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This paper is the report on the findings of a three-year study conducted in undergraduate computer science courses. It also describes the changes made to deal with COVID-19 during the summer and fall of 2020. We collected data on over 800 students with approximately 35 students identified as having learning disabilities (LD). These students were not professional programmers in a computer science department; rather, they were students from a Business College. Our preliminary results show that pair programming improved (a) teamwork and communication between the pairs; (b) confidence in students; and (c) comprehension and learning for all students. Thus, our preliminary results indicate that pair programming improved everyone’s performance. Educators should consider the importance of collaboration with other disciplines when creating inclusive environments for students with disabilities. 

Peer learning through pair programming is a type of collaborative learning that involves students working in pairs to discuss computer programming concepts or develop codes to solve problems. The Zoom breakout room method is applied to teach pair programming in a virtual classroom during the COVID-19 environment. By facilitating pair programming in a virtual learning environment, we gained valuable experience in promoting collaborative learning, active learning, and problem-based learning activities in a cloud setting. 

Persons with learning disabilities (LD) are underrepresented in computer science and information technology fields despite the explosion of related career opportunities and interest. In this study, we examine the use of pair programming as a collaborative intervention in with computer programming and compare students with learning disabilities to students who do not have learning disabilities. We concentrate on situational motivation constructs which tap into the desire to meet goals and acquire skills. We find that students with LD and similar students without LD fare the same. For the both groups, three of the four situational motivation subscales increase after the introduction of pair programming. The use of pair programming holds promise as an educational intervention for all students including those with learning disabilities. 

The inspector/executor paradigm permits using runtime information in concert with compiler optimization. An inspector collects information that is only available at runtime; this information is used by an optimized executor that was created at compile time. Inspectors are widely used in optimizing irregular computations, where information about data dependences, loop bounds, data structures, and memory access pa erns are collected at runtime and used to guide code transformation, parallelization, and data layout. Most research that uses inspectors relies on instantiating inspector templates, invoking inspector library code, or manually writing inspectors. is paper describes abstractions for generating inspectors for loop and data transformations for sparse matrix computations using the Sparse Polyhedral Framework (SPF). SPF is an extension of the polyhedral framework for transformation and code generation. SPF extends the polyhedral framework to represent runtime information with uninterpreted functions and inspector computations that explicitly realize such functions at runtime. It has previously been used to derive inspectors for data and iteration space reordering. is paper introduces data transformations into SPF, such as conversions between sparse matrix formats, and show how prior work can be supported by SPF. We also discuss possible extensions to support inspector composition and incorporate other optimizations. is work represents a step towards creating composable inspectors in keeping with the composability of a ne transformations on the executors.