Project Engage: Training Secondary Teachers to Deliver Computer Science and Engineering Instruction

Project Engage: Training Secondary Teachers to Deliver Computer Science and Engineering Instruction will support the implementation of an emerging Computer Science Principles course called Thriving in Our Digital World (TODW) in forty-five urban, suburban, and rural schools, including public, private, magnet, and charter schools, in Texas. The TODW course will be offered in a unique dual enrollment mode (concurrent high school and college credit). This project will test two innovative professional development techniques for expanding the reach to more schools: flipped classroom techniques, in which teachers receive recorded video-based professional development, and then use face-to-face time for more hands-on activities; and an automated system which will use artificial intelligence technology to support negotiation of common grades for student work between high school teachers and college faculty. This project's research agenda will address the following core challenge: In a field such as computer science where there is limited expertise, how can we most efficiently scale professional development so as many schoolteachers as possible can provide high quality instruction?

This STEM-C Partnerships' Computer Science Education Expansion project builds on prior funding of the UTeachEngineering: Training Secondary Teachers to Deliver Design-Based Engineering Instruction Partnerships through the National Science Foundation's Math and Science Partnership program. This project will produce the scientific foundation and the concrete artifacts needed to deliver a highly scalable curriculum for TODW. The artifacts will include a differentiated curriculum, scalable professional development, and scalable assessment tools and processes. The differentiated curriculum will include formative assessments which can be used by teachers to architect novel pathways through the curriculum for individual students or whole classes. Assessments will be created that can be delivered through a double-blind collaborative review (DBCR) rubric scoring tool, which will include machine learning algorithms that help detect consistent discrepancies between scores of individual (novice) computer science teachers and (expert) college-level computer science professors. The DBCR tool will use machine learning techniques such as basic text features (Bag of Words), syntactic and semantic modeling (n-grams and Latent Dirichlet Allocation), and cluster analysis against existing artifact corpora. Since the assessments of student artifacts are explicitly tied to rubrics, human evaluators may also use the DBCR tool to specify specific features that support line item rubric scores. The available features will vary from project to project but will leverage a variety of tools available in the DBCR user interface: (1) selectable categorical classifications, (2) the highlighting of relevant text features, and (3) annotations to mark document structures, such as cross references and supporting evidence. Project evaluation will focus on both implementation fidelity of the TODW course and student-level outcomes in classes that implement TODW. If successful, the professional development could serve as a model for more scalable teacher professional development in many domains.

Principal Investigator(s):

David Allen, University of Texas at Austin - PI
Calvin Lin, University of Texas at Austin - Co-PI
Pauline Dow, Austin Independent School District - Co-PI