This paper presents an innovative hybrid learning model as well as the tools, resources, and learning environment to promote active learning for both face-to-face students and online students. Most small universities in the United States lack adequate resources and cost justifiable enrollments to offer Computational Science and Engineering (CSE) courses. The goal of the project was to find an effective and affordable model for small universities to prepare underserved students with marketable analytical skills in CSE. As the primary outcome, the project created a cluster of collaborating institutions that combined students into common classes and used cyberlearning learning tools to deliver and manage instruction. The instrumental tools for educational technologies included Smart Podium, digital projector, teleconference systems such as AdobeConnect, auto tracking camera and high quality audio in both local and remote classrooms. As an innovative active learning environment, an 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. A wave lab and underwater robotics lab were used to facilitate the experimental components of hands-on research projects. Course delivery included interactive live online help sessions, immediate feedback to students, peer support, and teamwork which were crucial for student success. Another key feature of instruction of the project was using emerging technologies such as HIMATT (Highly integrated model assessment technology and tools) [11] to evaluate how students think through and model complex, illdefined and ill-structured realistic problems.

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Emerging technology has changed the focus of corporate learning systems from task-based, procedural training to knowledge-intensive problem-solving with deep conceptual learning. In addition, the deployment of open systems and distributing processing are adding new stresses to learning systems that can barely keep pace with the current rate of change. Learning environments to address these challenges a reviewed within a framework of the conventional learning curve, in which different learning elements are required to support different levels of expertise. An adaptive development model for creating and sustaining a learning environment is proposed that consists of the iterative application of three phases: (1) analysis and reflection, (2) architecture inception and revision, and (3) alignment. The model relies on the notion that analysis deals as much with synthesis and learning as it does with decomposition. We conclude that the concept of a “learning environment” provides a viable construct for making sense of the array of systems designed to support knowledge management, document management, e-learning, and performance support. A learning environment with a well-defined architecture can guide the convergence of multiple systems into a seamless environment providing access to content, multimedia learning modules, collaborative workspaces, and other forms of learning support. Finally, we see future learning environments consisting of networks of databases housing content objects, elegant access to the content, ubiquitous virtual spaces, and authoring tools that enable content vendors, guilds, and universities to rapidly develop and deliver a wide range of learning artifacts.

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