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canvasapp/outcomes2022.py

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# Outcomes 2023
# Tasks:
#
# - List all courses (in semester) in iLearn:
# + SLOs associated with the course
# + Whether they are current or inactive
# + Whether they are attached to an assessment
# + Whether and by how many students, they have been assessed
#
# - Fetch most current SLOs from Curricunet
# + Assemble multiple versions of a (CQ) course and determine which semesters they apply to
# + Whether they are present in the relevant classes in iLearn
# + Insert SLO into course if not present
# + Mark as inactive (change name) if necessary
#
# - Update shell with correct outcomes
# - Issue:
# + Course naming / sections joined...
import concurrent.futures
import pandas as pd
from pipelines import fetch, url, header
from outcomes import quick_add_course_outcomes, code_from_ilearn_name, all_linked_outcomes_in_term
from courses import getCoursesInTerm, getCourses
import codecs, json, sys, re, csv, requests, textwrap
from path_dict import PathDict
outputfile = ''
csvwriter = ''
# 289 2025 Fall
# 288 2025 Summer
# 287 2025 Spring
# 286 2025 Winter
# 184 2024 Fall
# 183 2024 Summer
# 181 2024 Spring
# 182 2024 Winter
TERM = 287
# TERM = 286 # fall = 287
def escape_commas(s):
if ',' in s:
return '"' + s.replace('"', '""') + '"'
else:
return s
def add_outcome_to_course(shell_id=''):
if shell_id == '':
shell_id = input("Enter shell id > ")
course = getCourses(str(shell_id))
dept, code, crn = code_from_ilearn_name(course['name'])
print(f"{dept} {code} {crn} for course named: {course['name']}")
#xyz = input(f"Using: {code} for {course['name']}. Enter a different code, q to skip or press enter to continue > ")
#if xyz == 'q':
# return
#if xyz != '':
# code = xyz
cq_course_id = find_recent_cqcourseid(code)
oc = codecs.open('cache/courses/alloutcomes.csv','r','utf-8')
reader = csv.reader(oc)
cols = next(reader) # skip header
# Filter rows matching the code
rows = [row for row in reader if row[1] == cq_course_id]
rows_dicts = [ {cols[i]: r[i] for i in range(len(cols))} for r in rows ]
#abc = input(f"Using outcomes:\n{rows_dicts}\n\nPress enter to continue > ")
quick_add_course_outcomes(shell_id, rows_dicts)
def course_slo_getter(q):
global outputfile, csvwriter
(name,id) = q
info = {'ilearnname':name,'ilearnid':id}
print(" + Thread getting %s %s" % (str(name),str(id)))
# Get GROUPS for a course
u1 = url + "/api/v1/courses/%s/outcome_groups" % str(id)
og_for_course = fetch(u1)
if len(og_for_course):
# There is a GROUP...
for og in og_for_course:
if "outcomes_url" in og:
# There are OUTCOMES...
outcomes = fetch(url + og["outcomes_url"])
og['outcomes'] = outcomes
og['full_outcomes'] = {}
for oo in outcomes:
print(" -> " + url + oo['outcome']['url'])
this_outcome = fetch( url + oo['outcome']['url'] )
og['full_outcomes'][this_outcome['id']] = this_outcome
saveme = [name, this_outcome['assessed'], id, this_outcome['id'], this_outcome['points_possible'], this_outcome['title'], this_outcome['display_name'], this_outcome['description'], this_outcome['vendor_guid'] ]
saveme2 = [escape_commas(str(x)) for x in saveme]
csvwriter.writerow([id, name, og['id'], this_outcome['id'], this_outcome['vendor_guid'], this_outcome['points_possible'], this_outcome['mastery_points'], this_outcome['assessed'], this_outcome['description']])
outputfile.write(",".join(saveme2) + "\n")
outputfile.flush()
if type(og_for_course) == list:
og_for_course.insert(0,info)
else:
og_for_course.update(info)
print(" - Thread %s DONE" % str(id))
