Data with 100.000 diverse problems from International Math Olympiads (AIME, IMO etc).

You can use it for example for RAG systems or just to fine-tune model. If you like it, please upvote. Have a good work with this data!

MathInstruct Dataset: Hybrid Math Instruction Tuning

A curated dataset for math instruction tuning models

By TIGER-Lab (From Huggingface) [source]

About this dataset

MathInstruct is a comprehensive and meticulously curated dataset specifically designed to facilitate the development and evaluation of models for math instruction tuning. This dataset consists of a total of 13 different math rationale datasets, out of which six have been exclusively curated for this project, ensuring a diverse range of instructional materials. The main objective behind creating this dataset is to provide researchers with an easily accessible and manageable resource that aids in enhancing the effectiveness and precision of math instruction.

One noteworthy feature of MathInstruct is its lightweight nature, making it highly convenient for researchers to utilize without any hassle. With carefully selected columns such as source, source, output, output, users can readily identify the origin or reference material from where the math instruction was obtained. Additionally, they can also refer to the expected output or solution corresponding to each specific math problem or exercise.

Overall, MathInstruct offers immense potential in refining hybrid math instruction by facilitating meticulous model development and rigorous evaluation processes. Researchers can leverage this diverse dataset to gain deeper insights into effective teaching methodologies while exploring innovative approaches towards enhancing mathematical learning experiences

How to use the dataset

Title: How to Use the MathInstruct Dataset for Hybrid Math Instruction Tuning

Introduction: The MathInstruct dataset is a comprehensive collection of math instruction examples, designed to assist in developing and evaluating models for math instruction tuning. This guide will provide an overview of the dataset and explain how to make effective use of it.

• Understanding the Dataset Structure: The dataset consists of a file named train.csv. This CSV file contains the training data, which includes various columns such as source and output. The source column represents the source of math instruction (textbook, online resource, or teacher), while the output column represents expected output or solution to a particular math problem or exercise.

• Accessing the Dataset: To access the MathInstruct dataset, you can download it from Kaggle's website. Once downloaded, you can read and manipulate the data using programming languages like Python with libraries such as pandas.

• Exploring the Columns: a) Source Column: The source column provides information about where each math instruction comes from. It may include references to specific textbooks, online resources, or even teachers who provided instructional material. b) Output Column: The output column specifies what students are expected to achieve as a result of each math instruction. It contains solutions or expected outputs for different math problems or exercises.

• Utilizing Source Information: By analyzing the different sources mentioned in this dataset, researchers can understand which instructional materials are more effective in teaching specific topics within mathematics. They can also identify common strategies used by teachers across multiple sources.

• Analyzing Expected Outputs: Researchers can study variations in expected outputs for similar types of problems across different sources. This analysis may help identify differences in approaches across textbooks/resources and enrich our understanding of various teaching methods.

• Model Development and Evaluation: Researchers can utilize this dataset to develop machine learning models that automatically assess whether a given math instruction leads to the expected output. By training models on this data, one can create automated systems that provide feedback on math problems or suggest alternative instruction sources.

• Scaling the Dataset: Due to its lightweight nature, the MathInstruct dataset is easily accessible and manageable. Researchers can scale up their training data by combining it with other instructional datasets or expand it further by labeling more examples based on similar guidelines.

Conclusion: The MathInstruct dataset serves as a valuable resource for developing and evaluating models related to math instruction tuning. By analyzing the source information and expected outputs, researchers can gain insights into effective teaching methods and build automated assessment

Research Ideas

• Model development: This dataset can be used for developing and training models for math instruction...

The dataset contains 150 Math Word Problems(MWP). Each problem consists of textual math problems that involve the application of first and second-degree mathematical equations for their resolution. To create this set, academic and educational sources containing first and second-degree math problems were selected, and some original problems were also included.

Each problem in the dataset is structured as follows:

"question": A textual description of the math problem in Spanish "mathml_equations": The corresponding equation for the problem, expressed in MathML format to facilitate processing and manipulation by machine learning models. "Difficulty": The number of variables in the equation. "Grade": The grade of the equation, with 1 indicating a linear equation and 2 indicating a quadratic equation. "Index: A unique identifier for each problem in the dataset. "Author": The creator or source of the problem. "Ref": The source or citation for the problem, if applicable.

Math Selection Dataset

This dataset contains 82619 examples from various math and general sources for data selection experiments. Each example contains:

text: The content, with math examples following the "Problem: ...

