
## 23 11 4 5 23
## 24 16 18 15 64
## 25 15 15 16 57
## 26 17 17 15 63
## 27 17 16 17 65
## 28 14 8 7 36
## 29 16 17 13 57
## 30 15 15 4 45
## 31 7 5 5 25
## 32 13 10 8 40
## 33 14 18 16 61
## 34 13 12 13 45
## 35 18 19 17 67
## 36 14 17 14 57
## 37 18 20 14 66
## 38 19 18 10 60
## 39 15 12 12 51
## 40 14 12 9 44
## 41 13 11 5 43
## 42 7 5 8 28
## 43 14 13 9 43
## 44 14 11 13 45
## 45 14 11 12 49
## 46 14 12 7 40
## 47 13 12 17 53
## 48 16 11 4 42
## 49 11 9 15 43
## 50 8 7 12 36
## 51 11 16 14 52
## 52 20 19 16 69
## 53 18 17 15 62
## 54 19 15 8 54
## 55 16 16 15 60
## 56 17 15 16 61
## 57 11 13 11 46
## 58 13 12 8 41
## 59 15 11 7 38
## 60 14 18 18 65
## 61 9 9 9 33
## 62 16 17 13 61
## 63 16 18 17 63
## 64 12 11 7 36
## 65 13 14 14 50read.csv("Latency_effort.csv") ## ID username Sum_of_Latency feedback ITS pre_test_type post_test_type
## 1 1 gocola 492 effort 12 B A
## 2 37 gogyoza 1367 effort 12 B A
## 3 38 goika 936 effort 12 B A
## 4 74 goimo 1762 effort 12 B A
## 5 39 gokaki 767 effort 12 B A
## 6 75 gonabe 863 effort 12 B A
## 7 76 gonigiri 653 effort 12 B A
## 8 4 goniku 1158 effort 12 B A
## 9 77 goocha 2456 effort 12 B A
## 10 40 goonigiri 1325 effort 12 B A
## 11 41 goramen 607 effort 12 B A
## 12 42 gosakana 729 effort 12 B A
## 13 5 gosoba 1619 effort 12 B A
## 14 79 gosomen 2242 effort 12 B A
## 15 43 gotako 1622 effort 12 B A
## 16 7 gotamago 1026 effort 12 B A
## 17 8 gotofu 1250 effort 10 B A
## 18 44 gounagi 2932 effort 12 B A
## 19 45 goyuba 923 effort 12 B A
## 20 28 yescola 1413 effort 12 A B
## 21 29 yesfugu 1157 effort 12 A B
## 22 100 yesice 713 effort 12 A B
## 23 101 yesimo 826 effort 12 A B
## 24 30 yesmiso 1322 effort 12 A B
## 25 102 yesnabe 701 effort 12 A B
## 26 103 yesnigiri 876 effort 12 A B
## 27 31 yesniku 925 effort 12 A B
## 28 69 yessakana 1061 effort 12 A B
## 29 105 yessake 911 effort 12 A B
## 30 106 yessomen 742 effort 12 A B
## 31 33 yessushi 1532 effort 12 A B
## 32 107 yestaiyaki 714 effort 12 A B
## 33 34 yestamago 665 effort 12 A B
## 34 35 yestofu 842 effort 12 A B
## class PreTest_Total_Score PostTest_Total_Score Post_Pre Intrinsic_Value
## 1 1 14 14 0 15
## 2 2 3 3 0 6
## 3 2 14 14 0 15
## 4 3 11 3 -8 5
## 5 2 9 4 -5 15
## 6 3 8 6 -2 10
## 7 3 10 12 2 13
## 8 1 13 12 -1 10
## 9 3 12 10 -2 15
## 10 2 11 12 1 13
## 11 2 2 1 -1 11
## 12 2 14 14 0 14
## 13 1 4 6 2 6
## 14 3 5 10 5 8
## 15 2 13 13 0 12
## 16 1 3 9 6 10
## 17 1 10 8 -2 13
## 18 2 5 8 3 8
## 19 2 12 12 0 13
## 20 1 13 14 1 11
## 21 1 9 11 2 8
## 22 3 9 6 -3 9
## 23 3 13 13 0 11
## 24 1 12 14 2 14
## 25 3 7 2 -5 12
## 26 3 13 13 0 13
## 27 1 12 11 -1 8
## 28 2 8 11 3 5
## 29 3 12 14 2 15
## 30 3 1 0 -1 6
## 31 1 11 11 0 15
## 32 3 14 13 -1 12
## 33 1 4 3 -1 6
## 34 1 3 10 7 9
## Self.regulation Self.efficacy Test_anxiety Survey_Total_Score
## 1 19 20 14 68
## 2 15 6 4 31
## 3 20 20 6 61
## 4 10 6 6 27
## 5 18 14 12 59
## 6 11 9 14 44
## 7 16 15 12 56
## 8 16 13 8 47
## 9 15 13 7 50
## 10 19 17 5 54
## 11 16 16 4 47
## 12 18 20 17 69
## 13 11 12 7 36
## 14 14 7 7 36
## 15 11 16 10 49
## 16 15 12 9 46
## 17 16 17 15 61
## 18 11 12 8 39
## 19 13 20 20 66
## 20 16 11 4 42
## 21 11 9 15 43
## 22 8 7 12 36
## 23 11 16 14 52
## 24 20 19 16 69
## 25 18 17 15 62
## 26 16 16 15 60
## 27 13 12 8 41
## 28 15 11 7 38
## 29 14 18 18 65
## 30 9 9 9 33
## 31 16 17 13 61
## 32 16 18 17 63
## 33 12 11 7 36
## 34 13 14 14 50read.csv("Latency_performance.csv")## ID username Sum_of_Latency feedback ITS pre_test_type post_test_type
## 1 10 nicecola 553 performance 11 A B
## 2 46 nicegyoza 1232 performance 12 A B
## 3 82 niceice 735 performance 12 A B
## 4 83 niceimo 1190 performance 12 A B
## 5 48 nicekaki 817 performance 12 A B
## 6 12 nicemiso 892 performance 12 A B
## 7 13 niceniku 941 performance 12 A B
## 8 49 niceonigiri 646 performance 12 A B
## 9 50 niceramen 766 performance 12 A B
## 10 51 nicesakana 1382 performance 12 A B
## 11 15 nicesushi 1618 performance 12 A B
## 12 52 nicetako 1826 performance 12 A B
## 13 16 nicetamago 1434 performance 12 A B
## 14 53 niceunagi 790 performance 12 A B
## 15 54 niceyuba 990 performance 12 A B
## 16 20 okfugu 859 performance 12 B A
## 17 91 okice 2485 performance 12 B A
## 18 56 okika 1355 performance 12 B A
## 19 57 okkaki 931 performance 12 B A
## 20 21 okmiso 1144 performance 12 B A
## 21 58 okonigiri 913 performance 12 B A
## 22 23 oksoba 691 performance 12 B A
## 23 98 oktaiyaki 957 performance 12 B A
## 24 25 oktamago 1428 performance 12 B A
## 25 26 oktofu 1473 performance 12 B A
## 26 27 okudon 1407 performance 12 B A
## 27 62 okunagi 1037 performance 12 B A
## 28 63 okyuba 665 performance 12 B A
## 29 87 nicesake NA performance 12 A B
## 30 93 oknabe NA performance 12 B A
## 31 96 oksake NA performance 12 B A
## class PreTest_Total_Score PostTest_Total_Score Post_Pre Intrinsic_Value
## 1 1 9 11 2 13
## 2 2 5 2 -3 10
## 3 3 3 1 -2 4
## 4 3 2 13 11 3
## 5 2 12 13 1 15
## 6 1 9 9 0 11
## 7 1 1 14 13 14
## 8 2 8 11 3 15
## 9 2 11 13 2 7
## 10 2 14 14 0 11
## 11 1 8 8 0 11
## 12 2 2 3 1 8
## 13 1 3 3 0 9
## 14 2 13 12 -1 13
## 15 2 3 12 9 7
## 16 1 13 13 0 13
## 17 3 8 12 4 12
## 18 2 11 13 2 14
## 19 2 13 14 1 13
## 20 1 11 14 3 12
## 21 2 11 10 -1 9
## 22 1 5 9 4 14
## 23 3 14 14 0 8
## 24 1 3 4 1 7
## 