MetaMath QA

Mathematical Questions for Large Language Models

By Huggingface Hub [source]

About this dataset

This dataset contains meta-mathematics questions and answers collected from the Mistral-7B question-answering system. The responses, types, and queries are all provided in order to help boost the performance of MetaMathQA while maintaining high accuracy. With its well-structured design, this dataset provides users with an efficient way to investigate various aspects of question answering models and further understand how they function. Whether you are a professional or beginner, this dataset is sure to offer invaluable insights into the development of more powerful QA systems!

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How to use the dataset

Data Dictionary

The MetaMathQA dataset contains three columns: response, type, and query. - Response: the response to the query given by the question answering system. (String) - Type: the type of query provided as input to the system. (String) - Query:the question posed to the system for which a response is required. (String)

Preparing data for analysis

It’s important that before you dive into analysis, you first familiarize yourself with what kind data values are present in each column and also check if any preprocessing needs to be done on them such as removing unwanted characters or filling in missing values etc., so that it can be used without any issue while training or testing your model further down in your process flow.

##### Training Models using Mistral 7B

Mistral 7B is an open source framework designed for building machine learning models quickly and easily from tabular (csv) datasets such as those found in this dataset 'MetaMathQA ' . After collecting and preprocessing your dataset accordingly Mistral 7B provides with support for various Machine Learning algorithms like Support Vector Machines (SVM), Logistic Regression , Decision trees etc , allowing one to select from various popular libraries these offered algorithms with powerful overall hyperparameter optimization techniques so soon after selecting algorithm configuration its good practice that one use GridSearchCV & RandomSearchCV methods further tune both optimizations during model building stages . Post selection process one can then go ahead validate performances of selected models through metrics like accuracy score , F1 Metric , Precision Score & Recall Scores .

##### Testing phosphors :

After successful completion building phase right way would be robustly testing phosphors on different evaluation metrics mentioned above Model infusion stage helps here immediately make predictions based on earlier trained model OK auto back new test cases presented by domain experts could hey run quality assurance check again base score metrics mentioned above know asses confidence value post execution HHO updating baseline scores running experiments better preferred methodology AI workflows because Core advantage finally being have relevancy inexactness induced errors altogether impact low

Research Ideas

• Generating natural language processing (NLP) models to better identify patterns and connections between questions, answers, and types.
• Developing understandings on the efficiency of certain language features in producing successful question-answering results for different types of queries.
• Optimizing search algorithms that surface relevant answer results based on types of queries

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 purposes, all without asking permission. See Other Information.

Columns

File: train.csv | Column name | Description | |:--------------|:------------------------------------| | response | The response to the query. (String) | | type | The type of query. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Huggingface Hub.

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!

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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...

% of pupils achieving 5+ A*-Cs GCSE inc. English & Maths at Key Stage 4 (new First Entry definition) - (Snapshot) *This indicator has been discontinued due to national changes in GCSEs in 2016.

A multidisciplinary repository of public data sets such as the Human Genome and US Census data that can be seamlessly integrated into AWS cloud-based applications. AWS is hosting the public data sets at no charge for the community. Anyone can access these data sets from their Amazon Elastic Compute Cloud (Amazon EC2) instances and start computing on the data within minutes. Users can also leverage the entire AWS ecosystem and easily collaborate with other AWS users. If you have a public domain or non-proprietary data set that you think is useful and interesting to the AWS community, please submit a request and the AWS team will review your submission and get back to you. Typically the data sets in the repository are between 1 GB to 1 TB in size (based on the Amazon EBS volume limit), but they can work with you to host larger data sets as well. You must have the right to make the data freely available.

