Omiyakita_Elementary_School
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
data<-read.csv("R.csv")
datae<-read.csv("effort.csv")
datap<-read.csv("performance.csv")
datav<-read.csv("R_value.csv")
datalatency<-read.csv("Latency.csv")
datalatencye<-read.csv("Latency_effort.csv")
datalatencyp<-read.csv("Latency_performance.csv")
datalatencyv<-read.csv("Latency_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(data$PreTest_Total_Score,main="PreTest_Total_Score/All students (N=65)",ylim=c(0,15))
#Post-test/All Students (N=65)
mean(data$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$PostTest_Total_Score,main="PostTest_Total_Score/Performance group (N=31)",ylim=c(0,15))
#Latency（学習時間） #All Students (N=65-3, 3:Exculuded Studetns Too long Latency seconds in Q1)
#Comment: Not significant difference between the effort and performance group.
#T-test
#Sum of Latency (seconds) of total 11 math problems on ITS
#effort vs performance group
var.test(datalatencye$Sum_of_Latency,datalatencyp$Sum_of_Latency)#等分散##
## F test to compare two variances
##
## data: datalatencye$Sum_of_Latency and datalatencyp$Sum_of_Latency
## F = 1.705, num df = 33, denom df = 27, p-value = 0.1589
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.8078479 3.5021008
## sample estimates:
## ratio of variances
## 1.704953t.test(Sum_of_Latency ~ feedback, var.equal=T,data = datalatency)##
## Two Sample t-test
##
## data: Sum_of_Latency by feedback
## t = 0.29926, df = 60, p-value = 0.7658
## alternative hypothesis: true difference in means between group effort and group performance is not equal to 0
## 95 percent confidence interval:
## -216.5814 292.7873
## sample estimates:
## mean in group effort mean in group performance
## 1150.853 1112.750#Analysis 1 #Compare Pre- vs. Post-Test #All Students (N=65)
#Comment:Overall, the test score increased significantly before and after the ITS learning activity.
#T-test
#Pre-test and Post-test
#p-value = 0.04363
t.test(data$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$PostTest_Total_Score
## F = 1.1809, num df = 33, denom df = 30, p-value = 0.6484
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.5758789 2.3924284
## sample estimates:
## ratio of variances
## 1.18094t.test(PostTest_Total_Score ~ feedback, var.equal=T,data = data)##
## Two Sample t-test
##
## data: PostTest_Total_Score by feedback
## t = -0.93176, df = 63, p-value = 0.355
## alternative hypothesis: true difference in means between group effort and group performance is not equal to 0
## 95 percent confidence interval:
## -3.040265 1.106678
## sample estimates:
## mean in group effort mean in group performance
## 9.323529 10.290323#Graph #Compare Pre-Test / Effort vs. Performance Group
#mean/sdの確認
#effort
mean(datae$PreTest_Total_Score)## [1] 9.235294sd(datae$PreTest_Total_Score)## [1] 4.068049#performance
mean(datap$PreTest_Total_Score)## [1] 8.548387sd(datap$PreTest_Total_Score)## [1] 4.121801# グラフの描画に必要なデータの入力
bardata <- data.frame(
Condition = c("Effort", "Performance"),
Mean = c(mean(datae$PreTest_Total_Score), mean(datap$PreTest_Total_Score)),
SD = c(sd(datae$PreTest_Total_Score), sd(datap$PreTest_Total_Score))
)
# ggplot2を使用してグラフを描画
library(ggplot2)
ggplot(bardata, aes(x = Condition, y = Mean, fill = Condition)) +
geom_bar(stat = "identity", position = "dodge", width = 0.7) +
geom_errorbar(
aes(ymin = Mean - SD, ymax = Mean + SD),
position = position_dodge(0.7),
width = 0.2,
size = 1
) +
labs(
title = "Pore-test/All Students (N=65)",
x = " ",
y = "Score"
) +
scale_y_continuous(limits = c(0, 15), breaks = seq(0, 15, 5)) + # y軸の範囲をに設定
theme_minimal()## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
#Graph #Compare Post-Test / Effort vs. Performance Group
#mean/sdの確認
#effort
mean(datae$PostTest_Total_Score)## [1] 9.323529sd(datae$PostTest_Total_Score)## [1] 4.339533#performance
