Omiyakita_Elementary_School_Pre-Test 75 percentile_Students
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 0-11 (Pre-Test 75 percentile)
data<-subset(read.csv("R.csv"),PreTest_Total_Score<=11)
datae<-subset(read.csv("effort.csv"),PreTest_Total_Score<=11)
datap<-subset(read.csv("performance.csv"),PreTest_Total_Score<=11)
datav<-subset(read.csv("R_value.csv"),PreTest_Total_Score<=11)
#Import the data with Latency（学習時間）
datalatency<-subset(read.csv("Latency.csv"),PreTest_Total_Score<=11)
datalatencye<-subset(read.csv("Latency_effort.csv"),PreTest_Total_Score<=11)
datalatencyp<-subset(read.csv("Latency_performance.csv"),PreTest_Total_Score<=11)
datalatencyv<-subset(read.csv("Latency_value.csv"),PreTest_Total_Score<=11)#Students who Marked 0〜75 Percentile in the Pre-test
#Pre-Test 75 percentile = score "12"
read.csv("R.csv") ## 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 28allstudentdata<-read.csv("R.csv")
quantile(allstudentdata$PreTest_Total_Score)## 0% 25% 50% 75% 100%
## 1 5 10 12 14summary(allstudentdata$PreTest_Total_Score)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 5.000 10.000 8.908 12.000 14.000#Demographic
#All Students (N=42)
table(data$feedback)##
## effort performance
## 20 22#Pre-test
mean(data$PreTest_Total_Score)## [1] 6.738095sd(data$PreTest_Total_Score)## [1] 3.457474median(data$PreTest_Total_Score)## [1] 8hist(data$PreTest_Total_Score,main="PreTest_Total_Score/Pre-Test Score 75 percentile Students (N=42)",ylim=c(0,15))
#Post-test
mean(data$PostTest_Total_Score)## [1] 8.214286sd(data$PostTest_Total_Score)## [1] 4.274346median(data$PostTest_Total_Score)## [1] 9hist(data$PostTest_Total_Score,main="PostTest_Total_Score/Pre-Test Score 75 percentile Students (N=42)",ylim=c(0,15))
#Pre-test
mean(datae$PreTest_Total_Score)## [1] 6.65sd(datae$PreTest_Total_Score)## [1] 3.344674median(datae$PreTest_Total_Score)## [1] 7.5hist(datae$PreTest_Total_Score,main="PreTest_Total_Score/Effort group (N=20)",ylim=c(0,15))
#Post-test/effort group
mean(datae$PostTest_Total_Score)## [1] 6.8sd(datae$PostTest_Total_Score)## [1] 3.914884median(datae$PostTest_Total_Score)## [1] 7hist(datae$PostTest_Total_Score,main="PostTest_Total_Score/Effort group (N=20)",ylim=c(0,15))
#Pre-test/performance group
mean(datap$PreTest_Total_Score)## [1] 6.818182sd(datap$PreTest_Total_Score)## [1] 3.633657median(datap$PreTest_Total_Score)## [1] 8hist(datap$PreTest_Total_Score,main="PreTest_Total_Score/Performance group (N=22)",ylim=c(0,15))
#Post-test/performance group
mean(datap$PostTest_Total_Score)## [1] 9.5sd(datap$PostTest_Total_Score)## [1] 4.262237median(datap$PostTest_Total_Score)## [1] 11hist(datap$PostTest_Total_Score,main="PostTest_Total_Score/Performance group (N=22)",ylim=c(0,15))
#Latency（学習時間） #Pre-Test Score 75 percentile（N=42）
#Comment: Not significant difference between the effort and performance group.
#T-test
#Sum of Latency (seconds) of total 11 math problems on ITS
#effort vs performance group
var.test(datalatencye$Sum_of_Latency,datalatencyp$Sum_of_Latency)#等分散##
## F test to compare two variances
##
## data: datalatencye$Sum_of_Latency and datalatencyp$Sum_of_Latency
## F = 1.6519, num df = 19, denom df = 19, p-value = 0.2828
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.6538541 4.1735207
## sample estimates:
## ratio of variances
## 1.65193t.test(Sum_of_Latency ~ feedback, var.equal=T,data = datalatency)##
## Two Sample t-test
##
## data: Sum_of_Latency by feedback
## t = 0.03146, df = 38, p-value = 0.9751
## alternative hypothesis: true difference in means between group effort and group performance is not equal to 0
## 95 percent confidence interval:
## -338.9111 349.6111
## sample estimates:
## mean in group effort mean in group performance
## 1191.30 1185.95#Analysis 1 #Compare Pre- vs. Post-Test #Pre-Test Score 75 percentile（N=42）
#Comment:Overall, the test score increased significantly before and after the ITS learning activity.