Data from a comparative judgement survey consisting of 62 working mathematics educators (ME) at Norwegian universities or city colleges, and 57 working mathematicians at Norwegian universities. A total of 3607 comparisons of which 1780 comparisons by the ME and 1827 ME. The comparative judgement survey consisted of respondents comparing pairs of statements on mathematical definitions compiled from a literature review on mathematical definitions in the mathematics education literature. Each WM was asked to judge 40 pairs of statements with the following question: “As a researcher in mathematics, where your target group is other mathematicians, what is more important about mathematical definitions?” Each ME was asked to judge 41 pairs of statements with the following question: “For a mathematical definition in the context of teaching and learning, what is more important?” The comparative judgement was done with No More Marking software (nomoremarking.com) The data set consists of the following data: comparisons made by ME (ME.csv) comparisons made by WM (WM.csv) Look up table of codes of statements and statement formulations (key.csv) Each line in the comparison represents a comparison, where the "winner" column represents the winner and the "loser" column the loser of the comparison.

These datatasets relate to the computational study presented in the paper "The Berth Allocation Problem with Channel Restrictions", authored by Paul Corry and Christian Bierwirth. They consist of all the randomly generated problem instances along with the computational results presented in the paper.

Results across all problem instances assume ship separation parameters of [delta_1, delta_2, delta_3] = [0.25, 0, 0.5].

Excel Workbook Organisation:

The data is organised into separate Excel files for each table in the paper, as indicated by the file description. Within each file, each row of data presented (aggregating 10 replications) in the corrsponding table is captured in two worksheets, one with the problem instance data, and the other with generated solution data obtained from several solution methods (described in the paper). For example, row 3 of Tab. 2, will have data for 10 problem instances on worksheet T2R3, and corresponding solution data on T2R3X.

Problem Instance Data Format:

On each problem instance worksheet (e.g. T2R3), each row of data corresponds to a different problem instance, and there are 10 replications on each worksheet.

The first column provides a replication identifier which is referenced on the corresponding solution worksheet (e.g. T2R3X).

Following this, there are n*(2c+1) columns (n = number of ships, c = number of channel segmenets) with headers p(i)_(j).(k)., where i references the operation (channel transit/berth visit) id, j references the ship id, and k references the index of the operation within the ship. All indexing starts at 0. These columns define the transit or dwell times on each segment. A value of -1 indicates a segment on which a berth allocation must be applied, and hence the dwell time is unkown.

There are then a further n columns with headers r(j), defining the release times of each ship.

For ChSP problems, there are a final n colums with headers b(j), defining the berth to be visited by each ship. ChSP problems with fixed berth sequencing enforced have an additional n columns with headers toa(j), indicating the order in which ship j sits within its berth sequence. For BAP-CR problems, these columnns are not present, but replaced by n*m columns (m = number of berths) with headers p(j).(b) defining the berth processing time of ship j if allocated to berth b.

Solution Data Format:

Each row of data corresponds to a different solution.

Column A references the replication identifier (from the corresponding instance worksheet) that the soluion refers to.

Column B defines the algorithm that was used to generate the solution.

Column C shows the objective function value (total waiting and excess handling time) obtained.

Column D shows the CPU time consumed in generating the solution, rounded to the nearest second.

Column E shows the optimality gap as a proportion. A value of -1 or an empty value indicates that optimality gap is unknown.

From column F onwards, there are are n*(2c+1) columns with the previously described p(i)_(j).(k). headers. The values in these columns define the entry times at each segment.

For BAP-CR problems only, following this there are a further 2n columns. For each ship j, there will be columns titled b(j) and p.b(j) defining the berth that was allocated to ship j, and the processing time on that berth respectively.

The HWRT database of handwritten symbols contains on-line data of handwritten symbols such as all alphanumeric characters, arrows, greek characters and mathematical symbols like the integral symbol.

The database can be downloaded in form of bzip2-compressed tar files. Each tar file contains:

• symbols.csv: A CSV file with the rows symbol_id, latex, training_samples, test_samples. The symbol id is an integer, the row latex contains the latex code of the symbol, the rows training_samples and test_samples contain integers with the number of labeled data.
• train-data.csv: A CSV file with the rows symbol_id, user_id, user_agent and data.
• test-data.csv: A CSV file with the rows symbol_id, user_id, user_agent and data.