mean(datap$PostTest_Total_Score)## [1] 10.29032sd(datap$PostTest_Total_Score)## [1] 3.993274# グラフの描画に必要なデータの入力
bardata <- data.frame(
Condition = c("Effort", "Performance"),
Mean = c(mean(datae$PostTest_Total_Score), mean(datap$PostTest_Total_Score)),
SD = c(sd(datae$PostTest_Total_Score), sd(datap$PostTest_Total_Score))
)
# ggplot2を使用してグラフを描画
library(ggplot2)
ggplot(bardata, aes(x = Condition, y = Mean, fill = Condition)) +
geom_bar(stat = "identity", position = "dodge", width = 0.7) +
geom_errorbar(
aes(ymin = Mean - SD, ymax = Mean + SD),
position = position_dodge(0.7),
width = 0.2,
size = 1
) +
labs(
title = "Post-test/All Students (N=65)",
x = " ",
y = "Score"
) +
scale_y_continuous(limits = c(0, 15), breaks = seq(0, 15, 5)) + # y軸の範囲をに設定
theme_minimal()
#Analysis 3 #Compare Test Score Increase (増加量) / Effort vs. Performance Group
#Comment:There is a trend (p=0.051) that greater test score increase [(Post_Test_Total_Score) - (Pre_Test_Total_Score)] among kids who received performance feedback compared to those who received effort-based feedback.
#T-test
#Score Increase [Post_pre = (Post_Test_Total_Score)ー(Pre_Test_Total_Score)] between the effort and performance group
#p-value = 0.05177
var.test(datae$Post_Pre,datap$Post_Pre)#等分散##
## F test to compare two variances
##
## data: datae$Post_Pre and datap$Post_Pre
## F = 0.62939, num df = 33, denom df = 30, p-value = 0.1963
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3069204 1.2750686
## sample estimates:
## ratio of variances
## 0.6293939t.test(Post_Pre ~ feedback,var.equal=T,data = data)##
## Two Sample t-test
##
## data: Post_Pre by feedback
## t = -1.9827, df = 63, p-value = 0.05177
## alternative hypothesis: true difference in means between group effort and group performance is not equal to 0
## 95 percent confidence interval:
## -3.32044774 0.01304737
## sample estimates:
## mean in group effort mean in group performance
## 0.08823529 1.74193548#Graph #Compare Test Score Increase (増加量) / Effort vs. Performance Group
#mean/sdの確認
#effort
mean(datae$Post_Pre)## [1] 0.08823529sd(datae$Post_Pre)## [1] 2.968191#performance
mean(datap$Post_Pre)## [1] 1.741935sd(datap$Post_Pre)## [1] 3.74137# グラフの描画に必要なデータの入力
bardata <- data.frame(
Condition = c("Effort", "Performance"),
Mean = c(mean(datae$Post_Pre), mean(datap$Post_Pre)),
SD = c(sd(datae$Post_Pre), sd(datap$Post_Pre))
)
# ggplot2を使用してグラフを描画
library(ggplot2)
ggplot(bardata, aes(x = Condition, y = Mean, fill = Condition)) +
geom_bar(stat = "identity", position = "dodge", width = 0.7) +
geom_errorbar(
aes(ymin = Mean - SD, ymax = Mean + SD),
position = position_dodge(0.7),
width = 0.2,
size = 1
) +
labs(
title = " ",
x = " ",
y = "Score gain"
) +
scale_y_continuous(limits = c(-7, 7), breaks = seq(-7, 7, 1)) + # y軸の範囲をに設定
theme_minimal()
#Analysis 4-1 #Regression Model/Predict “Post-test” #All Students (N=65)
#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
## -9.323 -2.290 1.677 2.710 4.676
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.3235 0.7166 13.012 <2e-16 ***
## feedbackperformance 0.9668 1.0376 0.932 0.355
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.178 on 63 degrees of freedom
## Multiple R-squared: 0.01359, Adjusted R-squared: -0.002064
## F-statistic: 0.8682 on 1 and 63 DF, p-value: 0.355#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
## -7.7402 -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-08library(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="Post-test",x="Condition","Latency","Pre-test"
modelnew<- lm(PostTest_Total_Score ~ feedback + Sum_of_Latency + PreTest_Total_Score, data = datalatency)
summary(modelnew)##
## Call:
## lm(formula = PostTest_Total_Score ~ feedback + Sum_of_Latency +
## PreTest_Total_Score, data = datalatency)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.8796 -2.0804 0.5884 1.5789 9.0490
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.2987006 1.5261787 1.506 0.1374
