Invited Speakers
ICFL 2021





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.

   



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.