All CSV files use ";" as delimiter and "'" as quotechar. The data is given in YAML format as a list of lists of dictinaries. Each dictionary has the keys "x", "y" and "time". (x,y) are coordinates and time is the UNIX time.

About 90% of the data was made available by Daniel Kirsch via github.com/kirel/detexify-data. Thank you very much, Daniel!

Abstract The article presents the development and results of a systematic review of the literature on Mathematics Education and Distance Learning. This review is part of a doctoral research in development on e-learning and b-learning practices in Brazilian Mathematics Teacher Education Programs. The main objective of the review was to identify in Mathematics Education how previous researches (January 2011 and December 2017) defined the e-learning and b-learning teaching models. In addition, it is possible to understand at what levels of education these investigations are situated: basic education, initial or continuing teacher education. Although focusing on a doctoral undergraduate research, it is believed that the previous research, reproduced at other school levels, can also add elements and reflections to understand these models of courses in Distance Education. We carried out a systematic review based on orientations from different organizations and researchers dedicated to this area of research. In this sense, we followed different phases in the process to make the review: definition of objectives/questions, research equations and databases; determination of inclusion, exclusion, and methodological validity criteria; presentation and discussion of results; and data. As supporting software, both Google spreadsheets and NVivo11 were herein used. In addition to a higher incidence of work that occur in the teacher training context, the review results show great dispersion about the concept of e-learning and a lower occurrence of studies on b-learning models. Also, a significant number of works refer to the need to create conditions, in Distance Teacher Education Programs, for the constitution of (virtual) learning communities.

Zhang et al. (https://link.springer.com/article/10.1140/epjb/e2017-80122-8) suggest a temporal random network with changing dynamics that follow a Markov process, allowing for a continuous-time network history moving from a static definition of a random graph with a fixed number of nodes n and edge probability p to a temporal one. Defining lambda = probability per time granule of a new edge to appear and mu = probability per time granule of an existing edge to disappear, Zhang et al. show that the equilibrium probability of an edge is p=lambda/(lambda+mu) Our implementation, a Python package that we refer to as RandomDynamicGraph https://github.com/ScanLab-ossi/DynamicRandomGraphs, generates large-scale dynamic random graphs according to the defined density. The package focuses on massive data generation; it uses efficient math calculations, writes to file instead of in-memory when datasets are too large, and supports multi-processing. Please note the datetime is arbitrary.

In scientific research, assessing the impact and influence of authors is crucial for evaluating their scholarly contributions. Whereas in literature, multitudinous parameters have been developed to quantify the productivity and significance of researchers, including the publication count, citation count, well-known h index and its extensions and variations. However, with a plethora of available assessment metrics, it is vital to identify and prioritize the most effective metrics. To address the complexity of this task, we employ a powerful deep learning technique known as the Multi-Layer Perceptron (MLP) classifier for the classification and the ranking purposes. By leveraging the MLP’s capacity to discern patterns within datasets, we assign importance scores to each parameter using the proposed modified recursive elimination technique. Based on the importance scores, we ranked these parameters. Furthermore, in this study, we put forth a comprehensive statistical analysis of the top-ranked author assessment parameters, encompassing a vast array of 64 distinct metrics. This analysis gives us treasured insights in between these parameters, shedding light on the potential correlations and dependencies that may affect assessment outcomes. In the statistical analysis, we combined these parameters by using seven well-known statistical methods, such as arithmetic means, harmonic means, geometric means etc. After combining the parameters, we sorted the list of each pair of parameters and analyzed the top 10, 50, and 100 records. During this analysis, we counted the occurrence of the award winners. For experimental proposes, data collection was done from the field of Mathematics. This dataset consists of 525 individuals who are yet to receive their awards along with 525 individuals who have been recognized as potential award winners by certain well known and prestigious scientific societies belonging to the fields’ of mathematics in the last three decades. The results of this study revealed that, in ranking of the author assessment parameters, the normalized h index achieved the highest importance score as compared to the remaining sixty-three parameters. Furthermore, the statistical analysis results revealed that the Trigonometric Mean (TM) outperformed the other six statistical models. Moreover, based on the analysis of the parameters, specifically the M Quotient and FG index, it is evident that combining these parameters with any other parameter using various statistical models consistently produces excellent results in terms of the percentage score for returning awardees.