## feedbackperformance 1.4455431 0.8251708 1.752 0.0851 .
## Sum_of_Latency 0.0005464 0.0008383 0.652 0.5171
## PreTest_Total_Score 0.6925547 0.1011832 6.845 5.34e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.215 on 58 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.4532, Adjusted R-squared: 0.4249
## F-statistic: 16.02 on 3 and 58 DF, p-value: 1.057e-07modelnew2<- lm(PostTest_Total_Score ~ feedback*Sum_of_Latency*PreTest_Total_Score, data = datalatency)
summary(modelnew2)##
## Call:
## lm(formula = PostTest_Total_Score ~ feedback * Sum_of_Latency *
## PreTest_Total_Score, data = datalatency)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.194 -1.365 0.272 1.667 6.521
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -2.4544389 2.8010285
## feedbackperformance 16.5092994 5.4738744
## Sum_of_Latency 0.0039959 0.0022480
## PreTest_Total_Score 1.2522294 0.3038346
## feedbackperformance:Sum_of_Latency -0.0114024 0.0044718
## feedbackperformance:PreTest_Total_Score -1.7674887 0.6142089
## Sum_of_Latency:PreTest_Total_Score -0.0004191 0.0002591
## feedbackperformance:Sum_of_Latency:PreTest_Total_Score 0.0013846 0.0005329
## t value Pr(>|t|)
## (Intercept) -0.876 0.38477
## feedbackperformance 3.016 0.00390 **
## Sum_of_Latency 1.778 0.08111 .
## PreTest_Total_Score 4.121 0.00013 ***
## feedbackperformance:Sum_of_Latency -2.550 0.01365 *
## feedbackperformance:PreTest_Total_Score -2.878 0.00573 **
## Sum_of_Latency:PreTest_Total_Score -1.618 0.11159
## feedbackperformance:Sum_of_Latency:PreTest_Total_Score 2.598 0.01206 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.092 on 54 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.5291, Adjusted R-squared: 0.4681
## F-statistic: 8.668 on 7 and 54 DF, p-value: 4.334e-07library(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()`).## 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
## -9.801 -3.150 1.315 3.224 4.340
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.9439353 1.3651855 7.284 8.19e-10 ***
## Sum_of_Latency -0.0001924 0.0011050 -0.174 0.862
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.274 on 60 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.0005051, Adjusted R-squared: -0.01615
## F-statistic: 0.03032 on 1 and 60 DF, p-value: 0.8623#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
## -9.614 -2.856 1.665 3.470 4.857
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.0079257 1.7279402 5.213 2.58e-06 ***
## feedbackperformance 2.3837705 2.8985019 0.822 0.414
## Sum_of_Latency 0.0002742 0.0013571 0.202 0.841
## feedbackperformance:Sum_of_Latency -0.0013323 0.0023831 -0.559 0.578
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.311 on 58 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.01675, Adjusted R-squared: -0.03411
## F-statistic: 0.3294 on 3 and 58 DF, p-value: 0.8041library(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
## -8.1238 -1.9789 0.4956 1.8279 9.8441
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.0108144 2.5335365 0.794 0.43062
## Sum_of_Latency 0.0015418 0.0020543 0.751 0.45597
## PreTest_Total_Score 0.8234269 0.2789843 2.952 0.00456 **
## Sum_of_Latency:PreTest_Total_Score -0.0001373 0.0002398 -0.573 0.56909
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.29 on 58 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.4275, Adjusted R-squared: 0.3978
## F-statistic: 14.43 on 3 and 58 DF, p-value: 3.895e-07#Analysis 4-2 #Regression Model/Predict “Score Increase” #All Students (N=65)
#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
## -8.0882 -1.7419 -0.0882 1.2581 11.2581
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.08824 0.57600 0.153 0.8787
