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Prof. Ann Armstrong
Northcentral University, USA |
With experience in business and education, I
provide consulting/coaching/mentoring services. I am an expert in instructional
technology/design, adult learning/leadership, project management, and online
earning. My career includes executive director at Columbia Teachers College
starting the Columbia Coaching Certification and the first online master’s
degree; an eBusiness/eLearning executive at IBM delivering the first global
asynchronous course; and a VP of Ernst & Young's Intellinex/eLearning Practice.
I am a certified IBM consultant and a confirmed IBM principal.
A Full Professor at Northcentral University, I am published in peer-reviewed
journals including Quarterly Review of Distance Education, AHRD, AACE, and AECT.
I received the Academy of Human Resource Development top ten papers award. I
received my EdD and EdM in instructional technology and media from Teachers
College, Columbia University; MA degree from Fairfield University in
instructional design; MA degree in economics from University of Cincinnati; and
MBA in Project Management from Capella University.
Speech Title: Research-Based Constructivist
Instructional Design Model for Online Learning
Abstract: The unexpected emergence of COVID-19 precipitously caused
teachers and students to move from traditional classroom education to fully
online. Chaos erupted throughout the world and was especially disruptive to K-12
and higher education institutions, their administration, staff, and faculties.
Armstrong and Gale designed and developed a research-based constructivist
instructional design model for online learning, R2D2/C3PO, which was later
validated by a team of expert instructional designers using the Nominal Group
Technique. The model was updated and can help educators deliver online learning.
This presentation provides an overview of the model and its development and
validation along with an in-depth discussion of each of the components of the
model. These include 1) Read/listening, 2) Reflect/Writing/Sharing, 3) Display,
4) Doing, 5) Coaching, Conviviality, Critical Incident Questionnaire, and
Planning and Organization. For each component of the model suggestions are made
for instructional strategies and learning activities along with synchronous
web-conferencing/classroom tools.
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Assoc. Prof. Lior Shamir
Kansas State University, USA |
Lior Shamir is an associate professor of computer
science and Nick Chong keystone research faculty scholar at Kansas State
University. His research interests focus on data science, combining a broad
range of approaches in machine learning, soft computing, and computational
statistics to develop new paradigms that can turn data into knowledge and
scientific discoveries. These research activities involve major scientific
collaboration such as the Vera Rubin Observatory, the Midwest Big Data Hub, the
Astrophysics Source Code Library, and more. In education, Dr. Shamir has led
efforts to develop inclusive research-based and culturally-responsive
educational experiences. Dr. Shamir has received over $5M in external funding as
PI, co-PI, or senior personnel, and he is the primary author of more than 100
peer-reviewed publications.
Speech Title: Using Machine Learning Undergraduate Research for Culturally
and Socially Responsive First-year Introductory Courses
Abstract: During the past decade, machine learning has been advancing
rapidly, with a virtually infinite number of real-world applications that also
attracted substantial public interest. Here I use machine learning as a research
tool that can add culturally-responsive research experience to freshman level
undergraduate courses. Research experience has been identified as an effective
intervention for increasing student engagement and retention, with especially
high impact on students from underrepresented minorities. However, most
undergraduate research experience is done during the junior or senior years,
which are less critical for retention. Moreover, undergraduate research depends
on selection and self-selection of the students, and therefore many students are
left outside of the intervention. Here I show how machine learning can be used
as the basis for freshmen level research experience that can also be embedded in
first-year courses, making the intervention accessible to all students. By using
culturally-responsive approach, students can express their culture and identity
through their research. The results show an increase in student engagement and
self-efficacy. Some of the products even include peer-reviewed papers where the
first authors are first-year students, publishing their research results of
their work during a freshman level course. In some cases the student research
and their results was also covered by the international mainstream media.
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