If this Data Set is useful, and upvote is appreciated. This data approach student achievement in secondary education of two Portuguese schools. The data attributes include student grades, demographic, social and school related features) and it was collected by using school reports and questionnaires. Two datasets are provided regarding the performance in two distinct subjects: Mathematics (mat) and Portuguese language (por). In [Cortez and Silva, 2008], the two datasets were modeled under binary/five-level classification and regression tasks. Important note: the target attribute G3 has a strong correlation with attributes G2 and G1. This occurs because G3 is the final year grade (issued at the 3rd period), while G1 and G2 correspond to the 1st and 2nd-period grades. It is more difficult to predict G3 without G2 and G1, but such prediction is much more useful (see paper source for more details).

📘 Description

The Student Academic Performance Dataset contains detailed academic and lifestyle information of 250 students, created to analyze how various factors — such as study hours, sleep, attendance, stress, and social media usage — influence their overall academic outcomes and GPA.

This dataset is synthetic but realistic, carefully generated to reflect believable academic patterns and relationships. It’s perfect for learning data analysis, statistics, and visualization using Excel, Python, or R.

The data includes 12 attributes, primarily numerical, ensuring that it’s suitable for a wide range of analytical tasks — from basic descriptive statistics (mean, median, SD) to correlation and regression analysis.

📊 Key Features

🧮 250 rows and 12 columns

💡 Mostly numerical — great for Excel-based statistical functions

🔍 No missing values — ready for direct use

📈 Balanced and realistic — ideal for clear visualizations and trend analysis

🎯 Suitable for:

Descriptive statistics

Correlation & regression

Data visualization projects

Dashboard creation (Excel, Tableau, Power BI)

💡 Possible Insights to Explore

How do study hours impact GPA?

Is there a relationship between stress levels and performance?

Does social media usage reduce study efficiency?

Do students with higher attendance achieve better grades?

⚙️ Data Generation Details

Each record represents a unique student.

GPA is calculated using a weighted formula based on midterm and final scores.

Relationships are designed to be realistic — for example:

Higher study hours → higher scores and GPA

Higher stress → slightly lower sleep hours

Excessive social media time → reduced academic performance

⚠️ Disclaimer

This dataset is synthetically generated using statistical modeling techniques and does not contain any real student data. It is intended purely for educational, analytical, and research purposes.

The dataset was carefully created to evaluate student performance using a more holistic approach that goes beyond the traditional metrics of Continuous Assessment (CA) and Examination (Exam) scores. This new dataset integrates additional variables to provide a comprehensive evaluation framework. The columns in the dataset are defined as follows: x1 (Attendance): Represents the student's attendance, measured on a scale of 0 to 10. x2 (Practical Skills): Captures the student's practical skills, also measured on a scale of 0 to 10. x3 (Demeanor): Reflects the student's demeanor, measured on a scale of 0 to 10. x4 (Presentation Quality): Assesses the quality of the student's presentations, with scores ranging from 0 to 10. x5 (Class Participation): Measures the level of participation in class, scored between 0 and 10. x6 (Continuous Assessment): Represents the continuous assessment scores, ranging from 0 to 10. x7 (Examination): Reflects the student's performance in examinations, with a range of 0 to 40 marks. total: This column is the sum of selected feature values, aggregating the performance across different metrics to provide a cumulative score. remarks: This column contains categorical values (1 to 3), which classify the overall performance based on the 'total' score or other feature values. The generated dataset contained approximately 72,000,000 records, which was too large to load as a single Excel file. To manage this, the dataset was divided into 200 files, each containing roughly 363,000 records. From each of these files, 1,000 records were randomly extracted, resulting in a final dataset of 200,000 records. These records span all the chosen grades, ensuring a comprehensive and balanced representation across different performance levels. This dataset provides a higher-dimensional representation of student performance, making it suitable for advanced analytical models and comprehensive evaluations of academic success.

Created a Dashboard to show the performance of students based on their ethnicity group and other variables such as parental education and their gender.

