Principal Investigator: Noboru Matsuda
CoPrincipal Investigator(s): Norman Bier, Larry Johnson
Organization: Texas A&M University
Abstract:
The Cyberlearning and Future Learning Technologies Program funds
efforts that support envisioning the future of learning technologies
and advance what we know about how people learn in technology-rich
environments. Cyberlearning Exploration (EXP) Projects explore the
viability of new kinds of learning technologies by designing and
building new kinds of learning technologies and studying their
possibilities for fostering learning and challenges to using them
effectively. Most online courseware helps teach facts and concepts,
while a different type of online learning software called intelligent
tutoring systems can effectively teach skills in a way that is
tailored to each learner. Unfortunately, these two tools are rarely
integrated because of the expense and specialized expertise required
to create intelligent tutors. This project will close this gap by
building and testing a new scalable technology that will allow
teachers without years of specialized training to author adaptive
online courses that combine the best of both these approaches. This
scalable cyberlearning platform will provide students with effective
online instruction, provide learning engineers with an efficient
authoring environment to build adaptive online courses, and provide
researchers with a sharable corpus of big learning data that they can
use to develop and refine theories of how students learn in adaptive
online-course learning environments.
This project will build a web-browser-based authoring environment that
supports the creation cognitive tutors and their seamless integration
into online courses and will measure how well the resulting adaptive
online courses promote facets of student learning such as synergetic
competency and engagement. The central hypotheses are: (1) that the
SimStudent technology — a machine-learning agent that learns
cognitive skills from demonstration — can be a practical authoring
tool for cognitive tutors that can be easily embedded into online
courses; (2) that this technology can represent a tight connection
between learners’ procedural competency and conceptual competency by
combining knowledge-tracing (a standard method used by existing
cognitive tutors) and text-mining (data-mining latent skills from
traditional online course instructions) into an innovative
student-modeling technique; and (3) that adaptive online courses
created with this technique can produce robust student learning by
promoting connections between their procedural and conceptual
understanding (synergetic competency). As part of the overall research
program, the project will: (a) develop a genetic application
programming interface (API) for an existing web-based authoring
technology to build cognitive tutors for online course integration;
(b) develop an adaptive instructional technology as a generic control
mechanism for adaptive online courses; (c) build new adaptive online
courses on Open edX and also convert an existing OLI course into an
adaptive online course; (d) conduct in-vivo studies using the adaptive
online courses to test their effectiveness; (e) test the efficacy of
the proposed adaptive online courses in supporting students to achieve
the aforementioned synergetic competency. Successful completion of the
project will yield the following expected outcomes: (i) a scalable
online course architecture with efficient authoring tools for building
cognitive tutors and integrating them into online courses in order to
make those courses adaptive; (ii) a practical technique to identify
relationships between procedural competency and conceptual competency;
and (iii) an expanded theory of how students learn with the adaptive
online course, and in particular of how students achieve robust
learning with synergetic competency.
