bretsw / meche24

Slide deck for FSU Mechanical Engineering seminar: “Self-Directed Learning for MechE Problem-Solving”

Self-Directed Learning for MechE Problem-Solving

Slide deck for FSU Mechanical Engineering seminar: Self-Directed Learning for MechE Problem-Solving

February 16, 2024

Self-Directed Learning as problem-solving:

1. Performance Objective
2. Starting Context
3. Practice Opportunities
4. Difficulty Level
5. Content Area
6. External Guide

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Abstract

In this seminar, you will have an opportunity to reflect on how you typically solve engineering problems and how you will be expected to solve problems throughout your career. The concept of self-directed learning will be introduced and explored, specifically a context-aware approach to self-teaching. Participants will have opportunities to practice the steps of context-aware self-teaching: (1) identify performance objectives/outcomes, (2) assess starting context, (3) maximize practice opportunities, (4) monitor difficulty level, (5) explore content area, and (6) find external guides. Most importantly, you will leave the seminar with, at minimum, a nominal improvement in how you can go figure out mechanical engineering challenges on your own.

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Bio

Dr. Bret Staudt Willet (Ph.D. Michigan State University, 2021) is an Assistant Professor of Instructional Systems & Learning Technologies at Florida State University. His research investigates self-directed learning, a subset of informal and networked learning. Dr. Bret is fascinated by how people figure things out on their own. He is most interested in what happens when students, learners, and trainees finish formal instruction, preparation, and training. What do they do after they walk out the door or log off? How do they continue to develop their knowledge, skills, and abilities? Where do they look for resources? Who do they talk to? He explores how self-directed learners navigate the affordances and constraints of social connections through the internet and exploration through games. Dr. Bret frequently investigates self-directed learning with the tools of educational data science, including learning analytics, social network analysis, discourse analysis, natural language processing, and educational data mining.