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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
course_id: string
cutoff_week: int64
students: list<item: struct<student_id: string, weeks: list<item: struct<week: int64, activities: list<item: s (... 69 chars omitted)
child 0, item: struct<student_id: string, weeks: list<item: struct<week: int64, activities: list<item: struct<name: (... 57 chars omitted)
child 0, student_id: string
child 1, weeks: list<item: struct<week: int64, activities: list<item: struct<name: string, type: string, weight: dou (... 22 chars omitted)
child 0, item: struct<week: int64, activities: list<item: struct<name: string, type: string, weight: double, score: (... 10 chars omitted)
child 0, week: int64
child 1, activities: list<item: struct<name: string, type: string, weight: double, score: double>>
child 0, item: struct<name: string, type: string, weight: double, score: double>
child 0, name: string
child 1, type: string
child 2, weight: double
child 3, score: double
course_info: struct<avg_gpa: double, pct_A: double, pct_B: double, pct_C: double, pct_D: double, pct_F: double, g (... 23 chars omitted)
child 0, avg_gpa: double
child 1, pct_A: double
child 2, pct_B: double
child 3, pct_C: double
child 4, pct_D: double
child 5, pct_F: double
child 6, grading_scale_A: double
to
{'course_id': Value('string'), 'course_info': {'avg_gpa': Value('float64'), 'pct_A': Value('float64'), 'pct_B': Value('float64'), 'pct_C': Value('float64'), 'pct_D': Value('float64'), 'pct_F': Value('float64'), 'grading_scale_A': Value('float64')}, 'students': List({'student_id': Value('string'), 'weeks': List({'week': Value('int64'), 'activities': List({'name': Value('string'), 'type': Value('string'), 'weight': Value('float64'), 'score': Value('float64')})}), 'final_grade': Value('string')})}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1816, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
course_id: string
cutoff_week: int64
students: list<item: struct<student_id: string, weeks: list<item: struct<week: int64, activities: list<item: s (... 69 chars omitted)
child 0, item: struct<student_id: string, weeks: list<item: struct<week: int64, activities: list<item: struct<name: (... 57 chars omitted)
child 0, student_id: string
child 1, weeks: list<item: struct<week: int64, activities: list<item: struct<name: string, type: string, weight: dou (... 22 chars omitted)
child 0, item: struct<week: int64, activities: list<item: struct<name: string, type: string, weight: double, score: (... 10 chars omitted)
child 0, week: int64
child 1, activities: list<item: struct<name: string, type: string, weight: double, score: double>>
child 0, item: struct<name: string, type: string, weight: double, score: double>
child 0, name: string
child 1, type: string
child 2, weight: double
child 3, score: double
course_info: struct<avg_gpa: double, pct_A: double, pct_B: double, pct_C: double, pct_D: double, pct_F: double, g (... 23 chars omitted)
child 0, avg_gpa: double
child 1, pct_A: double
child 2, pct_B: double
child 3, pct_C: double
child 4, pct_D: double
child 5, pct_F: double
child 6, grading_scale_A: double
to
{'course_id': Value('string'), 'course_info': {'avg_gpa': Value('float64'), 'pct_A': Value('float64'), 'pct_B': Value('float64'), 'pct_C': Value('float64'), 'pct_D': Value('float64'), 'pct_F': Value('float64'), 'grading_scale_A': Value('float64')}, 'students': List({'student_id': Value('string'), 'weeks': List({'week': Value('int64'), 'activities': List({'name': Value('string'), 'type': Value('string'), 'weight': Value('float64'), 'score': Value('float64')})}), 'final_grade': Value('string')})}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
course_id string | course_info dict | students list |
|---|---|---|
C1-03 | {
"avg_gpa": 3.9,
"pct_A": 94.3,
"pct_B": 3.1,
"pct_C": 1.4,
"pct_D": 0.5,
"pct_F": 0.7,
"grading_scale_A": 90
} | [{"student_id":"syn_000472","weeks":[{"week":5,"activities":[{"name":"Homework 1","type":"hw","weigh(...TRUNCATED) |
C1-01 | {
"avg_gpa": 3.6,
"pct_A": 76.8,
"pct_B": 14.8,
"pct_C": 4.4,
"pct_D": 1.8,