25 1 11 14 3 7
## 26 1 10 12 2 12
## 27 2 12 12 0 7
## 28 2 12 6 -6 11
## 29 3 12 12 0 12
## 30 3 5 8 3 13
## 31 3 11 13 2 11
## Self.regulation Self.efficacy Test_anxiety Survey_Total_Score
## 1 18 16 15 62
## 2 16 11 4 41
## 3 17 11 4 36
## 4 11 4 5 23
## 5 16 18 15 64
## 6 15 15 16 57
## 7 17 17 15 63
## 8 17 16 17 65
## 9 14 8 7 36
## 10 16 17 13 57
## 11 15 15 4 45
## 12 7 5 5 25
## 13 13 10 8 40
## 14 14 18 16 61
## 15 13 12 13 45
## 16 18 19 17 67
## 17 14 17 14 57
## 18 18 20 14 66
## 19 19 18 10 60
## 20 15 12 12 51
## 21 14 12 9 44
## 22 13 11 5 43
## 23 7 5 8 28
## 24 14 13 9 43
## 25 14 11 13 45
## 26 14 11 12 49
## 27 14 12 7 40
## 28 13 12 17 53
## 29 19 15 8 54
## 30 17 15 16 61
## 31 11 13 11 46read.csv("Latency_value.csv") ## ID Sum_of_Latency feedback ITS class PreTest_Total_Score
## 1 1 492 1 12 1 14
## 2 37 1367 1 12 2 3
## 3 38 936 1 12 2 14
## 4 74 1762 1 12 3 11
## 5 39 767 1 12 2 9
## 6 75 863 1 12 3 8
## 7 76 653 1 12 3 10
## 8 4 1158 1 12 1 13
## 9 77 2456 1 12 3 12
## 10 40 1325 1 12 2 11
## 11 41 607 1 12 2 2
## 12 42 729 1 12 2 14
## 13 5 1619 1 12 1 4
## 14 79 2242 1 12 3 5
## 15 43 1622 1 12 2 13
## 16 7 1026 1 12 1 3
## 17 8 1250 1 10 1 10
## 18 44 2932 1 12 2 5
## 19 45 923 1 12 2 12
## 20 10 553 2 11 1 9
## 21 46 1232 2 12 2 5
## 22 82 735 2 12 3 3
## 23 83 1190 2 12 3 2
## 24 48 817 2 12 2 12
## 25 12 892 2 12 1 9
## 26 13 941 2 12 1 1
## 27 49 646 2 12 2 8
## 28 50 766 2 12 2 11
## 29 51 1382 2 12 2 14
## 30 15 1618 2 12 1 8
## 31 52 1826 2 12 2 2
## 32 16 1434 2 12 1 3
## 33 53 790 2 12 2 13
## 34 54 990 2 12 2 3
## 35 20 859 2 12 1 13
## 36 91 2485 2 12 3 8
## 37 56 1355 2 12 2 11
## 38 57 931 2 12 2 13
## 39 21 1144 2 12 1 11
## 40 58 913 2 12 2 11
## 41 23 691 2 12 1 5
## 42 98 957 2 12 3 14
## 43 25 1428 2 12 1 3
## 44 26 1473 2 12 1 11
## 45 27 1407 2 12 1 10
## 46 62 1037 2 12 2 12
## 47 63 665 2 12 2 12
## 48 28 1413 1 12 1 13
## 49 29 1157 1 12 1 9
## 50 100 713 1 12 3 9
## 51 101 826 1 12 3 13
## 52 30 1322 1 12 1 12
## 53 102 701 1 12 3 7
## 54 103 876 1 12 3 13
## 55 31 925 1 12 1 12
## 56 69 1061 1 12 2 8
## 57 105 911 1 12 3 12
## 58 106 742 1 12 3 1
## 59 33 1532 1 12 1 11
## 60 107 714 1 12 3 14
## 61 34 665 1 12 1 4
## 62 35 842 1 12 1 3
## PostTest_Total_Score Post_Pre Intrinsic_Value Self.regulation Self.efficacy
## 1 14 0 15 19 20
## 2 3 0 6 15 6
## 3 14 0 15 20 20
## 4 3 -8 5 10 6
## 5 4 -5 15 18 14
## 6 6 -2 10 11 9
## 7 12 2 13 16 15
## 8 12 -1 10 16 13
## 9 10 -2 15 15 13
## 10 12 1 13 19 17
## 11 1 -1 11 16 16
## 12 14 0 14 18 20
## 13 6 2 6 11 12
## 14 10 5 8 14 7
## 15 13 0 12 11 16
## 16 9 6 10 15 12
## 17 8 -2 13 16 17
## 18 8 3 8 11 12
## 19 12 0 13 13 20
## 20 11 2 13 18 16
## 21 2 -3 10 16 11
## 22 1 -2 4 17 11
## 23 13 11 3 11 4
## 24 13 1 15 16 18
## 25 9 0 11 15 15
## 26 14 13 14 17 17
## 27 11 3 15 17 16
## 28 13 2 7 14 8
## 29 14 0 11 16 17
## 30 8 0 11 15 15
## 31 3 1 8 7 5
## 32 3 0 9 13 10
## 33 12 -1 13 14 18
## 34 12 9 7 13 12
## 35 13 0 13 18 19
## 36 12 4 12 14 17
## 37 13 2 14 18 20
## 38 14 1 13 19 18
## 39 14 3 12 15 12
## 40 10 -1 9 14 12
## 41 9 4 14 13 11
## 42 14 0 8 7 5
## 43 4 1 7 14 13
## 44 14 3 7 14 11
## 45 12 2 12 14 11
## 46 12 0 7 14 12
## 47 6 -6 11 13 12
## 48 14 1 11 16 11
## 49 11 2 8 11 9
## 50 6 -3 9 8 7
## 51 13 0 11 11 16
## 52 14 2 14 20 19
## 53 2 -5 12 18 17
## 54 13 0 13 16 16
## 55 11 -1 8 13 12
## 56 11 3 5 15 11
## 57 14 2 15 14 18
## 58 0 -1 6 9 9
## 59 11 0 15 16 17
## 60 13 -1 12 16 18
## 61 3 -1 6 12 11
## 62 10 7 9 13 14
## Test_anxiety Survey_Total_Score
## 1 14 68
## 2 4 31
## 3 6 61
## 4 6 27
## 5 12 59
## 6 14 44
## 7 12 56
## 8 8 47
## 9 7 50
## 10 5 54
## 11 4 47
## 12 17 69
## 13 7 36
## 14 7 36
## 15 10 49
## 16 9 46
## 17 15 61
## 18 8 39
## 19 20 66
## 20 15 62
## 21 4 41
## 22 4 36
## 23 5 23
## 24 15 64
## 25 16 57
## 26 15 63
## 27 17 65
## 28 7 36
## 29 13 57
## 30 4 45
## 31 5 25
## 32 8 40
## 33 16 61
## 34 13 45
## 35 17 67
## 36 14 57
## 37 14 66
## 38 10 60
## 39 12 51
## 40 9 44
## 41 5 43
## 42 8 28
## 43 9 43
## 44 13 45
## 45 12 49
## 46 7 40
## 47 17 53
## 48 4 42
## 49 15 43
## 50 12 36
## 51 14 52
## 52 16 69
## 53 15 62
## 54 15 60
## 55 8 41
## 56 7 38
## 57 18 65
## 58 9 33
## 59 13 61
## 60 17 63
## 61 7 36
## 62 14 50setwd("/Users/satoushintarou/Desktop/Master thesis/大宮北小/Data") #Directory
#Select Pre-test score 0-13/14 (exclude:score 14 students)
data<-subset(read.csv("R.csv"),PreTest_Total_Score<=13)
datae<-subset(read.csv("effort.csv"),PreTest_Total_Score<=13)
datap<-subset(read.csv("performance.csv"),PreTest_Total_Score<=13)
datav<-subset(read.csv("R_value.csv"),PreTest_Total_Score<=13)
#Import the data with Latency（学習時間）
datalatency<-subset(read.csv("Latency.csv"),PreTest_Total_Score<=13)
datalatencye<-subset(read.csv("Latency_effort.csv"),PreTest_Total_Score<=13)
datalatencyp<-subset(read.csv("Latency_performance.csv"),PreTest_Total_Score<=13)
datalatencyv<-subset(read.csv("Latency_value.csv"),PreTest_Total_Score<=13)#Demographic
#All Students (N=59)
table(data$feedback)##
## effort performance
## 30 29#Pre-test
mean(data$PreTest_Total_Score)## [1] 8.389831sd(data$PreTest_Total_Score)## [1] 3.921721median(data$PreTest_Total_Score)## [1] 9hist(data$PreTest_Total_Score,main="PreTest_Total_Score/Students who Marked less than 13 in the Pre-test（N=59)",ylim=c(0,15))
