Professional learning programs for computer science (CS) teachers primarily rely on self-reported data from participants to understand the learning impact on teachers and improve teacher growth. We developed and piloted a set of standardized measures of teacher growth aligned with Standards 2-5 of the Computer Science Teachers Association (CSTA) Standards for CS Teachers. We created a rubric from the 29 indicators across the four standards by merging similar concepts. We reduced these 29 indicators into 18 rubric items placed in one of three groups: Planning (9 items), Assessing (3 items), and Professional Development (6 items). We also created scales for measuring progress on each item based on the criteria for each standard. After creating an entry form based on the rubric items, we conducted a two-cycle pilot process, with teachers (n=24) completing the entry form and providing feedback in the form and in focus groups. We then applied revisions to the process, and conducted a second pilot with a different set of teachers (n=29). Teachers reported multiple ways to improve the process, including understanding their own growth path as a CS teacher. In this experience report, we describe the process of creating the rubric, the two-phase pilot used to gather feedback from the teachers, and the changes that we made to the rubric based on teacher feedback. We also provide a high-level description of the 18 items in the rubric, lessons learned, and recommendations.

Effective professional learning communities (PLCs) are important in supporting teacher learning. This study investigated computer science (CS) teacher leaders' perspectives on the lessons and the challenges in supporting CS teachers through local PLCs. We purposefully selected ten CSTA chapters and conducted focus group interviews with the chapter leaders between 2020 and 2022. Our findings indicated that these PLCs offered social-emotional support, continual networking opportunities, and rich professional learning resources. Also, they amplified teachers' voices and supported CS teachers' professional identity building. To engage CS teachers, the teacher leaders built trust, collaborated with other PLCs or organizations, and set an inclusive PLC culture. These PLCs had challenges in recruitment, leadership development and transition and building group identity.

The recent pandemic has resulted in challenges to the prioritization of CS curriculum and strained the structures that support and grow the professional development and identity building of the teachers who implement it. In this poster we examine how local chapters of a national CS teacher advocacy organization (the CSTA) support their members during a time of transition and change. Using focus group data collected from an ongoing multi-year longitudinal research project, we tried to better understand the challenges that these entities faced as their normal structures of communication and outreach were closed off, and how such challenges might affect the perceptions of the role that local chapters play in supporting the continued development of their members, including their identity as CS teachers.

In this special session, we present tools and resources created from educational theory to make teaching and scholarship easier and more effective. Included in these resources are CS Teachers Association, CSforAll, CS Teaching Tips, CT Pathways Toolkit, EngageCSEdu, CS Assessments Hub, Cybersecurity, and CS Ed Research Resource Center. These tools/resources provide assistance at the district and school level (administrators, curriculum designers), classroom level (teachers), and researcher/evaluator level. In addition to these overviews, we will provide information on where to find them and will leave ample time for questions from attendees.

Motivation: Recent efforts to expand K-12 computer science education highlight the great need for well-prepared computer science (CS) teachers. Teacher identity theory offers a particular conceptual lens for us to understand computer science teacher preparation and professional development. The emerging literature suggests that teacher identity is central to sustaining motivation, efficacy, job satisfaction, and commitment, and these attributes are crucial in determining teacher retention. While the benefits associated with a strong sense of teacher identity are great, teachers face unique challenges and tensions in developing their professional identity for teaching computer science.

This paper reports a study of CSTA chapter leaders' percep-tions of their chapter's roles in supporting computer science (CS) teachers. Intent on understanding the impact of member-ship in a professional organization on the development of teacher professional identity, our research revealed that in the chapter leaders' perceptions local CSTA chapters had an im-portant role in supporting the development of their mem-bers' professional identity as a CS teacher.

Current efforts to expand K-12 CS education highlight the great need of well-prepared CS teachers with a strong sense of professional identity. This study proposes the CS teacher identity scale, a quantitative instrument for measuring CS teachers' sense of professional identity. The survey was piloted through a national teacher survey and tested for its reliability, dimensionality, and validity. The analysis reveals a four-factor solution for the CS teacher identity scale.

As computer science is moving into K-12 education, most efforts have focused on getting new teachers into computer science or integrating computing within STEM topics. But one aspect that has received less attention, if any, is the continued professional development of experienced CS teachers. We know little about the particular learning needs of experienced CS teachers, knowledge that will be critical as the CS teaching force expands significantly in coming years. In this panel, we address this knowledge gap from the perspective of teacher education research and professional development-what we need to know about deepening teachers' pedagogical practices and content knowledge-an understanding that will be instrumental for retaining and enriching teachers in CS education.

The proliferation of lidar technology in remote sensing has resulted in extremely large, high resolution point clouds covering a wide variety of terrain. Constructing a grid digital elevation model (DEM) from these large data sets requires extensive computational resources and ample disk space. We propose a framework for leveraging modern computing resources including multi-core distributed systems and general purpose GPU computing to reduce computational bottlenecks and accelerate DEM construction. We employ an I/O-efficient strategy using quad trees to automatically partition the lidar point clouds into a set of independent work bundles. We then distribute these work bundles to multiple GPU-equipped hosts which independently interpolate a portion of the DEM and return partial results. Finally, we gather the partial results and assemble the final DEM I/O-efficiently. Our approach balances I/O, computation, and network communication to reduce bottlenecks. Experimental results show that our approach scales linearly with the number of compute hosts, and achieves speed-ups of 25 × or greater using GPU computing. These results make it practical to use more complex interpolation methods such as regularized splines with tension, which provide geomorphological advantages over simpler interpolation methods such as linear interpolation, nearest neighbor interpolation, or natural neighbor interpolation.