PREDICTING GRADE POINT AVERAGE OF ENGINEERING STUDENTS USING DEEP LEARNING TECHNIQUES

2021 ◽  
Author(s):  
Imran Zualkernan
Keyword(s):  
Deep Learning ◽  
Grade Point Average ◽  
Engineering Students ◽  
Grade Point ◽  
Learning Techniques

scholarly journals Differential associations of neurobehavioral traits and cognitive ability to academic achievement in higher education

2019 ◽  
Author(s):  
Graham Pluck
Keyword(s):  
Higher Education ◽  
Academic Achievement ◽  
Psychological Distress ◽  
Grade Point Average ◽  
Engineering Students ◽  
Executive Dysfunction ◽  
Grade Point ◽  
Strong Correlate ◽  
University Level ◽  
Current Psychological

BackgroundPeople vary between each other on several neurobehavioral traits, which may have implications for understanding academic achievement.MethodsUniversity-level Psychology or Engineering students were assessed for neurobehavioral traits, intelligence, and current psychological distress. Scores were compared with their grade point average (GPA) data.ResultsFactors associated with higher GPA differed markedly between groups. For Engineers, intelligence, but not neurobehavioral traits or psychological distress, was a strong correlate of grades. For Psychologists, grades were not correlated with intelligence but they were with the neurobehavioral traits of executive dysfunction, disinhibition, apathy, and positive schizotypy. However, only the latter two were associated independently of psychological distress. Additionally, higher mixed-handedness was associated with higher GPA in the combined sample.ConclusionsNeurological factors (i.e., neurobehavioral traits and intelligence), are differentially associated with university-level grades, depending on the major studied. However, mixed-handedness may prove to be a better general predictor of academic performance across disciplines.

scholarly journals Supervised Learning Applied to Graduation Forecast of Industrial Engineering Students

2022 ◽  
Vol 11 (1) ◽  
pp. 325-337
Author(s):  
Natalia Gil ◽  
Marcelo Albuquerque ◽  
Gabriela de
Keyword(s):  
Machine Learning ◽  
High School ◽  
Logistic Regression ◽  
Supervised Learning ◽  
Grade Point Average ◽  
Engineering Students ◽  
Learning Algorithm ◽  
Industrial Engineering ◽  
Machine Learning Algorithm ◽  
Grade Point

<p style="text-align: justify;">The article aims to develop a machine-learning algorithm that can predict student’s graduation in the Industrial Engineering course at the Federal University of Amazonas based on their performance data. The methodology makes use of an information package of 364 students with an admission period between 2007 and 2019, considering characteristics that can affect directly or indirectly in the graduation of each one, being: type of high school, number of semesters taken, grade-point average, lockouts, dropouts and course terminations. The data treatment considered the manual removal of several characteristics that did not add value to the output of the algorithm, resulting in a package composed of 2184 instances. Thus, the logistic regression, MLP and XGBoost models developed and compared could predict a binary output of graduation or non-graduation to each student using 30% of the dataset to test and 70% to train, so that was possible to identify a relationship between the six attributes explored and achieve, with the best model, 94.15% of accuracy on its predictions.</p>

Evaluation of TAPS Packages

2010 ◽  
pp. 128-147
Author(s):  
Manjit Singh Sidhu
Keyword(s):  
Problem Solving ◽  
Learning Styles ◽  
Grade Point Average ◽  
Engineering Students ◽  
Target Population ◽  
Grade Point ◽  
Slow Learners ◽  
Undergraduate Engineering ◽  
The Third ◽  
Engineering Mechanics

The evaluation was carried out to examine the distribution of learning styles (discussed in Chapter 2) of the third year undergraduate engineering students and suggest effective problem solving approaches that could increase the motivation and understanding of slow learners at UNITEN. For this study, a sample target population of 60 third year undergraduate engineering students who had taken the Engineering Mechanics subject was tested. These students were selected based on their second year grade point average (GPA) of less than 2.5 as this study emphasizes on slow learners.

scholarly journals Impact of Time Management Behaviors on Undergraduate Engineering Students’ Performance