#Post-test
mean(data$PostTest_Total_Score)## [1] 9.372881sd(data$PostTest_Total_Score)## [1] 4.164291median(data$PostTest_Total_Score)## [1] 11hist(data$PostTest_Total_Score,main="PostTest_Total_Score/Students who Marked less than 13 in the Pre-test（N=59）",ylim=c(0,15))
#Pre-test
mean(datae$PreTest_Total_Score)## [1] 8.6sd(datae$PreTest_Total_Score)## [1] 3.909317median(datae$PreTest_Total_Score)## [1] 9.5hist(datae$PreTest_Total_Score,main="PreTest_Total_Score/Effort group (N=30)",ylim=c(0,15))
#Post-test/effort group
mean(datae$PostTest_Total_Score)## [1] 8.733333sd(datae$PostTest_Total_Score)## [1] 4.282549median(datae$PostTest_Total_Score)## [1] 10hist(datae$PostTest_Total_Score,main="PostTest_Total_Score/Effort group (N=30)",ylim=c(0,15))
#Pre-test/performance group
mean(datap$PreTest_Total_Score)## [1] 8.172414sd(datap$PreTest_Total_Score)## [1] 3.991679median(datap$PreTest_Total_Score)## [1] 9hist(datap$PreTest_Total_Score,main="PreTest_Total_Score/Performance group (N=29)",ylim=c(0,15))
#Post-test/performance group
mean(datap$PostTest_Total_Score)## [1] 10.03448sd(datap$PostTest_Total_Score)## [1] 4.004308median(datap$PostTest_Total_Score)## [1] 12hist(datap$PostTest_Total_Score,main="PostTest_Total_Score/Performance group (N=29)",ylim=c(0,15))
#Latency（学習時間）
#Comment: Not significant difference between the effort and performance group.
#T-test
#Sum of Latency (seconds) of total 11 math problems on ITS
#effort vs performance group
var.test(datalatencye$Sum_of_Latency,datalatencyp$Sum_of_Latency)#等分散##
## F test to compare two variances
##
## data: datalatencye$Sum_of_Latency and datalatencyp$Sum_of_Latency
## F = 1.6608, num df = 29, denom df = 25, p-value = 0.2013
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.7583181 3.5572862
## sample estimates:
## ratio of variances
## 1.660817t.test(Sum_of_Latency ~ feedback, var.equal=T,data = datalatency)##
## Two Sample t-test
##
## data: Sum_of_Latency by feedback
## t = 0.73748, df = 54, p-value = 0.464
## alternative hypothesis: true difference in means between group effort and group performance is not equal to 0
## 95 percent confidence interval:
## -172.2252 372.6560
## sample estimates:
## mean in group effort mean in group performance
## 1208.600 1108.385#Analysis 1 #Compare Pre- vs. Post-Test #Students who Marked less than 13 in the Pre-test（N=59）
#Comment:Overall, the test score increased significantly before and after the ITS learning activity.
#T-test
#Pre-test and Post-test
#p-value = 0.04363
t.test(data$PreTest_Total_Score, data$PostTest_Total_Score, paired=TRUE)##
## Paired t-test
##
## data: data$PreTest_Total_Score and data$PostTest_Total_Score
## t = -2.1041, df = 58, p-value = 0.03972
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -1.91828025 -0.04782144
## sample estimates:
## mean difference
## -0.9830508#Analysis 2 #Compare Pre-Test / Effort vs. Performance Group #Compare Post-Test / Effort vs. Performance Group #Students who Marked less than 13 in the Pre-test（N=59）
#Comment:No significant difference in the pre- and post-test between the effort and performance group.
#T-test
#Pre-test between the effort and performance group
var.test(datae$PreTest_Total_Score,datap$PreTest_Total_Score)#等分散##
## F test to compare two variances
##
## data: datae$PreTest_Total_Score and datap$PreTest_Total_Score
## F = 0.95916, num df = 29, denom df = 28, p-value = 0.9104
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.4522781 2.0240482
## sample estimates:
## ratio of variances
## 0.9591591t.test(PreTest_Total_Score ~ feedback, var.equal=T,data = data)##
## Two Sample t-test
##
## data: PreTest_Total_Score by feedback
## t = 0.41568, df = 57, p-value = 0.6792
## alternative hypothesis: true difference in means between group effort and group performance is not equal to 0
## 95 percent confidence interval:
## -1.632225 2.487397
## sample estimates:
## mean in group effort mean in group performance
## 8.600000 8.172414#T-test
#Post-test between the effort and performance group
var.test(datae$PostTest_Total_Score,datap$PostTest_Total_Score)#等分散##
## F test to compare two variances
##
## data: datae$PostTest_Total_Score and datap$PostTest_Total_Score
## F = 1.1438, num df = 29, denom df = 28, p-value = 0.7241
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.5393426 2.4136819
## sample estimates:
## ratio of variances
## 1.143799t.test(PostTest_Total_Score ~ feedback, var.equal=T,data = data)##
## Two Sample t-test
##
## data: PostTest_Total_Score by feedback
## t = -1.2045, df = 57, p-value = 0.2334
## alternative hypothesis: true difference in means between group effort and group performance is not equal to 0
## 95 percent confidence interval:
## -3.464323 0.862024
## sample estimates:
## mean in group effort mean in group performance
## 8.733333 10.034483#Analysis 3 #Compare Test Score Increase (増加量) / Effort vs. Performance Group #Students who Marked less than 13 in the Pre-test（N=59）
#Comment:There is a trend (p=0.06) that greater test score increase [(Post_Test_Total_Score) - (Pre_Test_Total_Score)] among kids who received performance feedback compared to those who received effort-based feedback.