## feedbackperformance 1.65370 0.83407 1.983 0.0518 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.359 on 63 degrees of freedom
## Multiple R-squared: 0.05873, Adjusted R-squared: 0.04379
## F-statistic: 3.931 on 1 and 63 DF, p-value: 0.05177#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
## -7.7402 -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: 0.001646library(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
## -9.3369 -1.4963 -0.6079 1.2890 12.3140
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0600540 1.1211068 -0.054 0.957
## Sum_of_Latency 0.0007928 0.0009074 0.874 0.386
##
## Residual standard error: 3.509 on 60 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.01256, Adjusted R-squared: -0.003895
## F-statistic: 0.7633 on 1 and 60 DF, p-value: 0.3858#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
## -8.5923 -1.6609 -0.3346 1.1590 11.4098
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.8610377 1.3873749 -0.621 0.537
## feedbackperformance 1.5756770 2.3272268 0.677 0.501
## Sum_of_Latency 0.0008248 0.0010896 0.757 0.452
## feedbackperformance:Sum_of_Latency 0.0001056 0.0019134 0.055 0.956
##
## Residual standard error: 3.461 on 58 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.07143, Adjusted R-squared: 0.02341
## F-statistic: 1.487 on 3 and 58 DF, p-value: 0.2275library(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
## -8.1238 -1.9789 0.4956 1.8279 9.8441
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.0108144 2.5335365 0.794 0.431
## Sum_of_Latency 0.0015418 0.0020543 0.751 0.456
## PreTest_Total_Score -0.1765731 0.2789843 -0.633 0.529
## Sum_of_Latency:PreTest_Total_Score -0.0001373 0.0002398 -0.573 0.569
##
## Residual standard error: 3.29 on 58 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.1613, Adjusted R-squared: 0.1179
## F-statistic: 3.717 on 3 and 58 DF, p-value: 0.01629#Analysis 4-3 #Regression Model/Predict Latency（学習時間） #All Students (N=65)
#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
## -624.8 -336.1 -154.2 229.9 1743.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1260.28 150.78 8.358 1.2e-11 ***
## PreTest_Total_Score -14.25 15.42 -0.924 0.359
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 495.8 on 60 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.01404, Adjusted R-squared: -0.002394
## F-statistic: 0.8543 on 1 and 60 DF, p-value: 0.359#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
## -658.9 -341.3 -163.8 257.2 1781.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1150.85 85.56 13.450 <2e-16 ***
## feedbackperformance -38.10 127.32 -0.299 0.766
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 498.9 on 60 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.00149, Adjusted R-squared: -0.01515
## F-statistic: 0.08956 on 1 and 60 DF, p-value: 0.7658#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
## -617.5 -359.7 -155.7 209.9 1738.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1244.823 216.770 5.743 3.61e-07
## PreTest_Total_Score -10.175 21.532 -0.473 0.638
## feedbackperformance 38.122 306.438 0.124 0.901
## PreTest_Total_Score:feedbackperformance -9.932 31.500 -0.315 0.754
##
## (Intercept) ***
## PreTest_Total_Score
## feedbackperformance
## PreTest_Total_Score:feedbackperformance
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 503.2 on 58 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.01822, Adjusted R-squared: -0.03256
## F-statistic: 0.3587 on 3 and 58 DF, p-value: 0.783library(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) #All Students (N=65)
#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
## -26.007 -7.610 0.370 9.804 20.920
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 57.367276 3.813558 15.043 <2e-16 ***
## Sum_of_Latency -0.007025 0.003087 -2.276 0.0264 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.94 on 60 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.07947, Adjusted R-squared: 0.06413
## F-statistic: 5.18 on 1 and 60 DF, p-value: 0.02643#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
## -25.3199 -7.8377 0.5695 9.9492 20.3861
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 58.639327 4.855842 12.076 <2e-16 ***
