Cross Disciplinary Perceptions of the Computational Thinking among Freshmen Engineering Students

In this study, we analyzed the perception of Computational Thinking among engineering students from three engineering disciplines (Electrical, Mechanical, and Civil) and correlated their performance with their discipline. The goal of this analysis is to determine whether structuring discipline-specific Computational Thinking courses can improve the retention or having a diverse group of students is more beneficial by allowing multidisciplinary interaction. This analysis was quantitatively verified by assessing the students\u27 performance in over 40 different sections of Computing for Engineers course taught from Fall 2012 to Spring 2014. Our sample consisted of 861 students (142 Civil, 484 Mechanical, and 235 Electrical). We statistically analyzed students\u27 performance in this multi-section course to conclude that the perception of Computational Thinking differs among different engineering disciplines. This indicates that structuring Computational Thinking courses for engineering students from different engineering disciplines and using diverse pedagogy approaches will ultimately help improve students’ retention

Learning Computational Thinking Using Open-Source Hardware-based Programming

One of the first most fundamental skills that freshman engineering students learn is computational thinking. Computation thinking is the thought process carried out to solve problems. To develop this skill set usually computer programming fundamentals are introduced using a specific programming language. This approach falls short in sustaining the students’ interest in engineering. To rekindle the students’ interest in engineering, we proposed the utilization of the open-source electronics prototyping platform “Arduino”. Introducing the students to hardware programming and having them use project-based approaches to develop their computational thinking skill set increased their interest in the subject matter and significantly improved their performance

Can Computational Thinking Predict Academic Performance?

This research introduces the notion of predicting academic performance using Computational Thinking. The integral role that Computational Thinking modalities play in engineering disciplines can serve as an accurate indicator of the student future academic success. Therefore, this study investigated the students’ performance in a Computational Thinking course offered at the freshman-level to predict the student future academic success. To achieve this goal, a two-year study of the correlation between accumulative grade point averages and Computational Thinking course grades was conducted. The performance of 982 students was assessed over the two-year period. It was concluded that the students’ academic performance is strongly correlated to their Computational Thinking skills assessed at the freshman-level. This proves the viability of using Computational Thinking skills as a predictor of students’ academic success which can be used as an early intervention method to improve the students’ retention, progression, and graduation rates in STEM related disciplines

Calculus I for Engineers (GA Southern)

This Grants Collection for Calculus I for Engineers was created under a Round Eleven ALG Textbook Transformation Grant. Affordable Learning Georgia Grants Collections are intended to provide faculty with the frameworks to quickly implement or revise the same materials as a Textbook Transformation Grants team, along with the aims and lessons learned from project teams during the implementation process. Documents are in .pdf format, with a separate .docx (Word) version available for download. Each collection contains the following materials: Linked Syllabus Initial Proposal Final Reporthttps://oer.galileo.usg.edu/mathematics-collections/1038/thumbnail.jp

Deep Learning Image Classification for Disaster Relief

Deep Learning Image Classification for Disaster Relief Author: James K. Gaither, [email protected] Faculty Mentor: Rami J. Haddad, [email protected] Presentation Type: Poster Field of Study: Electrical and Computer Engineering Abstract: The aftermath of natural disasters leaves communities across the nation devastated for years after they occur. When communication systems go down, natural disasters become much more dangerous. Those who live in rural areas have an increased risk of supplies shortage because there are less stores with vital supplies (such as food and water). Furthermore, roads to stores with these supplies may be inaccessible. Utilizing recent advancements in artificial intelligence and consumer drone technology, we design a swarm of drones capable of identifying locations with inaccessible roads and post these locations to a virtual map. Those involved in the relief efforts would have access to this virtual map, and residents in need can be reached in a more efficient manner. The main result of our work is the design of a system of drones that stream their camera feeds to a base computer, and the use of the deep learning image classifier to process this data in real time. The design of this disaster relief system must be mobile. Drones must be capable of flying for at least 20 minutes and minimum range of 1 mile. The image classifier will be designed in MATLAB and implemented in Python. A virtual map will be designed using a reliable set of web technologies called a MEAN stack. The acronym MEAN stands for MongoDB(a non-relational database), embedded JavaScript(used for making HTML dynamic), Angular JavaScript (logic for making webpage interactive), and Node JS (A runtime environment similar to the Java Virtual Machine). Key Words: Artificial Intelligence, Image Classifier, Drones, MATLAB, Python, Natural Disaste

Continuous Improvement in the Assessment Process of Engineering Programs

In this paper, we present a multifaceted assessment process that was developed for our Electrical Engineering (EE) program at Georgia Southern University to meet the ABET criteria dealing with the student learning outcomes (SLOs). Both direct and indirect measures were used to collect and analyze data to assess the attainments of the student learning outcomes. To ensure data integrity, multiple faculty were involved in the development of a set of rubrics with benchmarks and performance indicators at both the program and curriculum levels. These tools provided action plans for this continuous improvement process to be implemented during the academic year. We also describe the mechanism used for assessing student performance at the curriculum level including the use of a course-level outcomes (CLO) form, a continuous improvement efforts (CIE) form, and a student course evaluation (SCE) form. These standardized forms are usually completed by faculty and submitted to the assessment committee for evaluation at the end of the semester. This feedback helped faculty to modify and/or develop new instructional methods to be incorporated into their courses, thus resulting in a more efficient assessment and continuous improvement process

A Unified Approach to the Assessment of Student Learning Outcomes in Electrical Engineering Programs

In this paper, a unified approach to the assessment of student and program learning outcomes to satisfy ABET and SACS accreditations criteria is proposed. This new approach takes into consideration the criteria of both accreditations to streamline the assessment process. As a result, a set of six skills categories were developed for SACS in which the eleven ABET student learning outcomes were embedded to satisfy both accreditation criteria. Furthermore, a standardized set of artifacts and rubrics were also developed to measure each skill category based on a given set of performance indicators. Data collected at the sophomore, junior and senior levels were recorded using a unified set of tables showing all the pertinent information needed to perform standard statistical analysis and to generate graphical presentation of the student performance at each level. For every outcome not meeting its benchmark, action plans were devised to address the shortcomings and close the loop on the assessment process. This novel approach was pilot tested this year for SACS and ABETS accreditations and has proved to be simpler and more efficient than any other assessment methods used

Learning Computational Thinking Using Open-Source Hardware-based Programming

Presentation given at the STEM Teaching and Learning Conference 2017, Savannah, GA

Gaming Against Plagiarism (GAP): A Game-Based Approach to Illustrate Research Misconduct to Undergraduate Engineering Students

In this paper, we discuss our experience using a set of games called Gaming Against Plagiarism to increase awareness in different types of research misconduct, and highlight the ramification of committing such misconducts among undergraduate engineering students. Gaming Against Plagiarism consists of three mini-games that address research misconduct. The types of research misconduct addressed are stealing, misquoting, patchwriting, insufficient paraphrasing, self-plagiarism, data falsification, and data fabrication. In these games, students are virtually put into situations involving research misconduct. The students either have to identify the type of misconduct or make an ethical decision by avoiding research misconduct. We assessed the impact of these games using qualitative and quantitative assessments techniques. Pre and post-surveys were conducted asking students to identity different research misconduct cases before and after they played the games. The results indicated that using this game-based approach to increase awareness of research misconduct among undergraduate students is effective. This conclusion was inferred by the statistical analysis with 98.7% confidence level. We also showed that the concepts of falsification and fabrication are somewhat confusing for students