The ways in which the components of a program interact with each other in a concurrent setting can be considerably more complex than in a sequential setting. The core problem is unrestricted shared mutable state. An alternative to unrestricted shared mutable state is to restrict the sharing using Ownership. Ownership can turn what would have been a race into a deterministic failure that can be explained to the programmer. However, Ownership has predominantly taken place in statically typed languages. In this paper, we explore retrofitting an existing dynamically typed programming language with an ownership model based on regions. Our core aim is to provide safe concurrency, that is, the ownership model should provide deterministic dynamic failures of ownership that can be explained to the programmer. We present a dynamic model of ownership that provides ownership of groups objects called regions. We provide dynamic enforcement of our region discipline, which we have implemented in a simple interpreter that provides a Pythonlike syntax and semantics, and report on our first steps into integrating it into an existing language, Python.

With the growth of computing education research in the last decade, we have found a call for a strengthening of empiricism within the computing education research community. Computer science education researchers are being asked to focus not only the innovation that the research creates or the question it answers, but also on validating the claims we made about the work. In this session, we will explore the relationship between evaluation and computing education research and why it is so vital to the success of the many computing education initiatives underway. It will also help computing faculty engaged in computer science education research understand why it is essential to integrate evaluation and validation from the very first conceptual stages of their intervention programs.

This study investigated patterns in the development of computational thinking practices in the context of the Exploring Computer Science (ECS) program, a high school introductory CS course and professional development program designed to foster deep engagement through equitable inquiry around CS concepts. Past research indicates that the personal relevance of the ECS experience influences students' expectancy-value towards computer science. Expectancy-value is a construct that is predictive of career choices. We extended our research to examine whether expectancy-value influences the development of computational thinking practices. This study took place in the context of two ECS implementation projects across two states. Twenty teachers, who implemented ECS in 2016-17, participated in the research. There were 906 students who completed beginning and end of year surveys and assessments. The surveys included demographic questions, a validated expectancy-value scale, and questions about students' course experiences. The assessments were developed and validated by SRI International as a companion to the ECS course. Overall, student performance statistically increased from pretest to posttest with effect size of 0.74. There were no statistically significant differences in performance by gender or race/ethnicity. These results are consistent with earlier findings that a personally relevant course experience positively influences students' expectancy for success. These results expanded on prior research by indicating that students' expectancy-value for computer science positively influenced student learning.

As K-12 computer science (CS) initiatives scale throughout the U.S., educators face increasing pressure from their school systems to provide evidence about student learning on hard-to-measure CS outcomes. At the same time, researchers studying curriculum implementation and student learning want reliable measures of how students apply their CS knowledge. This paper describes a two-year validation study focused on end-of-unit and cumulative assessments for Exploring Computer Science, an introductory high school CS curriculum. To develop the assessments, we applied a principled methodology called Evidence-Centered Design (ECD) to (1) work with various stakeholders to identify the important computer science skills to measure, (2) map those skills to a model of evidence that can support inferences about those skills, and (3) develop assessment tasks that elicit that evidence. Using ECD, we created assessments that measure the practices of computational thinking, in contrast to assessments that only measure CS conceptual knowledge. We iteratively developed and piloted the assessments with 941 students over two years and collected three types of validity evidence based on contemporary psychometric standards: test content, internal structure, and student response processes. Results show that reliability was moderate to high for each of the unit assessments; the assessment tasks within each assessment are well aligned with each other and with the targeted learning goals; and average scores were in the 60 to 70 percent range. These results indicate that the assessments validly measure students' computational thinking practices covered in the introductory CS curriculum. We discuss the broader issues we faced of balancing the need to use the assessment results for evaluation and research, and demands from teachers for use in the classroom.

Education researchers have extensively studied how secondary teachers adopt and adapt new curriculum and new teaching practices, especially in science and mathematics. Their goals are often to learn ways to help teachers enact new pedagogical approaches, so the results inform teacher professional development, as well as building knowledge in the field. Changing teaching practice often involves changes across a number of fronts: for example, inquiry-based teaching of science involves skills in developing questions, supporting student whole-class discussion and sense-making, and allowing students time to investigate authentic problems. Research is now underway to discover ways to similarly help teachers in K-12 computer science (CS). K-12 CS curricula have emphasized inquiry- and equity-focused teaching practices as ways to engage and include students while simultaneously deepening students? understanding of CS concepts and practices. While researchers have focused on measuring student attitudes to discern engagement and feelings of inclusivity, less work has been done on how implementation affects student learning. To study this, CS education researchers need frameworks and instruments to measure implementation, attitudes, and learning. We are developing and validating instruments that support mixed-methods study of curriculum enactment and teaching quality, with a focus on inquiry, equity, and computational thinking practices. This poster will present preliminary results from a large-scale study of implementation in secondary CS classrooms to build a more systematic understanding of evolving practices in measuring curriculum enactment and teaching quality.

