Student Placement Prediction Model: A Data Mining Perspective for Outcome-Based Education System

Abstract

Campus placement plays a vital role in every educational institution in helping students to achieve their goals. Data mining classification can be used as a useful tool for extracting the associated information from the large scale student dataset. Data mining methods have been used broadly in the area of the education system which involves various methods and approach for discovering knowledge. In this paper, a predictive model is designed which can predict the category of placements (dream companies, super dream companies and mass recruiter companies) in which students are eligible by considering their past performance in academics and other curricular activities. The model will also suggest further skills required for future recruitments which may help the students for placement preparation. The paper also provides real-time experimental results and findings along with performance measures used for model validation which helps in achieving the milestone of outcome-based education (OBE) in educational institutes as it isgiven utmost importance in present scenario to ensure better placement prospects in students, which would in turn help the students for carrier building.

Educational Data Mining in Outcome-Based Education: An Analysis of Predictive Models for Program Learning Outcome Attainment

“…The paper highlights the significance of accurately determining students' placement prospects by utilizing data mining techniques to predict placement likelihood based on various factors. The analysis was conducted using SVM, ANN, J48, NB, and Random Forest based on the student's previous four-year academic performance data, and among these methods, ANN yielded the most favorable outcomes [1].…”

“…Data pre-processing is an initial phase of data analysis and involves converting and processing raw data to make it suitable for further analysis [1]. The exploration and processing of data before experimentation ensures the reliability of the results.…”

“…Once collected, the data for each batch was integrated into a single dataset, making a total of 370 records. A thorough examination of the dataset during data preprocessing aimed to eliminate null or missing values that could compromise analysis accuracy [1]. This step, crucial for dataset integrity, involved locating and addressing any missing values along with rectifying any instances of inconsistent data.…”

“…The goal is to build real-world skills and enhance student performance through the attainment of knowledge. OBE serves to bridge the industry-academic gap, which ultimately enables students to meet industry standards and thus improve job opportunities [1][2][3]. Educational data mining (EDM) is an evolving field that analyzes data from diverse educational sources to better understand student learning patterns.…”

“…Course learning outcomes (CLOs) are specific lessons acquired from a course that students are expected to apply in subsequent circumstances, that emphasize key elements of the transition from student to professional, and that show the broad aspects of desired student skills. The mapping of CLOs to PLOs ensures that there are sufficient learning opportunities to support the acquisition of learning outcomes that meet the OBE standards [1][2][3][4][5][6][7][8][9].…”