Solution: ..." format original_source: Source information for the example

  Formats Available

Parquet: Available in the data/ directory Jsonlines: Available as src.jsonl in the root directory

  Sources:

TIGER-Lab/MathInstruct,train,3000:… See the full description on the dataset page: https://huggingface.co/datasets/zipfit/math-select-06062025.

Dataset Card for GSM8K

  Dataset Summary

GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning.

These problems take between 2 and 8 steps to solve. Solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to reach the… See the full description on the dataset page: https://huggingface.co/datasets/openai/gsm8k.

This dataset consists of mathematical question and answer pairs, from a range of question types at roughly school-level difficulty. This is designed to test the mathematical learning and algebraic reasoning skills of learning models.

## Example questions

 Question: Solve -42*r + 27*c = -1167 and 130*r + 4*c = 372 for r.
 Answer: 4
 
 Question: Calculate -841880142.544 + 411127.
 Answer: -841469015.544
 
 Question: Let x(g) = 9*g + 1. Let q(c) = 2*c + 1. Let f(i) = 3*i - 39. Let w(j) = q(x(j)). Calculate f(w(a)).
 Answer: 54*a - 30

It contains 2 million (question, answer) pairs per module, with questions limited to 160 characters in length, and answers to 30 characters in length. Note the training data for each question type is split into "train-easy", "train-medium", and "train-hard". This allows training models via a curriculum. The data can also be mixed together uniformly from these training datasets to obtain the results reported in the paper. Categories:

• algebra (linear equations, polynomial roots, sequences)
• arithmetic (pairwise operations and mixed expressions, surds)
• calculus (differentiation)
• comparison (closest numbers, pairwise comparisons, sorting)
• measurement (conversion, working with time)
• numbers (base conversion, remainders, common divisors and multiples, primality, place value, rounding numbers)
• polynomials (addition, simplification, composition, evaluating, expansion)
• probability (sampling without replacement)

Dataset Card for MathVista

Dataset Description Paper Information Dataset Examples Leaderboard Dataset Usage Data Downloading Data Format Data Visualization Data Source Automatic Evaluation

License Citation

  Dataset Description

MathVista is a consolidated Mathematical reasoning benchmark within Visual contexts. It consists of three newly created datasets, IQTest, FunctionQA, and PaperQA, which address the missing visual domains and are tailored to evaluate logical… See the full description on the dataset page: https://huggingface.co/datasets/AI4Math/MathVista.

Dataset Card

This dataset contains ~200K grade school math word problems. All the answers in this dataset is generated using Azure GPT4-Turbo. Please refer to Orca-Math: Unlocking the potential of SLMs in Grade School Math for details about the dataset construction.

  Dataset Sources

Repository: microsoft/orca-math-word-problems-200k Paper: Orca-Math: Unlocking the potential of SLMs in Grade School Math

  Direct Use

This dataset has been designed to… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/orca-math-word-problems-200k.

About the Math-Students Dataset

This dataset, originally sourced from the UCI Machine Learning Repository, offers a rich collection of data on student performance in a math program. It provides detailed insights into both the academic achievements and the socio-demographic backgrounds of the students, making it an excellent resource for educational data mining and predictive analytics.

Key Features & Attributes

• Demographics & Background:

  • School: Identifies the student's school (e.g., Gabriel Pereira or Mousinho da Silveira).
  • Sex & Age: Basic demographic information to help explore performance trends among different groups.
  • Address & Family Size: Details about the student’s home environment, including whether they live in an urban or rural area and their family size.

• Parental & Household Information:

  • Parental Cohabitation & Education: Data on whether parents live together and their education levels, which can correlate with student support and academic outcomes.
  • Parental Occupation: Information on the mother’s and father’s jobs, providing further context on socioeconomic factors.

• Educational & Behavioral Variables:

  • Study Time & Failures: Weekly study time and history of past class failures help gauge academic dedication and potential challenges.
  • Support & Extracurricular Activities: Records on whether the student has received extra educational support or participates in extracurricular activities, which can influence overall performance.
  • School-Related Factors: Travel time to school, attendance (absences), and participation in additional paid classes contribute to a holistic view of the educational environment.

• Lifestyle & Social Factors:

  • Internet Access, Free Time & Socializing: Variables like internet availability, free time, and how often students go out with friends help capture lifestyle and behavioral patterns.
  • Health & Well-being: Self-reported health status and alcohol consumption patterns during weekdays and weekends offer insights into personal well-being, which may impact academic performance.

