Treffer: Using LEGO Mindstorms and MATLAB in Curriculum Design of Active Learning Activities for a First-year Engineering Computing Course.

Title:
Using LEGO Mindstorms and MATLAB in Curriculum Design of Active Learning Activities for a First-year Engineering Computing Course.
Source:
Proceedings of the ASEE Annual Conference & Exposition; 2019, p21916-21937, 22p
Database:
Complementary Index

Weitere Informationen

This paper is an evidence-based practice research study to improve course delivery in computer programming. Courses and materials in computer programming tend to be abstract, which can lead to many students having difficulties learning and being engaged with the material. With a more hands-on practical approach, students may find themselves immersed in the material and motivated to understand and apply concepts learned in class to real-world applications. Previous studies in the literature have shown that LEGO® Mindstorms can be used to enhance active learning for students, particularly when used to demonstrate computer programming concepts. However, many of the studies have typically been limited to design courses and not first-year specific. There is not substantial information showing a developed curriculum for a first-year engineering programming course. The current paper examines the feasibility of using robotics (specifically LEGO® Mindstorms) combined with computer programming (MATLAB) as it relates to the curriculum of a first-year engineering computing course, and how it can be implemented. Specifically, the goals of this scholar activity were threefold: i) to investigate the literature to explore the use of active learning tools in first-year engineering education, ii) to determine the capabilities of the LEGO® Mindstorms platform as an "active learning" tool for first-year engineering and computer science students at MacEwan University and iii) to use the information gained to propose and test active learning lab activities for first-year programming courses. This research uses appreciative inquiry to examine the feasibility of using LEGO® Mindstorms EV3 robot with the MATLAB programming environment in our first-year engineering course. The functionality, specifically sensor and motor capabilities, of the robot was then compared with intended learning outcomes. A checklist of desirable curriculum learning outcomes was used as the rubric in the feasibility analysis. Motivation for the research was derived from the literature which showed potential for the use of robotics for active learning. Results of this investigation have shown that LEGO® Mindstorms is a viable teaching tool for a first-year engineering computing course to develop fundamental programming skills and handson problem-solving skills. The feasibility study focused on the five sensors and motors that are available for the Mindstorms EV3 robot to design active learning activities using the MATLAB toolbox. The capabilities of the motors and sensors were found to be more than adequate to cover the first-year computing curriculum. Through the assessment of the various sensors, learning activities were designed to reinforce learning outcomes and provide students with interactive practical opportunities to apply their knowledge. A set of assignments was created and tested by a first-year engineering student and reviewed to by the course coordinator to ensure that they satisfied the learning outcomes of the course and that the programming level was consistent with the status quo. Examples of the tested learning activities demonstrating curriculum relevant material in the context of our first-year computing course are presented. A discussion of the sensor capabilities which provide the framework for the curriculum is also provided. The sensors are discussed qualitatively in a broader context that would allow development of additional learning activities as the need arises. A detailed curriculum map using Bloom's taxonomy for the cognitive domain is presented for both the classroom and lab environment. This curriculum map is linked to learning outcomes for the course. [ABSTRACT FROM AUTHOR]

Copyright of Proceedings of the ASEE Annual Conference & Exposition is the property of ASEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)