## feedbackperformance -3.143786 8.145344 -0.386 0.7009
## Sum_of_Latency -0.007584 0.003814 -1.989 0.0515 .
## feedbackperformance:Sum_of_Latency 0.001554 0.006697 0.232 0.8174
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.12 on 58 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.08354, Adjusted R-squared: 0.03613
## F-statistic: 1.762 on 3 and 58 DF, p-value: 0.1644library(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
## -760.4 -277.1 -125.8 245.7 1701.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1503.78 158.62 9.480 1.55e-13 ***
## Test_anxiety -34.10 13.52 -2.523 0.0143 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 474.7 on 60 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.0959, Adjusted R-squared: 0.08083
## F-statistic: 6.364 on 1 and 60 DF, p-value: 0.01431library(ggplot2)
ggplot(datalatency, aes(x = Test_anxiety, 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()`).
## Removed 3 rows containing missing values (`geom_point()`).
#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
## -30.9038 -7.9038 0.1117 9.0962 22.8197
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 36.5034 3.4616 10.545 1.52e-15 ***
## PostTest_Total_Score 1.3385 0.3258 4.108 0.000117 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.88 on 63 degrees of freedom
## Multiple R-squared: 0.2113, Adjusted R-squared: 0.1988
## F-statistic: 16.88 on 1 and 63 DF, p-value: 0.0001171#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
## -29.253 -7.106 -0.972 9.168 23.566
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 35.2992 4.5121 7.823 8.87e-11
## feedbackperformance 2.5868 7.1315 0.363 0.718066
## PostTest_Total_Score 1.5673 0.4399 3.563 0.000719
## feedbackperformance:PostTest_Total_Score -0.4622 0.6670 -0.693 0.491023
##
## (Intercept) ***
## feedbackperformance
## PostTest_Total_Score ***
## feedbackperformance:PostTest_Total_Score
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.97 on 61 degrees of freedom
## Multiple R-squared: 0.224, Adjusted R-squared: 0.1858
## F-statistic: 5.87 on 3 and 61 DF, p-value: 0.001374library(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
## -26.2353 -8.7397 -0.5595 11.3324 19.4405
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 49.63159 1.56881 31.637 <2e-16 ***
## Post_Pre -0.03603 0.44579 -0.081 0.936
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.25 on 63 degrees of freedom
## Multiple R-squared: 0.0001037, Adjusted R-squared: -0.01577
## F-statistic: 0.006531 on 1 and 63 DF, p-value: 0.9358#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
## -25.7083 -8.8546 -0.2747 11.0929 19.0929
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 49.9071 2.1349 23.377 <2e-16 ***
## feedbackperformance -0.5456 3.2666 -0.167 0.868
## Post_Pre 0.0525 0.7298 0.072 0.943
## feedbackperformance:Post_Pre -0.1119 0.9493 -0.118 0.907
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.44 on 61 degrees of freedom
## Multiple R-squared: 0.0009744, Adjusted R-squared: -0.04816
## F-statistic: 0.01983 on 3 and 61 DF, p-value: 0.9962library(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
#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$Test_anxiety
## F = 1.0587, num df = 33, denom df = 30, p-value = 0.8786
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.5162518 2.1447136