#T-test
#Pre-test and Post-test
t.test(data$PreTest_Total_Score, data$PostTest_Total_Score, paired=TRUE)##
## Paired t-test
##
## data: data$PreTest_Total_Score and data$PostTest_Total_Score
## t = -2.3872, df = 41, p-value = 0.02167
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -2.7250286 -0.2273524
## sample estimates:
## mean difference
## -1.47619#Analysis 2 #Compare Pre-Test / Effort vs. Performance Group #Compare Post-Test / Effort vs. Performance Group #Pre-Test Score 75 percentile（N=42）
#Comment:There is a significant difference, "Post-test between the effort and performance group"（p-value = 0.03929）. This is not the case for all students (N=65).
#T-test
#Pre-test between the effort and performance group
var.test(datae$PreTest_Total_Score,datap$PreTest_Total_Score)#等分散##
## F test to compare two variances
##
## data: datae$PreTest_Total_Score and datap$PreTest_Total_Score
## F = 0.84727, num df = 19, denom df = 21, p-value = 0.7209
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3468983 2.1121928
## sample estimates:
## ratio of variances
## 0.8472657t.test(PreTest_Total_Score ~ feedback, var.equal=T,data = data)##
## Two Sample t-test
##
## data: PreTest_Total_Score by feedback
## t = -0.15556, df = 40, p-value = 0.8772
## alternative hypothesis: true difference in means between group effort and group performance is not equal to 0
## 95 percent confidence interval:
## -2.353277 2.016913
## sample estimates:
## mean in group effort mean in group performance
## 6.650000 6.818182#T-test
#Post-test between the effort and performance group
var.test(datae$PostTest_Total_Score,datap$PostTest_Total_Score)#等分散##
## F test to compare two variances
##
## data: datae$PostTest_Total_Score and datap$PostTest_Total_Score
## F = 0.84365, num df = 19, denom df = 21, p-value = 0.7138
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.345418 2.103180
## sample estimates:
## ratio of variances
## 0.8436504t.test(PostTest_Total_Score ~ feedback, var.equal=T,data = data)##
## Two Sample t-test
##
## data: PostTest_Total_Score by feedback
## t = -2.131, df = 40, p-value = 0.03929
## alternative hypothesis: true difference in means between group effort and group performance is not equal to 0
## 95 percent confidence interval:
## -5.2607167 -0.1392833
## sample estimates:
## mean in group effort mean in group performance
## 6.8 9.5#Analysis 3 #Compare Test Score Increase (増加量) / Effort vs. Performance Group #Pre-Test Score 75 percentile（N=42）
#Comment:Comment:This result is the same as all students (N=65).
#T-test
#Score Increase [Post_pre = (Post_Test_Total_Score)ー(Pre_Test_Total_Score)] between the effort and performance group
var.test(datae$Post_Pre,datap$Post_Pre)#等分散##
## F test to compare two variances
##
## data: datae$Post_Pre and datap$Post_Pre
## F = 0.95794, num df = 19, denom df = 21, p-value = 0.9305
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3922129 2.3881042
## sample estimates:
## ratio of variances
## 0.9579423t.test(Post_Pre ~ feedback,var.equal=T,data = data)##
## Two Sample t-test
##
## data: Post_Pre by feedback
## t = -2.1313, df = 40, p-value = 0.03926
## alternative hypothesis: true difference in means between group effort and group performance is not equal to 0
## 95 percent confidence interval:
## -4.9326576 -0.1309788
## sample estimates:
## mean in group effort mean in group performance
## 0.150000 2.681818#Analysis 4-1 #Regression Model/Predict “Post-test” #Pre-Test Score 75 percentile（N=42）
#Comment:"Feedback" predicts "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
## -8.50 -3.55 1.20 3.50 5.20
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.800 0.917 7.416 4.98e-09 ***
## feedbackperformance 2.700 1.267 2.131 0.0393 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.101 on 40 degrees of freedom
## Multiple R-squared: 0.102, Adjusted R-squared: 0.0795
## F-statistic: 4.541 on 1 and 40 DF, p-value: 0.03929#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
## -6.3787 -2.3356 0.5795 2.0475 8.0882
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.21454 1.85270 1.735 0.0908 .
