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This is a panel presentation on the role of dispositions in computing education. 

Since the early 21st century, ABET’s accreditation criteria have focused on learning outcomes (what students learn) rather than what professors teach. Such accreditation criteria bring to bear the need for programs to establish clear learning objectives and assessment processes that ensure that program graduates have the requisite technical and professional preparation. To this end, ABET defines student outcomes as “what students are expected to know and be able to do by the time of graduation,” further noting that these outcomes “relate to the knowledge, skills, and behaviors that students acquire as they progress through the program.” With the recent release of Computing Curricula 2020 (CC2020), the competencies of computing program graduates have received additional attention. CC2020 describes competency as “comprising knowledge, skills, and dispositions that are observable in accomplishing a task within a work context.” ABET’s student outcomes thus largely correspond to the CC2020 competencies of program graduates. This paper is a first attempt to reconcile the two notions in the context of computer science. It presents the relevant background and discusses student competencies and their assessments that focus on competency-based learning in computer science. The contributions of this paper are (1) forging an improved shared understanding of computing competencies and (2) an interpretation of ABET’s student outcomes to improve the competency, including dispositions, expectations of computer science graduates. 

A Joint Task Force of the Association for Computing Machinery (ACM), the Institute of Electrical and Electronics Engineers - Computer Society (IEEE-CS), and Association for the Advancement of Artificial Intelligence (AAAI) was constituted in early 2021 to begin the decennial process of revising the Computer Science curricular guidelines, which were last released as Computer Science Curricula 2013 (CS2013). This special session will present the first draft of the revised curricular guidelines, currently referred to as CS202X, and solicit feedback. The CS202X draft will include revisions to CS2013 Knowledge Areas, a proposed competency model being incorporated into the curricular guidelines, and other updates. Targeted towards educators, administrators and others interested in Computer Science curricular issues, this session will be led by the co-chairs and members of the CS202X Steering Committee as part of their process to engage the community and solicit feedback. 

We developed a tutor for imperative programming in C++. It covers algorithm formulation, program design and coding – all three stages involved in writing a program to solve a problem. The design of the tutor is epistemic, i.e., true to real-life programming practice. The student works through all the three stages of programming in interleaved fashion, and within the context of a single code canvas. The student has the sole agency to compose the program and write the code. The tutor uses goals and plans as prompts to scaffold the student through the programming process designed by an expert. It provides drill-down immediate feed-back at the abstract, concrete and bottom-out levels at each step. So, by the end of the session, the student is guaranteed to write the complete and correct program for a given problem. We used model-based architecture to implement the tutor be-cause of the ease with which it facilitates adding problems to the tutor. In a preliminary study, we found that practicing with the tutor helped students solve problems with fewer erroneous actions and less time. 

We studied long-term retention of the concepts that introductory programming students learned using two software tutors on tracing the behavior of functions and debugging functions. Whereas the concepts covered by the tutor on the behavior of functions were interdependent, the concepts covered by debugging tutor were independent. We analyzed the data of the students who had used the tutors more than once, hours to weeks apart. Our objective was to find whether students retained what they had learned during the first session till the second session. We found that the more the problems students solved during the first session, the greater the retention. Knowledge and retention varied between debugging and behavior tutors, even though they both dealt with functions, possibly because de-bugging tutor covered independent concepts whereas behavior tutor covered interdependent concepts. 

Parsons puzzles are jigsaw puzzles wherein students are given a program in scrambled order and tasked with reassembling the program in its correct order. Do students use program semantics when solving Parsons puzzles? The answer to this question has implications for the use of Parsons puzzles as a pedagogic tool. In order to answer this question, we considered semantics at the level of statements and control-flow. We analyzed the data collected by a Parsons puzzle tutor over 5 semesters and measured the extent to which students’ puzzle-solving behavior conformed with the use of statement-level and control-flow semantics. We found that students used statement-level semantics to assemble up to 73% of the lines in a puzzle and control-flow semantics to assemble up to 47% of the lines. They used statement-level semantics more than control-flow semantics and more on some puzzles than others. Whenever we found a significant difference between C++ and Java students, C++ students used semantics more than Java students. Finally, we did not find an increase in the use of semantics with in-creased practice. 

We conducted a study to see if using Bayesian Knowledge Tracing (BKT) models would save time and problems in programming tutors. We used legacy data collected by two programming tutors to compute BKT models for every concept covered by each tutor. The novelty of our model was that slip and guess parameters were computed for every problem presented by each tutor. Next, we used cross-validation to evaluate whether the resulting BKT model would have reduced the number of practice problems solved and time spent by the students represented in the legacy data. We found that in 64.23% of the concepts, students would have saved time with the BKT model. The savings varied among concepts. Overall, students would have saved a mean of 1.28 minutes and 1.23 problems per concept. We also found that BKT models were more effective at saving time and problems on harder concepts. 

Competency-based learning has been a successful pedagogical approach for centuries, but only recently has it gained traction within computing. Competencies, as defined in Computing Curricula 2020, comprise knowledge, skills, and professional dispositions. Building on recent developments in competency and computing education, this working group examined relevant pedagogical theories, investigates various skill frameworks, reviewed competencies and standard practices in other professional disciplines such as medicine and law. It also investigated the integrative nature of content knowledge, skills, and professional dispositions in defining professional competencies in computing education. In addition, the group explored appropriate pedagogies and competency assessment approaches. It also developed guidelines for evaluating student achievement against relevant professional competency frameworks and explores partnering with employers to offer students genuine professional experience. Finally, possible challenges and opportunities in moving from traditional knowledge-based to competency-based education were also examined. This report makes recommendations to inspire educators of future computing professionals and smooth students’ transition from academia to employment. 

This Birds-of-a-Feather session is for anyone interested in the NSF Scholarships in STEM (S-STEM) program, including current and former Principal Investigators (PIs) and those planning to apply. The S-STEM program funds scholarships and activities to support low-income, academically talented students in STEM. Any institution of higher education may apply, and the program supports a variety of projects. Designing and implementing a successful S-STEM project is challenging. The goal of this session is to catalyze a community of practice for S-STEM PIs. It will provide an opportunity to discuss lessons learned and best practices for proposal writing, project implementation, and providing student support. Specific topics to be discussed include the following: (1) Understanding the solicitation requirements and common proposal mistakes; (2) Scholar recruitment and data-driven approaches for selection; (3) Cohort building including activities for students from different majors or class years and integration of new students into existing cohorts; and (4) Remediation strategies including proactive interventions and peer support. Session leaders will introduce each topic; participants will then join a breakout group discussion of one topic. Lastly, participants will be invited to join a Slack workspace dedicated to S-STEM best practices and lessons.