Term definitions.

Parameters and data used in the hierarchical model for distance data.

Enhanced_NuminaMath_CoT

  Dataset Description

The Enhanced Mathematics Problem Solutions Dataset is designed to provide a comprehensive and structured collection of mathematical problems and their solutions, aimed at facilitating learning and teaching in educational settings. This dataset features clearly defined fields and presents problems that incorporate logical reasoning and problem-solving processes, making it particularly useful for educators and students alike. Key… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Enhanced_Math_Problem_Solutions.

Time series data for the statistic Administration of a nationally representative learning assessment in Grade 2 or 3 in mathematics (number) and country Zambia. Indicator Definition:

In scientific research, assessing the impact and influence of authors is crucial for evaluating their scholarly contributions. Whereas in literature, multitudinous parameters have been developed to quantify the productivity and significance of researchers, including the publication count, citation count, well-known h index and its extensions and variations. However, with a plethora of available assessment metrics, it is vital to identify and prioritize the most effective metrics. To address the complexity of this task, we employ a powerful deep learning technique known as the Multi-Layer Perceptron (MLP) classifier for the classification and the ranking purposes. By leveraging the MLP’s capacity to discern patterns within datasets, we assign importance scores to each parameter using the proposed modified recursive elimination technique. Based on the importance scores, we ranked these parameters. Furthermore, in this study, we put forth a comprehensive statistical analysis of the top-ranked author assessment parameters, encompassing a vast array of 64 distinct metrics. This analysis gives us treasured insights in between these parameters, shedding light on the potential correlations and dependencies that may affect assessment outcomes. In the statistical analysis, we combined these parameters by using seven well-known statistical methods, such as arithmetic means, harmonic means, geometric means etc. After combining the parameters, we sorted the list of each pair of parameters and analyzed the top 10, 50, and 100 records. During this analysis, we counted the occurrence of the award winners. For experimental proposes, data collection was done from the field of Mathematics. This dataset consists of 525 individuals who are yet to receive their awards along with 525 individuals who have been recognized as potential award winners by certain well known and prestigious scientific societies belonging to the fields’ of mathematics in the last three decades. The results of this study revealed that, in ranking of the author assessment parameters, the normalized h index achieved the highest importance score as compared to the remaining sixty-three parameters. Furthermore, the statistical analysis results revealed that the Trigonometric Mean (TM) outperformed the other six statistical models. Moreover, based on the analysis of the parameters, specifically the M Quotient and FG index, it is evident that combining these parameters with any other parameter using various statistical models consistently produces excellent results in terms of the percentage score for returning awardees.

About Dataset

Context

Analytics refers to the methodical examination and calculation of data or statistics. Its purpose is to uncover, interpret, and convey meaningful patterns found within the data. Additionally, analytics involves utilizing these data patterns to make informed decisions. It proves valuable in domains abundant with recorded information, employing a combination of statistics, computer programming, and operations research to measure performance.

Businesses can leverage analytics to describe, predict, and enhance their overall performance. Various branches of analytics encompass predictive analytics, prescriptive analytics, enterprise decision management, descriptive analytics, cognitive analytics, Big Data Analytics, retail analytics, supply chain analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modeling, web analytics, call analytics, speech analytics, sales force sizing and optimization, price and promotion modeling, predictive science, graph analytics, credit risk analysis, and fraud analytics. Due to the extensive computational requirements involved (particularly with big data), analytics algorithms and software utilize state-of-the-art methods from computer science, statistics, and mathematics.