• Academic Performance:

  • Grades: The dataset includes three key assessments—G1 (first period grade), G2 (second period grade), and G3 (final grade). G3, the final grade, serves as the primary target variable for predictive models.

Potential Applications

• Predictive Modeling:
  Researchers and data scientists can build regression models to predict final grades (G3) based on the numerous socio-demographic and educational features.
• Exploratory Data Analysis:
  The dataset is ideal for exploring relationships between family background, lifestyle choices, and academic success. For example, one could analyze how study time or parental education levels correlate with performance.
• Educational Interventions:
  By identifying key factors that contribute to academic outcomes, educators and policymakers can develop targeted interventions to support at-risk students.
• Comparative Studies:
  While this dataset focuses on math scores, its structure is similar to the Portuguese language course dataset. This similarity provides opportunities for cross-domain comparisons in educational research.

Additional Insights

• Data Complexity & Quality:
  Despite its moderate size, the dataset is rich in both categorical and numerical variables. This diversity requires careful preprocessing and feature engineering but also offers the chance to uncover complex interactions between various factors.
• Research Impact:
  The dataset has been widely used in the field of educational data mining. Its comprehensive nature has provided a basis for numerous studies examining the interplay between academic performance and a range of external factors.
• Historical Context:
  Originating from a study presented at the 5th FUBUTEC 2008 conference, the dataset has contributed valuable insights into secondary school performance and continues to serve as a benchmark for educational analytics research.

Summary data from a Calculus II class where students were required to watch an instructional video before or after lecture. Dataset includes gender (1=female; 2=male), vgroup (-1=before lecture; 1=after lecture), binary flag for 26 individual videos (1=watched 80% or more of length of video; 0=not watched), videosum (sum of number of videos watched), final_raw (raw grade student received on cumulative final course exam), sat_math (scaled SAT-Math score out of 800), math_place (institutional calculus readiness score out of 100), watched20 (grouping flag for students who watched 20 or more videos).

GSM8K - Grade School Math 8K Q&A

A Linguistically Diverse Dataset for Multi-Step Reasoning Question Answering

By Huggingface Hub [source]

About this dataset

This Grade School Math 8K Linguistically Diverse Training & Test Set is designed to help you develop and improve your understanding of multi-step reasoning question answering. The dataset contains three separate data files: the socratic_test.csv, main_test.csv, and main_train.csv, each containing a set of questions and answers related to grade school math that consists of multiple steps. Each file contains the same columns: question, answer. The questions contained in this dataset are thoughtfully crafted to lead you through the reasoning journey for arriving at the correct answer each time, allowing you immense opportunities for learning through practice. With over 8 thousand entries for both training and testing purposes in this GSM8K dataset, it takes advanced multi-step reasoning skills to ace these questions! Deepen your knowledge today and master any challenge with ease using this amazing GSM8K set!

More Datasets

For more datasets, click here.

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How to use the dataset

This dataset provides a unique opportunity to study multi-step reasoning for question answering. The GSM8K Linguistically Diverse Training & Test Set consists of 8,000 questions and answers that have been created to simulate real-world scenarios in grade school mathematics. Each question is paired with one answer based on a comprehensive test set. The questions cover topics such as algebra, arithmetic, probability and more.

The dataset consists of two files: main_train.csv and main_test.csv; the former contains questions and answers specifically related to grade school math while the latter includes multi-step reasoning tests for each category of the Ontario Math Curriculum (OMC). In addition, it has three columns - Question (Question), Answer ([Answer]) – meaning that each row contains 3 sequential question/answer pairs making it possible to take a single path from the start of any given answer or branch out from there according to the logic construction required by each respective problem scenario; these columns can be used in combination with text analysis algorithms like ELMo or BERT to explore different formats of representation for responding accurately during natural language processing tasks such as Q&A or building predictive models for numerical data applications like measuring classifying resource efficiency initiatives or forecasting sales volumes in retail platforms..

