SBIR-STTR Award

An embedded and in-context professional learning platform for math problem-solving instruction
Award last edited on: 7/11/2017

Sponsored Program
SBIR
Awarding Agency
NSF
Total Award Amount
$1,464,818
Award Phase
2
Solicitation Topic Code
EA
Principal Investigator
Sheela Sethuraman

Company Information

CueThink Inc

8 Furbish Pond Lane
North Reading, MA 01864
   (224) 338-9328
   info@cuethink.com
   www.cuethink.com
Location: Single
Congr. District: 08
County: Suffolk

Phase I

Contract Number: 1549094
Start Date: 1/1/2016    Completed: 6/30/2016
Phase I year
2016
Phase I Amount
$177,500
This SBIR Phase I project proposes to research and develop a web-based prototype that provides teachers with individualized, interactive, and timely supports for improving students? problem-solving skills and math communication. Despite the expectations placed on math teachers by the Common Core State Standards, most of them are insufficiently prepared to teach students how to become effective problem solvers. Research shows that the largest struggle for teachers is not learning new strategies to teaching but actually implementing them in the classroom. This project addresses this challenge by offering professional development that is embedded and ongoing and sets the stage for educators to develop essential 21st Century skills including critical thinking, communication, collaboration and creativity. These are essential job skills not only for educators but for the young minds they coach and mentor. Additionally, teachers who themselves approach problem solving with confidence and enthusiasm inspire students to do the same. This has great implications for how many students will continue to seek and enroll in STEM programs. The annual market for professional development is expected to grow to $1.8 to $2 billion dollars within 5 years just for math and science in K-12 schools, a significant market for this project to target.


The proposed project will result in a prototype that works seamlessly with the existing student-facing peer-to-peer application, developed by the same company and already in the marketplace. In order to achieve this level of embedded instruction, the project intends to build a sophisticated recommendation engine that not only analyzes the teacher's user profile but also their actions in using the student-facing platform and responds with suggested pathways to improve their teaching. This is a unique approach to professional development, enabling educators to determine how to introduce, instruct, and assess problem-solving skills in a sustained manner. This study is anchored in National Council on Teachers of Mathematics' (NCTM) "Principles to Actions: Ensuring Mathematical Success for All" and is designed to directly address three of their eight recommended essential practices and six of the specific recommended actions. The proposed project consists of a five-month iterative, formative evaluation-based development phase followed by a one-month pilot study where classroom teachers will evaluate the functional prototype.

Phase II

Contract Number: 1660216
Start Date: 3/15/2017    Completed: 2/28/2019
Phase II year
2017
(last award dollars: 2019)
Phase II Amount
$1,287,318

This project proposes to develop an innovative approach to improve and sustain math educators' problem solving teaching skills. Despite the expectations placed on math teachers by the Common Core State Standards, most are insufficiently prepared to teach students how to become critical thinkers. Much of this problem is due to limited pedagogical skills of teachers in providing adequate problem solving instruction and supports on top of teachers' own limited problem solving skills. This project remedies this with its integrated modules and powerful analytics engine that suggests learning pathways for both expert and novice teachers. They anchor their research in National Council of Teacher's of Mathematics Principles to Action. It will help teachers develop confidence and skills in planning and evaluating their lessons, as well as understanding student misconceptions and intervening in a timely manner. Teachers who approach problem solving with confidence inspire students to approach difficult math tasks the same way. This has great implications for how many students will continue to enroll in Science, Technology, Engineering and Math programs. In addition, the project sets the stage for educators to develop 21st Century skills including critical thinking, communication and collaboration - essential job skills for the young minds they mentor.This effort refines and scales up their product, which is a web and mobile application that works seamlessly in conjunction with our current student-facing platform, to provide teachers with timely supports for improving students' problem-solving skills and math communication. This project will deliver professional development continuously and in-context using virtual peers, rich rubrics, interactive tools and actionable data. The analytics engine leverages adaptive learning models in order to build robust modules. The Data Collector Layer will contain interfaces for users to get recommendations, receive user feedback and provide other analysis reports. The Analytics Core Layer will be implemented using a collection of machine learning algorithms. The Service Layer will calculate recommendations based on user profile, user feedback, pre-stored best practices and other use cases. The Persistence Layer will store and get calculated data to recommendation engine's own database. The company plans to conduct several formative evaluations during the course of the project, as well as two pilot studies at the end of each year with a control and experiment group. The results will enable them to determine the effectiveness of ongoing, just-in-time supports for improving teachers' skills and confidence inside and outside the classroom.