## sample estimates:
## ratio of variances
## 1.058664t.test(Test_anxiety ~ feedback,var.equal=T,data = data)##
## Two Sample t-test
##
## data: Test_anxiety by feedback
## t = -0.074085, df = 63, p-value = 0.9412
## alternative hypothesis: true difference in means between group effort and group performance is not equal to 0
## 95 percent confidence interval:
## -2.309021 2.143936
## sample estimates:
## mean in group effort mean in group performance
## 10.85294 10.93548#Graph #Compare Math Motivation Score (算数学習への意欲) / Effort vs. Performance Group
#mean/sdの確認
#effort
mean(datae$Survey_Total_Score)## [1] 49.91176sd(datae$Survey_Total_Score)## [1] 12.22607#performance
mean(datap$Survey_Total_Score)## [1] 49.25806sd(datap$Survey_Total_Score)## [1] 12.2664# グラフの描画に必要なデータの入力
bardata <- data.frame(
Condition = c("Effort", "Performance"),
Mean = c(mean(datae$Survey_Total_Score), mean(datap$Survey_Total_Score)),
SD = c(sd(datae$Survey_Total_Score), sd(datap$Survey_Total_Score))
)
# ggplot2を使用してグラフを描画
library(ggplot2)
ggplot(bardata, aes(x = Condition, y = Mean, fill = Condition)) +
geom_bar(stat = "identity", position = "dodge", width = 0.7) +
geom_errorbar(
aes(ymin = Mean - SD, ymax = Mean + SD),
position = position_dodge(0.7),
width = 0.2,
size = 1
) +
labs(
title = " ",
x = " ",
y = "Score"
) +
# scale_y_continuous(limits = c(-7, 7), breaks = seq(-7, 7, 1)) + # y軸の範囲をに設定
theme_minimal()
#Analysis 6-1 #Correlation #Math Motivation Score
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##
#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$PostTest_Total_Score
## t = 4.1083, df = 63, p-value = 0.0001171
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2430238 0.6326465
## sample estimates:
## cor
## 0.4596764#Pre-Test × Post-Test
#p-value = 3.582e-09, cor 0.6535961
cor.test(data$PreTest_Total_Score,data$PostTest_Total_Score)##
## Pearson's product-moment correlation
##
## data: data$PreTest_Total_Score and data$PostTest_Total_Score
## t = 6.8545, df = 63, p-value = 3.582e-09
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4873931 0.7740955
## sample estimates:
## cor
## 0.6535961#Intrinsic value × Post-test
#p-value = 0.0005316, cor 0.4179531
cor.test(data$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$PostTest_Total_Score
## t = 3.2491, df = 63, p-value = 0.001859
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1486740 0.5700625
## sample estimates:
## cor
## 0.3788359#Analysis 6-2 #Correlation #Score Increase
#Comment:There is not significant correlation between the Math motivation score and the Score increase.
#Math motivation score × Score increase（増加量）
cor.test(data$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$Post_Pre
## t = 0.31772, df = 63, p-value = 0.7517
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2059111 0.2811531
## sample estimates:
## cor
## 0.03999669#Posttest × Score increase（増加量）
cor.test(data$PostTest_Total_Score,data$Post_Pre)##
## Pearson's product-moment correlation
##
## data: data$PostTest_Total_Score and data$Post_Pre
## t = 3.884, df = 63, p-value = 0.0002491
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2191239 0.6172604
## sample estimates:
## cor
## 0.4395325#Pretest × Score increase（増加量)
cor.test(data$PreTest_Total_Score,data$Post_Pre)##
## Pearson's product-moment correlation
##
## data: data$PreTest_Total_Score and data$Post_Pre
## t = -3.3876, df = 63, p-value = 0.00122
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.5808305 -0.1643788