## feedbackperformance 2.08054 2.50344 0.831 0.4111
## PreTest_Total_Score 0.53917 0.25016 2.155 0.0375 *
## feedbackperformance:PreTest_Total_Score 0.07755 0.33249 0.233 0.8168
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.647 on 38 degrees of freedom
## Multiple R-squared: 0.3252, Adjusted R-squared: 0.272
## F-statistic: 6.105 on 3 and 38 DF, p-value: 0.001704library(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" does not predict "Post-Test".
#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
## -7.748 -4.388 1.101 3.483 6.095
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.162204 1.704277 4.202 0.000154 ***
## Sum_of_Latency 0.000789 0.001312 0.601 0.551153
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.349 on 38 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.009428, Adjusted R-squared: -0.01664
## F-statistic: 0.3617 on 1 and 38 DF, p-value: 0.5512#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
## -8.7611 -3.1335 0.7373 3.6125 6.1537
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.689339 2.128537 2.203 0.0341 *
## feedbackperformance 5.660421 3.374470 1.677 0.1021
## Sum_of_Latency 0.001772 0.001604 1.105 0.2766
## feedbackperformance:Sum_of_Latency -0.002573 0.002612 -0.985 0.3312
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.196 on 36 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1264, Adjusted R-squared: 0.05364
## F-statistic: 1.737 on 3 and 36 DF, p-value: 0.1768library(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 2 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 2 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
## -7.7364 -3.1721 0.0253 2.3874 9.4350
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.727e+00 3.825e+00 0.713 0.481
## Sum_of_Latency 1.324e-03 3.112e-03 0.425 0.673
## PreTest_Total_Score 6.802e-01 5.657e-01 1.203 0.237
## Sum_of_Latency:PreTest_Total_Score -9.317e-05 4.662e-04 -0.200 0.843
##
## Residual standard error: 3.958 on 36 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.2226, Adjusted R-squared: 0.1578
## F-statistic: 3.436 on 3 and 36 DF, p-value: 0.02692#Analysis 4-2 #Regression Model/Predict “Score Increase” #Pre-Test Score 75 percentile（N=42）
#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
## -8.1500 -2.1500 -0.6818 1.7170 10.3182
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1500 0.8597 0.174 0.8624
## feedbackperformance 2.5318 1.1879 2.131 0.0393 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.845 on 40 degrees of freedom
## Multiple R-squared: 0.102, Adjusted R-squared: 0.07953
## F-statistic: 4.543 on 1 and 40 DF, p-value: 0.03926#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
## -6.3787 -2.3356 0.5795 2.0475 8.0882
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.21454 1.85270 1.735 0.0908 .
## feedbackperformance 2.08054 2.50344 0.831 0.4111
## PreTest_Total_Score -0.46083 0.25016 -1.842 0.0733 .
## feedbackperformance:PreTest_Total_Score 0.07755 0.33249 0.233 0.8168
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.647 on 38 degrees of freedom
## Multiple R-squared: 0.2324, Adjusted R-squared: 0.1718
## F-statistic: 3.835 on 3 and 38 DF, p-value: 0.01711library(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
## -9.8381 -2.0616 -0.1292 1.6156 11.7534
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.5685535 1.6207965 0.351 0.728
## Sum_of_Latency 0.0007205 0.0012477 0.578 0.567
##
## Residual standard error: 4.136 on 38 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.0087, Adjusted R-squared: -0.01739
## F-statistic: 0.3335 on 1 and 38 DF, p-value: 0.567#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
## -8.9865 -2.2893 -0.4495 1.3924 10.1831
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.596053 2.027487 -0.787 0.436
## feedbackperformance 4.862228 3.214272 1.513 0.139
## Sum_of_Latency 0.001466 0.001528 0.959 0.344
## feedbackperformance:Sum_of_Latency -0.001943 0.002488 -0.781 0.440
##
## Residual standard error: 3.997 on 36 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.123, Adjusted R-squared: 0.04994
## F-statistic: 1.683 on 3 and 36 DF, p-value: 0.1878library(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 2 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 2 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
## -7.7364 -3.1721 0.0253 2.3874 9.4350
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.727e+00 3.825e+00 0.713 0.481
## Sum_of_Latency 1.324e-03 3.112e-03 0.425 0.673
## PreTest_Total_Score -3.198e-01 5.657e-01 -0.565 0.575
## Sum_of_Latency:PreTest_Total_Score -9.317e-05 4.662e-04 -0.200 0.843
##
## Residual standard error: 3.958 on 36 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1398, Adjusted R-squared: 0.06813
## F-statistic: 1.95 on 3 and 36 DF, p-value: 0.1389#Analysis 4-3 #Regression Model/Predict Latency（学習時間） #Pre-Test Score 75 percentile（N=42）
#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
## -639.36 -431.55 -90.66 246.77 1746.06
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1177.915 186.316 6.322 2.06e-07 ***
## PreTest_Total_Score 1.604 24.837 0.065 0.949
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 537.7 on 38 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.0001098, Adjusted R-squared: -0.0262
