Predicting Student Performance using Advanced Learning Analytics

Daud, Ali; Aljohani, Naif Radi; Abbasi, Rabeeh Ayaz; Lytras, Miltiadis D.; Abbas, Farhat; Alowibdi, Jalal S.

doi:10.1145/3041021.3054164

Cited by 179 publications

(112 citation statements)

References 16 publications

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“…From [3] we can infer that classifier can be used for the prediction purpose. In [4] we have studied different classification algorithms.…”

Section: Svmmentioning

confidence: 99%

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Automatic Student Analysis and Placement Prediction using Advanced Machine Learning Algorithms

Anvesh*¹,

Prasad²,

Laxman³

et al. 2019

IJITEE

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The amenable statement with respective to Company Organization, Institution and students is that the company organization are taking more time to recruit which is a big challenge to them and there is no specific platform to recruit candidates on preferred qualifications. The Institutions are unable to get 100% placements among eligible students. The institutions doesn't provide proper training on minimum and preferred qualifications to the students. The candidates are unable to get specific training from college organization. The college organization should provide the training to candidates at what they are lagging behind and make the students to get stronger in preferred qualifications and all other aspects. "52% of Talent Acquisition leaders say the hardest part of recruitment is screening candidates from a large applicant pool". The time spent on screening students from a large pool often takes up the largest portion of the time. Despite College Organization are also not training students effectively based on company requirements. The analysis of student is to be done to know where the student is failing to get the placement. The company doesn't know the personalityof the student while recruiting the students. To solve this bottleneck in recruiting we created this automation tool. The main process of determining whether a candidate is qualified based on minimum qualifications like CGPA, Certifications, Projects done, Internships and respectively. There are two main goals of this project are:1. To decide whether to move the student forward to an interview or to reject them. projects, education, skills and knowledge, personality traits, competencies. The minimum qualifications are the mandatory qualifications that the company organizations required and preferred qualifications are not mandatory but to make the student stronger from other students. The personality and also technical knowledge can be given accurately by the faculty, mentor, H.Based on the qualifications, personality analysis, we can shortlist the students and proper analysis is done. So the recruiters don't spend more time and college scan improve their placement percentage to 95% by improving more skills of the students, improving skills who are lagging behind (not shortlisted). The colleges can predict how many students are going to be placed and who are needed to be trained more. The students can evaluate themselves about their suitable job role which make organizations easy to give job role to the students. This paper mainly concentrates on the career area prediction of computer science domain candidates. The college organization can give more training to the students those who got rejected by small issues like communication, programming, aptitude… This process is based on minimum qualifications and preferred qualifications. Both types of qualifications are more useful to the recruiters. These qualifications can include experience on

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“…From [3] we can infer that classifier can be used for the prediction purpose. In [4] we have studied different classification algorithms.…”

Section: Svmmentioning

confidence: 99%

“…In [4] they explained the concepts how to predict the student performance using the machine learning algorithms. The solutions of various problems can be solved by supervised learning, unsupervised learning and semi supervised learning.…”

Section: Introductionmentioning

confidence: 99%

Automatic Student Analysis and Placement Prediction using Advanced Machine Learning Algorithms

Anvesh*¹,

Prasad²,

Laxman³

et al. 2019

IJITEE

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“…The study showed that the student's performance declines when it comes towards the end of the semester and that was due to the nature of studying programming subjects which increase in its difficulty week by week. Daud et al (2017) prepared a study that tackled the student's performance prediction problem, data has been collected from graduate and undergraduate universities which made up a 3000 record, after preprocessing the number of records was reduced to around 700 records. The study tried to answer the question of will the student complete his study or not.…”

Section: Related Workmentioning

confidence: 99%

A Classification and Prediction Model for Student's Performance in University Level

Abazeed¹,

Khder²

2017

Journal of Computer Science

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Educational Data Mining is a new discipline, focusing on studying the methods and creating models to utilize educational data, using those methods to better understand students and their performance. We implemented two different techniques on our dataset; classification used to build a prediction model and association rules were used to find interesting hidden information in the student's records. This study will help the student's to determine their direction and improve when necessary to cope up with their studies. It also provide a great tool to predict and evaluate those students who need attention and correction actions and find out any deviation before it happen and become a decrease in performance and reduce failure rate.

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“…However, the challenging task is to find the optimal algorithm which could produce satisfying results. Machine learning algorithms such as naïve Bayes, logistic regression, artificial neural networks, decision tree, random forest, support vector machine, k-nearest neighbor, and more, were popularly used to analyze and predict academic performance [3][4][5][6][7][8][9][10][11][12][13][14]. The performance of each model is varied from dataset to dataset, which relies on the characteristics and quality of data.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Machine Learning Algorithms for Predicting Academic Performance

Sokkhey¹,

Okazaki²

2020

IJACSA

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The large volume of data and its complexity in educational institutions require the sakes from informative technologies. In order to facilitate this task, many researchers have focused on using machine learning to extract knowledge from the education database to support students and instructors in getting better performance. In prediction models, the challenging task is to choose the effective techniques which could produce satisfying predictive accuracy. Hence, in this work, we introduced a hybrid approach of principal component analysis (PCA) as conjunction with four machines learning (ML) algorithms: random forest (RF), C5.0 of decision tree (DT), and naïve Bayes (NB) of Bayes network and support vector machine (SVM), to improve the performances of classification by solving the misclassification problem. Three datasets were used to confirm the robustness of the proposed models. Through the given datasets, we evaluated the classification accuracy and root mean square error (RSME) as evaluation metrics of the proposed models. In this classification problem, 10-fold cross-validation was proposed to evaluate the predictive performance. The proposed hybrid models produced very prediction results which shown itself as the optimal prediction and classification algorithms.

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Predicting Student Performance using Advanced Learning Analytics

Cited by 179 publications

References 16 publications

Automatic Student Analysis and Placement Prediction using Advanced Machine Learning Algorithms

Automatic Student Analysis and Placement Prediction using Advanced Machine Learning Algorithms

A Classification and Prediction Model for Student's Performance in University Level

Hybrid Machine Learning Algorithms for Predicting Academic Performance

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Predicting Student Performance using Advanced Learning Analytics

Cited by 179 publications

References 16 publications

Automatic Student Analysis and Placement Prediction using Advanced Machine Learning Algorithms

Automatic Student Analysis and Placement Prediction using Advanced Machine Learning Algorithms

A Classification and Prediction Model for Student&#039;s Performance in University Level

Hybrid Machine Learning Algorithms for Predicting Academic Performance

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Product

Resources

About

A Classification and Prediction Model for Student's Performance in University Level