Demonstrations provide researchers and practitioners with an exciting and interactive opportunity to present their systems, artifacts and/or research prototypes, either at a regular session or at the technical exhibition. In any case, it is required to avoid a commercial format, even if the demo consists of presenting a business product or service. Instead, the presentation should focus on technical aspects.
Any written support materials may be distributed locally but not published in the proceedings. Authors who already present a paper at the conference may apply for a demonstration, to complement but not to replace their paper presentation. Demonstrations can also be made by sponsor companies or as a mixed initiative involving researchers and industrial partners.
Demonstrations are based on an informal setting that encourages presenters and participants to engage in discussions about the presented work. This is an opportunity for the participants to disseminate practical results of their research and to network with other applied researchers or business partners.
March 6, 2017
Demonstration proposals are accepted until:
Concerning the format of the demo, we can accommodate it either as a demonstration in a booth (physical area of 4 sq. meter, with a table and 2 chairs) at the exhibition area, as a poster or as a 20 min oral presentation at a session especially set up for demonstrations. It is also possible to organize the presentation of the same demo in more than one format.
Please contact the event secretariat
In the Expression of Interest Form you should specify:
- What exactly will be demonstrated: a system, an artifact, a research prototype, other?
- What is the theoretical background and application possibilities of the object of demonstration?
- What exactly will a participant learn from the demonstration?
If you wish to propose a new Demo please kindly fill out and submit this Expression of Interest
The System and Method for using virtual student grouping, dynamic regrouping, and Deep Academic Learning Intelligence (DALI) advising and counseling to improve student academic outcomes via Scriyb
George Mason University
Dr. Scott M. Martin is an inventor, mentor, educator, entrepreneur, intrepreneur, and Founding Director of the Computer Game Design Program, and the Virginia Serious Game Institute (VSGI) at George Mason University in Fairfax, Virginia, U.S.A. In the last 30 years, Dr. Martin has taught courses in business entrepreneurship and management, art philosophy, theory, and criticism, music composition, acoustics, physics, and political theory, and American history. Dr. Martin received his B.M. (1988) and M.M. (1989) from Johns Hopkins University, with additional studies in Audio and Electrical Engineering. He received his Doctorate from the University of Maryland, College Park in 2000. In addition, Dr. Martin has founded multiple businesses, institutes, academics programs, research centers, schools, and colleges, and he holds several patent inventions in educational pedagogy and learning sciences. Dr. Martin and is the Founder and Chairman of PSTH LLC, a business consultancy, and Scriyb LLC, a learning sciences/AI platform ed-tech company.
Abstract: Software learning systems, conferencing software, or MOOCs used to offer online degrees, courses, and seminars experience higher student attrition rates, and lower student academic achievement outcomes than traditional classroom learning models (Biwa, 2016, Tyler-Smith, 2006). Although new interactive forums and devices, as well as analytics and assessments tools are being overlaid and integrated into LMS platforms to better provide student-to-student and student-teacher interaction, and to measure, track, and assess student performance, online student learning outcomes still fall below traditional on-site classroom results. Moreover, current learning systems are difficult or impossible to scale and maintain quality and integrity of instruction, not to mention provide the necessary support resources for larger numbers of online students (Moloney et al, 2010). Lastly, software learning systems are not designed to allow the inherent collating, indexing, and analysis of learning data to provide personalized learning solutions to individual students (Ross, 2016). This demonstration will outline a series of three inter-related and integrated learning engineering inventions that collectively propose to solve the problems outlined above, and provide preliminary evidence from these inventions of improved student academic outcomes from a small controlled randomized A/B trial sample set. The three learning inventions first virtually group students based on predetermined variables, and wall off each group from every other group within the same course of instruction. The second and third inventions then track, measure, and analyze each student’s academic achievement rates within a communication styles and social theory matrix and dynamically re-balances each virtual group, and deploys intelligent (deep neural network) algorithms to provide personal advising, mentoring, and counseling channels to consistently guide the student, and optimize their learning environment throughout an academic term.
Keywords: Online education, e-Learning, Education AI, Deep Learning, Streaming Education, Synchronous Education
Goal: To show how the three inventions can improve virtual educational outcomes in K-12 and higher education.
Plan: Demonstrate the three inventions via the Scriyb Platform, and present the preliminary data science to show evidence of improved student academic outcomes.
Equipment Requirements: Wifi or Ethernet. Projector.
Bawa, Papia. (2016). Retention in Online Courses: Exploring Issues and Solutions - A Literature Review. Sage Journals. Retrieved from http://journals.sagepub.com/doi/pdf/10.1177/2158244015621777.
Tyler-Smith, K. (2006). Early Attrition among First Time eLearners: A Review of Factors that Contribute
to Drop-out, Withdrawal and Non-completion Rates of Adult Learners undertaking eLearning Programmes. Journal of Online Learning and Teaching. Retrieved from http://jolt.merlot.org/Vol2_No2_TylerSmith.htm.
Moloney, J., & Oakley, B, II. (2010) Scaling Online Education: Increasing Access to Higher Education. Journal of Asynchronous Networks, v14 n1 p55-70. Needham, MA: Sloan Consortium. Retrieved from https://eric.ed.gov/?id=EJ909842.
Ross, T. (2016). Making Reflective Practice Visible: Supporting Shifts in Practice Towards Personalized Learning. MEd Thesis, University of Victoria. Retrieved from https://dspace.library.uvic.ca/handle/1828/7126.