scholarly journals Industrial Engineering Students' Perceptions of the Logistics and Supply Chain Industry

2016 ◽  
Author(s):  
Christina Scherrer ◽  
Michael Maloni ◽  
Elizabeth Boyd ◽  
Stacy Campbell
Keyword(s):  
Supply Chain ◽  
Engineering Students ◽  
Industrial Engineering

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>

scholarly journals SIMULATOR FOR LEAN MANUFACTURING APPLICATIONS

2018 ◽  
Vol 6 (2) ◽  
pp. 274-284
Author(s):  
MARIA GUADALUPE LOPEZ MOLINA ◽  
GABRIEL ATRISTAIN SUAREZ
Keyword(s):  
Lean Manufacturing ◽  
Engineering Students ◽  
Teaching Method ◽  
Knowledge Retention ◽  
Industrial Engineering ◽  
Learning Cycle ◽  
Training Time ◽  
Small College ◽  
Manufacturing Applications

An educational case implementing a SMED (Single Minute Exchange of Dies) teaching method with results that measure the participating student’s perception is reported. This method significantly reduces training time and increases knowledge retention as a result of an improvement aimed at shortening the learning cycle of industrial engineering students learning about lean manufacturing tools. This study was conducted with a hundred students who are the population of industrial engineering in a small college.

scholarly journals Evasion of Industrial Engineering students of FT/UFAM

2019 ◽  
Vol 7 (10) ◽  
pp. 551-564
Author(s):  
Jéssica Barbosa Da Silva ◽  
Jonas Gomes da Silva
Keyword(s):  
Engineering Students ◽  
Industrial Engineering ◽  
Electronic Questionnaire ◽  
First Semester ◽  
Vocational Orientation ◽  
The University ◽  
E Mail

The undergraduate degree in Industrial Engineering at the Faculty of Technology (FT) of the Federal University of Amazonas (UFAM) completed 15 years in the first semester of 2019. During this period, enrolled 837 students, of which 238 (28%) have already graduated, 335 (40 %) continue to study and 263 (32%) have left the course. Given this percentage of dropout and the need to research more about the topic, this article aims to investigate the main causes of abandonment in this course in order to propose strategies to minimize the problem. The method used was the Survey, which applied a five-part electronic questionnaire sent to 203 dropout students who had e-mail. After analyzing the answers of 39 (19.21%), it was concluded that most students did not receive vocational orientation before joining the University and the main reasons that influenced the students to quit the course were the didactic-pedagogical deficiency of the teachers, the difficulty in conciliating study and work, and the course did not satisfy their expectations.

INTERACTION BETWEEN ACADEMIA AND INDUSTRY TO BUILD STATISTICAL CAPACITY AMONG INDUSTRIAL-ENGINEERING STUDENTS

2020 ◽  
Vol 19 (1) ◽  
pp. 167-180
Author(s):  
JOSÉ LUIS ÁNGEL RODRÍGUEZ SILVA ◽  
MARIO SÁNCHEZ AGUILAR
Keyword(s):  
Applied Field ◽  
Engineering Students ◽  
Statistics Education ◽  
Teaching Experiment ◽  
Industrial Engineering ◽  
University Education ◽  
Theoretical Studies ◽  
Industrial Environment ◽  
Research Journal ◽  
Statistics Course

One of the aims of this work is to highlight the need for connecting the practice and theoretical studies of industrial engineers. One reason for this need is the fact that students tend to graduate without proper preparation for practice, spreading thus a bad reputation of statistics and its potential, and even affecting students’ dispositions and motivation towards the study and applications of statistics. This paper presents the results of a study conducted at two higher-education institutions in Mexico. The industrial engineering students who participated were introduced to a multivariate statistics course, one in a traditional way and the other through a problem-solving approach embedded within an industrial environment. The didactic intervention in both groups and the problems used to evaluate them are described. The results show that the experimental students had a significant increase in their qualifications and alower variance in their performance. From our study we can suggest that a university education in close connection with applications in an industrial environment significantly improves the students’ education. This teaching experiment provides students with opportunities to experience the genuine character of statistics as an applied field, giving meaning to the statistical techniques learnt in the classroom. It is one way to make the education in statistics more apt to the demand from outside and by the same time it enables the students to really understand statistics. First published February 2020 at Statistics Education Research Journal Archives

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