ASEE Computers in Education Journal

ASEE's Computers in Education Journal

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Archives for July 2021

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.

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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”

COVID-19 Technology Student Success Challenges : Influence of Tools and Strategies

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COVID-19 Technology Student Success Challenges : Influence of Tools and Strategies

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1 University of Houston

Abstract

COVID-19 brought rapid and substantial change to course formats as colleges and universities transitioned from on-campus to online instruction to mitigate the spread of the pandemic. While faculty and administrators sought solutions to maintain instructional quality and student success, students endeavored to adapt to the changes. This study investigated a) College of Technology students’ perceptions of their potential for success including initial reactions, adaptation, and perceptions of impacts to grades and learning; b) course features and tools preferred by Technology students; and c) factors that enabled Technology students’ course completion.

Keywords

covid-19, technology, tools

Introduction

The declaration of COVID-19 as a pandemic brought expansive changes to higher education including massive disruptions to students as they were forced to transition from on-campus to online classes (Binkley & Amy, 2020; Brownlee, 2020).

Read the full article here “COVID-19 Technology Student Success Challenges : Influence of Tools and Strategies”

gruepr, a Software Tool for Optimally Partitioning Students onto Teams

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gruepr, a Software Tool for Optimally Partitioning Students onto Teams


1 Northeastern University, Boston, MA, 02115, USA

Abstract

Abstract—Choosing how to split a group of students into teams for project work can be a time-intensive task for an instructor. An instructor might have a complex set of parameters to optimize, perhaps desiring each team to have a certain number of times throughout the week where they can meet, while also seeking to create teams that are homogeneous in some characteristics and heterogeneous in other characteristics. Demographic composition may also be considered, and perhaps the instructor has certain students that must be placed on the same team or must be placed on different teams. Maybe teams will be formed several times, and no student should have the same teammate twice. A few software tools can be found in the literature to assist an instructor with this task, but few of them seem to be easily and freely accessible.

Read the full article here “gruepr, a Software Tool for Optimally Partitioning Students onto Teams”