## sample estimates:
## cor
## -0.3925403#Analysis 6-3 #Correlation #Latency
library(corrplot)
round(cor(datalatencyv),2)## ID Sum_of_Latency feedback ITS class
## ID 1.00 -0.03 -0.13 0.24 0.93
## Sum_of_Latency -0.03 1.00 -0.04 0.04 0.04
## feedback -0.13 -0.04 1.00 0.04 -0.16
## ITS 0.24 0.04 0.04 1.00 0.20
## class 0.93 0.04 -0.16 0.20 1.00
## PreTest_Total_Score 0.09 -0.12 -0.09 -0.03 0.07
## PostTest_Total_Score -0.07 -0.02 0.11 0.03 -0.12
## Post_Pre -0.19 0.11 0.24 0.07 -0.22
## Intrinsic_Value -0.15 -0.16 -0.06 -0.13 -0.11
## Self.regulation -0.31 -0.22 0.00 -0.13 -0.24
## Self.efficacy -0.17 -0.21 -0.09 -0.13 -0.17
## Test_anxiety 0.07 -0.31 0.00 -0.16 -0.01
## Survey_Total_Score -0.15 -0.28 -0.05 -0.17 -0.15
## PreTest_Total_Score PostTest_Total_Score Post_Pre
## ID 0.09 -0.07 -0.19
## Sum_of_Latency -0.12 -0.02 0.11
## feedback -0.09 0.11 0.24
## ITS -0.03 0.03 0.07
## class 0.07 -0.12 -0.22
## PreTest_Total_Score 1.00 0.65 -0.39
## PostTest_Total_Score 0.65 1.00 0.45
## Post_Pre -0.39 0.45 1.00
## Intrinsic_Value 0.50 0.42 -0.07
## Self.regulation 0.29 0.29 0.01
## Self.efficacy 0.46 0.42 -0.03
## Test_anxiety 0.39 0.40 0.03
## Survey_Total_Score 0.50 0.47 -0.02
## Intrinsic_Value Self.regulation Self.efficacy Test_anxiety
## ID -0.15 -0.31 -0.17 0.07
## Sum_of_Latency -0.16 -0.22 -0.21 -0.31
## feedback -0.06 0.00 -0.09 0.00
## ITS -0.13 -0.13 -0.13 -0.16
## class -0.11 -0.24 -0.17 -0.01
## PreTest_Total_Score 0.50 0.29 0.46 0.39
## PostTest_Total_Score 0.42 0.29 0.42 0.40
## Post_Pre -0.07 0.01 -0.03 0.03
## Intrinsic_Value 1.00 0.58 0.77 0.53
## Self.regulation 0.58 1.00 0.71 0.20
## Self.efficacy 0.77 0.71 1.00 0.58
## Test_anxiety 0.53 0.20 0.58 1.00
## Survey_Total_Score 0.87 0.72 0.93 0.75
## Survey_Total_Score
## ID -0.15
## Sum_of_Latency -0.28
## feedback -0.05
## ITS -0.17
## class -0.15
## PreTest_Total_Score 0.50
## PostTest_Total_Score 0.47
## Post_Pre -0.02
## Intrinsic_Value 0.87
## Self.regulation 0.72
## Self.efficacy 0.93
## Test_anxiety 0.75
## Survey_Total_Score 1.00cor_matrix<-round(cor(datalatencyv),2)
corrplot(corr=cor_matrix)
### Test score × Latency ###
#Pre-test × Latency
#p-value = 0.359 , cor -0.118484
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$Sum_of_Latency
## t = -0.92428, df = 60, p-value = 0.359
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3576676 0.1352878
## sample estimates:
## cor
## -0.118484#Post-test × Latency
#p-value = 0.8623, cor -0.02247468
cor.test(datalatency$PostTest_Total_Score,datalatency$Sum_of_Latency,use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: datalatency$PostTest_Total_Score and datalatency$Sum_of_Latency
## t = -0.17413, df = 60, p-value = 0.8623
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2707232 0.2285766
## sample estimates:
## cor
## -0.02247468#Score increase（増加量）× Latency
#p-value = 0.3858, cor 0.1120818
cor.test(datalatency$Post_Pre,datalatency$Sum_of_Latency,use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: datalatency$Post_Pre and datalatency$Sum_of_Latency
## t = 0.87369, df = 60, p-value = 0.3858
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1416519 0.3519960
## sample estimates:
## cor
## 0.1120818#Comment:There is a negative correlation between "Math Motivation score" and "Latency". Especially, "Test anxiety" and "Latency" are significant.