## F-statistic: 0.004173 on 1 and 38 DF, p-value: 0.9488#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
## -632.95 -430.55 -86.12 243.55 1740.70
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1191.30 120.25 9.907 4.42e-12 ***
## feedbackperformance -5.35 170.06 -0.031 0.975
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 537.8 on 38 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 2.605e-05, Adjusted R-squared: -0.02629
## F-statistic: 0.0009897 on 1 and 38 DF, p-value: 0.9751#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
## -620.92 -409.96 -93.39 231.10 1756.96
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1125.751 280.313 4.016 0.000288
## PreTest_Total_Score 9.857 37.849 0.260 0.796017
## feedbackperformance 95.250 383.603 0.248 0.805311
## PreTest_Total_Score:feedbackperformance -15.088 51.196 -0.295 0.769902
##
## (Intercept) ***
## PreTest_Total_Score
## feedbackperformance
## PreTest_Total_Score:feedbackperformance
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 551.8 on 36 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.002543, Adjusted R-squared: -0.08058
## F-statistic: 0.0306 on 3 and 36 DF, p-value: 0.9927library(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 2 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 2 rows containing missing values (`geom_point()`).
#Analysis 4-3 #Regression Model/Predict Math Motivation score (算数学習への意欲) #Pre-Test Score 75 percentile（N=42）
#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
## -22.768 -7.900 -1.584 9.108 21.012
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 51.394461 4.462262 11.518 5.84e-14 ***
## Sum_of_Latency -0.004728 0.003435 -1.376 0.177
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.39 on 38 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.04748, Adjusted R-squared: 0.02242
## F-statistic: 1.894 on 1 and 38 DF, p-value: 0.1768#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
## -23.785 -6.716 -1.396 9.320 19.829
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 51.088753 5.905887 8.650 2.57e-10 ***
## feedbackperformance 0.124973 9.362882 0.013 0.989
## Sum_of_Latency -0.005321 0.004450 -1.196 0.240
## feedbackperformance:Sum_of_Latency 0.001599 0.007247 0.221 0.827
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.64 on 36 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.05668, Adjusted R-squared: -0.02193
## F-statistic: 0.7211 on 3 and 36 DF, p-value: 0.546library(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 2 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 2 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
## -724.3 -370.9 -120.4 266.4 1697.4
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1428.05 218.04 6.549 1.01e-07 ***
## Test_anxiety -24.18 20.35 -1.189 0.242
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 528 on 38 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.03585, Adjusted R-squared: 0.01048
## F-statistic: 1.413 on 1 and 38 DF, p-value: 0.2419#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
## -27.9518 -6.6812 -0.9397 7.8431 22.1016
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 37.8886 3.6358 10.421 5.8e-13 ***
## PostTest_Total_Score 1.0049 0.3936 2.553 0.0146 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.77 on 40 degrees of freedom
## Multiple R-squared: 0.1401, Adjusted R-squared: 0.1186
## F-statistic: 6.517 on 1 and 40 DF, p-value: 0.01461#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
## -28.2852 -6.5495 -0.9628 7.2023 21.4533
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 38.7953 5.0459 7.689 2.94e-09
## feedbackperformance -1.9072 7.7364 -0.247 0.807
## PostTest_Total_Score 0.8757 0.6471 1.353 0.184
## feedbackperformance:PostTest_Total_Score 0.2318 0.8593 0.270 0.789
##
## (Intercept) ***
## feedbackperformance
## PostTest_Total_Score
## feedbackperformance:PostTest_Total_Score
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.04 on 38 degrees of freedom
## Multiple R-squared: 0.1417, Adjusted R-squared: 0.07398
## F-statistic: 2.092 on 3 and 38 DF, p-value: 0.1174library(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
## -25.098 -9.032 -1.442 10.192 19.750
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 45.8399 1.9083 24.021 <2e-16 ***
## Post_Pre 0.2052 0.4516 0.454 0.652
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.59 on 40 degrees of freedom
## Multiple R-squared: 0.005137, Adjusted R-squared: -0.01973
## F-statistic: 0.2065 on 1 and 40 DF, p-value: 0.6519#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
## -26.848 -7.844 -1.591 10.117 18.791
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 44.7654 2.6417 16.946 <2e-16 ***
## feedbackperformance 1.8575 4.0590 0.458 0.650
## Post_Pre -0.1029 0.7124 -0.144 0.886
## feedbackperformance:Post_Pre 0.3961 0.9733 0.407 0.686
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.8 on 38 degrees of freedom
## Multiple R-squared: 0.0193, Adjusted R-squared: -0.05812
## F-statistic: 0.2493 on 3 and 38 DF, p-value: 0.8613library(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 #Pre-Test Score 75 percentile（N=42）
#Comment:This result is the same as all students (N=65).
