Using knowledge building to support deep learning, collaboration and innovation in engineering education

It can be envisaged that the use of multimedia computer technology as replacement, or supplement to, human educators in engineering education would become widespread in the future. Such technology can be employed to demonstrate and correlate real life application and theory thereby promoting deep learning. Interactive courseware for higher learning institutions may be extremely useful where trained human resources in the engineering education sector are limited. This Chapter discusses the current trends of incorporating new technologies with TAPS packages in the teaching of engineering subjects.

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Deep Learning in Engineering Education

Machine Learning and Deep Learning in Real-Time Applications - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-7998-3095-5.ch009 ◽

2020 ◽

pp. 187-218

Author(s):

Deepali R. Vora ◽

Kamatchi R. Iyer

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Deep Learning ◽

Information Systems ◽

Engineering Education ◽

Hybrid Model ◽

Cuckoo Search ◽

Support Vector ◽

Proposed Model ◽

Class Labels

The goodness measure of any institute lies in minimising the dropouts and targeting good placements. So, predicting students' performance is very interesting and an important task for educational information systems. Machine learning and deep learning are the emerging areas that truly entice more research practices. This research focuses on applying the deep learning methods to educational data for classification and prediction. The educational data of students from engineering domain with cognitive and non-cognitive parameters is considered. The hybrid model with support vector machine (SVM) and deep belief network (DBN) is devised. The SVM predicts class labels from preprocessed data. These class labels and actual class labels acts as input to the DBN to perform final classification. The hybrid model is further optimised using cuckoo search with Levy flight. The results clearly show that the proposed model SVM-LCDBN gives better performance as compared to simple hybrid model and hybrid model with traditional cuckoo search.

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Enhancement of learning through field study

Journal of Technology and Science Education ◽

10.3926/jotse.403 ◽

2018 ◽

Vol 8 (4) ◽

pp. 408 ◽

Cited By ~ 3

Author(s):

Tesila Kandamby

Keyword(s):

Deep Learning ◽

Engineering Education ◽

Field Study ◽

Construction Projects ◽

Time Frame ◽

Building Construction ◽

Real Situation ◽

Work Site ◽

Technical Personnel ◽

The Given

Learning is more concerned in engineering education as students need to do deep learning to understand the engineering principles for practice. Engineering is a practicing profession. Therefore, providing learning environment is required for the subjects of engineering disciplines to enable students to learn in depth. By knowing this phenomenon, field study was conducted as a group study for the civil engineering subject of building construction allowing students to learn and gain knowledge by observing construction activities in construction projects in addition to the lectures in usual classroom. At the end of the study, it is found that field study is useful for learning and students acquire knowledge and understand the application of theory at real situation. In addition, students develop skills for working as a team by organizing their works, sharing knowledge, discussing with relevant technical personnel at work site and achieving the targets within the given time frame. It is realized that teacher’s role is vital to make the study successful in the ways of organizing field study, conducting discussion classes to assist students, monitoring the progress and giving the feedback of the students’ performance during the course of field study producing excellent results while satisfying the objectives of both teacher and students.

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Low-Cost Education Kit for Teaching Basic Skills for Industry 4.0 Using Deep-Learning in Quality Control Tasks

Electronics ◽

10.3390/electronics11020230 ◽

2022 ◽

Vol 11 (2) ◽

pp. 230

Author(s):

Martin Pajpach ◽

Oto Haffner ◽

Erik Kučera ◽

Peter Drahoš

Keyword(s):

Quality Control ◽

Deep Learning ◽

Engineering Education ◽

Industry 4.0 ◽

Low Cost ◽

Basic Skills ◽

Free Software ◽

Educational Institutions ◽

Next Generation ◽

Suitable Environment

The main purposes of this paper are to offer a low-cost solution that can be used in engineering education and to address the challenges that Industry 4.0 brings with it. In recent years, there has been a great shortage of engineering experts, and therefore it is necessary to educate the next generation of experts, but the hardware and software tools needed for education are often expensive and access to them is sometimes difficult, but most importantly, they change and evolve rapidly. Therefore, the use of cheaper hardware and free software helps to create a reliable and suitable environment for the education of engineering experts. Based on the overview of related works dealing with low-cost teaching solutions, we present in this paper our own low-cost Education Kit, for which the price can be as low as approximately EUR 108 per kit, for teaching the basic skills of deep learning in quality-control tasks in inspection lines. The solution is based on Arduino, TensorFlow and Keras, a smartphone camera, and is assembled using LEGO kit. The results of the work serve as inspiration for educators and educational institutions.

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Undergraduate Cross-Class Research Projects For Deep Learning In Engineering Education

2017 Fall AEE Journal ◽

10.18260/3-1-1114-36024 ◽

2020 ◽

Vol 8 (2) ◽

Author(s):

Mansour Karkoub ◽

Chun-Lin Yang ◽

Wael Karkoub ◽

Moutafa Raslan

Keyword(s):

Deep Learning ◽

Engineering Education ◽

Research Projects

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Deep Learning in Engineering Education

Deep Learning Applications and Intelligent Decision Making in Engineering - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-2108-3.ch010 ◽

2021 ◽

pp. 222-255

Author(s):

Deepali R. Vora ◽

Kamatchi R. Iyer

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Deep Learning ◽

Information Systems ◽

Engineering Education ◽

Hybrid Model ◽

Cuckoo Search ◽

Support Vector ◽

Proposed Model ◽

Class Labels

The goodness measure of any institute lies in minimising the dropouts and targeting good placements. So, predicting students' performance is very interesting and an important task for educational information systems. Machine learning and deep learning are the emerging areas that truly entice more research practices. This research focuses on applying the deep learning methods to educational data for classification and prediction. The educational data of students from engineering domain with cognitive and non-cognitive parameters is considered. The hybrid model with support vector machine (SVM) and deep belief network (DBN) is devised. The SVM predicts class labels from preprocessed data. These class labels and actual class labels act as input to the DBN to perform final classification. The hybrid model is further optimised using cuckoo search with levy flight. The results clearly show that the proposed model SVM-LCDBN gives better performance as compared to simple hybrid model and hybrid model with traditional cuckoo search.

Download Full-text