The School Quality Reports share information about school performance, set expectations for schools, and promote school improvement. Due to size constraints only partial data is reflected, to view entire data open up the excel file that shown with data set name. These reports include information from multiple sources, including Quality Reviews, the NYC School Survey, and student performance. The School Quality Reports are organized around the Framework for Great Schools, which include six elements Rigorous Instruction, Collaborative Teachers, Supportive Environment, Effective School Leadership, Strong FamilyCommunity Ties, and Trust—that drive student achievement and school improvement.

This dataset contains information about pupils in primary education. The data attributes include student gender, race/ethnicity, parental education, lunch type, information about whether students have completed a test preparation course, and students scores for maths, reading and writing. Data has 8 columns and 1001 row. Data is formatted into a table. By analysing this data set we could answer the following questions: • How effective is the test preparation course? • Which major factors contribute to test outcomes? • Is there a correlation between race/ethnicity, parental education and pupils test score? • What patterns and interactions in the data can you find?

The dataset you've provided appears to be a record of student academic performance across several subjects: AI (Artificial Intelligence), DBMS (Database Management Systems), PP (Programming Principles), WP (Web Programming), and EMSML (possibly a subject related to Machine Learning or Statistics). Each student is identified by a name and roll number, followed by their scores in each subject.

From a quick glance, the scores vary widely among students and subjects, indicating diverse levels of proficiency. For instance, some students have high scores in AI but lower scores in DBMS, while others show a consistent performance across subjects. This data could be used to identify areas where students excel or need improvement, analyze subject-wise performance trends, or calculate overall academic standings.

If you need a more detailed analysis or specific calculations, please let me know how I can further assist you.

This data tracks four-year graduation rates from high schools located within the City of Tempe, with data publicly available through the Arizona Department of Education.Values of “8888” are used when there are too few to count, and values of “9999” are used where there is no data available. This page provides data for the High School Graduation Rate performance measure. The performance measure dashboard is available at 3.08 High School Graduation Rates. Additional Information Source: Contact: Marie RaymondContact E-Mail: Marie_Raymond@tempe.govContact Phone: 480-585-7818Data Source: Tempe High School DistrictData Source Type: Excel Preparation Method: Arizona Department of Education (ADE) generated Excel Spreadsheets- available at https://www.azed.gov/accountability-research/data/Publish Frequency: AnnuallyPublish Method: ManualData Dictionary

This research investigated the relationships among university students' behavioral intention to use H5P technology, their learning engagement, and their performance in an online learning environment through the testing of seven hypotheses. H1: Performance Expectancy (PE) has a positive effect on their Behavioral Intention (BI) to use H5P. H2: Effort Expectancy (EE) has a positive effect on their Behavioral Intention (BI) to use H5P. H3: Social Influence (SI) has a positive effect on their Behavioral Intention (BI) to use H5P. H4: Facilitating Conditions (FC) have a positive effect on their Behavioral Intention (BI) to use H5P. H5: Behavioral Intention (BI) has a positive impact on Learning Engagement (LE). H6: Behavioral Intention (BI) has a positive impact on Learning Performance (LP). H7: Learning Engagement (LE) has a positive impact on Learning Performance (LP). The analysis was conducted using PLS-SEM via SmartPLS software. Results indicate that H3, H4, H5, H6, and H7 are supported, while H1 and H2 are rejected. The data was collected from both undergraduate and postgraduate students who have used H5P technology in their online learning at a Sino-British joint venture international university in China. The dataset includes two Excel files: one named "Scale," which details the questions and descriptions of the integrated scale; and another named "H5P Dataset," containing participants' self-reported responses. Further details about the conceptual model and findings are available in the research paper.

