Teaching Workshops and Training
Teaching Workshops and Training
Enrolled and in-progress.
The course introduces the theoretical foundations of engineering education, student learning theories, educational research, and instructional design, as well as covering how to effectively teach, manage, and assess student performance in a practical approach. The primary pedagogies used were covered in Richard Felder and Rebecca Brent's Teaching and Learning STEM: A Practical Guide, with secondary material from How Learning Works: 8 Research-Based Principles for Smart Teaching.
The capstone project of the course involved a scaffolded project of improving a course in my field that a first-year instructor might teach; I chose CMSE 201. The project started a coarse level, from creating a syllabus and 15-week schedule, and then increased granularity, focusing on revising a week's worth of materials for one topic, and ending with detailed material and lecturer's notes for one class session.
For more details, please refer to my final report and presentation which can be found on this page.
As a doctoral student at Michigan State University, I'm in the process of completing the College of Engineering's Certification in College Teaching (CCT) program through the Graduate School. This is one of the only formal programs in the United States that provides graduate students a comprehensive preparation for teaching at the college level. The program involved coursework on teaching STEM, workshops, and completion of a mentored teaching project to advance five core competencies:
Developing Discipline-Related Teaching Strategies
Creating Effective Learning Environments
Incorporating Technology in Your Teaching
Understanding the University Context
Assessing Student Learning
For more information, refer to the CCT's homepage as well as my Teaching Portfolio where I go more in-depth in regard to the knowledge and practices learned while completing it.
I attended this virtual workshop while at MSU to discuss potential contentious discussions with students pertaining to their grade. We identified some reasons why these conversations get contentious, engaged with strategies to address difficult situations in a student-instructor interaction, practiced responding to these difficult scenarios using these strategies, reflected on experience of intentionally using practical conversation tools, and recognized that the discomfort in these situations is inevitable in the learning process.
It was quite helpful to discuss these scenarios before they happen, in order to game plan possible de-escalating or remedying steps. This ties into creating an effective learning environment by having well-defined learning objectives, grading rubrics, and the relationship with the students to be approachable. This approachability is paramount, as even while carefully cultivating this, the natural hierarchy makes it difficult for some students to express concerns.
This virtual workshop through MSU discussed methods Artificial Intelligence can streamline and automate course management tasks. This includes the using AI to design rubrics, assignments, and grading feedback through the intentional, careful drafting of prompts.
Most of the discussion was centered around how to best utilize AI to make the instructor's life easier while maintaining (or improving) instructional quality. AI doesn't always make the students' learning better or easier, and doesn't have a role for every task in course management; however, there are some tasks that AI can make more efficient.
One of these tasks that I've used while a Graduate Teaching Assistant in CMSE 382: Optimization in Data Science is rubric formatting. I don't use AI to create the rubric itself, but I will use it to format the rubric I've made into a table to increase readability. Additionally, I use AI to check alternative methods for solving a given problem. Although the instructor designed the problems to be solved in a given way, there are frequently other ways of solving them. (Of course, not all of these alternative methods are applicable due to not being taught earlier in the course or in a prerequisite, but some are.) One example of this while being a GTA is a problem proving convexity; of course there were usually one or two preferred theorems to employ that were more efficient, we covered numerous theorems that could be utilized.
It was beneficial to review strategies, and I'm sure that the AI landscape will continue to shift and grow quickly, with new models with a more customized, discipline-specific audience. In doing so, I hope that these models will become even more helpful in course management; however, caution will need to be deployed to ensure changes are equitable and truly conducive to the students' learning.
Subsequently, I ended up hosting a very similar workshop myself.
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