Using cyberlearning, coalitions, and model-based learning to provide specialized courses for small numbers of students
Large amounts data are now routinely collected in science and engineering courses, and access to large scale computing resources are common in colleges. Consequently undergraduate students in science and engineering programs need to learn how to leverage these resources, and need to know how to abstract the relevant problems in mathematics formalism so that they can make sense of their data. These needs are normally addressed by providing courses in computational sciences and engineering (CSE). However, there is a lack of available undergraduate CSE courses at most small colleges because these colleges cannot justify the expense of providing such courses for small enrollments.
In order to meet the need that CSE courses should be an option for every undergraduate student, our project strategy and goals were (a) establish a multi-institution coalition that collaboratively provides CSE courses and projects by combining students from different colleges into a single class and leveraging each collegeﾒs capabilities, (b) provide general CSE courses organized by computational methods (e.g. mathematical modeling and simulation) rather than domain specific courses such as computational biology; and (c) use cyberlearning technologies to deliver courses.
R&D process was used to provide a coherent framework for designing instruction and assessing learning. Course design centered on model-based learning which proposes that students learn complex content by elaborating on their mental model, developing a conceptual model, refining a mathematical model, and conducting experiments to validate and revise their conceptual and mathematical models. The courses were taught by a professor in the classroom at one location and students in the other two universities attended class in a classroom using live two-way communications. In addition, the professor at the distant campus sat in on the class which greatly reduced the student anxiety of taking a course from an unknown teacher. Software tool HIMATT for deep learning assessment was used to evaluate how students think through and model complex, ill-defined and ill-structured realistic problems.
The two year project resulted in the establishment of a coalition between Daytona Beach Campus of Embry-Riddle Aeronautical University (ERAU), ERAU Prescott Arizona Campus and Adams State University (ASU) in Colorado. Three PIs from different colleges of the coalition took turns to develop, review and teach the three CSE courses. Two courses in Mathematical Modeling & Simulation and a course in Data Mining and Visualization were developed and offered twice. In addition, two summer research workshops were provided.
Results of the project indicated that the courses were effective and appealed to students majoring in math, computer science, physics, engineering, and meteorology. However, students majoring in biology, chemistry, and environmental sciences did not enroll in the courses. Consequently, we are proposing a second project to include two more colleges to the coalition with strong capabilities in computational biology, chemistry, and environmental sciences and add four domain specific CSE courses relevant to freshmen and sophomores as well as junior and senior level students. The new courses are Problems in Atmosphere and Hydrosphere, Environmental Mathematics, Cloud Computing in Biology, Computational Chemistry, and Genomics and Bioinformatics.
Providing timely and informative feedback in an online or hybrid course is a challenge, especially in a course that involves analyzing models that reflect student reasoning. When the second professor is present in the remote classroom to address the emergent need and answer questions to the students, the students in remote classroom can gain almost the same classroom experience as the students in local classroom. Engaging advanced students as teaching assistants is the approach being adopted to address this potential problem.
 Liu, H. Klein, J. (2013), Using REU Project and Crowdsourcing to Facilitate Learning on Demand, Proceedings of IADIS International Conference on Cognition and Explorative Learning at Digit Ages, Fort Worth, Texas, pp 251-258.
 Simpson, A. Ludu , H. J. Cho and H. Liu, Simpson, A. Ludu, A. Cho H. J., Liu, H. 2014, Experimental and theoretical studies on visible light attenuation in water, Atmospheric and Oceanic Physics, retrievable from https://arxiv.org/abs/1408.3883
 Rickard, J. Robinson, P. Mascarenas, M. Ginther, T. Lyons, J. Braeger, H. Liu and A. Ludu, an oral presentation ﾓModel for Nonlinear Energy Resonant Damping for a Payload Impact to the Groundﾔ to be presented at The 9th IMACS International Conference on Nonlinear Evolution Equations and Wave Phenomena: Computation and Theory, April 1-4, 2015, Athens, GA.
 Book chapter, Hong P. Liu, Maria Ludu, and Douglas Holton, Can K-12 Math Teachers Train Students to Make Sound Logic Reasoning? ﾖ A Question Affecting 21st Century Skills, ﾓEmerging Technologies for STEAM Educationﾒ, Edited by Xu Ge, Michael Spector, and Dirk Ifenthaler, Springer, 2015
 Hong Liu, Xudong Shi, Junzhen Shao, Qi Zhou, Stacey Joseph-Ellison, Johnathan Jaworski , The Mechatronic System of Eco-Dolphin - a Fleet of Autonomous Underwater Vehicles, Proceeding of International Conference of Advanced Mechatronics System 2015, Beijing, August 24-27, 2015.