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.

Computer‐based technologies are changing at an accelerating pace and becoming increasingly complex. Smart and context‐aware devices and Internet access enable children and adult learners to gain factual and procedure knowledge about many things anywhere at any time. These computer‐based technologies, if well‐utilized in support of learning, can promote personalized and collaborative education, which research suggests are valuable pedagogies. However, new technologies create a burden on designers and educators to use them effectively in support of learning and instruction. Emphasis on 21st century skills suggests that education should emphasize the development of critical thinking and complex problem‐solving skills. Meanwhile, many educators encounter students who lack motivation and are ill‐prepared in reading and mathematics. In addition, engineering educators face another challenge—namely, the need to frequently update curricula so that students can attain marketable skills and adapt to rapidly changing technologies in the workplace. As it happens, proper use of educational technologies can provide solutions to these challenges. This paper reports one such approach to integrate new technologies in two hybrid synchronous courses using technology‐enabled scaffolds in support of deep learning and enhanced problem‐solving competence in engineering education at small colleges in the United States. The emphasis here is on the developmental approach rather than research analysis.

Probabilistic Logic Networks (PLN), a comprehensive framework for uncertain inference currently in use in the OpenCog and Novamente Cognition Engine AGI software architectures, has previously been described in terms of the "experiential semantics" of an intelligent agent embodied in a world. However, several aspects of PLN are more easily interpreted and formulated in terms of "possible worlds semantics"; here we use a formal model of intelligent agents to show how a form of possible worlds semantics can be derived from experiential semantics, and use this to provide new interpretations of several aspects of PLN (including uncertain quantifiers, intensional inheritance, and indefinite probabilities.) These new interpretations have practical as well as conceptual benefits, as they give a unified way of specifying parameters that in the previous interpretations of PLN were viewed as unrelated.

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