#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$Survey_Total_Score
## F = 0.84405, num df = 19, denom df = 21, p-value = 0.7146
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3455823 2.1041802
## sample estimates:
## ratio of variances
## 0.8440517t.test(Survey_Total_Score ~ feedback,var.equal=T,data = data)##
## Two Sample t-test
##
## data: Survey_Total_Score by feedback
## t = -0.74594, df = 40, p-value = 0.4601
## alternative hypothesis: true difference in means between group effort and group performance is not equal to 0
## 95 percent confidence interval:
## -9.863750 4.545568
## sample estimates:
## mean in group effort mean in group performance
## 44.75000 47.40909#T-test
#Intrinsic value between the effort and performance group
var.test(datae$Intrinsic_Value,datap$Intrinsic_Value)#等分散##
## F test to compare two variances
##
## data: datae$Intrinsic_Value and datap$Intrinsic_Value
## F = 0.9637, num df = 19, denom df = 21, p-value = 0.941
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.3945694 2.4024525
## sample estimates:
## ratio of variances
## 0.9636979t.test(Intrinsic_Value ~ feedback,var.equal=T,data = data)##
## Two Sample t-test
##
## data: Intrinsic_Value by feedback
## t = -0.72241, df = 40, p-value = 0.4742
## alternative hypothesis: true difference in means between group effort and group performance is not equal to 0
## 95 percent confidence interval:
## -2.796474 1.323747
## sample estimates:
## mean in group effort mean in group performance
## 9.40000 10.13636#T-test
#Self efficacy between the effort and performance group
var.test(datae$Self.efficacy,datap$Self.efficacy)#等分散##
## F test to compare two variances
##
## data: datae$Self.efficacy and datap$Self.efficacy
## F = 1.0201, num df = 19, denom df = 21, p-value = 0.959
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.4176529 2.5430030
## sample estimates:
## ratio of variances
## 1.020077t.test(Self.efficacy ~ feedback,var.equal=T,data = data)##
## Two Sample t-test
##
## data: Self.efficacy by feedback
## t = -0.5051, df = 40, p-value = 0.6163
## alternative hypothesis: true difference in means between group effort and group performance is not equal to 0
## 95 percent confidence interval:
## -3.000793 1.800793
## sample estimates:
## mean in group effort mean in group performance
## 11.9 12.5#T-test
#Self regulation between the effort and performance group
var.test(datae$Self.regulation,datap$Self.regulation)#等分散##
## F test to compare two variances
##
## data: datae$Self.regulation and datap$Self.regulation
## F = 1.5032, num df = 19, denom df = 21, p-value = 0.3647
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.6154652 3.7474422
## sample estimates:
## ratio of variances
## 1.503215t.test(Self.regulation ~ feedback,var.equal=T,data = data)##
## Two Sample t-test
##
## data: Self.regulation by feedback
## t = -0.79483, df = 40, p-value = 0.4314
## alternative hypothesis: true difference in means between group effort and group performance is not equal to 0
## 95 percent confidence interval:
## -2.512153 1.093971
## sample estimates:
## mean in group effort mean in group performance
## 13.70000 14.40909#T-test
#Anxiety between the effort and performance group
var.test(datae$Test_anxiety,datap$Test_anxiety)#等分散##
## F test to compare two variances
##
## data: datae$Test_anxiety and datap$Test_anxiety
## F = 0.73257, num df = 19, denom df = 21, p-value = 0.4992
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.2999372 1.8262562
## sample estimates:
## ratio of variances
## 0.7325678t.test(Test_anxiety ~ feedback,var.equal=T,data = data)##
## Two Sample t-test
##
## data: Test_anxiety by feedback
## t = -0.47248, df = 40, p-value = 0.6392
## alternative hypothesis: true difference in means between group effort and group performance is not equal to 0
## 95 percent confidence interval:
## -3.238530 2.011257
## sample estimates:
## mean in group effort mean in group performance
## 9.75000 10.36364#Analysis 6-1 #Correlation #Math Motivation Score #Pre-Test Score 75 percentile（N=42）
library(corrplot)## corrplot 0.92 loadedround(cor(datav),2)## ID feedback ITS class PreTest_Total_Score