This page provides data for the 3rd Grade Reading Level Proficiency performance measure.The dataset includes the student performance results on the English/Language Arts section of the AzMERIT from the Fall 2017 and Spring 2018. Data is representive of students in third grade in public elementary schools in Tempe. This includes schools from both Tempe Elementary and Kyrene districts. Results are by school and provide the total number of students tested, total percentage passing and percentage of students scoring at each of the four levels of proficiency. The performance measure dashboard is available at 3.07 3rd Grade Reading Level Proficiency.Additional InformationSource: Arizona Department of EducationContact: Ann Lynn DiDomenicoContact E-Mail: Ann_DiDomenico@tempe.govData Source Type: Excel/ CSVPreparation Method: Filters on original dataset: within "Schools" Tab School District [select Tempe School District and Kyrene School District]; School Name [deselect Kyrene SD not in Tempe city limits]; Content Area [select English Language Arts]; Test Level [select Grade 3]; Subgroup/Ethnicity [select All Students] Remove irrelevant fields; Add Fiscal YearPublish Frequency: Annually as data becomes availablePublish Method: ManualData Dictionary

Student Performance & Behavior Dataset

This dataset is real data of 5,000 records collected from a private learning provider. The dataset includes key attributes necessary for exploring patterns, correlations, and insights related to academic performance.

Columns: 01. Student_ID: Unique identifier for each student. 02. First_Name: Student’s first name. 03. Last_Name: Student’s last name. 04. Email: Contact email (can be anonymized). 05. Gender: Male, Female, Other. 06. Age: The age of the student. 07. Department: Student's department (e.g., CS, Engineering, Business). 08. Attendance (%): Attendance percentage (0-100%). 09. Midterm_Score: Midterm exam score (out of 100). 10. Final_Score: Final exam score (out of 100). 11. Assignments_Avg: Average score of all assignments (out of 100). 12. Quizzes_Avg: Average quiz scores (out of 100). 13. Participation_Score: Score based on class participation (0-10). 14. Projects_Score: Project evaluation score (out of 100). 15. Total_Score: Weighted sum of all grades. 16. Grade: Letter grade (A, B, C, D, F). 17. Study_Hours_per_Week: Average study hours per week. 18. Extracurricular_Activities: Whether the student participates in extracurriculars (Yes/No). 19. Internet_Access_at_Home: Does the student have access to the internet at home? (Yes/No). 20. Parent_Education_Level: Highest education level of parents (None, High School, Bachelor's, Master's, PhD). 21. Family_Income_Level: Low, Medium, High. 22. Stress_Level (1-10): Self-reported stress level (1: Low, 10: High). 23. Sleep_Hours_per_Night: Average hours of sleep per night.

The Attendance is not part of the Total_Score or has very minimal weight.

Calculating the weighted sum: Total Score=a⋅Midterm+b⋅Final+c⋅Assignments+d⋅Quizzes+e⋅Participation+f⋅Projects

Dataset contains: - Missing values (nulls): in some records (e.g., Attendance, Assignments, or Parent Education Level). - Bias in some Datae (ex: grading e.g., students with high attendance get slightly better grades). - Imbalanced distributions (e.g., some departments having more students).

Note: - The dataset is real, but I included some bias to create a greater challenge for my students. - Some Columns have been masked as the Data owner requested. "Students_Grading_Dataset_Biased.csv" contains the biased Dataset "Students Performance Dataset" Contains the masked dataset

