Academic Performance Of Engineering Students Research Articles

Application of Data Mining for the Prediction of Academic Performance in University Engineering Students at the National Autonomous University of Mexico, 2022

Introduction: In the present study, data mining is applied to predict the academic performance of university Engineering students at the National Autonomous University of Mexico during the year 2022. The introduction addresses the importance of understanding and anticipating academic performance as a means to implement more effective and personalized educational strategies.Objective: Develop a predictive model capable of identifying determining factors in the academic performance of students and predicting their future performance.Methodology: The methodology used includes the collection of academic and sociodemographic data from students, as well as the use of data mining techniques such as cluster analysis, decision trees and neural networks. The data was preprocessed to ensure quality and divided into training and test sets to validate the predictive model.Results: The results show that the developed model has a high accuracy in predicting academic performance, identifying key variables such as class attendance, participation in extracurricular activities and performance in previous exams. These variables were essential to build a robust and reliable model.Conclusion: the application of data mining has proven to be an effective tool to predict the academic performance of engineering students. This model not only provides a valuable tool for administrators and educators in decision making, but also opens new avenues for future research in the field of personalized education and improving academic performance.

Influence of Self-Regulated Learning on the Academic Performance of Engineering Students in a Blended-Learning Environment

The pandemic environment experienced for more than two years strongly accelerated the use of technological resources to support learning. Using these resources made it evident that higher education students expecting to succeed academically must master digital strategies. The combination of learning experiences and autonomous work required students to selfregulate their learning well to attain the expected performance levels for competitive work that characterizes the engineering profession. So, we designed an exploratory study to evaluate the effect of self-regulated learning on mechanical design students in a Blended Learning (BL) environment using Flipped Learning/Classroom didactics (FPC). Fifty-one students studying under two different educational-model curricula participated. The research employed a mixed methodology. For the quantitative part, the students’ academic grades and results of the Motivation and Learning Strategies Questionnaire (MSLQ SF) applied in its short Spanish version were employed. A questionnaire for self-study management and a competency observation rubric based on the Marzano & Kendall New Taxonomy were used for the qualitative part. The results showed that the students with the best levels of self-regulated learning in BL and FPC contexts obtained the best results in cognitive, organizational, and motivational resource management strategies. They also improved their disciplinary skills and had a higher academic performance.

Effects of Gender and Military Leave on the Academic Performance of Undergraduate Engineering Students

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Contribution:</i> This article examines how military leaves of absence affect the academic performance of engineering students compared to those who did not take a military leave of absence or took only a general break. Gender-related differences were also analyzed since only male students take a military break. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Background:</i> Recognizing the effects of military breaks can help students make informed decisions and, in turn, enhance their academic performance. Consequently, it can help students make data-based decisions about when taking a military break would be least disruptive or, perhaps, even beneficial to their academic careers. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Research Questions:</i> How does taking a long-term academic break to complete military service in the middle of one’s undergraduate education impact the student’s performance? Does the gender of students affect their academic performance? <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Methodology:</i> This research was conducted at the Korea University of Technology and Education (KOREATECH) with data from 1039 undergraduate students in the School of Electrical, Electronics, and Communication Engineering. The students were admitted between 1994 and 2013 and graduated before 2018. The Shapiro–Wilk test, along with the Kolmogorov–Smirnov test, assessed the normality of the distribution of the data. In addition, analysis of variances (ANOVA) and the Kruskal–Wallis tests were implemented for hypothesis testing. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Findings:</i> The research findings demonstrate that contrary to the assumption that having a break might be detrimental to academic success, returning to school after a military leave of absence positively affected students’ grade point averages (GPAs). Moreover, students with lower grades significantly increased their GPAs after returning to school from conscription.

Attitudes and Academic Performance of Engineering Students in both Prerequisite Courses to Final Year Project and Final Year Project

The aim of this study was to examine the attitudes of engineering students and their academic performance towards both prerequisite courses for and the final year project (FYP), given the need to increase our understanding of attitudes and performance in the context of engineering students, currently underexplored. Questionnaire surveys of 714 eligible students enrolled in the FYP across six engineering programs were conducted. The results show that students enrolled in Industrial, Mechanical and Civil engineering programs, have a negative attitude towards the FYP and its prerequisites, while students enrolled in Electrical, Electronic and Industrial Design and Technology programs have a positive attitude. A statistically strong positive correlation between project prerequisites and engineering FYP was found, confirmed by factor analysis. Majority of students struggle with project progress as compared to other stages of the FYP, due to inadequacy in fundamentals such as design. This study contributes to an understanding of existing knowledge by providing empirical evidence of not only challenges faced by engineering students (as opposed to other disciplines that have been widely covered) but also remedies to improve students’ academic performance. The findings also have implications on engineering education, in relation to informing policy decisions on engineering program structure.