To use this dataset efficiently you should first get familiar with its structure by reading through its documentation so you are aware all available info regarding items content definition & format requirements then study examples that best suits your specific purpose whether is performing an experiment inspired by education research needs, generate insights related marketing analytics reports making predictions over artificial intelligence project capacity improvements optimization gains etcetera having full access knowledge about available source keeps you up & running from preliminary background work toward knowledge mining endeavor completion success Support User success qualitative exploration sessions make sure learn all variables definitions employed heterogeneous tools before continue Research journey starts experienced Researchers come prepared valuable resource items employed go beyond discovery false alarm halt advancement flow focus unprocessed raw values instead ensure clear cutting vision behind objectives support UserHelp plans going mean project meaningful campaign deliverables production planning safety milestones dovetail short deliveries enable design interfaces session workforce making everything automated fun entry functioning final transformation awaited offshoot Goals outcome parameters monitor life cycle management ensures ongoing projects feedbacks monitored video enactment resources tapped Proficiently balanced activity sheets tracking activities progress deliberation points evaluation radius highlights outputs primary phase visit egress collaboration agendas Client cumulative returns records capture performance illustrated collectively diarized successive setup sweetens conditions researched environments overview debriefing arcane matters turn acquaintances esteemed directives social

Research Ideas

• Training language models for improving accuracy in natural language processing applications such as question answering or dialogue systems.
• Generating new grade school math questions and answers using g...

Mathematical reasoning, a fundamental aspect of human cognition, poses significant challenges for artificial intelligence (AI) systems. Despite recent advancements in natural language processing (NLP) and large language models (LLMs), AI's ability to replicate human-like reasoning, generalization, and efficiency remains an ongoing research challenge. In this dissertation, we address key limitations in MWP solving, focusing on the accuracy, generalization ability and efficiency of AI-based mathematical reasoners by applying human-like reasoning methods and principles.

This dissertation introduces several innovative approaches in mathematical reasoning. First, a numeracy-driven framework is proposed to enhance math word problem (MWP) solvers by integrating numerical reasoning into model training, surpassing human-level performance on benchmark datasets. Second, a novel multi-solution framework captures the diversity of valid solutions to math problems, improving the generalization capabilities of AI models. Third, a customized knowledge distillation technique, termed Customized Exercise for Math Learning (CEMAL), is developed to create tailored exercises for smaller models, significantly improving their efficiency and accuracy in solving MWPs. Additionally, a multi-view fine-tuning paradigm (MinT) is introduced to enable smaller models to handle diverse annotation styles from different datasets, improving their adaptability and generalization. To further advance mathematical reasoning, a benchmark, MathChat, is introduced to evaluate large language models (LLMs) in multi-turn reasoning and instruction-following tasks, demonstrating significant performance improvements. Finally, new inference-time verifiers, Math-Rev and Code-Rev, are developed to enhance reasoning verification, combining language-based and code-based solutions for improved accuracy in both math and code reasoning tasks.

In summary, this dissertation provides a comprehensive exploration of these challenges and contributes novel solutions that push the boundaries of AI-driven mathematical reasoning. Potential future research directions are also discussed to further extend the impact of this dissertation.

This dataset contains the 30 questions that were posed to the chatbots (i) ChatGPT-3.5; (ii) ChatGPT-4; and (iii) Google Bard, in May 2023 for the study “Chatbots put to the test in math and logic problems: A preliminary comparison and assessment of ChatGPT-3.5, ChatGPT-4, and Google Bard”. These 30 questions describe mathematics and logic problems that have a unique correct answer. The questions are fully described with plain text only, without the need for any images or special formatting. The questions are divided into two sets of 15 questions each (Set A and Set B). The questions of Set A are 15 “Original” problems that cannot be found online, at least in their exact wording, while Set B contains 15 “Published” problems that one can find online by searching on the internet, usually with their solution. Each question is posed three times to each chatbot.

This dataset contains the following: (i) The full set of the 30 questions, A01-A15 and B01-B15; (ii) the correct answer for each one of them; (iii) an explanation of the solution, for the problems where such an explanation is needed, (iv) the 30 (questions) × 3 (chatbots) × 3 (answers) = 270 detailed answers of the chatbots. For the published problems of Set B, we also provide a reference to the source where each problem was taken from.