## ID 1.00 -0.11 0.28 0.94 0.01
## feedback -0.11 1.00 0.08 -0.14 0.02
## ITS 0.28 0.08 1.00 0.23 -0.18
## class 0.94 -0.14 0.23 1.00 0.00
## PreTest_Total_Score 0.01 0.02 -0.18 0.00 1.00
## PostTest_Total_Score -0.16 0.32 -0.04 -0.20 0.48
## Post_Pre -0.18 0.32 0.11 -0.21 -0.35
## Intrinsic_Value -0.21 0.11 -0.21 -0.20 0.38
## Self.regulation -0.23 0.12 -0.19 -0.16 0.23
## Self.efficacy -0.23 0.08 -0.25 -0.25 0.27
## Test_anxiety 0.00 0.07 -0.25 -0.10 0.35
## Survey_Total_Score -0.20 0.12 -0.28 -0.21 0.38
## PostTest_Total_Score Post_Pre Intrinsic_Value
## ID -0.16 -0.18 -0.21
## feedback 0.32 0.32 0.11
## ITS -0.04 0.11 -0.21
## class -0.20 -0.21 -0.20
## PreTest_Total_Score 0.48 -0.35 0.38
## PostTest_Total_Score 1.00 0.65 0.30
## Post_Pre 0.65 1.00 -0.01
## Intrinsic_Value 0.30 -0.01 1.00
## Self.regulation 0.20 0.02 0.57
## Self.efficacy 0.26 0.04 0.74
## Test_anxiety 0.42 0.15 0.53
## Survey_Total_Score 0.37 0.07 0.86
## Self.regulation Self.efficacy Test_anxiety
## ID -0.23 -0.23 0.00
## feedback 0.12 0.08 0.07
## ITS -0.19 -0.25 -0.25
## class -0.16 -0.25 -0.10
## PreTest_Total_Score 0.23 0.27 0.35
## PostTest_Total_Score 0.20 0.26 0.42
## Post_Pre 0.02 0.04 0.15
## Intrinsic_Value 0.57 0.74 0.53
## Self.regulation 1.00 0.72 0.23
## Self.efficacy 0.72 1.00 0.50
## Test_anxiety 0.23 0.50 1.00
## Survey_Total_Score 0.74 0.91 0.74
## Survey_Total_Score
## ID -0.20
## feedback 0.12
## ITS -0.28
## class -0.21
## PreTest_Total_Score 0.38
## PostTest_Total_Score 0.37
## Post_Pre 0.07
## Intrinsic_Value 0.86
## Self.regulation 0.74
## Self.efficacy 0.91
## Test_anxiety 0.74
## 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$PreTest_Total_Score
## t = 2.5954, df = 40, p-value = 0.01315
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.08558573 0.61285844
## sample estimates:
## cor
## 0.3796418#Math Motivation Score × Post-test
cor.test(data$Survey_Total_Score,data$PostTest_Total_Score)##
## Pearson's product-moment correlation
##
## data: data$Survey_Total_Score and data$PostTest_Total_Score
## t = 2.5528, df = 40, p-value = 0.01461
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.07938783 0.60894701
## sample estimates:
## cor
## 0.3742882#Pre-Test × Post-Test
cor.test(data$PreTest_Total_Score,data$PostTest_Total_Score)##
## Pearson's product-moment correlation
##
## data: data$PreTest_Total_Score and data$PostTest_Total_Score
## t = 3.453, df = 40, p-value = 0.001324
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2051516 0.6835756
## sample estimates:
## cor
## 0.4792036#Intrinsic value × Post-test
cor.test(data$Intrinsic_Value,data$PostTest_Total_Score)##
## Pearson's product-moment correlation
##
## data: data$Intrinsic_Value and data$PostTest_Total_Score
## t = 2.0206, df = 40, p-value = 0.05005
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.0004391956 0.5567632484
## sample estimates:
## cor
## 0.3043299#Self efficacy × Post-test
cor.test(data$Self.efficacy,data$PostTest_Total_Score)##
## Pearson's product-moment correlation
##
## data: data$Self.efficacy and data$PostTest_Total_Score
## t = 1.6765, df = 40, p-value = 0.1014
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.05172905 0.51969044
## sample estimates:
## cor
## 0.2562308#Self regulation × Post-test
#p-value = 0.02902, cor 0.270957
cor.test(data$Self.regulation,data$PostTest_Total_Score)##
## Pearson's product-moment correlation
##
## data: data$Self.regulation and data$PostTest_Total_Score
## t = 1.2724, df = 40, p-value = 0.2106
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1135045 0.4728193
## sample estimates:
## cor
## 0.1972309#Test anxiety × Post-test
#p-value = 0.001859, cor 0.3788359
cor.test(data$Test_anxiety,data$PostTest_Total_Score)##
## Pearson's product-moment correlation
##
## data: data$Test_anxiety and data$PostTest_Total_Score
## t = 2.9373, df = 40, p-value = 0.005469
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1345126 0.6428545
## sample estimates:
## cor
## 0.4212233#Analysis 6-2 #Correlation #Score Increase #Pre-Test Score 75 percentile（N=42）
#Comment:There is not significant correlation between the Math motivation score and the Score increase.