Prediction of Academic Performance of Engineering Students by Using Data Mining Techniques

In the current age, students' academic performance deterioration is a very crucial problem in engineering education. Prediction of low-performing students at an early stage is important so that their faculties and administration could provide timely support. The present study attempts to perform this prediction task at the entry-time with the help of four single supervised educational data mining algorithms, namely: Decision tree, Naïve Bayes, k-Nearest Neighbor, and Support Vector Machine along with an ensemble method called “Random Forest”. These classifiers have been applied to a students‟ dataset of an Indian Engineering College, having four categories of parameters viz., student‟s background, academic, social, and psychological parameters. Different libraries of Python programming language such as Pandas, Seaborn, Scikit-learn, and Scipy were used for analysis, visualization, classification, and statistics computation, respectively. The present study shows that among all of the five algorithms, Naïve Bayes gives the highest accuracy with 89%, and finally to improve the results, a model is proposed in which three Naïve Bayes classifiers were integrated with the help of 'Bagging'. The achieved accuracy with the proposed model was 91%, with the highest recall and highest precision for identifying low performers.

Investigating the Relationship between English Language Proficiency and Academic Performance of Engineering Students in Mehran University Jamshoro, Pakistan

The present study investigated the relationship between language proficiency and academic performance of students at Mehran university Jamshoro, Sindh. The study employed quantitative correlational research design to measure the variables. The researchers adapted English language test to measure the language proficiency of the learners. And CGPA (cumulative grade point average) to measure the academic performance of the research participants. The researchers collected data from 100 undergraduate students by doing simple random sampling. The data was collected in two phases: first pilot study and then main study. The pilot study was done before the actual research to measure validity and feasibility of the adopted English language proficiency test. The cronbach alpha was used to check reliability of the data. SPSS version 26 was used to perform the Pearson correlation analysis. The findings revealed that data was reliable with alpha value. 702 and the correlation was highly positive with a value of .826 at the significance value .000. The study confirms that the language has played a pivotal role to improve academic performance of the students studying in Mehran university of science and technology.

Impact of College Provided Transportation on the Absenteeism and Academic Performance of Engineering Students

Students use different modes of transport to go to college. While many transportation programs exist at different universities and many experts find these programs to have a positive impact, no studies have investigated the impact of such programs on the absenteeism and academic performance of college students. The main purpose of this study is to investigate the impact of a college provided transportation program on the absenteeism and performance of engineering students. Different types of data were collected from a sample of engineering students, including attendance records, grade point average (GPA), course grades, majors, and bus ridership information for two years. The findings suggest that there is a positive impact of providing a college transportation service to engineering students in the form of better attendance and higher GPA. The outcomes of this study can be used to evaluate similar programs in the future and can be used by public agencies and policymakers to make decisions on expanding investments in such programs.

Predicting Academic Performance of Engineering Students Using Ensemble Method

One of the common problems that most of the engineering institutions face in recent times is poor academic results. The statistics of the IOE semester result show that since 2009 A.D., the average pass percentage has been reducing in an average from 50% to 40% and moving towards decreasing scenarios. The study aims to predict an engineering student's academic performance based on their past educational records, demographic factors, family backgrounds, and other related factors. Firstly, a predictive model is built using the traditional classifiers Decision Tree, SVM, and Linear Regression, which had shown good performance in similar types of study. After that, we have implemented one of the popular ensemble Methods, voting, which is known for improving the individual classifier's performance. Voting classifier combines the predictions of base classifiers by averaging those predictions. The result revealed that the accuracy, precision, recall, and F1 score had been considerably improved by using ensemble voting than that of the individual classifiers. The data used in the study was collected directly from the hard copy personal files of each pass out student of Paschimanchal Engineering Campus, Pokhara between the years 2004 to 2015 AD.

Correlation between Classroom Engagement and Academic Performance of Engineering Students

Egyptian schools of engineering (government faculties, private faculties and private higher engineering institutes) award degrees to 35000 graduates annually. There have been concerns from the Egyptian Engineers Syndicate, experienced engineers, local &amp; international employers, parents and other society stakeholders about the knowledge and skills gained by engineering graduates. In this article, first the institutions were analyzed from a fresh business perspective. The analysis highlighted several constraints that hinder radical reforms. In part two of this article the aspirations of stakeholders were collected and identified. In part three, recommendations were made based on the experiences of engineering education providers worldwide.

