JOURNAL ARTICLE

Supporting Self-Directed Learning in a Project-Based Embedded Systems Design Course

James LarsonShawn JordanMicah LandeSteven Weiner

Year: 2020 Journal:   IEEE Transactions on Education Vol: 63 (2)Pages: 88-97   Publisher: IEEE Education Society

Abstract

Contribution: This article shares the learning ecosystem of a project-based embedded systems course, identifying course elements that support self-directed learning and how assignments guide students toward becoming adaptive experts. Background: The technology advances while the fundamentals of electrical engineering remain static. Educators can increasingly prepare students to identify what they need to know to solve problems and avail themselves of resources to learn. This article seeks to further understand ways that a project-based learning approach in an undergraduate embedded systems course can facilitate students' self-directed learning. Research Question: In what ways can a project-based learning approach in an undergraduate embedded systems course facilitates the self-directed learning amongst students? Methodology: This article, conducted in the context of an existing embedded systems design (ESD) course, relied on interviews of students, teaching assistants, and faculty along with document analysis and a mixed inductive-deductive thematic analysis. Findings: A learning ecology of the course is presented. This includes descriptions of space and facilities that influence student motivation, means by which the pedagogical intent of the instructor impacts the student experience, how the course builds on project-based learning knowledge, how the content is distributed using knowledge sharing, how Making supported the ecosystem, how students and instructor occupy similar roles, how the curricular design process was conducted, and how the open ecology promotes student self-direction.

Keywords:
Computer science Context (archaeology) Thematic analysis Process (computing) Active learning (machine learning) Cooperative learning Project-based learning Knowledge management Mathematics education Teaching method Psychology Qualitative research Artificial intelligence

Metrics

40
Cited By
4.59
FWCI (Field Weighted Citation Impact)
43
Refs
0.95
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Experimental Learning in Engineering
Physical Sciences →  Engineering →  Media Technology
Teaching and Learning Programming
Physical Sciences →  Computer Science →  Computer Science Applications
Engineering Education and Curriculum Development
Physical Sciences →  Engineering →  Media Technology

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