#T-test
#Score Increase [Post_pre = (Post_Test_Total_Score)ー(Pre_Test_Total_Score)] between the effort and performance group
var.test(datae$Post_Pre,datap$Post_Pre)#等分散##
## F test to compare two variances
##
## data: datae$Post_Pre and datap$Post_Pre
## F = 0.67599, num df = 29, denom df = 28, p-value = 0.3002
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3187516 1.4264864
## sample estimates:
## ratio of variances
## 0.6759855t.test(Post_Pre ~ feedback,var.equal=T,data = data)##
## Two Sample t-test
##
## data: Post_Pre by feedback
## t = -1.8904, df = 57, p-value = 0.0638
## alternative hypothesis: true difference in means between group effort and group performance is not equal to 0
## 95 percent confidence interval:
## -3.5599761 0.1025048
## sample estimates:
## mean in group effort mean in group performance
## 0.1333333 1.8620690#Analysis 4-1 #Regression Model/Predict “Post-test” #Students who Marked less than 13 in the Pre-test（N=59）
#Comment:"Feedback" and "Pre-Test" has a trend to predict "Post-Test".
#y="Post-test", x="Feedback"
model1 <- lm(PostTest_Total_Score ~ feedback, data = data)
summary(model1)##
## Call:
## lm(formula = PostTest_Total_Score ~ feedback, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.034 -2.733 1.966 2.966 5.267
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.7333 0.7574 11.531 <2e-16 ***
## feedbackperformance 1.3011 1.0803 1.204 0.233
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.148 on 57 degrees of freedom
## Multiple R-squared: 0.02482, Adjusted R-squared: 0.007712
## F-statistic: 1.451 on 1 and 57 DF, p-value: 0.2334#y="Post-test", x="Pre-test","Feedback"
model2 <- 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
## -7.5897 -1.8788 0.6369 1.9103 7.8372
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.0815 1.4601 1.426 0.1596
## feedbackperformance 3.5414 2.0232 1.750 0.0856
## PreTest_Total_Score 0.7735 0.1550 4.990 6.42e-06
## feedbackperformance:PreTest_Total_Score -0.2337 0.2189 -1.068 0.2903
##
## (Intercept)
## feedbackperformance .
## PreTest_Total_Score ***
## feedbackperformance:PreTest_Total_Score
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.263 on 55 degrees of freedom
## Multiple R-squared: 0.4177, Adjusted R-squared: 0.3859
## F-statistic: 13.15 on 3 and 55 DF, p-value: 1.382e-06library(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:"Latency".
#y="Score Increase",x="Condition","Latency","Pre-test"
modelnew<- lm(Post_Pre ~ feedback + Sum_of_Latency + PreTest_Total_Score, data = datalatency)
summary(modelnew)##
## Call:
## lm(formula = Post_Pre ~ feedback + Sum_of_Latency + PreTest_Total_Score,
## data = datalatency)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.7241 -2.1798 0.5033 1.6932 8.8496
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.1667389 1.6382292 1.323 0.19175
## feedbackperformance 1.6341039 0.9089154 1.798 0.07801 .
## Sum_of_Latency 0.0007321 0.0009046 0.809 0.42202
## PreTest_Total_Score -0.3393276 0.1152132 -2.945 0.00482 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.365 on 52 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.2051, Adjusted R-squared: 0.1593
## F-statistic: 4.474 on 3 and 52 DF, p-value: 0.007212modelnew2<- lm(Post_Pre ~ feedback*Sum_of_Latency*PreTest_Total_Score, data = datalatency)
summary(modelnew2)##
## Call:
## lm(formula = Post_Pre ~ feedback * Sum_of_Latency * PreTest_Total_Score,
## data = datalatency)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.3102 -1.4664 0.2997 1.8757 6.5209
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -2.5088935 3.1745843
## feedbackperformance 17.1805755 5.9879508
## Sum_of_Latency 0.0040480 0.0024857
## PreTest_Total_Score 0.2626103 0.3897198
## feedbackperformance:Sum_of_Latency -0.0120196 0.0048988
## feedbackperformance:PreTest_Total_Score -1.8736626 0.7047428
## Sum_of_Latency:PreTest_Total_Score -0.0004286 0.0003081
## feedbackperformance:Sum_of_Latency:PreTest_Total_Score 0.0014856 0.0006072
## t value Pr(>|t|)
## (Intercept) -0.790 0.4332
## feedbackperformance 2.869 0.0061 **
## Sum_of_Latency 1.628 0.1100
## PreTest_Total_Score 0.674 0.5036
## feedbackperformance:Sum_of_Latency -2.454 0.0178 *
## feedbackperformance:PreTest_Total_Score -2.659 0.0106 *
## Sum_of_Latency:PreTest_Total_Score -1.391 0.1707
## feedbackperformance:Sum_of_Latency:PreTest_Total_Score 2.447 0.0181 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.264 on 48 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.3098, Adjusted R-squared: 0.2091
## F-statistic: 3.077 on 7 and 48 DF, p-value: 0.009254library(ggplot2)
ggplot(datalatency, aes(x = Sum_of_Latency, y = Post_Pre, col = feedback)) + geom_point() +geom_smooth(method = "lm",se=FALSE)## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 3 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 3 rows containing missing values (`geom_point()`).
#y="Post-test",x="Latency"
model3 <- lm(PostTest_Total_Score ~ Sum_of_Latency, data = datalatency)
summary(model3)##
## Call:
## lm(formula = PostTest_Total_Score ~ Sum_of_Latency, data = datalatency)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.160 -3.162 1.730 3.596 4.790
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.9372399 1.4406924 6.203 8.03e-08 ***
## Sum_of_Latency 0.0002999 0.0011386 0.263 0.793
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.265 on 54 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.001283, Adjusted R-squared: -0.01721
## F-statistic: 0.06936 on 1 and 54 DF, p-value: 0.7933#y="Post-test",x="Feedback", "Latency"
model4 <- lm(PostTest_Total_Score ~ feedback*Sum_of_Latency, data = datalatency)
summary(model4)##
## Call:
## lm(formula = PostTest_Total_Score ~ feedback * Sum_of_Latency,
## data = datalatency)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.363 -2.981 1.714 3.339 5.639
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.219150 1.871071 3.858 0.000317 ***
## feedbackperformance 4.008821 2.981867 1.344 0.184655
## Sum_of_Latency 0.001253 0.001408 0.890 0.377732
## feedbackperformance:Sum_of_Latency -0.002430 0.002409 -1.009 0.317754
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.258 on 52 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.04133, Adjusted R-squared: -0.01398
## F-statistic: 0.7473 on 3 and 52 DF, p-value: 0.5289library(ggplot2)
ggplot(datalatency, aes(x = Sum_of_Latency, y = PostTest_Total_Score, col = feedback)) + geom_point() +geom_smooth(method = "lm",se=FALSE)## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 3 rows containing non-finite values (`stat_smooth()`).
## Removed 3 rows containing missing values (`geom_point()`).
#y="Post-test",x="Pre-Test", "Latency"
model5 <- lm(PostTest_Total_Score ~ Sum_of_Latency*PreTest_Total_Score, data = datalatency)
summary(model5)##
## Call:
## lm(formula = PostTest_Total_Score ~ Sum_of_Latency * PreTest_Total_Score,
## data = datalatency)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.0758 -2.4982 0.5566 1.9913 9.7117
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.3006323 2.8030323 0.821 0.4155
## Sum_of_Latency 0.0014052 0.0022502 0.624 0.5350
## PreTest_Total_Score 0.7716664 0.3310250 2.331 0.0237 *
## Sum_of_Latency:PreTest_Total_Score -0.0001129 0.0002750 -0.411 0.6830
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.463 on 52 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.3661, Adjusted R-squared: 0.3295
## F-statistic: 10.01 on 3 and 52 DF, p-value: 2.598e-05#Analysis 4-2 #Regression Model/Predict “Score Increase” #Students who Marked less than 13 in the Pre-test（N=59）
#Comment: "Feedback" has a trend to predict "Score Increase（増加量）".