Influence of Computer Based Test (CBT) Examination on Academic Performance of Engineering Students in Nigerian Universities

Influence of Computer Based Test (CBT) Examination on Academic Performance of Engineering Students in Nigerian Universities

Influence of Computer Based Test (CBT) Examination on Academic Performance of Engineering Students in Nigerian Universities

Influence of Computer Based Test (CBT) Examination on Academic Performance of Engineering Students in Nigerian Universities

Improvement of Academic Performance of Engineering Students through an Adaptive Educational Hypermedia System (AEHS)

This paper describes how to improve the academic performance of engineering university students through an Adaptive Educational Hypermedia System (AEHS). The psychological basis, learning styles and MOOMH methodology for the development of the system are exposed, which with its implementation achieves adaptability and works for students as an “intelligent tutor”, allowing them to guide their education as academic tutor. Not only shows its content that meets the needs of the student, but it is also represented in elements such as color adaptation, work tools and even academic recommendations based on the interactions that the user makes within the system, the system recognizes its pattern of use, and when the student is logged in again, it presents a friendlier interface that the student prefers use, it is wider in content and, above all, easy to use and understand. In addition, the AEHS allows to extend education allowing the assignment of more domain areas, in the field of engineering, that is, the SHAE can be adapted to various engineering specialties such as: industrial, software, telecommunications, mechanics and other.

Improvement of Academic Performance of Engineering Students through an Adaptive Educational Hypermedia System (AEHS)

This paper describes how to improve the academic performance of engineering university students through an Adaptive Educational Hypermedia System (AEHS). The psychological basis, learning styles and MOOMH methodology for the development of the system are exposed, which with its implementation achieves adaptability and works for students as an “intelligent tutor”, allowing them to guide their education as academic tutor. Not only shows its content that meets the needs of the student, but it is also represented in elements such as color adaptation, work tools and even academic recommendations based on the interactions that the user makes within the system, the system recognizes its pattern of use, and when the student is logged in again, it presents a friendlier interface that the student prefers use, it is wider in content and, above all, easy to use and understand. In addition, the AEHS allows to extend education allowing the assignment of more domain areas, in the field of engineering, that is, the SHAE can be adapted to various engineering specialties such as: industrial, software, telecommunications, mechanics and other.

Predicting Academic Performance of Engineering Students After Approving a Mathematics Leveling Course using Decision Trees

Predicting Academic Performance of Engineering Students After Approving a Mathematics Leveling Course using Decision Trees

Impact of Classroom Behavior of Faculty on Academic Performance of Engineering Students

Teachers and their strategies of teaching play a very vital role in making the student a successful engineer in this very competitive world .The article thus focuses on how teaching engineering can be done effectively by studying the class room behavior of faculty. This new line of research will generate important findings that are of practical implications for teacher education and will examine multiple criteria of effective teaching in terms of classroom behavior variables and their relationship with the academic performance of the students taught by that faculty. Thus, creating better faculty and better engineers in future is the primary benefit for the society from this research paper.

Sources of Self-Efficacy Influencing Academic Performance of Engineering Students

This study examined (a) the correlation of the four hypothesized sources of self-efficacy (mastery experience, vicarious experience, social persuasion, emotional arousal) with academic performance, and (b) the prediction of the main source of self-efficacy that affects academic performance. A 40-item survey measuring sources of mathematics self-efficacy was administered to 178 third-year engineering students. Academic performance, which includes mathematics module grades and cumulative grade point average (GPA) scores, were collated. The results of the present study showed that self-efficacy sources were correlated with mathematics achievement scores as well as cumulative GPA of electronics-related engineering diplomas. More importantly, mastery experience was found to be the main predictor for academic achievements of mathematics and related engineering modules. Finally, suggestions are offered to help curriculum developers in instructional design so as to improve students’ engineering academic performance.

Emotional intelligence and academic performance of engineering students

Professional technical expertise is no longer the main standard by which employers evaluate college graduates. ‘Soft skills’, such as emotional intelligence (EI also referred to as EQ), are viewed as effective ways to distinguish potential high-performance workers. It is posited that high performing students will also exhibit more proficiency at these ‘soft skills’ than students with more modest academic success. The purpose of this paper is to attempt to correlate EI with the academic performance of civil engineering students. A student's grade point ratio (GPR) is the criterion used in this research to measure academic performance. The hypothesis is that students with high GPRs will score better on measures of EI than other students. Literature reviews found that there is a growing area of research into EI and its relationship with job performance, specifically through the research presented in Emotional Intelligence: Why It Can Matter More than IQ [Goleman, D. (1995) Emotional Intelligence: Why It Can ...

Academic failure of first-year engineering and technological students in India and assessment of motivation factors – a case study

This study has been conducted to show that there is a recent trend in engineering colleges in India that students who are considered to be highly intelligent show poor academic performance during their 1st year. This article is proposed to examine the role of motivation factors such as teaching methods and learning material in the academic performance of engineering and technological students in India. A test was carried out among engineering students. A total of 200 male and female students participated in this test. A 2-group discriminator analysis was done to analyse the data. The results showed that there was a significant correlation (p = 0.000) between motivation factors and academic performance of engineering students. The higher the rating of these factors by the students, the higher their performance.

The Prediction of Academic Performance of Engineering Students

The Prediction of Academic Performance of Engineering Students