Nationwide, efforts are focusing on taking computer science (CS) to scale in high school classrooms through the Exploring Computer Science (ECS) and AP CS Principles (CSP) courses. Recent inroads are also being made to take structured introductory curricula to middle school classrooms. Often, a starting point for teaching CS in middle and high school is a discussion around the seemingly simple question "What is a computer?" The question is aimed to help learners understand through debate and discussion what makes a computer a computer. This paper reports our analysis of (a) middle school students' discussions around this question, and (b) high school students' responses to an assessment question measuring this understanding. Our analyses of students' comments and responses reveal that a discussion around "what is a computer?" may be problematic for students, as it tends to focus on the tool, the "computer." We suggest that the discussion needs re-framing to focus instead on computing and computation.

As computer science (CS) and computational thinking (CT) make their way into all levels of K-12 education, valid assessments aligned with new curricula can assist in measuring student learning, easing the way for adoption of new computing courses, and evaluation of pedagogical approaches for teaching computing ideas and concepts. Without attention to rigorous assessment, CT can have little hope of making its way successfully into K-12 school education settings at scale. This BoF session will involve discussion around ongoing work at SRI International (under several NSF-funded projects) on the design and development of formative and summative assessments for the ECS curriculum. Additionally, various forms of assessment (including free response and multiple-choice questions, and computational artifacts), and insights from past research on their use will be discussed. BOF attendees will be able to discuss multiple viewpoints, connect with others who care about assessment of CT, and share resources and ideas.

Several techniques have been proposed to automatically grade students' free-text answers in e-learning systems. However, these techniques provide no or limited support for the evaluation of acquired procedural knowledge. To address this issue, we propose a new approach, named ProcMark, specifically designed to assess answers containing procedural knowledge. It requires a teacher to provide the ideal answer as a semantic network (SN) that is used to automatically score learners' answers in plain text. The novelty of our approach resides mainly in three areas: a) the variable granularity levels possible in the SN and the parameterizing of ontology concepts, thus allowing the students free expression of their ideas, b) the new similarity measures of the grading system that give refined numerical scores, c) the language-independence of the grading system as all linguistic information is given as data files or dictionaries and is distinct of the semantic knowledge of the SN. Experimental results in a Computer Algorithms course show that the approach gives marks that are very close to those of human graders, with a very strong (0.70, 0.79, and 0.79) positive correlation.

In this paper we describe the parallelization of a dynamic programming algorithm used to find common RNA secondary structures including pseudoknots and similar structures. The sequential algorithm is recursive and uses memoization and data-driven selective allocation of the tables, in order to cope with the high space and time demands. These features, in addition to the irregular nature of the data access pattern, present particular challenges to parallelization. We present a new manager-worker approach, where workers are responsible for task creation and the manager's sole responsibility is overseeing load balancing. Special considerations are given to the management of distributed, dynamic task creation and data structures, along with general inter-process communication and load balancing on a heterogeneous computational platform. Experimental results show a modest level of speedup with a highly-scalable level of memory usage, allowing the comparison of much longer RNA molecules than is possible in the sequential implementation.

Flexible, large area electronics - macroelectronics - using amorphous silicon, low-temperature polysilicon, or various organic and inorganic nanocrystalline semiconductor materials is beginning to show great promise. While much of the activity in macroelectronics has been display-centric, a number of applications where macroelectronics is needed to enable solutions that are otherwise not feasible are beginning to attract technical and/or commercial interest. In this paper, we discuss the application drivers and the technology needs and device performance requirements to enable high performance applications to include RF systems.

In this paper, we describe the use of proximal probes, such as the atomic force microscope (AFM) and the scanning tunneling microscope (STM), for nanofabrication. A resistless proximal probe-based lithographic technique has been developed that uses the local electric field of an STM or conductive AFM tip that is operated in air to selectively oxidize regions of a sample surface. The resulting oxide, typically 1-10 nm thick, can be used either as a mask for selective etching or to directly modify device properties by patterning insulating oxides on thin conducting layers. In addition to this resistless approach, we also describe the use of the STM/AFM to modify the chemical functionality of self-assembling monolayer films. Such modified films are used as a template for the selective electroless plating of metal films. The above processes are fast simple to perform, and well suited for device fabrication. We apply the anodic oxidation process to the fabrication of both semiconductor and metal-oxide devices. In these latter structures, sub-10 nm-sized device features are easily achieved, and we describe the fabrication of the smallest possible device, a single, atomic-sized metallic point contact by using in situ-controlled AFM oxidation.