#y="Score Increase(増加量)", x="Feedback"
model6 <- lm(Post_Pre ~ feedback, data = data)
summary(model6)##
## Call:
## lm(formula = Post_Pre ~ feedback, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.1333 -1.8621 -0.1333 1.5023 11.1379
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1333 0.6411 0.208 0.8360
## feedbackperformance 1.7287 0.9145 1.890 0.0638 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.512 on 57 degrees of freedom
## Multiple R-squared: 0.05899, Adjusted R-squared: 0.04249
## F-statistic: 3.574 on 1 and 57 DF, p-value: 0.0638#y="Score Increase(増加量)", x="Feedback", "Pre-test"
model7 <- lm(Post_Pre ~ feedback*PreTest_Total_Score, data = data)
summary(model7)##
## Call:
## lm(formula = Post_Pre ~ feedback * PreTest_Total_Score, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.5897 -1.8788 0.6369 1.9103 7.8372
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.0815 1.4601 1.426 0.1596
## feedbackperformance 3.5414 2.0232 1.750 0.0856 .
## PreTest_Total_Score -0.2265 0.1550 -1.461 0.1496
## feedbackperformance:PreTest_Total_Score -0.2337 0.2189 -1.068 0.2903
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.263 on 55 degrees of freedom
## Multiple R-squared: 0.2159, Adjusted R-squared: 0.1732
## F-statistic: 5.049 on 3 and 55 DF, p-value: 0.003676library(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'
#Comment:"Latency" does not predict "Score Increase (増加量)".
#y="Score Increase (増加量)", x= "Latency"
model8 <- lm(Post_Pre ~ Sum_of_Latency, data = datalatency)
summary(model8)##
## Call:
## lm(formula = Post_Pre ~ Sum_of_Latency, data = datalatency)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.3712 -1.6575 -0.5625 1.3540 12.2101
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1236950 1.2452875 0.099 0.921
## Sum_of_Latency 0.0007080 0.0009842 0.719 0.475
##
## Residual standard error: 3.686 on 54 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.009492, Adjusted R-squared: -0.008851
## F-statistic: 0.5175 on 1 and 54 DF, p-value: 0.475#y="Score Increase (増加量)", x="Feedback", "Latency"
model9 <- lm(Post_Pre ~ feedback*Sum_of_Latency, data = datalatency)
summary(model9)##
## Call:
## lm(formula = Post_Pre ~ feedback * Sum_of_Latency, data = datalatency)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.5968 -1.8212 -0.3351 1.4483 11.2819
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.8787748 1.5972286 -0.550 0.585
## feedbackperformance 1.6610013 2.5454523 0.653 0.517
## Sum_of_Latency 0.0008374 0.0012021 0.697 0.489
## feedbackperformance:Sum_of_Latency 0.0001572 0.0020564 0.076 0.939
##
## Residual standard error: 3.635 on 52 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.07265, Adjusted R-squared: 0.01915
## F-statistic: 1.358 on 3 and 52 DF, p-value: 0.2659library(ggplot2)
ggplot(datalatency, aes(x = Sum_of_Latency, y = Post_Pre, col = feedback)) + geom_point() +geom_smooth(method = "lm",se=FALSE)## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 3 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 3 rows containing missing values (`geom_point()`).
#"Score Increase (増加量)",x="Pre-Test", "Latency"
model10 <- lm(Post_Pre ~ Sum_of_Latency*PreTest_Total_Score, data = datalatency)
summary(model10)##
## Call:
## lm(formula = Post_Pre ~ Sum_of_Latency * PreTest_Total_Score,
## data = datalatency)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.0758 -2.4982 0.5566 1.9913 9.7117
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.3006323 2.8030323 0.821 0.416
## Sum_of_Latency 0.0014052 0.0022502 0.624 0.535
## PreTest_Total_Score -0.2283336 0.3310250 -0.690 0.493
## Sum_of_Latency:PreTest_Total_Score -0.0001129 0.0002750 -0.411 0.683
##
## Residual standard error: 3.463 on 52 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.1585, Adjusted R-squared: 0.1099
## F-statistic: 3.264 on 3 and 52 DF, p-value: 0.02856#Analysis 4-3 #Regression Model/Predict Latency（学習時間） #Students who Marked less than 13 in the Pre-test（N=59）
#Comment:"Pre-test" and "Feedback" does not predict "Latency".
#y=Latency, x="Pre-test"
model11 <- lm(Sum_of_Latency ~ PreTest_Total_Score, data = datalatency)
summary(model11)##
## Call:
## lm(formula = Sum_of_Latency ~ PreTest_Total_Score, data = datalatency)
##
## Residuals:
## Min 1Q Median 3Q Max
## -604.7 -344.7 -185.8 231.9 1747.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1217.554 159.888 7.615 4.14e-10 ***
## PreTest_Total_Score -6.653 17.351 -0.383 0.703
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 509 on 54 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.002715, Adjusted R-squared: -0.01575
## F-statistic: 0.147 on 1 and 54 DF, p-value: 0.7029#y=Latency, x="Feedback"
model12<- lm(Sum_of_Latency ~ feedback, data = datalatency)
summary(model12)##
## Call:
## lm(formula = Sum_of_Latency ~ feedback, data = datalatency)
##
## Residuals:
## Min 1Q Median 3Q Max
## -601.6 -350.9 -157.5 259.6 1723.4
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1208.60 92.59 13.053 <2e-16 ***
## feedbackperformance -100.22 135.89 -0.737 0.464
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 507.2 on 54 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.009971, Adjusted R-squared: -0.008362
## F-statistic: 0.5439 on 1 and 54 DF, p-value: 0.464#y=Latency, x="Pre-test", "Feedback type"
model13 <- lm(Sum_of_Latency ~ PreTest_Total_Score*feedback, data = datalatency)
summary(model13)##
## Call:
## lm(formula = Sum_of_Latency ~ PreTest_Total_Score * feedback,
## data = datalatency)
##
## Residuals:
## Min 1Q Median 3Q Max
## -567.6 -343.4 -123.5 201.5 1754.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1134.90 228.82 4.960 7.93e-06
## PreTest_Total_Score 8.57 24.29 0.353 0.726
## feedbackperformance 174.12 321.46 0.542 0.590
## PreTest_Total_Score:feedbackperformance -33.53 34.98 -0.959 0.342
##
## (Intercept) ***
## PreTest_Total_Score
## feedbackperformance
## PreTest_Total_Score:feedbackperformance
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 511.4 on 52 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.03063, Adjusted R-squared: -0.02529
## F-statistic: 0.5477 on 3 and 52 DF, p-value: 0.6519library(ggplot2)
ggplot(datalatency, aes(x = PreTest_Total_Score, y = Sum_of_Latency, col = feedback)) + geom_point() +geom_smooth(method = "lm",se=FALSE)## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 3 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 3 rows containing missing values (`geom_point()`).
#Analysis 4-3 #Regression Model/Predict Math Motivation score (算数学習への意欲) #Students who Marked less than 13 in the Pre-test（N=59）
#Comment:Latency (学習時間) predict Math Motivation score (算数学習への意欲).