2018 DC School Report Card. STAR Framework student group scores by school and school framework. The STAR Framework measures performance for 10 different student groups with a minimum n size of 10 or more students at the school. The student groups are All Students, Students with Disabilities, Student who are At Risk, English Learners, and students who identify as the following ESSA-defined racial/ethnic groups: American Indian or Alaskan Native, Asian, Black or African American, Hispanic/Latino of any race, Native Hawaiian or Other Pacific Islander, White, and Two or more races. The Alternative School Framework includes an eleventh student group, At-Risk Students with Disabilities.Some students are included in the school- and LEA-level aggregations that will display on the DC School Report Card but are not included in calculations for the STAR Framework. These students are included in the “All Report Card Students” student group to distinguish from the “All Students” group used for the STAR Framework.Supplemental:Metric scores are not reported for n-sizes less than 10; metrics that have an n-size less than 10 are not included in calculation of STAR scores and ratings.At the state level, teacher data is reported on the DC School Report Card for all schools, high-poverty schools, and low-poverty schools. The definition for high-poverty and low-poverty schools is included in DC's ESSA State Plan. At the school level, teacher data is reported for the entire school, and at the LEA-level, teacher data is reported for all schools only.On the STAR Framework, 203 schools received STAR scores and ratings based on data from the 2017-18 school year. Of those 203 schools, 2 schools closed after the completion of the 2017-18 school year (Excel Academy PCS and Washington Mathematics Science Technology PCHS). Because those two schools closed, they do not receive a School Report Card and report card metrics were not calculated for those schools.Schools with non-traditional grade configurations may be assigned multiple school frameworks as part of the STAR Framework. For example, a K-8 school would be assigned the Elementary School Framework and the Middle School Framework. Because a school may have multiple school frameworks, the total number of school framework scores across the city will be greater than the total number of schools that received a STAR score and rating.Detailed information about the metrics and calculations for the DC School Report Card and STAR Framework can be found in the 2018 DC School Report Card and STAR Framework Technical Guide (https://osse.dc.gov/publication/2018-dc-school-report-card-and-star-framework-technical-guide).

Dataset for the evaluation of student-level outcomes of a primary school Computer Science curricular reform

• If you publish material based on this dataset, please cite the following :

    • The Zenodo repository : Laila El-Hamamsy, Barbara Bruno, Jessica Dehler Zufferey, and Francesco Mondada (2023). Dataset for the evaluation of student-level outcomes of a primary school Computer Science curricular reform [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7489244


    • The associated peer reviewed article that will appear in the International Journal of STEM education : El-Hamamsy, L., Bruno, B., Audrin, C., Chevalier, M., Avry S., Dehler Zufferey, J., and Mondada, F. (2023). How are Primary School Computer Science Curricular Reforms Contributing to Equity? Impact on Student Learning, Perception of the Discipline, and Gender Gaps. arXiv, to appear in the International Journal of STEM Education. https://doi.org/10.48550/arXiv.2306.00820

• License: This work is licensed under a Creative Commons Attribution 4.0 International license (CC-BY-4.0)

• Creator: El-Hamamsy, L., Bruno, B., Dehler Zufferey, J., and Mondada, F.

• Date: May 2nd 2023

• Subject: Computer Science; Curricular Reform; Elementary Education; Learning Achievement; Computational Thinking; Perception Survey; Equity; Gender Gaps

• Dataset format: CSV

• Dataset collection: January 2021 to May 2022

• Dataset size : < 100 kB

• Dataset content : three excel files. We provide the detailed description of each of the files below. The original questions are available in the associated publication [1]. Please note that these datasets contain missing values due to students either not being present for all data collections or not having the associated teacher-related data.

• Abbreviations : - CS : Computer Science - CT : Computational Thinking - PD : Professional Development

• Funding : This work was funded by the the NCCR Robotics, a National Centre of Competence in Research, funded by the Swiss National Science Foundation (grant number 51NF40_185543)

References

[1] El-Hamamsy, L., Bruno, B., Audrin, C., Chevalier, M., Avry S., Dehler Zufferey, J., and Mondada, F. (2023). How are Primary School Computer Science Curricular Reforms Contributing to Equity? Impact on Student Learning, Perception of the Discipline, and Gender Gaps. arXiv, to appear in the International Journal of STEM Education. https://doi.org/10.48550/arXiv.2306.00820

Many socio-economically disadvantaged students excel in PISA. Students who succeed at school despite a disadvantaged background -- resilient students -- are the focus of Against the Odds. The report shows that overcoming barriers to achievement is possible, and provides students, parents, policy makers and other education stakeholders insights into what enables socio-economically disadvantaged students to fulfil their potential. Resilient students are characterised by positive approaches to learning, for example, having more interest in science or having more self-confidence. The evidence in PISA shows that positive approaches to learning tend to boost the performance of advantaged students more than that of disadvantaged ones. From an equity perspective, therefore, policies aimed at fostering positive approaches to learning ought to target disadvantaged students more than others.