##No significant##
#Math motivation score × Score increase（増加量）
cor.test(data$Survey_Total_Score,data$Post_Pre)##
## Pearson's product-moment correlation
##
## data: data$Survey_Total_Score and data$Post_Pre
## t = 0.45447, df = 40, p-value = 0.6519
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2374297 0.3675975
## sample estimates:
## cor
## 0.07167384#Intrinsic value × Score increase
cor.test(data$Intrinsic_Value,data$Post_Pre)##
## Pearson's product-moment correlation
##
## data: data$Intrinsic_Value and data$Post_Pre
## t = -0.04359, df = 40, p-value = 0.9654
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3101737 0.2976628
## sample estimates:
## cor
## -0.006892088#Self efficacy × Score increase
cor.test(data$Self.efficacy,data$Post_Pre)##
## Pearson's product-moment correlation
##
## data: data$Self.efficacy and data$Post_Pre
## t = 0.28029, df = 40, p-value = 0.7807
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2631990 0.3435821
## sample estimates:
## cor
## 0.04427403#Self regulation × Score increase
cor.test(data$Self.regulation,data$Post_Pre)##
## Pearson's product-moment correlation
##
## data: data$Self.regulation and data$Post_Pre
## t = 0.1014, df = 40, p-value = 0.9197
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2893105 0.3184104
## sample estimates:
## cor
## 0.01603043#Test anxiety × Score increase
cor.test(data$Test_anxiety,data$Post_Pre)##
## Pearson's product-moment correlation
##
## data: data$Test_anxiety and data$Post_Pre
## t = 0.96865, df = 40, p-value = 0.3385
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1598975 0.4352936
## sample estimates:
## cor
## 0.1513912#Analysis 6-3 #Correlation #Latency #Pre-Test Score 75 percentile（N=42）
library(corrplot)
round(cor(datalatencyv),2)## ID Sum_of_Latency feedback ITS class
## ID 1.00 -0.01 -0.20 0.29 0.93
## Sum_of_Latency -0.01 1.00 -0.01 0.07 0.04
## feedback -0.20 -0.01 1.00 0.07 -0.22
## ITS 0.29 0.07 0.07 1.00 0.22
## class 0.93 0.04 -0.22 0.22 1.00
## PreTest_Total_Score -0.02 0.01 0.01 -0.19 -0.03
## PostTest_Total_Score -0.22 0.10 0.31 -0.05 -0.25
## Post_Pre -0.21 0.09 0.31 0.11 -0.24
## Intrinsic_Value -0.29 -0.15 0.08 -0.22 -0.26
## Self.regulation -0.25 -0.26 0.13 -0.20 -0.17
## Self.efficacy -0.28 -0.12 0.06 -0.26 -0.29
## Test_anxiety -0.07 -0.19 0.04 -0.27 -0.16
## Survey_Total_Score -0.27 -0.22 0.09 -0.30 -0.28
## PreTest_Total_Score PostTest_Total_Score Post_Pre
## ID -0.02 -0.22 -0.21
## Sum_of_Latency 0.01 0.10 0.09
## feedback 0.01 0.31 0.31
## ITS -0.19 -0.05 0.11
## class -0.03 -0.25 -0.24
## PreTest_Total_Score 1.00 0.46 -0.36
## PostTest_Total_Score 0.46 1.00 0.66
## Post_Pre -0.36 0.66 1.00
## Intrinsic_Value 0.40 0.30 -0.02
## Self.regulation 0.28 0.24 0.01
## Self.efficacy 0.28 0.26 0.04
## Test_anxiety 0.37 0.43 0.14
## Survey_Total_Score 0.41 0.39 0.06
## Intrinsic_Value Self.regulation Self.efficacy Test_anxiety
## ID -0.29 -0.25 -0.28 -0.07
## Sum_of_Latency -0.15 -0.26 -0.12 -0.19
## feedback 0.08 0.13 0.06 0.04
## ITS -0.22 -0.20 -0.26 -0.27
## class -0.26 -0.17 -0.29 -0.16
## PreTest_Total_Score 0.40 0.28 0.28 0.37
## PostTest_Total_Score 0.30 0.24 0.26 0.43
## Post_Pre -0.02 0.01 0.04 0.14
## Intrinsic_Value 1.00 0.58 0.73 0.51
## Self.regulation 0.58 1.00 0.73 0.21
## Self.efficacy 0.73 0.73 1.00 