#y="Math Motivation score", x="Latency"
model14 <- lm(Survey_Total_Score ~ Sum_of_Latency, data = datalatency)
summary(model14)##
## Call:
## lm(formula = Survey_Total_Score ~ Sum_of_Latency, data = datalatency)
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.3557 -7.3972 -0.6233 9.9936 21.4110
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 55.266998 3.902555 14.162 <2e-16 ***
## Sum_of_Latency -0.005808 0.003084 -1.883 0.0651 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.55 on 54 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.06162, Adjusted R-squared: 0.04424
## F-statistic: 3.546 on 1 and 54 DF, p-value: 0.06509#y="Math Motivation score", x="Feedback type", "Latency"
model15 <- lm(Survey_Total_Score ~ feedback*Sum_of_Latency, data = datalatency)
summary(model15)##
## Call:
## lm(formula = Survey_Total_Score ~ feedback * Sum_of_Latency,
## data = datalatency)
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.6742 -7.5911 -0.4767 9.4167 21.6905
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 53.804749 5.163402 10.420 2.46e-14 ***
## feedbackperformance 3.544751 8.228750 0.431 0.668
## Sum_of_Latency -0.004913 0.003886 -1.264 0.212
## feedbackperformance:Sum_of_Latency -0.002377 0.006648 -0.358 0.722
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.75 on 52 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.06515, Adjusted R-squared: 0.01121
## F-statistic: 1.208 on 3 and 52 DF, p-value: 0.3161library(ggplot2)
ggplot(datalatency, aes(x = Sum_of_Latency, y = Survey_Total_Score, col = feedback)) + geom_point() +geom_smooth(method = "lm",se=FALSE)## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 3 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 3 rows containing missing values (`geom_point()`).
#Since there is a significant correlation between "Test_anxiety"and "Latency" in Analysis 6-3, this is the extra analysis of it. "Test_anxiety"predicts "Latency".
#y="Latency", x="Test_anxiety"
model16 <- lm(Sum_of_Latency ~ Test_anxiety, data = datalatency)
summary(model16)##
## Call:
## lm(formula = Sum_of_Latency ~ Test_anxiety, data = datalatency)
##
## Residuals:
## Min 1Q Median 3Q Max
## -775.84 -292.22 -94.75 231.87 1681.38
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1515.07 169.16 8.957 2.91e-12 ***
## Test_anxiety -33.06 14.62 -2.261 0.0278 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 487.2 on 54 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.08648, Adjusted R-squared: 0.06956
## F-statistic: 5.112 on 1 and 54 DF, p-value: 0.02782#Comment:Post-test predict the Math Motivation score (算数学習への意欲).
#y="Math Motivation score", x="Post-test"
model17 <- lm(Survey_Total_Score ~ PostTest_Total_Score, data = data)
summary(model17)##
## Call:
## lm(formula = Survey_Total_Score ~ PostTest_Total_Score, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.4023 -7.6278 -0.6577 8.7839 22.6168
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 36.8343 3.3775 10.906 1.42e-15 ***
## PostTest_Total_Score 1.2745 0.3298 3.865 0.000287 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.46 on 57 degrees of freedom
## Multiple R-squared: 0.2076, Adjusted R-squared: 0.1937
## F-statistic: 14.94 on 1 and 57 DF, p-value: 0.0002872#y="Math Motivation score", x="Feedback", "PostTest_Total_Score"
model18 <- lm(Survey_Total_Score ~ feedback*PostTest_Total_Score, data = data)
summary(model18)##
## Call:
## lm(formula = Survey_Total_Score ~ feedback * PostTest_Total_Score,
## data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.5702 -7.6332 -0.6009 8.6721 22.5386
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 36.96478 4.47548 8.259 3.3e-11
## feedbackperformance -0.25464 7.02534 -0.036 0.97122
## PostTest_Total_Score 1.24831 0.46161 2.704 0.00909
## feedbackperformance:PostTest_Total_Score 0.04862 0.68229 0.071 0.94345
##
## (Intercept) ***
## feedbackperformance
## PostTest_Total_Score **
## feedbackperformance:PostTest_Total_Score
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.65 on 55 degrees of freedom
## Multiple R-squared: 0.2078, Adjusted R-squared: 0.1646
## F-statistic: 4.808 on 3 and 55 DF, p-value: 0.004805library(ggplot2)
ggplot(datalatency, aes(x = PostTest_Total_Score, y = Survey_Total_Score, col = feedback)) + geom_point() +geom_smooth(method = "lm",se=FALSE)## `geom_smooth()` using formula = 'y ~ x'
#Comment:Score Increse does not predict Math Motivation score (算数学習への意欲).
#y="Math Motivation score", x="Score Increse"
model19 <- lm(Survey_Total_Score ~ Post_Pre, data = data)
summary(model19)##
## Call:
## lm(formula = Survey_Total_Score ~ Post_Pre, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.233 -8.213 -1.690 11.242 20.174
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 48.73521 1.58673 30.714 <2e-16 ***
## Post_Pre 0.04522 0.42983 0.105 0.917
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.75 on 57 degrees of freedom
## Multiple R-squared: 0.0001941, Adjusted R-squared: -0.01735
## F-statistic: 0.01107 on 1 and 57 DF, p-value: 0.9166#y="Math Motivation score", x="Feedback", "Score Increse"
model20 <- lm(Survey_Total_Score ~ feedback*Post_Pre, data = data)
summary(model20)##
## Call:
## lm(formula = Survey_Total_Score ~ feedback * Post_Pre, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.6168 -9.2709 -0.7236 11.1714 20.8977
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 47.8498 2.1771 21.978 <2e-16 ***
## feedbackperformance 2.1000 3.2901 0.638 0.526
## Post_Pre 0.1262 0.7002 0.180 0.858
## feedbackperformance:Post_Pre -0.2474 0.9130 -0.271 0.787
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.91 on 55 degrees of freedom
## Multiple R-squared: 0.007824, Adjusted R-squared: -0.04629
## F-statistic: 0.1446 on 3 and 55 DF, p-value: 0.9327library(ggplot2)
ggplot(datalatency, aes(x = Post_Pre, y = Survey_Total_Score, 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 #Students who Marked less than 13 in the Pre-test（N=59）
#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
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$Survey_Total_Score
## F = 0.92339, num df = 29, denom df = 28, p-value = 0.8313
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.4354137 1.9485759
## sample estimates:
## ratio of variances
## 0.9233941t.test(Survey_Total_Score ~ feedback,var.equal=T,data = data)##
## Two Sample t-test
##
## data: Survey_Total_Score by feedback
## t = -0.60907, df = 57, p-value = 0.5449
## alternative hypothesis: true difference in means between group effort and group performance is not equal to 0
## 95 percent confidence interval:
## -7.964378 4.249435
## sample estimates:
## mean in group effort mean in group performance
## 47.86667 49.72414#T-test
#Intrinsic value between the effort and performance group
var.test(datae$Intrinsic_Value,datap$Intrinsic_Value)#等分散##
## F test to compare two variances
##
## data: datae$Intrinsic_Value and datap$Intrinsic_Value
## F = 1.0044, num df = 29, denom df = 28, p-value = 0.9925
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.4735988 2.1194632