The dataset provides simulated insights into student engagement and performance within the THM platform. It outlines mathematical representations of student learning profiles, detailing behaviors ranging from high achievers to inconsistent performers. Additionally, the dataset includes key performance indicators, offering metrics like room completion, points earned, and time spent to gauge student progress and interaction within the platform's modules. Here are definitions of the learning profiles, along with mathematical representations of their behaviors:

High Achiever: These are students who consistently perform well across all modules. Their performance can be described as a normal distribution centered at a high mean value. Their performance P in a given module can be modelled as: P = N(90, 5) where N is the normal distribution function, 90 is the mean, and 5 is the standard deviation. Average Performer: These are students who typically perform at the average level across all modules. Their performance can be described as a normal distribution centered at a medium mean value: P = N(70, 10), where 70 is the mean, and 10 is the standard deviation. Late Bloomer: These are students whose performance improves as they progress through the modules. Their performance can be modelled as: P = N(50 + i*10, 10), where i is the module index and shows an increasing trend. Specialized Talent: These are students who have average performance in most modules but excel in a particular module (e.g., module5). Their performance can be described as: P = N(90, 5) if the module is module 5, else P = N(70, 10). Inconsistent Performer: These are students whose performance varies significantly across modules. Their performance can be described as a normal distribution with a high standard deviation: P = N(70, 30), where 70 is the mean, and 30 is the high standard deviation, reflecting inconsistency. Note that the actual performances are bounded between 0 and 100 using the function max(0, min(100, performance)) to ensure valid percentages. In these formulas, the np.random.normal function is used to simulate the variability in student performance around the mean values. The first argument to this function is the mean, and the second argument is the standard deviation, reflecting the level of variability around the mean. The function returns a number drawn from the normal distribution described by these parameters. Note that the proposed method is experimental and has not been validated.

List of Key Performance Indicators (KPIs) for Student Engagement and Progress within the Platform:

Room Name: This represents the unique identifier or name of a specific room (or module). Think of each room as a separate module or lesson within an educational platform. For example, Room1, Room2, etc. Total rooms completed: Indicates the cumulative number of rooms that a student has fully completed. Completion is typically determined by meeting certain criteria, like answering all questions or achieving a certain score. Rooms registered in: Represents the number of rooms a student has registered or enrolled in. This could be different from the total number of rooms they've completed. Ratio of Questions completed per room: This gives an insight into a student's progress in a particular room. For instance, a ratio of 7/10 suggests the student has completed 7 out of 10 available questions in that room. Room Completed (yes no): Indicates whether a student has fully completed a specific room or not. This could be determined by the percentage of material covered, questions answered, or a certain score achieved. Room Last deploy (count of days): Refers to the number of days since the last update or deployment was made to that room. It can give an idea about the effort of the student. Points in room used for the leaderboard (range 0-560): Each room assigns points based on student performance, and these points contribute to leaderboards. The range suggests that a student can earn anywhere from 0 to 560 points in a particular room. Last answered question in a room (27th Jan 2023): This indicates the date when a student last answered a question in a specific room. It can provide insights into a student's recent activity and engagement. Total points in all rooms (range 0-560): The cumulative score a student has achieved across all rooms. Path Percentage completed (range 0-100): Indicates the percentage of the overall learning path that the student has completed. A path could consist of multiple modules or rooms. Module Percentage completed (range 0-100): Represents how much of a specific module (which could have multiple lessons or topics) a student has completed. Room Percentage completed (range 0-100): Shows the percentage of a specific room that has been completed by a student. Time Spent on the platform (seconds): This provides an aggregate of the total time a student has spent on the entire educational platform. Time spent on each room (seconds): Represents the amount of time a student has dedicated to a specific room. This can give insights into which rooms or modules are the most time-consuming or engaging for students.

The relationship between multiple intelligences and academic performance looks at how Howard Gardner's theory of multiple intelligences, which identifies various types of intelligence, affects students' success in academic settings. Gardner proposed that intelligence is not a single entity but consists of various dimensions, including linguistic, logical-mathematical, spatial, musical, bodily kinesthetic, interpersonal, intrapersonal, and naturalistic intelligence. Each student may excel in one or more of these intelligences, which influences how they learn and perform in school.

