With the rapid growth of computer science in K-12 education has come a significant increase in the need for computer science teachers. This demand has meant that a large number of K-12 educators are being asked to teach computer science with very little formal preparation in the subject. For many, this creates a unique experience in which they are often learning the content for the first time while simultaneously preparing to teach or actively teaching in their own classrooms. Although this can be a challenging situation, the circumstances present a somewhat rare situation in which educators can meaningfully reflect on the intersection between teaching and learning by examining their experiences and lessons learned as they participate in both worlds simultaneously. With this poster, we discuss some of the more common observations about learning programming from an analysis of responses from over 220 participants in a state-wide CS teacher preparation program. These observations may be broadly valuable for educators teaching programming courses.

Training teachers to teach high-quality computer science courses is an important step towards increasing participation in computer science. To meet this need, our Research Practitioner Partnership (RPP) was formed with the primary goal of creating a comprehensive, graduate-level program to train in-service CS teachers with little to no prior CS coursework. Since its formation, the RPP has iteratively designed, implemented, and evaluated a five-course program to improve participants' knowledge of computer science content and pedagogy while allowing them to earn the state's grades 5-12 computer science endorsement. Our program has successfully scaled from a single-site pilot to a truly statewide, multi-site program, emphasizing educator-based, standards-based, and cohort-based instruction. Currently, the RPP serves 250 in-service participants.

The work described in this poster builds on previous work funded by the NSF through a CSForAll:RPP grant (NSF 17-525, small-scale, Award #1738784). In our original work we developed and piloted a professional development (PD) sequence which allows in-service high school teachers to teach high quality computer science courses and earn the state's then newly created computer science endorsement.

One of the impacts of the CS For All movement has been an increased demand for K-12 teachers who are well-qualified to teach computer science. Unfortunately, the pool of qualified educators is much smaller than the demand, and the pipeline producing newly-qualified educators is smaller yet.

In Grading for EQUITY: What It Is, Why It Matters, and How It Can Transform Schools and Classrooms, Joe Feldman states 'The ways we grade disproportionately favor students with privilege and disproportionately harm students with less privilege.' (2019, pp. xxii-xxiii) This workshop provides a process to make grading practices more equitable. The process includes: 1) analyzing course goals to yield desired course outcomes; 2) developing assessments of the outcomes; 3) developing learning activities; 4) adopting a different approach to grading assessments; and 5) developing an appropriate overall grading scheme. With some practice and discussion with peers you can implement an equitable grading process that revolutionizes your instructional practice. Grading will: no longer address student behavior subject to bias; incorporate better mathematical practices becoming more accurate, lack bias; and be motivating. Grading For Equity alleviates: the homework grading burden with feedback ignored by students; worries about cheating on homework; and arguing with students about points while it allows instructors to focus on working with students in a professional and less stressful manner. Workshop activity will address all five process elements noted above. Participants need to view a background presentation prior to the workshop and will receive sample outcomes, assessments, and grading suggestions for various types of courses in addition to workshop products.

In order to fully prepare high-quality teachers to deliver on the promise of CS for All, teacher preparation programs must ensure teacher candidates have mastered both content and the pedagogy to teach that content well. University CS departments are well-positioned to teach the content, but "methods courses" are an important source of pedagogical and pedagogical content knowledge for future computer science teachers. This panel will describe a variety of approaches: Colleges of Education or CS departments; in-person, hybrid, or online course delivery; and standalone "CS Methods" courses or CS integrated in broader methods courses. Each panelist will describe their course. The moderator will elicit common features between the courses as well as the strengths and weaknesses between different approaches.

One of the major perceived barriers to the adoption of Classroom Response Systems, a.k.a. "clickers", is limited interactivity. Students using dedicated clicker hardware are often only able to provide basic multiple choice or simple numeric responses. The accessibility and flexibility of smartphones make them an intriguing platform for managing some of the shortfalls of traditional classroom response systems. This approach not only leverages a resource that a growing number of students already own, but also aims to enhance the effectiveness of the system by tapping into the robust interaction capabilities that these devices afford. This poster will describe a smartphone implementation of a classroom response system and discuss efforts to extend the basic clicker interaction model.

In this paper, we discuss in-person or face-to-face grading: what it is, a rationale for its use, our use of it, and an experiment we conducted to evaluate its use. While no statistically significant differences in instructional outcome effects were found, several interesting affective results were seen. Additionally, a number of research methodological suggestions arose from the study.

In a world where the number of choices can be overwhelming, recommender systems help users find and evaluate items of interest. They do so by connecting users with information regarding the content of recommended items or the opinions of other individuals. Such systems have become powerful tools in domains such as electronic commerce, digital libraries, and knowledge management. In this paper, we address such systems and introduce a new class of recommender system called meta-recommenders. Meta-recommenders provide users with personalized control over the generation of a single recommendation list formed from a combination of rich data using multiple information sources and recommendation techniques. We discuss experiments conducted to aid in the design of interfaces for a meta-recommender in the domain of movies. We demonstrate that meta-recommendations fill a gap in the current design of recommender systems. Finally, we consider the challenges of building real-world, usable meta-recommenders across a variety of domains.

Recommender systems are changing from novelties used by a few E-commerce sites, to serious business tools that are re-shaping the world of E-commerce.Many of the largest commerce Web sites are already using recommender systems to help their customers find products to purchase.A recommender system learns from a customer and recommends products that she will find most valuable from among the available products.In this paper we present an explanation of how recommender systems help Ecommerce sites increase sales, and analyze six sites that use recommender systems including several sites that use more than one recommender system.Based on the examples, we create a taxonomy of recommender systems, including the interfaces they present to customers, the technologies used to create the recommendations, and the inputs they need from customers.We conclude with ideas for new applications of recommender systems to E-commerce.

This paper presents the results of introducing an agent into a real-world work situation -production of the online edition of a daily newspaper.Quantitative results show that agents helped users accomplish the task more rapidly without increasing user error and that users consistently underestimated the quality of their own performance.Qualitative results show that users accepted agents rapidly and that they unknowingly altered their working styles to adapt to the agent.