Description

This dataset provides a comprehensive overview of various factors affecting student performance in exams. It includes information on study habits, attendance, parental involvement, and other aspects influencing academic success.

Column Descriptions

Student Performance Dataset 2024 Overview This dataset comprises detailed information about high school students in China, collected from various universities and schools. It is designed to analyze the factors influencing student performance, well-being, and engagement. The data includes a wide range of features such as demographic details, academic performance, health status, parental support, and more. The participating institutions include prominent universities such as Tsinghua University, Peking University, Fudan University, Shanghai Jiao Tong University, and Zhejiang University.

Dataset Description Features Student ID: Unique identifier for each student. Gender: Gender of the student (Male/Female). Age: Age of the student. Grade Level: The grade level of the student (e.g., 9, 10, 11, 12). Attendance Rate: The percentage of days the student attended school. Study Hours: Average number of hours the student spends studying daily. Parental Education Level: The highest level of education attained by the student's parents. Parental Involvement: The level of parental involvement in the student's education (High, Medium, Low). Extracurricular Activities: Whether the student participates in extracurricular activities (Yes/No). Socioeconomic Status: Socioeconomic status of the student's family (High, Medium, Low). Previous Academic Performance: Previous academic performance level (High, Medium, Low). Class Participation: The level of participation in class (High, Medium, Low). Health Status: General health status of the student (Good, Average, Poor). Access to Learning Resources: Whether the student has access to necessary learning resources (Yes/No). Internet Access: Whether the student has access to the internet (Yes/No). Learning Style: Preferred learning style of the student (Visual, Auditory, Kinesthetic). Teacher-Student Relationship: Quality of the relationship between the student and teachers (Positive, Neutral, Negative). Peer Influence: Influence of peers on the student's behavior and performance (Positive, Neutral, Negative). Motivation Level: Student's level of motivation (High, Medium, Low). Hours of Sleep: Average number of hours the student sleeps per night. Diet Quality: Quality of the student's diet (Good, Average, Poor). Transportation Mode: Mode of transportation used by the student to commute to school (Bus, Car, Walk, Bike). School Type: Type of school attended by the student (Public, Private). School Location: Location of the school (Urban, Rural). Homework Completion Rate: The rate at which the student completes homework assignments. Reading Proficiency: Proficiency level in reading. Math Proficiency: Proficiency level in mathematics. Science Proficiency: Proficiency level in science. Language Proficiency: Proficiency level in language. Physical Activity Level: The level of physical activity (High, Medium, Low). Screen Time: Average daily screen time in hours. Bullying Incidents: Number of bullying incidents the student has experienced. Special Education Services: Whether the student receives special education services (Yes/No). Counseling Services: Whether the student receives counseling services (Yes/No). Learning Disabilities: Whether the student has any learning disabilities (Yes/No). Behavioral Issues: Whether the student has any behavioral issues (Yes/No). Attendance of Tutoring Sessions: Whether the student attends tutoring sessions (Yes/No). School Climate: Overall perception of the school's environment (Positive, Neutral, Negative). Parental Employment Status: Employment status of the student's parents (Employed, Unemployed). Household Size: Number of people living in the student's household. Performance Score: Overall performance score of the student (Low, Medium, High).

Description: This repository contains the datasets used as part of the OC2 lab's work on Student Performance prediction and student engagement prediction in eLearning environments using machine learning methods.

This dataset contains the 100 level first semester results of 229 students in South East University in Nigeria. The average score for each student is computed based on 8 courses offered in that semester. The dataset contains both the CA and Exam scores respectively. The CA amd Exam score were subsequently conveerted to percentage

This dataset has been collected to support research on predicting the academic performance of Secondary School Certificate (SSC) and Higher Secondary Certificate (HSC) students in Bangladesh. It comprises responses from many students across various institutions in the country.