MLFMF MLFMF (Machine Learning for Mathematical Formalization) is a collection of data sets for benchmarking recommendation systems used to support formalization of mathematics with proof assistants. These systems help humans identify which previous entries (theorems, constructions, datatypes, and postulates) are relevant in proving a new theorem or carrying out a new construction. The MLFMF data sets provide solid benchmarking support for further investigation of the numerous machine learning approaches to formalized mathematics. With more than 250,000 entries in total, this is currently the largest collection of formalized mathematical knowledge in machine learnable format. In addition to benchmarking the recommendation systems, the data sets can also be used for benchmarking node classification and link prediction algorithms. The four data sets Each data set is derived from a library of formalized mathematics written in proof assistants Agda or Lean. The collection includes

the largest Lean 4 library Mathlib, the three largest Agda libraries:

the standard library the library of univalent mathematics Agda-unimath, and the TypeTopology library. Each data set represents the corresponding library in two ways: as a heterogeneous network, and as a list of syntax trees of all the entries in the library. The network contains the (modular) structure of the library and the references between entries, while the syntax trees give complete and easily parsed information about each entry. The Lean library data set was obtained by converting .olean files into s-expressions (see the lean2sexp tool). The Agda data sets were obtained with an s-expression extension of the official Agda repository (use either master-sexp or release-2.6.3-sexp branch). For more details, see our arXiv copy of the paper. Directory structure First, the mlfmf.zip archive needs to be unzipped. It contains a separate directory for every library (for example, the standard library of Agda can be found in the stdlib directory) and some auxiliary files. Every library directory contains

the network file from which the heterogeneous network can be loaded, a zip of the entries directory that contains (many) files with abstract syntax trees. Each of those files describes a single entry of the library. In addition to the auxiliary files which are used for loading the data (and described below), the zipped sources of lean2sexp and Agda s-expression extension are present. Loading the data In addition to the data files, there is also a simple python script main.py for loading the data. To run it, you will have to install the packages listed in the file requirements.txt: tqdm and networkx. The easiest way to do so is calling pip install -r requirements.txt. When running main.py for the first time, the script will unzip the entry files into the directory named entries. After that, the script loads the syntax trees of the entries (see the Entry class) and the network (as networkx.MultiDiGraph object). Note. The entry files have extension .dag (directed acyclic graph), since Lean uses node sharing, which breaks the tree structure (a shared node has more than one parent node). More information For more information about the data collection process, detailed data (and data format) description, and baseline experiments that were already performed with these data, see our arXiv copy of the paper. For the code that was used to perform the experiments and data format description, visit our github repository https://github.com/ul-fmf/mlfmf-data. Funding Since not all the funders are available in the Zenodo's database, we list them here:

This material is based upon work supported by the Air Force Office of Scientific Research under award number FA9550-21-1-0024. The authors also acknowledge the financial support of the Slovenian Research Agency via the research core funding No. P2-0103 and No. P1-0294.

optimized-cot-math-problems

  Dataset Description

The Optimized Math Problem Dataset is designed to enhance mathematical problem-solving skills across various domains. This dataset features a variety of mathematical scenarios, including optimization problems, value calculations, and cost estimations, making it suitable for educational purposes, academic research, and AI training. Each entry contains a clear problem statement, a structured solution, and comprehensive… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/optimized-math-problems.

Mathematical Problems Dataset: Various Mathematical Problems and Solutions

Mathematical Problems Dataset: Questions and Answers

By math_dataset (From Huggingface) [source]

About this dataset

This dataset comprises a collection of mathematical problems and their solutions designed for training and testing purposes. Each problem is presented in the form of a question, followed by its corresponding answer. The dataset covers various mathematical topics such as arithmetic, polynomials, and prime numbers. For instance, the arithmetic_nearest_integer_root_test.csv file focuses on problems involving finding the nearest integer root of a given number. Similarly, the polynomials_simplify_power_test.csv file deals with problems related to simplifying polynomials with powers. Additionally, the dataset includes the numbers_is_prime_train.csv file containing math problems that require determining whether a specific number is prime or not. The questions and answers are provided in text format to facilitate analysis and experimentation with mathematical problem-solving algorithms or models

How to use the dataset

• Introduction: The Mathematical Problems Dataset contains a collection of various mathematical problems and their corresponding solutions or answers. This guide will provide you with all the necessary information on how to utilize this dataset effectively.

• Understanding the columns: The dataset consists of several columns, each representing a different aspect of the mathematical problem and its solution. The key columns are:

  • question: This column contains the text representation of the mathematical problem or equation.
  • answer: This column contains the text representation of the solution or answer to the corresponding problem.

• Exploring specific problem categories: To focus on specific types of mathematical problems, you can filter or search within the dataset using relevant keywords or terms related to your area of interest. For example, if you are interested in prime numbers, you can search for prime in the question column.

