Including Active Learning in an Online Database Management Course for Industrial Engineering Students

Author(s):  
Christina Andersson ◽  
Gerald Kroisandt ◽  
Doina Logofatu
2022 ◽  
Vol 11 (1) ◽  
pp. 325-337
Author(s):  
Natalia Gil ◽  
Marcelo Albuquerque ◽  
Gabriela de

<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>


2018 ◽  
Vol 6 (2) ◽  
pp. 274-284
Author(s):  
MARIA GUADALUPE LOPEZ MOLINA ◽  
GABRIEL ATRISTAIN SUAREZ

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.


Author(s):  
Jéssica Barbosa Da Silva ◽  
Jonas Gomes da Silva

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.


2016 ◽  
Author(s):  
Christina Scherrer ◽  
Michael Maloni ◽  
Elizabeth Boyd ◽  
Stacy Campbell

2012 ◽  
Vol 13 (2) ◽  
Author(s):  
BRENO BARROS TELLES DO CARMO ◽  
RENATA LOPES JAGUARIBE PONTES

The Internet changed the way of learning; it promotes interactivity and autonomy. Through Web 2.0 many tools could be used to plan strategies to motivate students for autonomous learning. This paper presents an analysis of such strategies applied to an industrial engineering course. It discusses an application in an Organizational Productive Arrangement (OPA) course using web tools to promote autonomous learning using an active strategy methodology. Two tools were used: a blog to promote interaction and a wiki to motivate research and collaboration. An information system was used to support an active strategy methodology. A survey of 40 students was conducted; the data is presented and discussed.


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