Data Dictionary

Content

This Dataset contains information of 22700+ Software Professionals with different features like their Salaries (₹), Name of the Company, Company Rating, Number of times Salaries Reported, and Location of the Company.

Extra Features Added: 1. Employment Status 2. Job Roles

Acknowledgements

This Dataset is created from https://www.glassdoor.co.in/. If you want to learn more, you can visit the Website.

Roles Included:

Android Developer Android Developer - Intern Android Developer - Contractor Android Developer Contractor Senior Android Developer Android Software Engineer Android Engineer Android Applications Developer - Intern Android Applications Developer Android App Developer - Intern Senior Android Developer and Team Lead Android Tech Lead Product Engineer (Android) Software Engineer - Android Android Software Developer Android Software Developer - Intern Senior Android Developer Contractor Junior Android Developer - Intern Junior Android Developer Android Applications Developer - Contractor Android App Developer Lead Android Developer Android Engineer - Intern Sr. Android Developer Senior Android Engineer Senior Software Engineer - Android Android - Intern Android Android & Flutter Developer - Intern Associate Android Developer Senior Android Applications Developer Android Developer Trainee Sr Android developer Android Trainee Android Trainee - Intern Trainee Android Developer Android Lead Android Lead Developer Android Development - Intern Android Development Android Team Lead Senior, Android Developer Lead Android Engineer Tech Lead- Android Applications Developer Senior Android Software Developer Full Stack Android Developer Android Framework Developer Android Architect Android & Flutter Developer Senior Software Engineer, Android Android App Development Sr Android Engineer Android Team Leader Android Technical Lead SDE2(Android) Web Developer/Android Developer - Intern Android Applications Develpoers Android Platform Developer - Intern Android Test Engineer Senior Engineer - Android Android Framework Engineer Game Developer ( Android, Windows) Android Testing Senior Software Engineer (Android/Mobility) Ace - Android Development Software Developer (Android) - Intern Android Mobile Developer Android and Flutt...

Context

Publicly available datasets have helped the computer vision community to compare new algorithms and develop applications. Especially MNIST [LBBH98] was used thousands of times to train and evaluate models for classification. However, even rather simple models consistently get about 99.2 % accuracy on MNIST [TF-16a]. The best models classify everything except for about 20 instances correct. This makes meaningful statements about improvements in classifiers hard. A possible reason why current models are so good on MNIST are 1) MNIST has only 10 classes 2) there are very few (probably none) labelling errors in MNIST 3) every class has 6000 training samples 4) the feature dimensionality is comparatively low. Also, applications that need to recognize only Arabic numerals are rare. Similar to MNIST, HASY is of very low resolution. In contrast to MNIST, the HASYv2 dataset contains 369 classes, including Arabic numerals and Latin characters. Furthermore, HASYv2 has much fewer recordings per class than MNIST and is only in black and white whereas MNIST is in grayscale. HASY could be used to train models for semantic segmentation of non-cursive handwritten documents like mathematical notes or forms.

Content

The dataset contains the following:

• a pickle file: HASYv2
• a txt file: cite.txt

The pickle file contains the 168233 observations in a dictionary form. The simplest way to use the HASYv2 dataset is to download the pickle file below (HASYv2). You can use the following lines of code to load the data:

def unpickle(file):
  import pickle
  with open(file, 'rb') as fo:
    dict = pickle.load(fo, encoding='bytes')
  return dict

HASYv2 = unpickle("HASYv2")

The data comes in a dictionary format, you can get the data and the labels separately by extracting the content from the dictionary: data = HASYv2['data'] labels = HASYv2['labels'] symbols = HASYv2['latex_symbol'] Note that the shape of the data is directly (32 x 32 x 3 x 168233), with the first and second dimensions as the height and width respectively, the third dimension correspond to the channels and the fourth to the observation number.