SAGE Open ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 215824401882450 ◽  
Author(s):  
Richelle V. Adams ◽  
Erik Blair
Keyword(s):  
Grade Point Average ◽  
Time Management ◽  
Perceived Control ◽  
Engineering Students ◽  
Grade Point ◽  
Cumulative Grade Point Average ◽  
Undergraduate Engineering ◽  
Effective Time ◽  
Behavior Scale ◽  
Management Behaviors

Effective time management is associated with greater academic performance and lower levels of anxiety in students; however many students find it hard to find a balance between their studies and their day-to-day lives. This article examines the self-reported time management behaviors of undergraduate engineering students using the Time Management Behavior Scale. Correlation analysis, regression analysis, and model reduction are used to attempt to determine which aspects of time management the students practiced, which time management behaviors were more strongly associated with higher grades within the program, and whether or not those students who self-identified with specific time management behaviors achieved better grades in the program. It was found that students’ perceived control of time was the factor that correlated significantly with cumulative grade point average. On average, it was found that time management behaviors were not significantly different across gender, age, entry qualification, and time already spent in the program.

scholarly journals Penerapan algoritma C5.0 pada analisis faktor-faktor pengaruh kelulusan tepat waktu mahasiswa Teknik Informatika UMM

Repositor ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 131
Author(s):  
Vinna Rahmayanti ◽  
Yufis Azhar ◽  
Andriani Eka Pramudita
Keyword(s):  
High School ◽  
Feature Selection ◽  
Grade Point Average ◽  
Engineering Students ◽  
Senior High School ◽  
Credit System ◽  
Grade Point ◽  
Academic Credit ◽  
Entry Status ◽  
School Status

AbstrakKelulusan tepat waktu mahasiswa merupakan salah satu permasalahan yang sulit untuk diatasi oleh setiap pihak perguruan tinggi, begitu pula pada jurusan Teknik Informatika Universitas Muhammadiyah Malang. Permasalahan ini harus segera diatasi mengingat kualitas mahasiswa akan mempengaruhi sebuah akreditasi perguruan tinggi maupun jurusan. Oleh karena itu, perlu dilakukan analisis faktor-faktor pengaruh kelulusan tepat waktu mahasiswa Teknik Informatika UMM. Penelitian ini menggunakan algoritma C5.0 untuk melakukan seleksi fitur penting dan analisis regresi untuk melakukan estimasi peluang kelulusan tepat waktu mahasiswa. Variabel bebas yang digunakan adalah jenis kelamin, asal daerah, status masuk, SKS semester 4, SKS semester 6, IP semester 2, IP semester 4, IP semester 6, IPK semester 2, IPK semester 4, IPK semester 6, jenis SMA, status SMA, pendidikan orang tua, dan pekerjaan orang tua. Hasil implementasi algoritma C5.0 pada penelitian ini mampu melakukan seleksi fitur dengan menghasilkan 8 dari total keseluruhan 15 fitur dengan nilai akurasi yang lebih baik dibandingkan nilai akurasi yang menggunakan keseluruhan fitur. Serta, penelitian ini mampu memberikan model regresi dengan nilai akurasi sebesar 82%.Abstract Timely graduation of college students is one of the problems that is difficult to overcome by each college, as well as in the Department of Informatics, University of Muhammadiyah Malang. This problem must be resolved immediately, considering the quality of students will affect the accreditation of university and its majors. So, it is necessary to analyze the factors that influence the timely graduation of Informatics Engineering students in UMM. This study uses the C5.0 algorithm to do feature selection and regression analysis to estimate the opportunities of timely graduation. The independent variables used are gender, regional origin, entry status, academic credit system in 4th semester, academic credit system in 6th semester, grade point of 2nd semester, grade point of 4th semester, grade point of 6th semester, grade point average of 2nd semester, grade point average of 4th semester, grade point average of 6th semester, type of senior high school, status of senior high school, parent’s education, and parent’s job. The results of the implementation of the C5.0 algorithm in this study were able to do feature selection by producing 8 out of total 15 features with better accuracy than the value of accuracy using all features. And this study is able to provide a regression model with an accuracy value of 82%.

scholarly journals The Empirical Analysis of Industrial Work Challenges in the Industrial Revolution 5.0 Towards a Grade Point Average (GPA) for Electronic Engineering Education Students

