Student grade Prediction
Description
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.
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Data Collection and Methodology 1. Context and Purpose Objective: The dataset was collected to analyze and predict the academic performance of BTech students at GIET University based on their semester-wise grades. 2. Data Source Institution: GIET University Program: BTech (Bachelor of Technology) Population: Students enrolled in the BTech program. Sample Size: 379 student records were collected for this dataset. 3. Data Collection Process Data Collection Period: The data was collected over [insert time period, e.g., the academic year 2022–2023]. Data Type: Historical academic records (semester-wise grades). Inclusion Criteria: Only students who had completed at least 5 semesters were included in the dataset. Exclusion Criteria: Students with incomplete records or those who dropped out were excluded. 4. Variables Collected Independent Variables: 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. Dependent Variable: SEM 5: Grade obtained in the 5th semester (target variable for prediction). 5. Data Collection Methods Source: Data was extracted from the university's academic records system. Tools: Microsoft Excel or Google Sheets for initial data entry and organization. Python or R for data cleaning and preprocessing (if applicable). Protocol: Requested access to anonymized student grade records from the university administration. Extracted semester-wise grades for students who met the inclusion criteria. Ensured data anonymization by removing personally identifiable information (PII). Verified the accuracy of the data by cross-checking with official records.