This dataset contains anonymized interaction data from French primary and secondary school students, including minors. It is released strictly for non-commercial research purposes. Users must not attempt to re-identify any individual in the dataset, and must report any personally identifiable information (PII) encountered to the dataset authors. By submitting this form, you confirm that you have read and agree to the CC-BY-NC-SA 4.0 license terms.

Mathematics Dataset Card: AM, Adaptiv World, and Adaptiv College

Overview

This folder contains a cross-country mathematics interaction dataset built from three classroom-grounded digital learning sources deployed in France and Côte d’Ivoire. It spans three educational age bands, from early primary school to middle school and high-school remediation, and covers several mathematical domains, including number sense, arithmetic problem solving, geometry, fractions and proportional reasoning, and data organization and functions.

Adaptiv'Math (AM) is an adaptive digital learning resource for early primary mathematics. It was developed in the context of the French P2IA program and is designed to help young learners build a robust understanding of core mathematical concepts through interactive exercises, structured remediation, and adaptive progression.
Adaptiv College contributes mathematics work on geometry, proportions and fractions, and data organization.
Adaptiv World contributes mathematics work on number sense and the organization and management of data and functions. The data were collected in Côte d’Ivoire.

The broader source platforms contain a larger content universe than the selected modules shared here.

Both sources are part of an AI-driven teaching platform that can personalize learning pathways through the ZPDES logic. Content is organized hierarchically as:

module -> objective -> activity -> exercise

A companion visualization tool is available at: https://miaamdatav2.streamlit.app/. It provides an interactive interface for inspecting the dataset.

Dataset scope

Adaptiv'Math modules kept

M1 — Nombres et calcul
M31 — Résolution de problèmes arithmétiques - Niveau 1
M32 — Résolution de problèmes arithmétiques - Niveau 2
M33 — Résolution de problèmes arithmétiques - Niveau 3

Adaptiv College modules kept

M101 — Espace et Géométrie
M102 — Proportions et fractions
M103 — Organisation et gestion des données

Adaptiv World modules kept

M101 — Réapprentissage du sens des nombres
M103 — Organisation et gestion de données, fonctions

Pedagogical and interaction modes

`adaptive-test`

At the beginning of a module, students can complete an adaptive placement test that estimates their level before the main recommendation phase begins. The test chooses subsequent questions dynamically from previous answers, allowing it to identify a suitable module entry point with relatively few attempts.

The goal is to place the student at a suitable entry point in the module rather than starting everyone from the same activity. Adaptive-test rows are present in all three sources.

`playlist`

In playlist mode, a teacher assigns a predefined set of exercises. This is not the adaptive recommendation mode: the sequence is selected by the teacher rather than online by the recommendation system. In the current dataset, playlist rows occur in Adaptiv World.

`zpdes`

zpdes is the main adaptive learning mode. The system recommends subsequent work from the student's current estimated level and the pedagogical dependency structure, with the aim of selecting tasks that are neither too easy nor too difficult.

The recommendation logic operates over a structured exercise graph organized by modules, objectives, and activities. The aim is to keep students working on tasks that are neither too easy nor too difficult, so that the sequence stays supportive of learning progress.

Data files

`maths_data.parquet`

Main attempt-level dataset.

26,302 source-specific students
5,392,610 attempts
9,427 distinct exercises observed in the parquet

Columns:

user_id
classroom_id
playlist_or_module_id
exercise_id
created_at
data_correct
work_mode
data_answer
data_duration
source
attempt_index
session_id

Current source breakdown:

am: 24,360 students, 5,188,084 attempts
adaptiv_world: 1,075 students, 148,846 attempts
adaptiv_college: 867 students, 55,680 attempts

Current work modes present in the dataset:

adaptive-test
playlist
zpdes

Source-specific work modes:

am: adaptive-test, zpdes
adaptiv_world: adaptive-test, playlist, zpdes
adaptiv_college: adaptive-test, zpdes

Note: the full interaction table preserves all retained attempts, including 614 attempts across 75 (source, exercise_id) pairs that are not present in the exercise table. The preprocessing notebook removes 599 of these attempts after first removing adaptive-test-only students.

`maths_data_filtered.parquet`

Stricter filtered version of the main dataset. This file is produced by notebooks/pre_processing_maths_data.ipynb, which applies three optional filters:

remove students whose retained history is adaptive-test only
remove individual attempts whose (source, exercise_id) pair is absent from the exercise table
remove students whose remaining trajectory contains fewer than 5 attempts overall

The filtered dataset contains:

22,245 source-specific students
5,355,783 attempts
9,324 distinct exercises

Compared with maths_data.parquet, this removes:

4,057 students, or 15.42%
36,827 attempts, or 0.68%
103 distinct exercises

Current source breakdown:

am: 20,832 students, 5,156,308 attempts, 6,226 exercises
adaptiv_world: 856 students, 146,821 attempts, 1,355 exercises
adaptiv_college: 557 students, 52,654 attempts, 2,027 exercises

`maths_exercises_table.parquet`

Exercise metadata table.

9,978 exercise rows
9,975 unique (source, exercise_id) pairs

Note: the table has 9,978 rows but 9,975 unique (source, exercise_id) pairs because three Adaptiv College exercise IDs occur in two hierarchy locations.

Current source breakdown:

am: 6,411 rows and unique pairs
adaptiv_world: 1,527 rows and unique pairs
adaptiv_college: 2,040 rows and 2,037 unique pairs

Columns:

exercise_id
gameplay_type
content
module_id
module_name
objective_id
objective_name
objective_pedagogical_intent
activity_id
activity_name
activity_pedagogical_intent
source

`maths_dependencies.json`

Simplified pedagogical dependency graph for the selected modules.

9 modules
102 objectives
525 activities
586 activity dependency edges

For each activity, the dependency file stores:

code
title
exercise_ids
prerequisite_activity_ids
unlocks_activity_ids

`screenshots/`

Exercise screenshots, one PNG per exercise. Two versions are provided:

screenshots/raw/ — original screenshots at full resolution (3840 px wide), split into 'am/' 'adaptiv_world/' and 'adaptiv_college/' subfolders
screenshots/compressed/ — compressed version at 960 px wide (1/4 of the original width), same subfolder structure
9,132 images total
one image per exercise with an available screenshot, named <exercise_id>.png

Note: 12 Adaptiv'Math Module 1 memory-card exercises could not be rendered as screenshots. This explains the discrepancy between the 7,118 available screenshots and the 7,130 exercises in maths_exercises_table.parquet.

`descriptions.json`

`preprocessing/pre_processing_maths_data.ipynb`

Notebook that reproduces the optional stricter filtering used to generate maths_data_filtered.parquet. It is provided for transparency and reproducibility.

Companion visualization tool

We provide a companion Streamlit application to facilitate dataset inspection and exploratory analysis: https://miaamdatav2.streamlit.app/

It allows users to explore global usage statistics, work-mode distributions, student-level trajectories, gameplay distributions, activity bottlenecks, transition patterns, curriculum structure, and module-level dependency graphs. The tool is intended to support dataset auditing, sanity checks, and hypothesis generation before downstream modeling.

Licensing

This repository contains two types of artefacts governed by different licenses:

Dataset: released under CC-BY-NC-SA 4.0. You are free to share and adapt the data for non-commercial research purposes, provided you give appropriate credit and distribute any derivatives under the same license.
Preprocessing scripts: released under the MIT License.

By accessing this dataset, you agree to the terms of the CC-BY-NC-SA 4.0 license.

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