The dataset includes a diverse set of features that are believed to influence academic outcomes. These features cover a wide range of domains such as:

Demographic Information: Age, gender, parental education, and occupation.

Academic History: Previous grades, subject preferences, study time, tutoring, etc.

Socioeconomic Factors: Family income, number of siblings, living location (urban/rural).

Institutional Factors: Type of school/college (public/private), distance from home, teacher-student ratio, etc.

Lifestyle and Behavioral Aspects: Sleep habits, screen time, daily routines, mental health indicators, and parental support.

The dataset is labeled with the actual academic performance (grades or GPA) of students in SSC and HSC examinations. The goal is to facilitate the development of predictive models and interpretability studies, with a focus on early intervention and academic counseling.

The dataset is anonymized and free from personally identifiable information. It is intended for academic research, education policy analysis, and machine learning experimentation.

if you use the dataset, please cite "A. A. Maruf, R. Ara Rumy, R. I. Sony and Z. Aung, "Predictive Analysis of Bangladeshi Students’ Academic Performances Using Ensemble Machine Learning with Explainable AI Techniques," 2024 27th International Conference on Computer and Information Technology (ICCIT), Cox's Bazar, Bangladesh, 2024, pp. 1200-1205, doi: 10.1109/ICCIT64611.2024.11021990."

The dataset on the academic performance of students in 12 programmes from a private university. The overall people sampled for the observation is 2490 undergraduates excavated from 12 programmes which are as follows Computer Science (CIS), Mathematics (MAT), Electrical and Electronics Engineering (EEE), Biochemistry (BCH), Mechanical Engineering (MCE), Microbiology (MCB), Civil Engineering (CVE), Computer Engineering (CEN), Chemical Engineering (CHE), Industrial Chemistry (CHM), Information and Communication (ICE), Petroleum Engineering (PET).

The dataset is related to student data, from an educational research study focusing on student demographics, academic performance, and related factors. Here’s a general description of what each column likely represents:

Sex: The gender of the student (e.g., Male, Female). Age: The age of the student. Name: The name of the student. State: The state where the student resides or where the educational institution is located. Address: Indicates whether the student lives in an urban or rural area. Famsize: Family size category (e.g., LE3 for families with less than or equal to 3 members, GT3 for more than 3). Pstatus: Parental cohabitation status (e.g., 'T' for living together, 'A' for living apart). Medu: Mother's education level (e.g., Graduate, College). Fedu: Father's education level (similar categories to Medu). Mjob: Mother's job type. Fjob: Father's job type. Guardian: The primary guardian of the student. Math_Score: Score obtained by the student in Mathematics. Reading_Score: Score obtained by the student in Reading. Writing_Score: Score obtained by the student in Writing. Attendance_Rate: The percentage rate of the student’s attendance. Suspensions: Number of times the student has been suspended. Expulsions: Number of times the student has been expelled. Teacher_Support: Level of support the student receives from teachers (e.g., Low, Medium, High). Counseling: Indicates whether the student receives counseling services (Yes or No). Social_Worker_Visits: Number of times a social worker has visited the student. Parental_Involvement: The level of parental involvement in the student's academic life (e.g., Low, Medium, High). GPA: The student’s Grade Point Average, a standard measure of academic achievement in schools.

This dataset provides a comprehensive look at various factors that might influence a student's educational outcomes, including demographic factors, academic performance metrics, and support structures both at home and within the educational system. It can be used for statistical analysis to understand and improve student success rates, or for targeted interventions based on specific identified needs.