## sample estimates:
## ratio of variances
## 1.004374t.test(Intrinsic_Value ~ feedback,var.equal=T,data = data)##
## Two Sample t-test
##
## data: Intrinsic_Value by feedback
## t = -0.3019, df = 57, p-value = 0.7638
## alternative hypothesis: true difference in means between group effort and group performance is not equal to 0
## 95 percent confidence interval:
## -1.930171 1.424423
## sample estimates:
## mean in group effort mean in group performance
## 10.33333 10.58621#T-test
#Self efficacy between the effort and performance group
var.test(datae$Self.efficacy,datap$Self.efficacy)#等分散##
## F test to compare two variances
##
## data: datae$Self.efficacy and datap$Self.efficacy
## F = 1.0183, num df = 29, denom df = 28, p-value = 0.9635
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.4801612 2.1488315
## sample estimates:
## ratio of variances
## 1.018292t.test(Self.efficacy ~ feedback,var.equal=T,data = data)##
## Two Sample t-test
##
## data: Self.efficacy by feedback
## t = -0.2724, df = 57, p-value = 0.7863
## alternative hypothesis: true difference in means between group effort and group performance is not equal to 0
## 95 percent confidence interval:
## -2.322956 1.766634
## sample estimates:
## mean in group effort mean in group performance
## 13.06667 13.34483#T-test
#Self regulation between the effort and performance group
var.test(datae$Self.regulation,datap$Self.regulation)#等分散##
## F test to compare two variances
##
## data: datae$Self.regulation and datap$Self.regulation
## F = 1.2945, num df = 29, denom df = 28, p-value = 0.497
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.6103994 2.7316771
## sample estimates:
## ratio of variances
## 1.294491t.test(Self.regulation ~ feedback,var.equal=T,data = data)##
## Two Sample t-test
##
## data: Self.regulation by feedback
## t = -1.1649, df = 57, p-value = 0.2489
## alternative hypothesis: true difference in means between group effort and group performance is not equal to 0
## 95 percent confidence interval:
## -2.3408790 0.6190399
## sample estimates:
## mean in group effort mean in group performance
## 13.96667 14.82759#T-test
#Anxiety between the effort and performance group
var.test(datae$Test_anxiety,datap$Test_anxiety)#等分散##
## F test to compare two variances
##
## data: datae$Test_anxiety and datap$Test_anxiety
## F = 0.95968, num df = 29, denom df = 28, p-value = 0.9115
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.4525225 2.0251421
## sample estimates:
## ratio of variances
## 0.9596774t.test(Test_anxiety ~ feedback,var.equal=T,data = data)##
## Two Sample t-test
##
## data: Test_anxiety by feedback
## t = -0.39929, df = 57, p-value = 0.6912
## alternative hypothesis: true difference in means between group effort and group performance is not equal to 0
## 95 percent confidence interval:
## -2.800134 1.869099
## sample estimates:
## mean in group effort mean in group performance
## 10.50000 10.96552#Analysis 6-1 #Correlation #Math Motivation Score #Students who Marked less than 13 in the Pre-test（N=59）
library(corrplot)## corrplot 0.92 loadedround(cor(datav),2)## ID feedback ITS class PreTest_Total_Score
## ID 1.00 -0.09 0.25 0.94 0.08
## feedback -0.09 1.00 0.06 -0.11 -0.05
## ITS 0.25 0.06 1.00 0.20 -0.06
## class 0.94 -0.11 0.20 1.00 0.04
## PreTest_Total_Score 0.08 -0.05 -0.06 0.04 1.00
## PostTest_Total_Score -0.06 0.16 0.02 -0.13 0.61
## Post_Pre -0.17 0.24 0.08 -0.19 -0.39
## Intrinsic_Value -0.06 0.04 -0.14 -0.06 0.47
## Self.regulation -0.20 0.15 -0.14 -0.16 0.26
## Self.efficacy -0.09 0.04 -0.16 -0.12 0.43
## Test_anxiety 0.08 0.05 -0.17 -0.01 0.35
## Survey_Total_Score -0.06 0.08 -0.19 -0.10 0.47
## PostTest_Total_Score Post_Pre Intrinsic_Value
## ID -0.06 -0.17 -0.06
## feedback 0.16 0.24 0.04
## ITS 0.02 0.08 -0.14
## class -0.13 -0.19 -0.06
## PreTest_Total_Score 0.61 -0.39 0.47
## PostTest_Total_Score 1.00 0.50 0.40
## Post_Pre 0.50 1.00 -0.05
## Intrinsic_Value 0.40 -0.05 1.00
## Self.regulation 0.26 0.02 0.53
## Self.efficacy 0.41 0.00 0.76
## Test_anxiety 0.38 0.06 0.54
## Survey_Total_Score 0.46 0.01 0.87
## Self.regulation Self.efficacy Test_anxiety
## ID -0.20 -0.09 0.08
## feedback 0.15 0.04 0.05
## ITS -0.14 -0.16 -0.17
## class -0.16 -0.12 -0.01
## PreTest_Total_Score 0.26 0.43 0.35
## PostTest_Total_Score 0.26 0.41 0.38
## Post_Pre 0.02 0.00 0.06
## Intrinsic_Value 0.53 0.76 0.54
## Self.regulation 1.00 0.62 0.17
## Self.efficacy 0.62 1.00 0.59
## Test_anxiety 0.17 0.59 1.00
## Survey_Total_Score 0.66 0.92 0.77
## Survey_Total_Score
## ID -0.06
## feedback 0.08
## ITS -0.19
## class -0.10
## PreTest_Total_Score 0.47
## PostTest_Total_Score 0.46
## Post_Pre 0.01
## Intrinsic_Value 0.87
## Self.regulation 0.66
## Self.efficacy 0.92
## Test_anxiety 0.77
## 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##
#Math Motivation Score × Pre-test
cor.test(data$Survey_Total_Score,data$PreTest_Total_Score)##
## Pearson's product-moment correlation
##
## data: data$Survey_Total_Score and data$PreTest_Total_Score
## t = 4.0322, df = 57, p-value = 0.000166
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2445143 0.6488977
## sample estimates:
## cor
## 0.4710983#Math Motivation Score × Post-test
cor.test(data$Survey_Total_Score,data$PostTest_Total_Score)##
## Pearson's product-moment correlation
##
## data: data$Survey_Total_Score and data$PostTest_Total_Score
## t = 3.8647, df = 57, p-value = 0.0002872
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2259477 0.6373733
## sample estimates:
## cor
## 0.4556647#Pre-Test × Post-Test
cor.test(data$PreTest_Total_Score,data$PostTest_Total_Score)##
## Pearson's product-moment correlation
##
## data: data$PreTest_Total_Score and data$PostTest_Total_Score
## t = 5.7741, df = 57, p-value = 3.362e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4161518 0.7473173
## sample estimates:
## cor
## 0.607494#Intrinsic value × Post-test
cor.test(data$Intrinsic_Value,data$PostTest_Total_Score)##
## Pearson's product-moment correlation
##
## data: data$Intrinsic_Value and data$PostTest_Total_Score
## t = 3.2649, df = 57, p-value = 0.001855
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1567772 0.5927554
## sample estimates:
## cor
## 0.3969238#Self efficacy × Post-test
cor.test(data$Self.efficacy,data$PostTest_Total_Score)##
## Pearson's product-moment correlation
##
## data: data$Self.efficacy and data$PostTest_Total_Score
## t = 3.3656, df = 57, p-value = 0.001372
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1686657 0.6006193
## sample estimates:
## cor
## 0.4071618#Self regulation × Post-test
cor.test(data$Self.regulation,data$PostTest_Total_Score)##
## Pearson's product-moment correlation
##
## data: data$Self.regulation and data$PostTest_Total_Score
## t = 2.0653, df = 57, p-value = 0.04345
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.008341907 0.487033914
## sample estimates:
## cor
## 0.2638605#Test anxiety × Post-test
cor.test(data$Test_anxiety,data$PostTest_Total_Score)##
## Pearson's product-moment correlation
##
## data: data$Test_anxiety and data$PostTest_Total_Score
## t = 3.1377, df = 57, p-value = 0.002695
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1416115 0.5826016
## sample estimates:
## cor
## 0.3837765#Analysis 6-2 #Correlation #Score Increase #Students who Marked less than 13 in the Pre-test（N=59）
#Comment:There is not significant correlation between the Math motivation score and the Score increase.
