Effect of Different Attributes on the Academic Performance of Engineering Students

Verma, Swati; Yadav, Rakesh Kumar

doi:10.1109/icatmri51801.2020.9398442

Cited by 6 publications

(5 citation statements)

References 8 publications

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“…In this study, it is observed that the factors related to deafness also need to be considered in the case of students with hearing impairment. For a hearing student, the prior academic results along with background features have a strong relationship with the grade he obtained in the first semester [17]. In the case of a student with hearing impairment, one of the major barriers he faces throughout his life is the communication barrier.…”

Section: Discussionmentioning

confidence: 99%

Factors Affecting the Academic Performance of Students with Hearing Impairment

Rajamoni¹,

Kumar²,

Leela³

2022

RIA

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Around 5 million people in India are with hearing impairment. The higher education opportunities for students with hearing impairment are very limited. Only six institutes in the country provide undergraduate degrees to students with hearing impairment, according to accessible records. Academic achievement in the past, student background characteristics, and eLearning elements are all aspects that influence a student's academic performance. Hearing impairment-related characteristics may also need to be considered for a student with hearing impairment. Identification of these elements may aid teachers in developing individualized teaching plans for students. This paper tries to find the features that affect the performance of students with hearing impairment. The features included are socioeconomic, previous academic scores, and deafness-related factors. The dataset includes data of 224 undergraduate students who have hearing impairment. The preadmission data is used to analyze the performance of students with hearing impairment. The students are classified into different levels – low, medium, and high according to their performance. Different machine learning models are used to classify the students – Logistic Regression, Decision Tree, Support Vector Machine, KNN, Random Forest, and Naïve Bayes. The Random Forest model performed better compared to other models.

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Section: Discussionmentioning

confidence: 99%

Factors Affecting the Academic Performance of Students with Hearing Impairment

Rajamoni¹,

Kumar²,

Leela³

2022

RIA

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“…Khairy et al (2018) utilized a similar method (the results of a psychometric test) to predict secondary school students' performance. However, according to the findings, most psychological attributes play a minor role in predicting students' performance (Verma & Yadav, 2020). In short, past academic performance is crucial to predicting students' performance in English and Mathematics and certain demographics and psychological attributes are also influencing the predictions.…”

Section: Predictors Of Students' Performance In English and Mathematicsmentioning

confidence: 99%

Predicting students’ performance in English and Mathematics using data mining techniques

Roslan

Chen

2022

Educ Inf Technol

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This study attempts to predict secondary school students' performance in English and Mathematics subjects using data mining (DM) techniques. It aims to provide insights into predictors of students' performance in English and Mathematics, characteristics of students with different levels of performance, the most effective DM technique for students' performance prediction, and the relationship between these two subjects. The study employed the archival data of students who were 16 years old in 2019 and sat for the Malaysian Certificate of Examination (MCE) in 2021. The learning of English and Mathematics is a concern in many countries. Three main factors, namely students' past academic performance, demographics, and psychological attributes were scrutinized to identify their impact on the prediction. This study utilized the Orange software for the DM process. It employed Decision Tree (DT) rules to determine the characteristics of students with low, moderate, and high performance in English and Mathematics subjects. DT and Naïve Bayes (NB) techniques show the best predictive performance for English and Mathematics subjects, respectively. Such characteristics and predictions may cue appropriate interventions to improve students' performance in these subjects. This study revealed students' past academic performance as the most critical predictor, as well as a few demographics and psychological attributes. By examining top predictors derived using four different classifier types, this study found that students' past Mathematics performance predicts their MCE English performance and students' past English performance predicts their MCE Mathematics performance. This finding shows students' performances in both subjects are interrelated.

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“…They concluded that students" engagement and past performance data have a significant influence, while www.iijacsa.thesai.org demographic attributes have a slight impact, on students" performance. Further, Verma and Yadav [8] used the crosstabulation method and the chi-square test to analyze the effects of different attributes such as background, academic, social, and psychological characteristics on students' academic performance. In their finding, it was concluded that students" academic and background attributes were the most influential factors that may affect students" grades.…”

Section: Related Workmentioning

confidence: 99%

“…There are three main feature selection techniques: manual selection based on pedagogical theories or expert experience; filter-based selection; and wrapper feature selection [19]. In the present study, as all the attributes were categorical, a filterbased feature selection technique, namely "chi-square", was used by which p-values were calculated for each attribute [8]. The attributes having a p-value of less than 0.01 show a highly significant correlation with the student's grades.…”

Section: ) Data Transformationmentioning

confidence: 99%

A Scalable Machine Learning-based Ensemble Approach to Enhance the Prediction Accuracy for Identifying Students at-Risk

Verma¹,

Yadav²,

Kholiya³

2022

IJACSA

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Among the educational data mining problems, the early prediction of the students' academic performance is the most important task, so that timely and requisite support may be provided to the needy students. Machine learning techniques may be used as an important tool for predicting low-performers in educational institutions. In the present paper, five singlesupervised machine learning techniques have been used, including Decision Tree, Naïve Bayes, k-Nearest-Neighbor, Support Vector Machine, and Logistic Regression. To analyze the effect of an imbalanced dataset, the performance of these algorithms has been checked with and without various resampling methods such as Synthetic Minority Oversampling Technique (SMOTE), Borderline SMOTE, SVM-SMOTE, and Adaptive Synthetic (ADASYN). The Random hold-out method and GridSearchCV were used as model validation techniques and hyper-parameter tuning respectively. The results of the present study indicated that Logistic Regression is the best performing classifier with every balanced dataset generated using all of the four resampling techniques and also achieved the highest accuracy of 94.54% with SMOTE. Furthermore, to improve the prediction results and to make the model scalable, the most suitable classifier was integrated with the help of bagging, and a well-accepted accuracy of 95.45% was achieved.

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Effect of Different Attributes on the Academic Performance of Engineering Students

Cited by 6 publications

References 8 publications

Factors Affecting the Academic Performance of Students with Hearing Impairment

Factors Affecting the Academic Performance of Students with Hearing Impairment

Predicting students’ performance in English and Mathematics using data mining techniques

A Scalable Machine Learning-based Ensemble Approach to Enhance the Prediction Accuracy for Identifying Students at-Risk

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