We are very excited to invite you to the Summer Institute for Distance Learning and Instructional Technology (SIDLIT “Side Light”) 2024 Annual Conference, which will be held on July 29-31, 2024 in Kansas City, Kansas. This year marks the 25th anniversary of SIDLIT, and we have prepared a special program to celebrate this milestone. For the first time ever, our physical location this year is: The University of Kansas Medical Center (KUMC), 3901 Rainbow Blvd., Kansas City, KS, 66160 – or join us virtually!
The theme of SIDLIT 2024 is “Lensing the Past, Zooming In on the Present, Developing Future Passions for Learning.” We will explore together how we can use the lessons learned from the past, the challenges, opportunities, and innovations of the present, and the emerging trends of the future to enhance our online learning and instructional design practices. We will also collaborate on how we can foster and activate a passion for lifelong learning among ourselves and our students.
Commitment to Diversity, Equity, Inclusion, and Accessibility
C2C aims to host SIDLIT 2024—in-person and virtually—in a mutually respectful environment. SIDLIT 2024 is inclusive of people from all backgrounds: ethnicities, races, countries of origin, ages, gender identities, sexual orientations, religions, body sizes, economic backgrounds, educational backgrounds, professional backgrounds, employment statuses, abilities, and other dimensions. We are getting together, staying together, and building the future together, with all of our strengths together.
Host Institution
KU Med Center - 3901 Rainbow Blvd, Kansas City, KS 66160
Asynchronous courses present several challenges. There is a time lag between when the content is made available, when the student views it, and when the instructor receives feedback. This mode of teaching brings students from various parts of the world together and hence provides flexibility. However, this mode makes it harder to know student skills and their performance in previous courses. There are several challenges in teaching machine learning and deep learning asynchronously. Here are a few, 1) the complexity of the concept, 2) student completing pre-req tasks to understand the presented concept, 3) student participating in discussion after learning the concept. These challenges do exist in in-person teaching, however, they are exacerbated in asynchronous teaching. In this presentation, I will discuss techniques that I practice in my courses to make asynchronous courses effective. Using a case study of teaching machine learning and deep learning asynchronously, I will focus on three main aspects 1) student engagement with the material, 2) instructor’s interactions with the student and 3) students’ interactions with each other.
Wednesday July 31, 2024 10:00am - 10:45am CDT
HEB 2249