##No significant##
#Math motivation score × Score increase（増加量）
cor.test(data$Survey_Total_Score,data$Post_Pre)##
## Pearson's product-moment correlation
##
## data: data$Survey_Total_Score and data$Post_Pre
## t = 0.10521, df = 57, p-value = 0.9166
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2430158 0.2690559
## sample estimates:
## cor
## 0.01393362#Intrinsic value × Score increase
cor.test(data$Intrinsic_Value,data$Post_Pre)##
## Pearson's product-moment correlation
##
## data: data$Intrinsic_Value and data$Post_Pre
## t = -0.38174, df = 57, p-value = 0.7041
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3026664 0.2082775
## sample estimates:
## cor
## -0.05049815#Self efficacy × Score increase
cor.test(data$Self.efficacy,data$Post_Pre)##
## Pearson's product-moment correlation
##
## data: data$Self.efficacy and data$Post_Pre
## t = -0.0074283, df = 57, p-value = 0.9941
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2570014 0.2551627
## sample estimates:
## cor
## -0.0009839029#Self regulation × Score increase
cor.test(data$Self.regulation,data$Post_Pre)##
## Pearson's product-moment correlation
##
## data: data$Self.regulation and data$Post_Pre
## t = 0.17065, df = 57, p-value = 0.8651
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2348440 0.2770761
## sample estimates:
## cor
## 0.02259723#Test anxiety × Score increase
cor.test(data$Test_anxiety,data$Post_Pre)##
## Pearson's product-moment correlation
##
## data: data$Test_anxiety and data$Post_Pre
## t = 0.44744, df = 57, p-value = 0.6563
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1999509 0.3105382
## sample estimates:
## cor
## 0.0591606#Analysis 6-3 #Correlation #Latency #Students who Marked less than 13 in the Pre-test（N=59）
library(corrplot)
round(cor(datalatencyv),2)## ID Sum_of_Latency feedback ITS class
## ID 1.00 -0.04 -0.18 0.25 0.93
## Sum_of_Latency -0.04 1.00 -0.10 0.05 0.05
## feedback -0.18 -0.10 1.00 0.05 -0.19
## ITS 0.25 0.05 0.05 1.00 0.20
## class 0.93 0.05 -0.19 0.20 1.00
## PreTest_Total_Score 0.07 -0.05 -0.07 -0.06 0.02
## PostTest_Total_Score -0.10 0.04 0.14 0.01 -0.17
## Post_Pre -0.19 0.10 0.24 0.08 -0.21
## Intrinsic_Value -0.11 -0.11 0.01 -0.15 -0.10
## Self.regulation -0.25 -0.20 0.14 -0.15 -0.22
## Self.efficacy -0.12 -0.18 0.02 -0.16 -0.15
## Test_anxiety 0.07 -0.29 0.04 -0.18 -0.02
## Survey_Total_Score -0.10 -0.25 0.06 -0.20 -0.14
## PreTest_Total_Score PostTest_Total_Score Post_Pre
## ID 0.07 -0.10 -0.19
## Sum_of_Latency -0.05 0.04 0.10
## feedback -0.07 0.14 0.24
## ITS -0.06 0.01 0.08
## class 0.02 -0.17 -0.21
## PreTest_Total_Score 1.00 0.60 -0.39
## PostTest_Total_Score 0.60 1.00 0.51
## Post_Pre -0.39 0.51 1.00
## Intrinsic_Value 0.48 0.40 -0.06
## Self.regulation 0.28 0.28 0.03
## Self.efficacy 0.44 0.41 0.00
## Test_anxiety 0.39 0.41 0.05
## Survey_Total_Score 0.50 0.47 0.01
## Intrinsic_Value Self.regulation Self.efficacy Test_anxiety
## ID -0.11 -0.25 -0.12 0.07
## Sum_of_Latency -0.11 -0.20 -0.18 -0.29
## feedback 0.01 0.14 0.02 0.04
## ITS -0.15 -0.15 -0.16 -0.18
## class -0.10 -0.22 -0.15 -0.02
## PreTest_Total_Score 0.48 0.28 0.44 0.39
## PostTest_Total_Score 0.40 0.28 0.41 0.41
## Post_Pre -0.06 0.03 0.00 0.05
## Intrinsic_Value 1.00 0.54 0.76 0.54
## Self.regulation 0.54 1.00 0.63 0.18
## Self.efficacy 0.76 0.63 1.00 0.60
## Test_anxiety 0.54 0.18 0.60 1.00
## Survey_Total_Score 0.86 0.67 0.92 0.77
## Survey_Total_Score
## ID -0.10
## Sum_of_Latency -0.25
## feedback 0.06
## ITS -0.20
## class -0.14
## PreTest_Total_Score 0.50
## PostTest_Total_Score 0.47
## Post_Pre 0.01
## Intrinsic_Value 0.86
## Self.regulation 0.67
## Self.efficacy 0.92
## Test_anxiety 0.77
## Survey_Total_Score 1.00cor_matrix<-round(cor(datalatencyv),2)
corrplot(corr=cor_matrix)
### Test score × Latency ###
#Pre-test × Latency
cor.test(datalatency$PreTest_Total_Score,datalatency$Sum_of_Latency,use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: datalatency$PreTest_Total_Score and datalatency$Sum_of_Latency
## t = -0.38343, df = 54, p-value = 0.7029
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3107514 0.2137204
## sample estimates:
## cor
## -0.05210792#Post-test × Latency
cor.test(datalatency$PostTest_Total_Score,datalatency$Sum_of_Latency,use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: datalatency$PostTest_Total_Score and datalatency$Sum_of_Latency
## t = 0.26336, df = 54, p-value = 0.7933
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2292431 0.2959300
## sample estimates:
## cor
## 0.03581595#Score increase（増加量）× Latency
cor.test(datalatency$Post_Pre,datalatency$Sum_of_Latency,use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: datalatency$Post_Pre and datalatency$Sum_of_Latency
## t = 0.71935, df = 54, p-value = 0.475
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1698244 0.3513277
## sample estimates:
## cor
## 0.09742585#Comment:There is a negative correlation between "Math Motivation score" and "Latency". Especially, "Test anxiety" and "Latency" are significant.
### Math Motivation score × Latency ###
#Math Motivation Score × Latency
cor.test(datalatency$Survey_Total_Score,datalatency$Sum_of_Latency,use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: datalatency$Survey_Total_Score and datalatency$Sum_of_Latency
## t = -1.883, df = 54, p-value = 0.06509
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.47981359 0.01570014
## sample estimates:
## cor
## -0.248225#Intrinsic value × Latency
cor.test(datalatency$Intrinsic_Value,datalatency$Sum_of_Latency,use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: datalatency$Intrinsic_Value and datalatency$Sum_of_Latency
## t = -0.80883, df = 54, p-value = 0.4222
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3618985 0.1580389
## sample estimates:
## cor
## -0.1094073#Self efficacy × Latency
cor.test(datalatency$Self.efficacy,datalatency$Sum_of_Latency,use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: datalatency$Self.efficacy and datalatency$Sum_of_Latency
## t = -1.3167, df = 54, p-value = 0.1935
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.41980706 0.09073549
## sample estimates:
## cor
## -0.1763723#Self regulation × Latency
cor.test(datalatency$Self.regulation,datalatency$Sum_of_Latency,use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: datalatency$Self.regulation and datalatency$Sum_of_Latency
## t = -1.4866, df = 54, p-value = 0.1429
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4383352 0.0681694
## sample estimates:
## cor
## -0.1982847#Test anxiety × Latency
cor.test(datalatency$Test_anxiety,datalatency$Sum_of_Latency,use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: datalatency$Test_anxiety and datalatency$Sum_of_Latency
## t = -2.2609, df = 54, p-value = 0.02782
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.51699991 -0.03378016
## sample estimates:
## cor
## -0.2940691