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STTR Phase I: Cloud-Based Pluggable Learning Analytics Engine for Educational Games

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 1549811
Agency Tracking Number: 1549811
Amount: $225,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: EA
Solicitation Number: N/A
Solicitation Year: 2015
Award Year: 2016
Award Start Date (Proposal Award Date): 2016-01-01
Award End Date (Contract End Date): 2016-12-31
Small Business Information
United States
DUNS: 079829812
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Gey-Hong Gweon
 (831) 222-5511
Business Contact
 Gey-Hong Gweon
Phone: (831) 222-5511
Research Institution
 Concord Consortium
 Rina Hoffer
25 Love Lane
Concord, MA 01742
United States

 Domestic Nonprofit Research Organization

This STTR Phase I project will carry out research and development on a cloud-based pluggable data analytics engine to address the educational game market?s need of real-time assessment for learning. Educational games will become much more successful if learning from games can be well quantified so that buyers will be assured that the time spent using games is productive. However, currently game makers are not qualified or funded to provide the statistics and cognitive assessment required for such analysis. This project will thus build a prototype of commercial pluggable third-party engine that traces the growth of the learner's knowledge in real time without interference and provides customized assessment summary and feedback to educational stakeholders. The prototype will be developed and tested with games that teach data literacy in three high schools representing diverse demographic groups. The testing in a commercial environment will begin in collaboration with two successful educational game companies. The innovative use of data-intensive assessment technology will aid in currently struggling STEM education in the United States by providing streamlined and accurate information while learning occurs. This project will also help launch a new business that has potential to boost the market value of educational games and digital learning. This STTR Phase I project utilizes the Monte-Carlo Bayesian Knowledge Tracing (MC-BKT) algorithm. This algorithm was recently developed in-house based on techniques distilled through years of research in physics, education, and computation, and makes it possible to perform individualized knowledge tracing in real-time for the first time. In prior research, post hoc MC-BKT analysis led to identification of up to seven distinct patterns associated with knowledge growth during game segments, with 84% accuracy as compared with human judgments based on video analysis of game screens and players' discourse. This project will conduct research to test whether this assessment potential of the MC-BKT algorithm can be extended beyond initial research to players with games involving different content domains, in a greater number of classrooms with diverse demographics (involving around 600 high school students), and in real time. Based on research results, this project will build a prototype commercial product around the MC-BKT algorithm in the form of a cloud-based pluggable engine. Two popular commercial educational games as well as various games internally sourced within this project will be test-connected to the engine for real-time testing of knowledge tracing, learning problem detection, and feedback delivery to teachers, parents, game designers, and learners.

* Information listed above is at the time of submission. *

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