File size: 4,541 Bytes
a093ba2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 | #!/usr/bin/env python3
"""
ASSISTments 2012-2013 Processing Script for DIF Analysis
=========================================================
The 2012-2013 "School Data with Affect" version is the ONLY ASSISTments
release with both demographics (gender, ELL, IEP, economically_disadvantaged)
AND school_id. NO ASSISTments version has question text.
MANUAL DOWNLOAD REQUIRED:
1. Go to: https://sites.google.com/site/assistmentsdata/datasets/2012-13-school-data-with-affect
2. Click the Google Drive link to download the CSV
3. Save as 'assist2012.csv' in the same directory as this script
4. Run: python process_assistments_2012.py
Expected columns in 2012-2013:
student_id, problem_id, skill_id, skill_name, correct, attempt_count,
hint_count, ms_first_response, school_id, teacher_id, gender,
economically_disadvantaged, ELL, IEP
NO question text in any column.
"""
import os, sys, json
import pandas as pd
# Find the CSV
candidates = [
"assist2012.csv",
"2012-2013-data-with-predictions-4-students.csv",
"ASSISTments2012-13.csv",
]
csv_path = None
for c in candidates:
if os.path.exists(c):
csv_path = c
break
if csv_path is None:
# Try any CSV in current dir
csvs = [f for f in os.listdir('.') if f.endswith('.csv') and 'assist' in f.lower()]
if csvs:
csv_path = csvs[0]
if csv_path is None:
print("ERROR: No ASSISTments CSV found.")
print("Download from: https://sites.google.com/site/assistmentsdata/datasets/2012-13-school-data-with-affect")
print("Save as 'assist2012.csv' and re-run.")
sys.exit(1)
OUT = "assistments_2012_processed"
os.makedirs(OUT, exist_ok=True)
# Load
print(f"Loading {csv_path}...")
df = pd.read_csv(csv_path, low_memory=False)
print(f"Shape: {df.shape}")
print(f"Columns: {list(df.columns)}")
# Check demographics
demo_cols = ['gender', 'school_id', 'economically_disadvantaged', 'ELL', 'IEP']
for col in demo_cols:
matches = [c for c in df.columns if col.lower() in c.lower()]
if matches:
c = matches[0]
print(f"\n{c}:")
print(f" Distribution: {df[c].value_counts().head(5).to_dict()}")
print(f" Missing: {df[c].isna().sum()} ({df[c].isna().mean()*100:.1f}%)")
else:
print(f"\n{col}: NOT FOUND")
# Save as parquet
print(f"\nSaving to {OUT}/...")
df.to_parquet(os.path.join(OUT, "assist2012_full.parquet"), index=False)
# Item stats by skill
if 'correct' in df.columns:
skill_col = [c for c in df.columns if 'skill' in c.lower() and 'name' in c.lower()]
if not skill_col:
skill_col = [c for c in df.columns if 'skill' in c.lower() and 'id' in c.lower()]
if skill_col:
item_stats = df.groupby(skill_col[0]).agg(
n_responses=('correct', 'count'),
p_value=('correct', 'mean'),
).reset_index().sort_values('n_responses', ascending=False)
item_stats.to_parquet(os.path.join(OUT, "item_stats.parquet"), index=False)
print(f"\nItem stats: {len(item_stats)} skills")
print(item_stats.head(10).to_string())
# Student stats
if 'student_id' in [c.lower() for c in df.columns]:
sid_col = [c for c in df.columns if c.lower() == 'student_id' or c.lower() == 'user_id'][0]
stu = df.groupby(sid_col).agg(
n_items=('correct', 'count'),
accuracy=('correct', 'mean'),
).reset_index()
stu.to_parquet(os.path.join(OUT, "student_stats.parquet"), index=False)
print(f"\nStudents: {len(stu)}")
# DIF-ready export: gender × item response
gender_col = [c for c in df.columns if 'gender' in c.lower()]
if gender_col:
g = gender_col[0]
print(f"\nGender column '{g}': {df[g].value_counts().to_dict()}")
print(f"Missing gender: {df[g].isna().sum()} ({df[g].isna().mean()*100:.1f}%)")
# Filter to rows with valid gender
dif_data = df[df[g].notna()].copy()
dif_data.to_parquet(os.path.join(OUT, "dif_ready_with_gender.parquet"), index=False)
print(f"DIF-ready rows (with gender): {len(dif_data):,}")
summary = {
"dataset": "ASSISTments 2012-2013 (School Data with Affect)",
"rows": len(df),
"columns": list(df.columns),
"has_question_text": False,
"question_text_note": "NO question text in any public ASSISTments release. Only skill_name available.",
}
with open(os.path.join(OUT, "summary.json"), "w") as f:
json.dump(summary, f, indent=2, default=str)
print(f"\nDone! Files in: {OUT}/")
for f in sorted(os.listdir(OUT)):
size = os.path.getsize(os.path.join(OUT, f)) / 1e6
print(f" {f}: {size:.1f} MB")
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