The six data sets were created for an undergraduate course at the Babes-Bolyai University, Faculty of Mathematics and Computer Science, held for second year students in the autumn semester. The course is taught both in Romanian and English with the same content and evaluation rules in both languages. The six data sets are the following: - FirstCaseStudy_RO_traditional_2019-2020.txt - contains data about the grades from the 2019-2020 academic year (when traditional face-to-face teaching method was used) for the Romanian language - FirstCaseStudy_RO_online_2020-2021.txt - contains data about the grades from the 2020-2021 academic year (when online teaching was used) for the Romanian language - SecondCaseStudy_EN_traditional_2019-2020.txt - contains data about the grades from the 2019-2020 academic year (when traditional face-to-face teaching method was used) for the English language - SecondCaseStudy_EN_online_2020-2021.txt - contains data about the grades from the 2020-2021 academic year (when online teaching was used) for the English language - ThirdCaseStudy_Both_traditional_2019-2020.txt - the concatenation of the two data sets for the 2019-2020 academic year (so all instances from FirstCaseStudy_RO_traditional_2019-2020 and SecondCaseStudy_EN_traditional_2019-2020 together) - ThirdCaseStudy_Both_online_2020-2021.txt - the concatenation of the two data sets for the 2020-2021 academic year (so all instances from FirstCaseStudy_RO_online_2020-2021 and SecondCaseStudy_EN_online_2020-2021 together)Instances from the data sets for the 2019-2020 academic year contain 12 attributes (in this order): - the grades received by the student for 7 laboratory assignments that were presented during the semester. For assignments that were not turned in a grade of 0 was given. Possible values are between 0 and 10 - the grades received by the student for 2 practical exams. If a student did not participate in a practical exam, de grade was 0. Possible values are between 0 and 10. - the number of seminar activities that the student had. Possible values are between 0 and 7. - the final grade the student received for the course. It is a value between 4 and 10. - the category of the final grade: - E for grades 10 or 9 - G for grades 8 or 7 - S for grades 6 or 5 - F for grade 4Instances from the data sets for the 2020-2021 academic year contain 10 attributes (in this order): - the grades received by the student for 7 laboratory assignments that were presented during the semester. For assignments that were not turned in a grade of 0 was given. Possible values are between 0 and 10 - a seminar bonus computed based on the number of seminar activities the student had during the semester, which was added to the final grade. Possible values are between 0 and 0.5. - the final grade the student received for the course. It is a value between 4 and 10. - the category of the final grade: - E for grades 10 or 9 - G for grades 8 or 7 - S for grades 6 or 5 - F for grade 4

This study examined how students’ academic performance changed after undergoing a transition to online learning during the coronavirus disease 2019 (COVID-19) pandemic, based on the test results of 16 integrated courses conducted in 3 semesters at Hanyang This study was conducted at Hanyang University College of Medicine (HYUCM), a private medical school in Seoul, South Korea. The average number of students per year is about 100. In HYUCM, the transition to online teaching was first implemented after COVID-19. Almost all face-to-face classroom lectures were replaced by online recorded videos, while fewer than 5% of classes were conducted as live online lectures. The major examinations’ raw scores were collected for each student. Because the total score was different for each examination, percent-correct scores were used in subsequent analyses. For courses that conducted more than 1 major examination, student achievement was calculated as an average of the percent-correct scores obtained from the examinations.

This dataset was used in the Bayesian multiple membership multiple classification logistic regression model on student performance with random effects in university instructors and majors.

This dataset contains semester-wise academic performance data of BTech students from GIET University. It includes the grades of students from their 1st to 4th semesters, along with their corresponding 5th-semester grades. The dataset is intended for use in educational data mining and machine learning applications, specifically for predicting the 5th-semester grades of students based on their past performance.The dataset consists of 379 student records, with each record containing the following attributes:

SEM 1: Grade obtained in the 1st semester.

SEM 2: Grade obtained in the 2nd semester.

SEM 3: Grade obtained in the 3rd semester.

SEM 4: Grade obtained in the 4th semester.

SEM 5: Grade obtained in the 5th semester (target variable for prediction).The grades are represented on a scale of 0 to 10, where 10 is the highest achievable grade. This dataset can be used to develop predictive models for academic performance, identify trends in student performance, and support decision-making in educational institutions.

