scholarly journals Machine Learning Approach to Predict Student Academic Performance

2018 ◽  
Vol 6 (4) ◽  
pp. 734-742 ◽  
Author(s):  
Amandeep Kaur
Keyword(s):  
Machine Learning ◽  
Academic Performance ◽  
Learning Approach ◽  
Student Academic Performance ◽  
Machine Learning Approach

scholarly journals A Machine Learning Approach for Predicting Student Performance

2019 ◽  
pp. 462-465
Author(s):  
C. Selvi ◽  
R. Shalini ◽  
V. Navaneethan ◽  
L. Santhiya
Keyword(s):  
Machine Learning ◽  
Academic Performance ◽  
Student Performance ◽  
Learning Model ◽  
Learning Approach ◽  
Economic Prosperity ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Novel Method ◽  
Predicting Student Performance

An University’s reputation and its standard are weighted by its students performance and their part in the future economic prosperity of the nation, hence a novel method of predicting the student’s upcoming academic performance is really essential to provide a pre-requisite information upon their performances. A machine learning model can be developed to predict the student’s upcoming scores or their entire performance depending upon their previous academic performances.

scholarly journals Performance Based Prediction of the Students in the Physics Subject using Traditional and Machine Learning Approach at Higher Education Level

2020 ◽  
Vol 6 (1) ◽  
pp. 174-190
Author(s):  
Sarfraz Ahmad Ahmad ◽  
Ishtiaq Hussain ◽  
Rashid Ahmad ◽  
Muhammad Naseer Ud Din
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Academic Performance ◽  
Undergraduate Students ◽  
Sampling Technique ◽  
Simple Random Sampling ◽  
Education Level ◽  
Learning Approach ◽  
Learning Approaches ◽  
Machine Learning Approach

In higher educational institutions, it is not an easy task to judge the performance of the students timely which is becoming more challenging. Although institutions have gathered a lot of data about their students. They do not have some specific methods to extract meanings from it. The main objective of this study was to find out the performance-based prediction of the students using their demographic and academic factors by using traditional and machine learning approaches. Graduates and undergraduate students studying in KUST were the population of the study. The study was delimited to the department of physics. A total of ninety graduate and undergraduate students were selected randomly using a simple random sampling technique as the entire sample.  The result indicated that percentage in matric (Correlation = 0.304), intermediate (Correlation = 0.245) and National Aptitude Test scores (Correlation = 0.480) found the best predictors. Further research was recommended to predict students’ academic performance by taking other aspects of the students like personality, cognitive, psychological, and economic domain for making a dataset of the features which may be used in machine learning approach which is more reliable to judge the academic performance of the students at the higher education level. Keywords: Performance, Challenging, Demographic, Prediction, Examination

scholarly journals Urban–Rural Gradients Predict Educational Gaps: Evidence from a Machine Learning Approach Involving Academic Performance and Impervious Surfaces in Ecuador

2021 ◽  
Vol 10 (12) ◽  
pp. 830
Author(s):  
Fabián Santos-García ◽  
Karina Delgado Valdivieso ◽  
Andreas Rienow ◽  
Joaquín Gairín
Keyword(s):  
Machine Learning ◽  
Academic Performance ◽  
Cognitive Skills ◽  
Learning Approach ◽  
Impervious Surfaces ◽  
School Students ◽  
Urban Transition ◽  
Natural Region ◽  
Machine Learning Approach ◽  
Urban Rural

Academic performance (AP) is explained by a multitude of factors, principally by those related to socioeconomic, cultural, and educational environments. However, AP is less understood from a spatial perspective. The aim of this study was to investigate a methodology using a machine learning approach to determine which answers from a questionnaire-based survey were relevant for explaining the high AP of secondary school students across urban–rural gradients in Ecuador. We used high school locations to construct individual datasets and stratify them according to the AP scores. Using the Boruta algorithm and backward elimination, we identified the best predictors, classified them using random forest, and mapped the AP classification probabilities. We summarized these results as frequent answers observed for each natural region in Ecuador and used their probability outputs to formulate hypotheses with respect to the urban–rural gradient derived from annual maps of impervious surfaces. Our approach resulted in a cartographic analysis of AP probabilities with overall accuracies around 0.83–0.84% and Kappa values of 0.65–0.67%. High AP was primarily related to answers regarding the academic environment and cognitive skills. These identified answers varied depending on the region, which allowed for different interpretations of the driving factors of AP in Ecuador. A rural-to-urban transition ranging 8–17 years was found to be the timespan correlated with achievement of high AP.

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use
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