Purposive sampling was the method we chose to collect the data. We obtained information from two after-school coaching programs that voluntarily provided their online learning data to us in 2020 during the pandemic. Batches of 45 and 75 students each were used to organize the data, which were then combined to create a single dataset with 399 entries. Two phases of collection took place: on January 17, 2023, and on February 12, 2023. The initial data recording was done using Google Learning Management System's Google Classroom. The data was then exported to local storage by the classroom faculties and then passed onto the researchers. Excel was used to organize the data, with rows representing individual students and columns representing different topics. The dataset, which consists of four mock tests and sixteen physics topics, was gathered from grade 10 physics instructors and students. Every pupil was given a unique ID to protect their privacy, resulting in 399 distinct entries overall. The coaching institution standardized the dataset to score it out of 100 for consistency. It is important to note that for students who did not take the majority of the exams, the institutions did not gather or transmit missing data. The dataset displays a spread with a standard deviation of 20.5 and an average score of 69.547.

This dataset is derived from a random questionnaire survey on the psychological and academic conditions of junior college students in Jinan City, China. The research adopted a method combining stratified sampling and random sampling, and randomly selected students from 18 junior colleges in Jinan City as the survey subjects. Data collection was conducted through the distribution of questionnaires. A set of anonymous questionnaires containing four standardized scales was used, namely the "Discrimination Perception Questionnaire for Junior College Students" (14 questions), the "Academic Burnout Questionnaire for Junior College Students" (14 questions), the "Belief Questionnaire for a Just World for Junior College Students" (13 questions), and the "Sense of Family Obligation Questionnaire for Junior College Students". All scales were scored using the Likert five-point scoring method and underwent pre-tests and reliability and validity tests. The Cronbach's α coefficients were all greater than 0.9, and the KMO values were all greater than 0.9, indicating that the measurement tools have extremely high reliability and validity.The dataset contains a total of 1,499 valid sample records. The data is organized in tabular form, and the main data files contain the following core contents: demographic variables (such as gender, grade, major); The 14 items of discrimination perception cover four dimensions: intelligence and academic performance, physical traits, language communication, and family background. The 14 items of academic burnout cover four dimensions: low learning efficiency, low mood, physical exhaustion and academic alienation. The 13 items of the belief in a just world include two dimensions: general belief in a just world and personal belief in a just world. The multiple items of family obligation cover three dimensions: current assistance to the family, respect for family members, and future support for the family.In addition, the dataset usually also contains the total scores of each dimension and the total score of the scale calculated from these items. The study has passed the Harman single-factor test, indicating that the deviation of the common method is not significant.The dataset is stored in common CSV and Excel formats, ensuring that it can be directly opened and analyzed using a variety of statistical software. This dataset provides detailed first-hand data support for exploring the complex mechanisms among perception of discrimination, belief in a just world, sense of family obligation and academic burnout among junior college students. It can be used for advanced statistical modeling such as structural equation models and moderating effect analysis, and has significant reuse value for subsequent research in fields such as educational psychology, student development and mental health.

📊 Student Performance Analysis

Project: Data Analysis using Excel Pivot Tables & Charts

Executive Summary

Based on the analysis of 6,607 students, this project identifies that active student habits (Attendance, Tutoring) are stronger predictors of success than environmental factors (Income, Resources).

Key Insights

1. Show Up: Attendance is the #1 driver of success.
2. Get Help: Students attending 6 tutoring sessions/week scored 5 points higher on average.
3. Sleep Myth: Sleep duration showed no correlation with exam scores.

Tools Used

• Microsoft Excel: Pivot Tables, Advanced Charting, Statistical Analysis, Data Cleaning.

Source of Dataset(.csv)

• https://www.kaggle.com/datasets/ayeshaseherr/student-performance

Excel dataset showing all of the survey questions and answers.