### Math Motivation score × Latency ###
#Math Motivation Score × Latency
#p-value = 0.02643, cor -0.2819058
cor.test(datalatency$Survey_Total_Score,datalatency$Sum_of_Latency,use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: datalatency$Survey_Total_Score and datalatency$Sum_of_Latency
## t = -2.2759, df = 60, p-value = 0.02643
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.49670078 -0.03457188
## sample estimates:
## cor
## -0.2819058#Intrinsic value × Latency
#p-value = 0.2249, cor -0.1563486
cor.test(datalatency$Intrinsic_Value,datalatency$Sum_of_Latency,use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: datalatency$Intrinsic_Value and datalatency$Sum_of_Latency
## t = -1.2262, df = 60, p-value = 0.2249
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.39085358 0.09721601
## sample estimates:
## cor
## -0.1563486#Self efficacy × Latency
#p-value = 0.09369, cor -0.2147641
cor.test(datalatency$Self.efficacy,datalatency$Sum_of_Latency,use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: datalatency$Self.efficacy and datalatency$Sum_of_Latency
## t = -1.7033, df = 60, p-value = 0.09369
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.44088278 0.03698822
## sample estimates:
## cor
## -0.2147641#Self regulation × Latency
#p-value = 0.08645, cor -0.2195386
cor.test(datalatency$Self.regulation,datalatency$Sum_of_Latency,use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: datalatency$Self.regulation and datalatency$Sum_of_Latency
## t = -1.7431, df = 60, p-value = 0.08645
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.44491071 0.03198333
## sample estimates:
## cor
## -0.2195386#Test anxiety × Latency
#p-value = 0.01431, cor -0.3096778
cor.test(datalatency$Test_anxiety,datalatency$Sum_of_Latency,use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: datalatency$Test_anxiety and datalatency$Sum_of_Latency
## t = -2.5228, df = 60, p-value = 0.01431
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.51928085 -0.06493195
## sample estimates:
## cor
## -0.3096778Pre vs post
library(dplyr)##
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##
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##
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## ✔ readr 2.1.4## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(ggpubr)
library(rstatix)##
## Attaching package: 'rstatix'
##
## The following object is masked from 'package:stats':
##
## filter# Data preparation
# Wide format
data<-read.csv("R.csv")
data <- data %>%
gather(key = "test", value = "score", PreTest_Total_Score, PostTest_Total_Score) %>%
convert_as_factor(ID, test)
data %>%
group_by(test) %>%
get_summary_stats(score, type = "mean_sd")## # A tibble: 2 × 5
## test variable n mean sd
## <fct> <fct> <dbl> <dbl> <dbl>
## 1 PostTest_Total_Score score 65 9.78 4.17
## 2 PreTest_Total_Score score 65 8.91 4.08bxp <- ggboxplot(data, x = "test", y = "score", add = "point")
bxp
data %>%
group_by(test) %>%
identify_outliers(score)## # A tibble: 1 × 17
## test ID username feedback ITS pre_test_type post_test_type class
## <fct> <fct> <chr> <chr> <int> <chr> <chr> <int>
## 1 PostTest_Tot… 106 yessomen effort 12 A B 3
## # ℹ 9 more variables: Post_Pre <int>, Intrinsic_Value <int>,
## # Self.regulation <int>, Self.efficacy <int>, Test_anxiety <int>,
## # Survey_Total_Score <int>, score <int>, is.outlier <lgl>, is.extreme <lgl>data %>%
group_by(test) %>%
shapiro_test(score)## # A tibble: 2 × 4
## test variable statistic p
## <fct> <chr> <dbl> <dbl>
## 1 PostTest_Total_Score score 0.854 0.00000185
## 2 PreTest_Total_Score score 0.892 0.0000368ggqqplot(data, "score", facet.by = "test")
#ANOVA Table (type III tests)
res.aov <- anova_test(data = data, dv = score, wid = ID, within = test)
get_anova_table(res.aov)## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 test 1 64 4.237 0.044 * 0.011print(res.aov)## ANOVA Table (type III tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 test 1 64 4.237 0.044 * 0.011# pairwise comparisons
pwc <- data %>%
pairwise_t_test(
score ~ test, paired = TRUE,
p.adjust.method = "bonferroni"
)
pwc## # A tibble: 1 × 10
## .y. group1 group2 n1 n2 statistic df p p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 score PostTest_To… PreTe… 65 65 2.06 64 0.044 0.044 *#Visualization: box plots with p-values
pwc <- pwc %>% add_xy_position(x = "test")
bxp +
stat_pvalue_manual(pwc) +
labs(
subtitle = get_test_label(res.aov, detailed = TRUE),
caption = get_pwc_label(pwc)
)
## https://www.datanovia.com/en/lessons/repeated-measures-anova-in-r/#data-preparation