& (assessment1A['perCorrect1A'] < 0.6)]
ex2_excellent = assessment2A.loc[(assessment2A['perCorrect2A'] <= 1)
& (assessment2A['perCorrect2A'] >= 0.5)]
ex2_weak = assessment2A.loc[(assessment2A['perCorrect2A'] >= 0)
& (assessment2A['perCorrect2A'] < 0.5)]
nonExUpload = dataUpload.drop(
dataUpload.loc[dataUpload['task'].str.match('ex')].index)
nonExUploadByWeek = [
g for n, g in nonExUpload.groupby(pd.Grouper(key='date', freq='W'))
]
nonExUpload['version'].unique()
#merge exam result with transition data matrix:
reLabelIndex = dataProcessing.reLabelStudentId(assessment.index)
#practice results
workingWeekExcercise = []
cummulativeExerciseWeeks = []
for week in range(0, 12):
workingWeekExcercise.append(nonExUploadByWeek[week])
practiceResult = pd.concat(workingWeekExcercise) #nonExUploadByWeek[week]
#adjust number of correct: For each task, number of correct submission/number of submission for that task
practiceResultSum = practiceResult.groupby(
[pd.Grouper(key='user'),
pd.Grouper(key='task')]).sum()
practiceResultSum['correct_adjusted'] = practiceResultSum[
'correct'] / practiceResult.groupby(
listIndex = []
for w in range(0, 10):
# ca1162018_transitionDataMatrixWeeks[w]['covid'] = 0
# ca1162019_transitionDataMatrixWeeks[w]['covid'] = 0
# ca1162020_transitionDataMatrixWeeks[w]['covid'] = 1
transitionDataMatrixWeeks.append(
pd.concat([
ca1162018_transitionDataMatrixWeeks[w],
ca1162019_transitionDataMatrixWeeks[w],
ca1162020_transitionDataMatrixWeeks[w]
],
join='inner'))
listIndex = listIndex + list(transitionDataMatrixWeeks[w].index)
listIndex = list(set(listIndex))
reLabelIndex = dataProcessing.reLabelStudentId(listIndex)
for w in range(0, 10):
ca1162018_transitionDataMatrixWeeks[w] = graphLearning.mapNewLabel(
ca1162018_transitionDataMatrixWeeks[w], reLabelIndex)
ca1162019_transitionDataMatrixWeeks[w] = graphLearning.mapNewLabel(
ca1162019_transitionDataMatrixWeeks[w], reLabelIndex)
ca1162020_transitionDataMatrixWeeks[w] = graphLearning.mapNewLabel(
ca1162020_transitionDataMatrixWeeks[w], reLabelIndex)
transitionDataMatrixWeeks[w] = graphLearning.mapNewLabel(
transitionDataMatrixWeeks[w], reLabelIndex)
transitionDataMatrixWeeks_directFollow_standardised = []
for w in range(0, 10):
transitionDataMatrixWeeks_directFollow_standardised.append(
dataProcessing.normaliseData(transitionDataMatrixWeeks[w].T))