ASEE Computers in Education Journal

ASEE's Computers in Education Journal

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Archives for 2021

Active Learning Techniques in Digital Design Education for Engineering Technology Students

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Active Learning Techniques in Digital Design Education for Engineering Technology Students


1 Farmingdale State College, State University of New York

Abstract

The goal of this paper is to describe the motivation, methodology and results of introducing Active Learning Techniques in a Digital Design course. Digital Design is a four-credit junior level course for electrical and computer-engineering technology majors at Farmingdale State College, State University of New York. The students enrolled in this course have a large range of skills in term of experience with laboratory equipment, computer-based tools, and programming. The course introduces students to VHDL Hardware Description Language as the design entry method for digital circuits and to Field Programmable Gate Arrays (FPGA) platforms for the implementation of the digital circuits. Active learning techniques implemented in the course offer students more learning opportunities, potentially improving students’ knowledge and skills in digital design.

Read the full article here “Active Learning Techniques in Digital Design Education for Engineering Technology Students”

Simulation-Based Reflection in a Digital Circuits Course

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Simulation-Based Reflection in a Digital Circuits Course

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1 Electrical and Computer Engineering, University of Pittsburgh, PA, Pittsburgh, USA
2 Industrial Engineering, University of Pittsburgh, Pittsburgh, PA, USA

Abstract

A unique method for promoting reflection among engineering students was used in the present study involving a digital circuits course. The method combined computer-based simulation for digital circuit design with reflective-thought prompts after a midterm exam for post-exam analysis and reflection. This method was first implemented in a microelectronics course using the SPICE simulator, and lessons learned from the initial implementation were applied to the digital circuits course. These lessons learned included the need to scaffold students in the use of the simulation tool for reflection, the need to balance frequency of reflection with student workload and fatigue, and the need for question prompts that voluntarily elicit broad thought after a milestone event such as a midterm exam (versus a quiz).

Read the full article here “Simulation-Based Reflection in a Digital Circuits Course”

Use of Open-source Software in Mechatronics and Robotics Engineering Education – Part I: Model Simulation and Analysis

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Special Feature

This paper is the first in two-part series on the use of Open-source Software in Mechatronics and Robotics Engineering. Part two is available in Vol 13 Issue 1.

– The ASEE COED, Editorial Team

Use of Open-source Software in Mechatronics and Robotics Engineering Education – Part I: Model Simulation and Analysis

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1 Mechanical and Mechatronics Engineering Department, Southern Illinois University Edwardsville, Edwardsville, Il, USA
2 Mechanical Engineering, University of California Berkeley, Berkeley, CA, USA
3 Mechanical Engineering, Milwaukee School of Engineering, Milwaukee, WI, USA
4 Robotics Engineering, Worcester Polytechnic Institute, Worcester, MA, USA
5 Electrical and Computer Engineering, Rose-Hulman Institute of Technology, Terre Haute, IN, USA

Abstract

Open-source Software (OSS) provide attractive solutions for complementing Mechatronics and Robotics Engineering (MRE) education due to their numerous advantages such as free access, customizability and wide community support, increased adoption and utilization in industry, etc.

Read the full article here “Use of Open-source Software in Mechatronics and Robotics Engineering Education – Part I: Model Simulation and Analysis”

Using MULTISIM software as a replacement or supplement for physical labs

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Using MULTISIM software as a replacement or supplement for physical labs


1 College of Engineering Engineering Technology Department, The University of Toledo Toledo, Ohio, Ohio

Abstract

MULTISIM is a computer simulation software that among other things simulates the performance of electric circuits. The article presented here demonstrates using MULTISIM as an educational tool to enhance student learning process by discussing the use of MULTISIM for teaching basic electrical circuits consisting of analysis of Series-Parallel Circuits, Superposition Theory, Thevenin Theory and Norton Theory. The traditional textbooks only demonstrate the final result of a circuit analysis using MULTISIM. However, the article presented here discusses using the software as an aid to check the intermediate steps of hand calculation techniques that must be used if no computer software were being used. By using the mixed approach of using a hand calculation technique and MULTISIM, the students have the opportunity to verify the accuracy of every step of the process which will improve their learning and will allow them to find and correct their own errors.

Read the full article here “Using MULTISIM software as a replacement or supplement for physical labs”

Learning Analytics of Outcomes-Based Engineering Programs’ Data

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Learning Analytics of Outcomes-Based Engineering Programs’ Data


1 College of Computer Science and Information Systems, Najran University, Saudi Arabia
2 Faculty of Computer Science and Information Systems, Thamar University, Thamar, Yemen

Abstract

In the recent years, learning analytics is attracting attention in tertiary education sector. This paper presents a case study of applying learning analytics approaches to discover knowledge from Outcome Based (OB) engineering programs’ data. More specifically, Association Rule Mining approach is applied to a dataset extracted from the Self-Study Reports of 152 engineering programs accredited by American Board of Engineering and Technology (ABET). In doing so, the dataset has been processed and transformed into a suitable representation. Apriori algorithm is then applied to generate rules involving PEOs and ABET SOs. The generated rules are filtered, and the filtered rules are used to draw a set of generic rules for mapping each PEO to ABET SOs and to discover the correlations among ABET SOs.

Read the full article here “Learning Analytics of Outcomes-Based Engineering Programs’ Data”