return og_for_course
# I duplicate???
def outcomes_in_shell(course_id):
print(f"Getting root outcome group for course id {course_id}")
root_og = fetch(f"{url}/api/v1/courses/{course_id}/root_outcome_group")
print(f"Getting outcomes")
u1 =f"{url}/api/v1/courses/{course_id}/outcome_groups/{root_og['id']}/outcomes"
outcomes_list = fetch( u1 )
the_outcomes = []
if 'errors' in outcomes_list:
print(f"Error: {outcomes_list}")
if len(outcomes_list):
for oo in outcomes_list:
print(f"Getting outcome id {oo['outcome']['id']}")
outcome = fetch( url + '/api/v1/outcomes/%s' % str(oo['outcome']['id']) )
outcome['parent_group'] = root_og['id']
the_outcomes.append(outcome)
u2 = f"{url}/api/v1/courses/{course_id}/outcome_groups/{root_og['id']}/subgroups"
g2 = fetch( u2 )
for subgroup in g2:
print("doing subgroup id %s" % str(subgroup['id']))
u3 = f"{url}/api/v1/courses/{course_id}/outcome_groups/{subgroup['id']}/outcomes"
outcomes_list = fetch( u3 )
if 'errors' in outcomes_list:
print(f"Error: {outcomes_list}")
continue
if len(outcomes_list):
for oo in outcomes_list:
outcome = fetch( f"{url}/api/v1/outcomes/{oo['outcome']['id']}" )
outcome['parent_group'] = subgroup['id']
the_outcomes.append(outcome)
return root_og, the_outcomes, g2
def ilearn_shell_slo_to_csv(shell_slos):
L = ['canvasid','name','crn','has_outcomes',]
for i in range(1,11):
L.append("o%i_id" % i)
L.append("o%i_vendor_guid" % i)
L.append("o%i_desc" % i)
L.append("o%i_assd" % i)
df = pd.DataFrame(columns=L)
for S in shell_slos:
try:
short = S[0]
this_crs = {'canvasid':short['ilearnid'], 'name':short['ilearnname'], 'has_outcomes':0, }
if len(S)>1:
full = S[1]
this_crs['has_outcomes'] = 1
i = 1
for o in full['outcomes']:
try:
this_id = int(o['outcome']['id'])
this_crs['o%i_id' % i] = o['outcome']['id']
except Exception as e:
this_crs['o%i_id' % i] = '!'
try:
this_crs['o%i_desc' % i] = full['full_outcomes'][this_id]['description']
except Exception as e:
this_crs['o%i_desc' % i] = '!'
try:
assessed = 0
if full['full_outcomes'][this_id]['assessed'] == 'True':
assessed = 1
this_crs['o%i_assd' % i] = assessed
except Exception as e:
this_crs['o%i_assd' % i] = '!'
try:
this_crs['o%i_vendor_guid' % i] = full['full_outcomes'][this_id]['vendor_guid']
except Exception as e:
this_crs['o%i_vendor_guid' % i] = '!'
i += 1
df2 = pd.DataFrame(this_crs, columns = df.columns, index=[0])
df = pd.concat( [df, df2], ignore_index = True )
except Exception as e:
print(f"*** Exception {e} with {S}\n\n")
df.to_csv(f'cache/outcome_{TERM}.csv')
print(df)
def get_outcomes_term_index():
global outputfile, csvwriter
NUM_THREADS = 20
get_fresh = 0
sem_courses = getCoursesInTerm(TERM,get_fresh)