Citation

fedesoriano. (October 2021). HASYv2 - Symbol Recognizer. Retrieved [Date Retrieved] from https://www.kaggle.com/fedesoriano/hasyv2-symbol-recognizer.

Source

The dataset was originally uploaded by Martin Thoma, see https://arxiv.org/abs/1701.08380.

Thoma, M. (2017). The HASYv2 dataset. ArXiv, abs/1701.08380.

The original paper describes the HASYv2 dataset. HASY is a publicly available, free of charge dataset of single symbols similar to MNIST. It contains 168233 instances of 369 classes. HASY contains two challenges: A classification challenge with 10 pre-defined folds for 10-fold cross-validation and a verification challenge. The HASYv2 dataset (PDF Download Available). Available from: https://arxiv.org/pdf/1701.08380.pdf [accessed Oct 11, 2021].

About Dataset

Context

Analytics refers to the methodical examination and calculation of data or statistics. Its purpose is to uncover, interpret, and convey meaningful patterns found within the data. Additionally, analytics involves utilizing these data patterns to make informed decisions. It proves valuable in domains abundant with recorded information, employing a combination of statistics, computer programming, and operations research to measure performance.

Businesses can leverage analytics to describe, predict, and enhance their overall performance. Various branches of analytics encompass predictive analytics, prescriptive analytics, enterprise decision management, descriptive analytics, cognitive analytics, Big Data Analytics, retail analytics, supply chain analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modeling, web analytics, call analytics, speech analytics, sales force sizing and optimization, price and promotion modeling, predictive science, graph analytics, credit risk analysis, and fraud analytics. Due to the extensive computational requirements involved (particularly with big data), analytics algorithms and software utilize state-of-the-art methods from computer science, statistics, and mathematics.

Data Dictionary

Content

This Dataset consists of salaries for Data Scientists, Machine Learning Engineers, Data Analysts, and Data Engineers in various cities across India (2022).

-Salary Dataset.csv -Partially Cleaned Salary Dataset.csv

Acknowledgements

This Dataset is created from https://www.glassdoor.co.in/. If you want to learn more, you can visit the Website.

CONTEXT

Practice Scenario: The UIW School of Engineering wants to recruit more students into their program. They will recruit students with great math scores. Also, to increase the chances of recruitment,​ the department will look for students who qualify for financial aid. Students who qualify for financial aid more than likely come from low socio-economic backgrounds. One way to indicate this is to view how much federal revenue a school district receives through its state. High federal revenue for a school indicates that a large portion of the student base comes from low incomes families.

The question we wish to ask is as follows: Name the school districts across the nation where their Child Nutrition Programs(c25) are federally funded between the amounts $30,000 and $50,000. And where the average math score for the school districts corresponding state is greater than or equal to the nations average score of 282.

The SQL query below in 'Top5MathTarget.sql' can be used to answer this question in MySQL. To execute this process, one would need to install MySQL to their local system and load the attached datasets below from Kaggle into their MySQL schema. The SQL query below will then join the separate tables on various key identifiers.

DATA SOURCE Data is sourced from The U.S Census Bureau and The Nations Report Card (using the NAEP Data Explorer).

Finance: https://www.census.gov/programs-surveys/school-finances/data/tables.html

Math Scores: https://www.nationsreportcard.gov/ndecore/xplore/NDE

COLUMN NOTES

All data comes from the school year 2017. Individual schools are not represented, only school districts within each state.

FEDERAL FINANCE DATA DEFINITIONS

t_fed_rev: Total federal revenue through the state to each school district.

C14- Federal revenue through the state- Title 1 (no child left behind act).

C25- Federal revenue through the state- Child Nutrition Act.

Title 1 is a program implemented in schools to help raise academic achievement ​for all students. The program is available to schools where at least 40% of the students come from low inccom​​e families.