"pct_F": 2.2,
"grading_scale_A": 90
} | [{"student_id":"syn_000978","weeks":[{"week":1,"activities":[{"name":"Lecture Activity 1","type":"ot(...TRUNCATED) |
C1-02 | {"avg_gpa":3.11,"pct_A":49.5,"pct_B":27.2,"pct_C":13.8,"pct_D":5.8,"pct_F":3.7,"grading_scale_A":90.(...TRUNCATED) | [{"student_id":"syn_005052","weeks":[{"week":2,"activities":[{"name":"Lab 1","type":"lab","weight":0(...TRUNCATED) |
C2-03 | {"avg_gpa":3.61,"pct_A":68.8,"pct_B":26.0,"pct_C":3.9,"pct_D":0.3,"pct_F":1.0,"grading_scale_A":90.0(...TRUNCATED) | [{"student_id":"syn_007343","weeks":[{"week":1,"activities":[{"name":"Weekly Assignments 1","type":"(...TRUNCATED) |
C2-01 | {
"avg_gpa": 3.87,
"pct_A": 92.5,
"pct_B": 5.6,
"pct_C": 0.7,
"pct_D": 0.3,
"pct_F": 0.9,
"grading_scale_A": 90
} | [{"student_id":"syn_008220","weeks":[{"week":1,"activities":[{"name":"Weekly Review 1","type":"other(...TRUNCATED) |
C2-04 | {"avg_gpa":3.47,"pct_A":71.3,"pct_B":15.4,"pct_C":6.9,"pct_D":3.0,"pct_F":3.4,"grading_scale_A":90.0(...TRUNCATED) | [{"student_id":"syn_012178","weeks":[{"week":1,"activities":[{"name":"Laboratory Assignment 1","type(...TRUNCATED) |
C2-02 | {"avg_gpa":3.43,"pct_A":59.2,"pct_B":27.6,"pct_C":11.2,"pct_D":1.0,"pct_F":1.0,"grading_scale_A":90.(...TRUNCATED) | [{"student_id":"syn_015850","weeks":[{"week":1,"activities":[{"name":"Homework 1","type":"hw","weigh(...TRUNCATED) |
C3-03 | {"avg_gpa":3.76,"pct_A":82.2,"pct_B":13.9,"pct_C":2.6,"pct_D":1.0,"pct_F":0.3,"grading_scale_A":90.0(...TRUNCATED) | [{"student_id":"syn_015949","weeks":[{"week":1,"activities":[{"name":"Lab assignment 1","type":"lab"(...TRUNCATED) |
C3-02 | {"avg_gpa":3.52,"pct_A":69.0,"pct_B":20.7,"pct_C":6.6,"pct_D":1.8,"pct_F":1.9,"grading_scale_A":90.0(...TRUNCATED) | [{"student_id":"syn_016576","weeks":[{"week":1,"activities":[{"name":"Homework 1","type":"hw","weigh(...TRUNCATED) |
C3-01 | {"avg_gpa":3.12,"pct_A":42.7,"pct_B":35.3,"pct_C":16.4,"pct_D":4.0,"pct_F":1.6,"grading_scale_A":90.(...TRUNCATED) | [{"student_id":"syn_018605","weeks":[{"week":1,"activities":[{"name":"Homework 1","type":"hw","weigh(...TRUNCATED) |
End of preview.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
PredAct-CS Dataset
PredAct-CS is the synthetic student-records dataset of PredAct-Bench: 53,401 students
across 60 computer science courses at a large public research university. Each course
provides a real syllabus structure (assignment names, weights, weekly schedule) and each
student a real final letter grade; weekly per-assignment scores are synthetically
generated to produce realistic trajectories that terminate at each student's real final
grade. Course identifiers are anonymized (e.g., C4-20); no real course numbers, student
identifiers, or personal information appear anywhere in the data.
Files
| File | Description |
|---|---|
students_data.csv |
Source dataset. One row per student: course id, course-level grade statistics, and weekly assignment slots (name, type, weight, score) for up to 16 weeks. |
cs_db.json.gz |
Full benchmark database built from students_data.csv (all 60 courses, grouped by course). |
cs_db_train.json.gz |
Training pool only — the historical students the k-NN predictor searches. No overlap with test students. |
test_sets/ |
776 evaluation files, one per (course, cutoff week): C3-01_week8.json contains that course's test students truncated at week 8. |
ground_truth_for_cutoff_data.json |
Answer key: final grades for every test set. |
convert_to_json.py |
Builds cs_db.json from students_data.csv. |
split_data.py |
Builds the train/test split, test sets, and ground truth from cs_db.json. |
Usage
The released JSON files are exactly the ones used in the paper's experiments (course identifiers renamed). To rebuild from source:
python convert_to_json.py --students students_data.csv --output cs_db.json
python split_data.py --db cs_db.json --output-dir .
Decompress the databases with gunzip cs_db.json.gz cs_db_train.json.gz.
License
Released under Creative Commons Attribution 4.0 International (CC BY 4.0).
See the LICENSE file for the full terms.
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