0.49
## Test_anxiety 0.51 0.21 0.49 1.00
## Survey_Total_Score 0.86 0.74 0.91 0.73
## Survey_Total_Score
## ID -0.27
## Sum_of_Latency -0.22
## feedback 0.09
## ITS -0.30
## class -0.28
## PreTest_Total_Score 0.41
## PostTest_Total_Score 0.39
## Post_Pre 0.06
## Intrinsic_Value 0.86
## Self.regulation 0.74
## Self.efficacy 0.91
## Test_anxiety 0.73
## 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$Sum_of_Latency
## t = 0.0646, df = 38, p-value = 0.9488
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3020163 0.3209406
## sample estimates:
## cor
## 0.01047894#Post-test × Latency
cor.test(datalatency$PostTest_Total_Score,datalatency$Sum_of_Latency,use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: datalatency$PostTest_Total_Score and datalatency$Sum_of_Latency
## t = 0.60139, df = 38, p-value = 0.5512
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2211000 0.3966101
## sample estimates:
## cor
## 0.09709686#Score increase（増加量）× Latency
cor.test(datalatency$Post_Pre,datalatency$Sum_of_Latency,use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: datalatency$Post_Pre and datalatency$Sum_of_Latency
## t = 0.57751, df = 38, p-value = 0.567
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2247645 0.3933556
## sample estimates:
## cor
## 0.09327565#Comment:There is no significant correlation between "Math Motivation score" and "Latency". This result is opposite of N=65 and N=59.
### Math Motivation score × Latency ###
#Math Motivation Score × Latency
cor.test(datalatency$Survey_Total_Score,datalatency$Sum_of_Latency,use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: datalatency$Survey_Total_Score and datalatency$Sum_of_Latency
## t = -1.3763, df = 38, p-value = 0.1768
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4957634 0.1004194
## sample estimates:
## cor
## -0.2179064#Intrinsic value × Latency
cor.test(datalatency$Intrinsic_Value,datalatency$Sum_of_Latency,use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: datalatency$Intrinsic_Value and datalatency$Sum_of_Latency
## t = -0.95926, df = 38, p-value = 0.3435
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4440040 0.1656836
## sample estimates:
## cor
## -0.1537616#Self efficacy × Latency
cor.test(datalatency$Self.efficacy,datalatency$Sum_of_Latency,use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: datalatency$Self.efficacy and datalatency$Sum_of_Latency
## t = -0.76664, df = 38, p-value = 0.448
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4188231 0.1956143
## sample estimates:
## cor
## -0.1234155#Self regulation × Latency
cor.test(datalatency$Self.regulation,datalatency$Sum_of_Latency,use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: datalatency$Self.regulation and datalatency$Sum_of_Latency
## t = -1.6329, df = 38, p-value = 0.1108
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.5256385 0.0602598
## sample estimates:
## cor
## -0.2560561#Test anxiety × Latency
cor.test(datalatency$Test_anxiety,datalatency$Sum_of_Latency,use = "complete.obs")##
## Pearson's product-moment correlation
##
## data: datalatency$Test_anxiety and datalatency$Sum_of_Latency
## t = -1.1887, df = 38, p-value = 0.2419
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4729542 0.1298264
## sample estimates:
## cor
## -0.1893403