Child Nutrition Programs ensure the children are getting the food they need to grow and learn. Schools with high federal revenue to these programs indicate students that also come from low income​ families.

MATH SCORES DATA DEFINITIONS

Note: Mathematics, Grade 8, 2017, All Students (Total)

average_scale_score - The state's average score for eighth graders taking the NAEP math exam.

Assume you are the new superintendent of School District which has a Junior High School that consists of approximately 500 students in grades 7-8. Students are randomly assigned to grade-level, subject-specific classroom teachers. The school is diverse socioeconomically with several students qualifying for free or reduced-price meals. The ethnic composition of the school is relatively diverse consisting primarily of African-American, Hispanic, Asian, and Caucasian students.

There are three teachers who teach 8th-grade math at the school, each doing their own thing when it comes to teaching math. Ms. Ruger, a young African-American lady who is certified to teach science and math, has been teaching for a total of 5 years and has taught math for the past 3 years. Ms. Smith, a Caucasian lady in her 40s who is certified to teach Spanish and math, has taught Spanish for 12 years but has taught math for the past 3 years. Ms. Wesson, an older Caucasian lady and the sister of the school board president, has been teaching PE for 24 years and has been assigned to teach math for the past 3 years. Each teacher was allowed to use their preferred teaching method and to select their own textbook three years ago. All three use different textbooks.

Ms. Wesson’s approach to teaching math would be broadly defined as the traditional method. The traditional math teacher adheres to a top-down approach in which knowledge originates from the teacher and is disseminated to the students. The teacher is recognized by the students (and often by the teacher herself) as the authority on the subject matter. Traditional math teachers tend to thrive on structure and order, resulting in quiet, calm learning environments. There is research that indicates certain behavioral issues are minimized in a traditional classroom resulting in effective, direct instruction.

Ms. Ruger and Ms. Smith’s approach to teaching math would be more broadly defined as the standards-based method. The standards-based math teacher adheres to a literal interpretation of well-written standards. The teacher facilitates the learning in a constructivist environment in which students develop, explore, conjecture and test their conjectures within the confines of the standard. The teacher believes there is research that a majority of children learn more and deeper mathematics and are better problem solvers when in the standards-based classroom.

During a meeting with the math department it was suggested that the three 8th-grade math teachers should be using the same teaching method and the same textbook. Ms. Wesson, being quite vocal, feels strongly that her approach is the better of the two because of the ethnic composition and sociological background of the students. She further believes and proposes that the students should be grouped among the three teachers according to the students’ ethnicity. She suggests that Ms. Ruger who is African-American teach the majority of the African-American students and that she, Ms. Wesson, would primarily teach the Caucasian and Asian students. Ms. Smith, who speaks fluent Spanish, would teach the majority of the Hispanic students. She also proposes that students be grouped within each teacher’s class by their ability with the high-ability students in a group by themselves and the lower-ability students in a group by themselves because she believes, based on a “gut” feeling, that the students will perform better if they are segregated into groups within the classroom. To support her argument she provides a copy of an article she located in the ATU library (see the Ross article entitled “Math and Reading Instruction in Tracked First-Grade Classes”) to each member of the department. She mentions that she has discussed this with her brother, the school board president, and that it will probably be discussed at the next board meeting. She further states that math is math and teachers should be allowed to teach using the style in which they are most comfortable.

Ms. Smith does not agree with Ms. Wesson’s proposal and shares an article that she has read (see the Thompson article about standards-based math). She states that research indicates students in traditional programs may have better procedural skills, but definitely lack in problem-solving creativity. She proposes that all three teachers should be using the standards-based approach to teaching.

Knowing that you have less than 30 days before the next board meeting you know that you need to have a proposal prepared based on school performance data. You have access to the latest student standardized math scores and personal data for the students taught by the 3 teachers (see file named 1_Research_Project_Data).  In order to protect confidentially, student names have been replaced by numbers. You try to anticipate and list any question that might be rais...