A unified integrated teaching-learning modular approach to education: application to computer engineering education and to machine learning

2003 ◽  
Author(s):  
A.-R.M. Zaghloul ◽  
A. Saad
Keyword(s):  
Machine Learning ◽  
Engineering Education ◽  
Modular Approach ◽  
Computer Engineering ◽  
Teaching Learning

scholarly journals Application of Machine Learning in Predicting Performance for Computer Engineering Students: A Case Study

2019 ◽  
Vol 11 (10) ◽  
pp. 2833 ◽  
Author(s):  
Diego Buenaño-Fernández ◽  
David Gil ◽  
Sergio Luján-Mora
Keyword(s):  
Machine Learning ◽  
Information And Communication Technologies ◽  
Common Good ◽  
Learning Algorithm ◽  
Learning Processes ◽  
Machine Learning Techniques ◽  
Technology Enhanced Learning ◽  
Computer Engineering ◽  
Learning Techniques ◽  
Teaching Learning

The present work proposes the application of machine learning techniques to predict the final grades (FGs) of students based on their historical performance of grades. The proposal was applied to the historical academic information available for students enrolled in the computer engineering degree at an Ecuadorian university. One of the aims of the university’s strategic plan is the development of a quality education that is intimately linked with sustainable development goals (SDGs). The application of technology in teaching–learning processes (Technology-enhanced learning) must become a key element to achieve the objective of academic quality and, as a consequence, enhance or benefit the common good. Today, both virtual and face-to-face educational models promote the application of information and communication technologies (ICT) in both teaching–learning processes and academic management processes. This implementation has generated an overload of data that needs to be processed properly in order to transform it into valuable information useful for all those involved in the field of education. Predicting a student’s performance from their historical grades is one of the most popular applications of educational data mining and, therefore, it has become a valuable source of information that has been used for different purposes. Nevertheless, several studies related to the prediction of academic grades have been developed exclusively for the benefit of teachers and educational administrators. Little or nothing has been done to show the results of the prediction of the grades to the students. Consequently, there is very little research related to solutions that help students make decisions based on their own historical grades. This paper proposes a methodology in which the process of data collection and pre-processing is initially carried out, and then in a second stage, the grouping of students with similar patterns of academic performance was carried out. In the next phase, based on the identified patterns, the most appropriate supervised learning algorithm was selected, and then the experimental process was carried out. Finally, the results were presented and analyzed. The results showed the effectiveness of machine learning techniques to predict the performance of students.

MNC-Sponsored Multidisciplinary Industrial-Strength Capstone Design Projects in China

2014 ◽  
Vol 2 (10) ◽  
pp. 54-65
Author(s):  
Vincent Chang
Keyword(s):  
Engineering Education ◽  
Biomedical Engineering ◽  
Multinational Corporation ◽  
Engineering Students ◽  
Internet Technology ◽  
Computer Engineering ◽  
University Of Michigan ◽  
Design Projects ◽  
Industrial Strength ◽  
Capstone Design

With a growing need to reform Chinese higher engineering education, University of Michigan—Shanghai Jiao Tong University Joint Institute (JI) initiated multinational corporation-sponsored industrial-strength Capstone Design Projects (CDP) in 2011. Since 2011, JI has developed 96 corporate-sponsored CDPs since its inception, which include multinational corporation sponsors such as Covidien, Dover, GE, HP, Intel, NI, Philips, and Siemens. Of these projects, healthcare accounts for 27%, energy 24%, internet technology (IT) 22%, electronics 16%, and other industries 11%. This portfolio reflects the trends and needs in the industry, which provides opportunities for engineering students to develop their careers. An accumulated 480 JI students have been teamed up based on their individual backgrounds, specifically electrical engineering, computer engineering, computer science, mechanical engineering, and biomedical engineering. The corporate-sponsored rate grew from 0% in 2010 to 86% in 2014.

scholarly journals Scenario Based E-Learning in Electrical Engineering Education

2017 ◽  
Vol 10 (3) ◽  
pp. 26
Author(s):  
Hamonangan Tambunan ◽  
Amirhud Dalimunte ◽  
Marsangkap Silitonga
Keyword(s):  
Engineering Education ◽  
Communication Technology ◽  
Electrical Engineering ◽  
Learning Outcome ◽  
Learning Motivation ◽  
Information Communication ◽  
Electrical Engineering Education ◽  
E Learning ◽  
Teaching Learning ◽  
Better Than

