Analyzing Student Performance in Engineering Placement Using Data Mining

Agarwal, Krishnanshu; Maheshwari, Ekansh; Roy, Chandrima; Pandey, Manjusha; Rautray, Siddharth Swarup

doi:10.1007/978-981-13-6459-4_18

Cited by 10 publications

(4 citation statements)

References 6 publications

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“…The Naïve Bayes classifier is very effective on many real data applications and the KNN is work on similarity measures and the accuracy of KNN is 95.18%, for Logistic regression 97.59%, for Random forest 96.38%, and for SVM accuracy is 100%. Krishnanshu Agarwal et al [9] had implemented various data mining techniques for student placement prediction and they have used algorithms like K-nearest neighbor(KNN) which is used to labeled points to learn to label other points and random forest(RF) which works by creating a group of random uncorrelated decision trees(DT) and increase the accuracy by taking grade point average(GPA), cumulative grade point average(CGPA) as in their dataset from final year students of B.tech from Kalinga Institute of Industrial Technology(KIIT). Their analysis gives that KNN gives 93.54% and random forest gives the 83.87% accuracy and they have concluded that KNN gives more accuracy for their dataset.…”

Section: B Life Cycle Of Machine Learningmentioning

confidence: 99%

Model Construction Using ML for Prediction of Student Placement

Nutipalli¹

2022

IJRASET

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“Model construction using ML for prediction of student placement” aims to predict the placement of a student using various performance metrics on the Machine Learning algorithms. Early prediction makes the institutional growth as well as the student to get placed. It helps the student to prepare all the company requirements at early stage and monitors the student performance. Existed work was done on the algorithms like Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes. In the proposed work to predict the student placement considered dataset and applied data preprocessing to make the data easier to train the model for prediction using Decision Tree (DT) and XG Boost along with the existing algorithms. Accuracies are calculated using different performance metrics like Accuracy and F1-score, Precision, Recall. The algorithm that worked with the best accuracy is SVM with 91%, and the LR and DT algorithms got 88% accuracy whereas Naïve Bayes got 86% and then the XG Boost stood last with an accuracy of 84%. We are able to make a decision which algorithm is better than other algorithms. Higher accuracy algorithm is mostly preferred to predict the student performance.

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Section: B Life Cycle Of Machine Learningmentioning

confidence: 99%

Model Construction Using ML for Prediction of Student Placement

Nutipalli¹

2022

IJRASET

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“…A machine-algorithm was used by [33] to know about the undergrads at engineering colleges who dropped out in their freshmen year. To conduct, student identification and to classify them according to their academic assessments that will comprise of quiz scores, assessment grades, exam scores and practical test scores, [34] used various data mining algorithms.…”

Section: Education Data Mining (Edm)mentioning

confidence: 99%

“…In accordance with [32], the diffusion of BD gives an account of how BD goes from discovery to widespread use, and how this is aided by steps taken by service providers of important technologies necessary to enhance the resources and capacities of academic institutions. [34] Stated that countries may take advantage of the numerous BD opportunities that are accessible to them in order to gain value from the huge volumes of data that are generated and, in the long term, aid in their development. BD and analytics in HEd have the potential to be transformative, affecting existing processes of administration, teaching, and learning, as well as contributing to policy outcomes by assisting in the resolution of existing difficulties facing educational institutions [4,35].…”

Section: Proposed Framework Future Research and Limitationsmentioning

confidence: 99%

A Proposed Framework for Big Data Analytics in Higher Education

Mago¹,

Khan²

2021

IJACSA

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Students, faculties, and other members of the higher education (HEd) system are increasingly reliant on various information technologies. Such a reliance results in a plethora of data that can be explored to obtain relevant statistics or insights. Another reason to explore the data is to acquire valuable insight regarding the novel unstructured forms of data that are discovered and often found to have a connection with elements of social media such as pictures, videos, Web pages, audio files, etc. Moreover, the data can bring additional valuable benefits when processed in the context of HEd. When used strategically, Big Data (BD) provides educational institutions with the chance to improve the quality of education from all the perspectives and steer students of HEd toward higher rates of completion. Further, this will improve student persistence and results, all of which are facilitated by technology. With this aim, the current research proposes a framework that analyzes the data collected from heterogeneous sources and analyzes using BD analytics tools to do various types of analysis that will be beneficial for different learners, faculties and other members of HEd system. Moreover, current research also focuses on the challenges of acquiring BD from various sources.

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“…Decision Tree (J48) had accuracy of 76.2712%, REPTree and OneR had the same accuracy of 76.7334%. The study [19] by Agarwal, Maheshwari, Roy, Pandey and Rautray analyzed 306 students' data in higher education for predicting student performance using two classification algorithms K-Nearest Neighbor and Random Forest. The result of their study showed that Random Forest had the highest prediction accuracy of 93.54%.…”

Section: A Predicting Student Performancementioning

confidence: 99%

Educational Data Mining Applications and Techniques

Alshareef¹,

Alhakami²,

Alsubait³

et al. 2020

IJACSA

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Educational data mining (EDM) uses data mining techniques to analyze huge amounts of student data in the educational environments. The main purpose of EDM is to analyze and solve educational issues and, consequently, improve educational processes. With the emergence of EDM applications in the educational environments, several techniques have been identified to implement these applications. This paper reviews the relevant studies in EDM including datasets and techniques used in those studies and identifies the most effective techniques. The most prevalent applications include predicting student performance, detecting undesirable student behaviors, grouping students and student modeling. These applications aim to help decision makers in the educational institutions to understand student situations, improve students' performance, identify learning priorities for different groups of students and develop learning process. The prediction accuracy is selected as the evaluation criteria for the effectiveness of educational data mining techniques. The results show that Bayesian Network and Random Forest are the most effective techniques for predicting student performance, Social Network Analysis is the best technique for detecting undesirable student behaviors, Clustering and Social Network Analysis are the most effective techniques for grouping students and student modeling, respectively. This study recommends conducting more comprehensive and extended studies to evaluate the effectiveness of EDM techniques with an extended evaluation criteria.

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Analyzing Student Performance in Engineering Placement Using Data Mining

Cited by 10 publications

References 6 publications

Model Construction Using ML for Prediction of Student Placement

Model Construction Using ML for Prediction of Student Placement

A Proposed Framework for Big Data Analytics in Higher Education

Educational Data Mining Applications and Techniques

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