# shorter list for test?
#sem_courses = sem_courses[:50]
print("Got %i courses in current semester." % len(sem_courses))
outputfile = codecs.open(f'cache/slo/outcomes_bycourse_{TERM}.output.txt','w','utf-8')
outputfile.write( "coursename,assessed,courseid,outcome_id,points,title,displayname,description,guid\n")
csvfile = codecs.open(f'cache/slo/linked_slos_term_{TERM}_compact.csv','w','utf-8')
csvwriter = csv.writer(csvfile)
csvwriter.writerow('courseid coursename ogid oid vendorguid points mastery assessed desc'.split(' '))
raw_log = codecs.open('cache/outcome_raw_log.txt','w','utf-8')
#raw_log.write( json.dumps(output,indent=2) )
output = []
with concurrent.futures.ThreadPoolExecutor(max_workers=NUM_THREADS) as pool:
futures = []
for C in sem_courses:
print("Adding ", C['name'], C['id'], " to queue")
futures.append( pool.submit(course_slo_getter, [C['name'], C['id']] ) )
for future in concurrent.futures.as_completed(futures):
output.append(future.result())
print(future.result())
raw_log.write( json.dumps(future.result(),indent=2) + "\n" )
csvfile.close()
ilearn_shell_slo_to_csv(output)
def classify_shell(lines):
# given a list of lines like this, determine status of shell
# (from linked_slos_term_180_compact.csv) outcomes.py all_linked_outcomes_in_term()
#
# courseid,coursename,ogid,oid,vendorguid,points,mastery,assessed,desc
# 16909,AH11 FA23 10003/10014/12251,10860,819,,5,3,False,Use scientific facts and principles to critically analyze nutrition information and use the information to assess personal diet and the diets of other cultures.
# 16909,AH11 FA23 10003/10014/12251,10860,820,,5,3,False,Evaluate nutrition information for accuracy and reliability.
# 16909,AH11 FA23 10003/10014/12251,10860,821,,5,3,False,Analyze and identify the relationship between nutrition and health.
# 16909,AH11 FA23 10003/10014/12251,10860,822,,5,3,False,Differentiate among food habits and practices related to traditional foods and preparation techniques in selected cultures or religions.
# 16909,AH11 FA23 10003/10014/12251,10860,823,,5,3,False,Analyze nutritional problems of selected cultures and create a nutritionally balanced menu.
#
# 1. number of outcomes
# 2. points are correct (max=3,mastery=2) or incorrect (max=5,mastery=3)
# 3. assessed or not
course_status = {'outcome_count':0, 'id':0, 'name':'', 'assessed_count':0, 'points_ok':1}
for L in lines:
#print(L)
#L = L.split(',')
course_status['outcome_count'] += 1
course_status['id'] = L[0]
course_status['name'] = L[1]
outcome_status = {'courseid':L[0],'coursename':L[1],'ogid':L[2],'oid':L[3],'vendorguid':L[4],'points':L[5],'mastery':L[6],'assessed':L[7],'desc':L[8], 'pointscorrect':0}
if L[5] == '5' and L[6] == '3':
outcome_status['pointscorrect'] = 0
course_status['points_ok'] = 0
elif (L[5] == '3.0' or L[5] == '3') and L[6] == '2':
outcome_status['pointscorrect'] = 1
else:
outcome_status['pointscorrect'] = -1
if L[7] == 'True':
course_status['assessed_count'] += 1
return course_status
def find_recent_cqcourseid(code):
# code example: CSIS42
with open('cache/courses/all_courses_ranked.csv', 'r') as f:
reader = csv.reader(f)
next(reader) # skip header
# Filter rows matching the code
rows = [row for row in reader if row[0] == code]
print(f"All entries for {code}:\n{rows}")
if not rows:
raise ValueError(f"No rows found for code {code}")
# Sort by 'termineffect', in descending order
rows.sort(key=lambda row: row[3], reverse=True)
# Return cqcourseid of the first row
myrow = rows[0][1]
print(f"Using: {myrow}")
return myrow
def remove_old_outcomes(course_id):
root_og, current_outcomes, subgroups = outcomes_in_shell(course_id)
print(f"Got {len(current_outcomes)} outcomes for course id {course_id}")
print(f"Current outcomes:\n{json.dumps(current_outcomes,indent=2)}")
# Try deleting them
if 1:
for deleted_outcome in current_outcomes:
print(f"Deleting outcome id {deleted_outcome['id']}")
u9 = f"{url}/api/v1/courses/{course_id}/outcome_groups/{deleted_outcome['parent_group']}/outcomes/{deleted_outcome['id']}"
print(u9)
# make the DELETE request (update with your actual access token)
response = requests.delete(u9, headers=header)
# check the status of the request
if response.status_code == 200:
print(' Delete operation was successful')
else:
print(' Failed to delete, response code:', response.status_code)
print(' Response message:', response.text)
def repair_outcome_points(course_id):
# Compare to what Outcomes SHOULD be
course = getCourses(course_id)
dept, code, crn = code_from_ilearn_name(course['name'])
xyz = input(f"Using: {code} for {course['name']}. Enter a different code or press enter to continue > ")
if xyz != '':
code = xyz
cq_course_id = find_recent_cqcourseid(code)
oc = codecs.open('cache/courses/alloutcomes.csv','r','utf-8')
reader = csv.reader(oc)
cols = next(reader) # skip header
# Filter rows matching the code
rows = [row for row in reader if row[1] == cq_course_id]
rows_dicts = [ {cols[i]: r[i] for i in range(len(cols))} for r in rows ]
abc = input(f"Using outcomes:\n{json.dumps(rows_dicts,indent=2)}\n\nPress enter to continue > ")
return
outcome_id = 0
data = {
'mastery_points': '2',
'calculation_method': 'decaying_average',
'calculation_int': '65',
'ratings[0][description]': 'Exceeds Expectations',
'ratings[0][points]': '3',
'ratings[1][description]': 'Meets Expectations',
'ratings[1][points]': '2',
'ratings[2][description]': 'Does Not Meet Expectations',
'ratings[2][points]': '0'
}
response = requests.put(f'{url}/api/v1/outcomes/{outcome_id}.json', headers=header, data=data)
if response.status_code == 200:
print(f"Successfully updated outcome with id {outcome_id}.")
else:
print(f"Failed to update outcome with id {outcome_id}. Error: {response.text}")
def add_o_dept_dry_run():
add_o_dept(1)
def add_o_whole_term():
course_groups = full_term_overview(0)
dept_shells_to_add = [ a for a in course_groups['no outcomes'] ]
sorted_dept_shells_to_add = sorted(dept_shells_to_add, key=lambda x: f"{x['dept']}{x['code']}")
print(f"Adding to {len(sorted_dept_shells_to_add)} shells.")
for shell in sorted_dept_shells_to_add:
print(f"Adding outcomes to {shell['name']}")
try:
add_outcome_to_course(shell['id'])
except Exception as e:
print(f"Failed on {shell['id']}: {e}")
def add_o_dept(dry_run=0):
d = input("Enter dept or deps separated with a space > ")
d_list = d.split(' ')
course_groups = full_term_overview(0)
dept_shells_to_add = [ a for a in course_groups['no outcomes'] if a['dept'] in d_list ]
sorted_dept_shells_to_add = sorted(dept_shells_to_add, key=lambda x: f"{x['dept']}{x['code']}")
print(f"Adding to {len(sorted_dept_shells_to_add)} shells.")