The scenario based e-learning in Electrical Engineering Education Learning (EEEL) was developed by covering the scope and characteristics of all subjects and the competence unit of graduates in the field of pedagogy, professional, social and personality, with url addresed http://jpte-ft-unimed.edu20.org. The scenario incorporates the concept of Problem Based Learning (PBL) and Contextual Teaching Learning (CTL), by supporting of Information Communication Technology (ICT) to establish the competence of the students, from beginners to become proficient, as the teachers of electrical engineering, and the electrical technicians. Based on the analysis, it obtained the students’ learning motivation, the lecturers’ attitude in teaching, and the students’ learning outcome are tend to be high, and the competence of the students who used the model are better than not use.

scholarly journals Project Based Learning Activities in Engineering Education

2013 ◽  
Vol 3 (S2) ◽  
pp. 27 ◽  
Author(s):  
Tiago Faustino Andrade
Keyword(s):  
Engineering Education ◽  
Mechanical Engineering ◽  
Project Based Learning ◽  
Learning Activities ◽  
Academic Environment ◽  
Font Size ◽  
Master Degree ◽  
Excellent Opportunity ◽  
Teaching Learning

<span style="font-size: 10.0pt; font-family: &quot;Times New Roman&quot;,&quot;serif&quot;; mso-fareast-font-family: SimSun; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">In the present work, the author reports examples of his involvement in different teaching/learning methodologies during his five years of the Integrated Master Degree in Mechanical Engineering at the Faculty of Engineering of University of Porto. The aim is to explain how useful those experiences have been, allowing him to explore many techno-scientific activities within his engineering education while student as well as other <span style="letter-spacing: -.05pt;">transferable</span> skills and later, up to the present, as a professional in academic environment. The author wishes to underline the excellent opportunity he had to practice reflection processes as an essential methodology of his engineering education.</span>

Improved Intrusion Detection Algorithm based on TLBO and GA Algorithms

2021 ◽  
Vol 18 (2) ◽  
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Optimization Algorithm ◽  
Feature Subset Selection ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Feature Subset ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

Optimization algorithms are widely used for the identification of intrusion. This is attributable to the increasing number of audit data features and the decreasing performance of human-based smart Intrusion Detection Systems (IDS) regarding classification accuracy and training time. In this paper, an improved method for intrusion detection for binary classification was presented and discussed in detail. The proposed method combined the New Teaching-Learning-Based Optimization Algorithm (NTLBO), Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Logistic Regression (LR) (feature selection and weighting) NTLBO algorithm with supervised machine learning techniques for Feature Subset Selection (FSS). The process of selecting the least number of features without any effect on the result accuracy in FSS was considered a multi-objective optimization problem. The NTLBO was proposed in this paper as an FSS mechanism; its algorithm-specific, parameter-less concept (which requires no parameter tuning during an optimization) was explored. The experiments were performed on the prominent intrusion machine-learning datasets (KDDCUP’99 and CICIDS 2017), where significant enhancements were observed with the suggested NTLBO algorithm as compared to the classical Teaching-Learning-Based Optimization algorithm (TLBO), NTLBO presented better results than TLBO and many existing works. The results showed that NTLBO reached 100% accuracy for KDDCUP’99 dataset and 97% for CICIDS dataset

An Example Application of an Artificial Intelligence-Supported Blended Learning Education Program in Computer Engineering

2018 ◽  
pp. 1304-1323
Author(s):  
Tuncay Yigit ◽  
Arif Koyun ◽  
Asim Sinan Yuksel ◽  
Ibrahim Arda Cankaya ◽  
Utku Kose
Keyword(s):  
Artificial Intelligence ◽  
Blended Learning ◽  
Engineering Education ◽  
Education Program ◽  
Learning Approach ◽  
Computer Engineering ◽  
Student Records ◽  
Educational Processes ◽  
Engineering Department ◽  
Teachers And Students

Blended Learning is a learning model that is enriched with traditional learning methods and online education materials. Integration of face-to-face and online learning with blending learning can enhance the learning experience and optimize seat time. In this chapter, the authors present the teaching of an Algorithm and Programming course in Computer Engineering Education via an artificial intelligence-supported blended learning approach. Since 2011, Computer Engineering education in Suleyman Demirel University Computer Engineering Department is taught with a blended learning method. Blended learning is achieved through a Learning Management System (LMS) by using distance education technology. The LMS is comprised of course materials supported with flash animations, student records, user roles, and evaluation systems such as surveys and quizzes that meet SCORM standards. In this chapter, the related education process has been supported with an intelligent program, which is based on teaching C programming language. In this way, it has been aimed to improve educational processes within the related course and the education approach in the department. The blended learning approach has been evaluated by the authors, and the obtained results show that the introduced artificial intelligence-supported blended learning education program enables both teachers and students to experience better educational processes.

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