Keywords: Grade Prediction, Student Performance, Educational Data Mining, Academic Analytics, Machine Learning, GIET University

Potential Applications:

Predicting student performance in future semesters.

Identifying at-risk students for early intervention.

Analyzing trends in academic performance over time.

Several studies have highlighted the link between sleep, learning, and memory. Strong evidence shows that sleep deprivation can affect a student’s ability to learn and academic performance. While delayed sleep-wake phase disorder was prevalent among young adults, available evidence showed an inconclusive association between sleep times and academic performance in university students. Therefore, we conducted a cross-sectional study among university students in Indonesia to collect their sleep duration, bedtime, wake-up time, and academic performance. An analysis of 588 university students in Indonesia found that only 38.6% of students sufficiently slept, and their median bedtime and wake-up time was 11:30 pm and 5:30 am, respectively. Gender and wake-up time accounted for a 5.8% variation in academic performance (adjusted R2 = 4.5%) after controlling for sleep duration, bedtime, body mass index, the field of study, batch year, and physical activity. Male had 0.116 [95% Confidence Interval (CI) −0.167 to −0.064] lower grade point average (p < 0.001) than female and students who wake up later had 0.077 (95% Confidence Interval 0.025 to 0.129) greater grade point average (p = 0.004) than students who wake-up earlier. The prevalence of sleep deprivation related to the delayed sleep-wake phase among university students in Indonesia was high. Since wake-up time was related to the increased grade point average, the university should consider developing sleep-friendly policies and interventions to improve their academic performance.

This data contains student questionnaire assessment data and student performance.

Dataset

This dataset was created by mohammed shahbaaz

Contents

The dataset contains anonymized academic performance data of undergraduate engineering students from 2011 to 2020 at the nine campuses of the Universidad Nacional de Colombia. Key variables include socioeconomic status, faculty, gender, academic program, age, and cumulative weighted academic average (CWAA). This dataset provides insights into academic outcomes across various demographics, enabling analysis of patterns that may inform educational strategies and improve equity in engineering education.

This dataset contains information about the relationship between the frequency of library visits by undergraduate residential students and their academic performance, measured by GPA, during the Fall 2023 and Spring 2024 semesters at a private University in the United States of America. The data comprises anonymized library gate entry records and GPA scores, filtered to include students aged 17 and older.Dataset Details:Total Records: 3,340Semester Coverage: Fall 2023 and Spring 2024Student Demographics: Undergraduate residential students aged 17 and olderData Fields:Student ID (Anonymized)AgeSemesterGPALibrary Visits (Total)Library Visits per WeekWeeks with Library Visits

Dataset

This dataset was created by Saud Ahmad Basra

Contents

Food insecurity among college students is a serious problem that can impact student performance in the classroom and ultimately effect student success. The Center for Healthy Communities (CHC) developed the Basic Needs Student Success Survey (BNS3) and administered it to undergraduate students participating in the Educational Opportunity Program (EOP) at three California State Universities between November 2020 and March 2021.

The purpose of this second cross-sectional pilot study was to revise the BNS3 tool and validate student perception of the following:

1. The impact of receiving CalFresh assistance.
2. The impact of utilization of the campus food pantry on their health, nutrition, cooking confidence, time management and academic performance.

This entry contains

1. The anonymized and cleaned data set
2. A codebook (data dictionary)
3. The survey tool as a Qualtrics export to Word file

Course Success rate is the percent of students obtaining grades A‐C and P out of the total number of students enrolled at the beginning of the term. Course success is the building block toward student program completion. Without successful completion of courses, City Colleges of Chicago students will not be able to earn credits toward a degree or certificate, nor will they progress from remedial to college-level coursework.

Contains data files (.csv format) related to compensation, salaries, diversity, and student academic performance at American colleges and universities. Data is from 2008-2023, with data primarily from 2018-2021. Each file has a related data dictionary in a .txt file.