for shell in sorted_dept_shells_to_add:
print(f"Adding outcomes to {shell['name']}")
if not dry_run:
try:
add_outcome_to_course(shell['id'])
except Exception as e:
print(f"Failed on {shell['id']}: {e}")
else:
print(" Dry run, not adding")
def remove_all_bad_points():
course_groups = full_term_overview(0)
dept_shells_to_zap = [ a for a in course_groups['fix_points'] ]
for shell in dept_shells_to_zap:
print(f"Removing outcomes from {shell['name']}")
remove_old_outcomes(shell['id'])
def full_term_overview(verbose=1):
out2 = codecs.open(f'cache/slo/slo_status_{TERM}.json','w','utf-8')
out3 = codecs.open(f'cache/slo/slo_status_{TERM}.csv','w','utf-8')
csv_fields = 'outcome_count,id,name,dept,code,crn,assessed_count,points_ok'.split(',')
fn1 = f"cache/courses_in_term_{TERM}.json"
all_courses = json.loads(codecs.open(fn1,'r','utf-8').read())
all_courses_status = {}
# default values for all courses
for C in all_courses:
dept,code,crn = code_from_ilearn_name(C['name'])
all_courses_status[str(C['id'])] = {'outcome_count':0, 'id':C['id'], 'name':C['name'], 'dept':dept, 'code':code, 'crn':crn, 'assessed_count':0, 'points_ok':1}
# read the existing ilearn outcomes and group by shell
filename = f"cache/slo/linked_slos_term_{TERM}_compact.csv"
with open(filename, 'r') as csvfile:
reader = csv.reader(csvfile)
next(reader) # skip header
# Read the rows into a list
rows = list(reader)
# Sort the rows based on a specific column (e.g., column 0)
sorted_rows = sorted(rows, key=lambda x: x[0])
groups = []
current_group = []
last_courseid = None
for row in sorted_rows:
courseid = row[0]
if last_courseid != courseid and current_group:
# courseid changed from last row to current row
groups.append(current_group)
current_group = []
current_group.append(row)
last_courseid = courseid
# append the last group if any
if current_group:
groups.append(current_group)
for g in groups:
classified = classify_shell(g)
dept,code,crn = code_from_ilearn_name(g[0][1])
classified['dept'] = dept
classified['code'] = code
classified['crn'] = crn
all_courses_status[str(classified['id'])] = classified
#for C in all_courses_status:
# print(all_courses_status[C])
course_groups = {
'no outcomes': [],
'ok': [],
'fix_points_and_scores': [],
'fix_points': []
}
for d in all_courses_status.values():
outcome_count = d['outcome_count']
points_ok = d['points_ok']
assessed_count = d['assessed_count']
if outcome_count == 0:
course_groups['no outcomes'].append(d)
elif points_ok == 1 and assessed_count > 0:
course_groups['fix_points_and_scores'].append(d)
elif points_ok == 0 and assessed_count > 0:
course_groups['fix_points_and_scores'].append(d)
elif points_ok == 1:
course_groups['ok'].append(d)
elif points_ok == 0:
course_groups['fix_points'].append(d)
# Print out the groups
out2.write(json.dumps(course_groups,indent=2))
if verbose:
cwriter = csv.DictWriter(out3,fieldnames=csv_fields)
cwriter.writeheader()
for group, dicts in course_groups.items():
sorted_dicts = sorted(dicts, key=lambda x: f"{x['dept']}{x['code']}")
print(f"{group} - {len(sorted_dicts)} item(s)")
cwriter.writerows(sorted_dicts)
# out3.write(f"{group} - {len(sorted_dicts)} item(s)\n")
for d in sorted_dicts:
print(d)
#out3.write(str(d) + "\n")
print("\n")
out3.write("\n")
return course_groups
def fetch_term_outcomes_and_report():
get_outcomes_term_index()
full_term_overview()
if __name__ == "__main__":
options = { 1: ['Refresh term outcome list & report', fetch_term_outcomes_and_report],
2: ['Add outcomes to unset courses in whole term', add_o_whole_term],
3: ['Add outcomes to course id', add_outcome_to_course],
4: ['Fix outcome points', remove_old_outcomes],
5: ['Add outcomes to dept, dry run', add_o_dept_dry_run],
6: ['Add outcomes to dept', add_o_dept],
7: ['Remove all outcomes with wrong points', remove_all_bad_points],
}
print ('')
if len(sys.argv) > 1 and re.search(r'^\d+',sys.argv[1]):
resp = int(sys.argv[1])
print("\n\nPerforming: %s\n\n" % options[resp][0])
else:
print ('')
for key in options:
print(str(key) + '.\t' + options[key][0])
print('')
resp = input('Choose: ')
# Call the function in the options dict
options[ int(resp)][1]()
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