• Applying machine learning techniques: This dataset can be used for training machine learning models related to natural language understanding and mathematics. You can explore various techniques such as text classification, sentiment analysis, or even sequence-to-sequence models for solving mathematical problems based on their textual representations.

• Generating new questions and solutions: By analyzing patterns in this dataset, you can generate new questions and solutions programmatically using techniques like data augmentation or rule-based methods.

• Validation and evaluation: As with any other machine learning task, it is essential to validate your models on separate validation sets not included in this dataset properly. You can also evaluate model performance by comparing predictions against known answers provided in this dataset's answer column.

• Sharing insights and findings: After working with this datasets, it would be beneficial for researchers or educators to share their insights, approaches taken during analysis/modelling as Kaggle notebooks/ discussions/ blogs/ tutorials etc., so that others could get benefited from such shared resources too.

Note: Please note that the dataset does not include dates.

By following these guidelines, you can effectively explore and utilize the Mathematical Problems Dataset for various mathematical problem-solving tasks. Happy exploring!

Research Ideas

• Developing machine learning algorithms for solving mathematical problems: This dataset can be used to train and test models that can accurately predict the solution or answer to different mathematical problems.
• Creating educational resources: The dataset can be used to create a wide variety of educational materials such as problem sets, worksheets, and quizzes for students studying mathematics.
• Research in mathematical problem-solving strategies: Researchers and educators can analyze the dataset to identify common patterns or strategies employed in solving different types of mathematical problems. This analysis can help improve teaching methodologies and develop effective problem-solving techniques

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purpos...

The dataset contains data from the algebra (Math) environment in capabench as well as code and trajectory examples.

FrugalMath Dataset: Easy Samples as Length Regularizers in Math RLVR

Paper: Shorter but not Worse: Frugal Reasoning via Easy Samples as Length Regularizers in Math RLVR Base Model: Qwen/Qwen3-4B-Thinking-2507 Authors: Abdelaziz Bounhar et al. License: Apache 2.0

  Overview

The FrugalMath dataset was designed to study implicit length regularization in Reinforcement Learning with Verifiable Rewards (RLVR). Unlike standard pipelines that discard easy problems, this dataset… See the full description on the dataset page: https://huggingface.co/datasets/MBZUAI-Paris/frugal-maths-data-split-v1.

OlympiadBench Data set used in the Putnam-AXIOM Paper

The Putnam-AXIOM dataset is a benchmark for measuring advanced mathematical reasoning in large language models (LLMs). It includes challenging mathematical problems from the William Lowell Putnam Mathematical Competition, with both original problems and functional variations to address data contamination. The dataset aims to provide rigorous evaluations by requiring models to answer in boxed format, simplifying automatic answer… See the full description on the dataset page: https://huggingface.co/datasets/brando/olympiad-bench-imo-math-boxed-825-v2-21-08-2024.

The purpose of the ASSISTments Replication Study is to conduct a replication study of the impact of a fully developed, widely adopted intervention called ASSISTments on middle school student mathematics outcomes. ASSISTments is an online formative assessment platform that provides immediate feedback to students and supports teachers in their use of homework to improve math instruction and learning. Findings from a previous IES-funded efficacy study, conducted in Maine, indicated this intervention led to beneficial impacts on student learning outcomes in 7th grade. The current study examined the impacts of this intervention with a more diverse sample and relied on trained local math coaches (instead of the intervention developers) to provide professional development and support to teachers. Participating schools (and all 7th grade math teachers in the school) in this study were randomly assigned to either a treatment or control group. Teachers participated in the project over a two year period, the 2018-19 school year and the 2019-20 school year. The 2018-19 school year was to serve as a ramp-up year. Data used in the final analysis was collected during the second year of the study, the 2019-20 school year. The data contained in this project is primarily from the 2019-20 school year and includes student ASSISTments usage data, teacher ASSISTments usage data, student outcome data, and teacher instructional log data. Student outcome data is from the online Mathematics Readiness Test for Grade 8 developed by Math Diagnostic Test Project (MDTP). The teacher instructional log had teachers to answer questions about their daily instructional practices over the span of 5 consecutive days of instruction. They were asked to participate in 3 rounds of logs over the course of the 2019-2020 school year. Student and teacher usage data of ASSISTments were collected automatically as they used the system. The usage data was limited to treatment group only. Other data (outcome data, teacher instructional log data) were collected from both treatment and control groups.