2021 ◽  
Vol 17 (09) ◽  
pp. 21
Author(s):  
Hendra Hidayat ◽  
Elfi Tasrif ◽  
Putra Jaya ◽  
Muhammad Anwar ◽  
Thamrin Thamrin ◽  
...  
Keyword(s):  
Engineering Education ◽  
Grade Point Average ◽  
Engineering Students ◽  
Industrial Revolution ◽  
Work Motivation ◽  
Work Readiness ◽  
Education Students ◽  
Grade Point ◽  
Industrial Work ◽  
Electronic Engineering

This study aims to analyze the challenges of industrial work in the industrial revolution 5.0 towards the grade point average (GPA) for electronic engineering education students. This study was conducted in accordance with the development of the industrial world with the entry of the 5.0 industrial revolution and the demands of the world of education that must be met to improve the quality of graduates. The research objects are Electronics Engineering Students’. Sampling in this study uses the Slovin formula. A total of 75 students were involved in this study, with a sample of 43 people. To analyze the research data uses SPSS Version 20. The result shows that (1) industrial work motivation has a positive and significant on students’ grade point average (GPA) of electronics engineering students’ (2) work readiness in the industry has a positive and significant on students’ grade point average (GPA) of electronics engineering students’ (3) industrial work motivation and work readiness in the industry together have a significant on students’ grade point average (GPA) of electronics engineering students’.

The Comparison of Forward and Backward Neural Network Model – A Study on the Prediction of Student Grade

2021 ◽  
Vol 16 ◽  
pp. 422-429
Author(s):  
Saikat Gochhait ◽  
Yagyanath Rimal ◽  
Sakuntala Pageni
Keyword(s):  
Neural Network ◽  
Grade Point Average ◽  
Network Model ◽  
Neural Network Model ◽  
Engineering Students ◽  
Machine Language ◽  
Background Information ◽  
Neuron Network ◽  
Grade Point ◽  
Confusion Matrices

A neural network model can be used effectively in predicting training accuracy using machine learning. Based on the comparison of forward and backward neural networks, coded to communicate their output in the requisite manner using machine language is the basis of the present study. With the help of students' background information, to predict the Grade Point Average (GPA) of 580 engineering students based on various parameters, including mental health. The study is based on the Boruta algorithm and the random forest methods for data preparation in the matrices (12 * 2 = 24) of single-layered, multiple-layers, and forward and reverse algorithms adopted to test the prediction and accuracy of the grade point average by analyzing histograms, confusion matrices, and regression analysis. This study suggests the best model for predictions with the help of artificial neuron network that has roughly half the number of single layers and with three hidden layers.

University Dropout

2008 ◽  
Vol 29 (3) ◽  
pp. 134-147 ◽  
Author(s):  
Manuel C. Voelkle ◽  
Nicolas Sander
Keyword(s):  
Grade Point Average ◽  
Structural Equation ◽  
Unobserved Heterogeneity ◽  
School Grade ◽  
Grade Point ◽  
High School Grade ◽  
Hands On ◽  
University Dropout ◽  
The Individual ◽  
A Prospective Study

University dropout is a politically and economically important factor. While a number of studies address this issue cross-sectionally by analyzing different cohorts, or retrospectively via questionnaires, few of them are truly longitudinal and focus on the individual as the unit of interest. In contrast to these studies, an individual differences perspective is adopted in the present paper. For this purpose, a hands-on introduction to a recently proposed structural equation (SEM) approach to discrete-time survival analysis is provided ( Muthén & Masyn, 2005 ). In a next step, a prospective study with N = 1096 students, observed across four semesters, is introduced. As expected, average university grade proved to be an important predictor of future dropout, while high-school grade-point average (GPA) yielded no incremental predictive validity but was completely mediated by university grade. Accounting for unobserved heterogeneity, three latent classes could be identified with differential predictor-criterion relations, suggesting the need to pay closer attention to the composition of the student population.

Anomaly Detection and Categorization in Cloud Environment using Deep Learning Techniques

2019 ◽  
Vol 7 (5) ◽  
pp. 211-214
Author(s):  
Nidhi Thakkar ◽  
Miren Karamta ◽  
Seema Joshi ◽  
M. B. Potdar
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Cloud Environment ◽  
Learning Techniques
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