The formulation of conceptual models is an important intermediate step in the mathematizing process. Conceptual modeling allows visualization of students' internal mental state as they work to express ideas in formal, mathematical terms. The objective of this Birds of a Feather session is to discuss, ''What makes a good conceptual model for understanding computer science theory?'' Students across all areas of computer science find the skills of computer science theory literacy, e.g. formal modeling and algorithms building, difficult to acquire. Building students' capacity to extensively build, critique and revise models, and doing so in groups, has great potential to help all CS majors achieve CST literacy and become valuable problem-solvers in their own subareas of computer science. In this BoF session we will examine examples of model-producing instructional prompts from a model-based unit in the course ''xTreme Theory'', part of an NSF-funded educational research project. The prompts were selected for breadth of accessibility regardless of CS area of expertise. We will also review the corresponding anonymized student submissions. Table groups will discuss similarities and differences in the student submissions and how they might assess the student-produced models for quality, then will contribute to a discussion of conceptual models and modeling in the teaching and learning of CSTheory. This session will support instructors who are interested in increasing students' capabilities for formal modeling and algorithms building and interested in connecting with colleagues in ways not afforded during the academic semester.

This innovative practice full paper examines mindset understandings of three cohorts of first-year student scholars in a College of Computing at a private technical Carnegie-classified Doctoral University in the northeastern United States. Grounded in theories of intelligence, a growth mindset posits that intelligence and skills can be developed through continued practice and learning, while a fixed mindset situates one with the skills they have at birth, never to evolve or grow. Thirty-two undergraduate students across three years (10 students in year one, cohort one; 10 students in year two, cohort two; and 12 students in year three, cohort three) participated in a holistic growth mindset program that included three pillars: (a) faculty-student mentoring infused with growth mindset, (b) growth-mindset augmentations to the introductory programming course and (c) a growth mindset-scholar seminar - a series of meetings where each cohort met as a group to discuss and practice activating a growth mindset. Previous work with students has focused on more limited growth mindset interventions rather than a holistic approach. Prior to the scholars arriving on campus, the faculty involved in each of the pillars were part of a Community of Practice to learn about and activate their own growth mindset. At the end of their first semester in the project, each of the student cohorts participated in a focus group to learn about their understanding and application of growth and fixed mindset. We report findings from the student scholar data after one semester of participating in the three programmatic pillars in the context of growth mindset: mentoring, programming instruction, and the scholar seminar. Summary findings from the student perspectives are described including the use of illustrative quotes, in the students' own words, serving as a powerful reminder of the importance of growth mindset and relationship building. This has implications for addressing mindset in the future by considering how the innovative practice of embedding a growth mindset holistically into mentoring, instruction and a student seminar can provide support for students that standalone interventions cannot.

A web-based learning platform is useful as it allows students with limited or no programming background to conduct in-depth hands-on practice in data science. Background: The need for data science coursework for non-computing majors has grown in recent years, given the demand in various disciplines. However, a substantial number of current data science courses are inappropriate for non-computing majors as they typically require a long chain of prerequisite courses in computer science and mathematics. Moreover, courses designed for computing majors do not match the preparation and interests of students majoring in other disciplines. Outcomes: This paper presents a platform for Learning Data Science (DSLP), a web-based platform, which assists in the teaching and learning of data science topics by students with limited or no coding experience, including those that have completed a high school AP Computer Science Principles (CSP) class or an equivalent CSP course increasingly offered in many colleges. Application Design: The platform helps students understand fundamental data science concepts and techniques, as well as provides them with an in-depth hands-on experience that goes beyond their coding capabilities. The platform offers various data visualization supports to help students understand data and analysis results. Students can use the platform to work on in-house datasets or their own data. This allows students to focus more on how to solve data science problems in various domains than how to write code. The platform also has several unique features that make it particularly helpful for teaching and learning data science topics such as code exemplification and sandbox, informative instructions, and progress monitoring. Findings: The platform has been used multiple times in data science courses for non-computing majors offered at the authors' institution. Preliminary student feedback indicated that the platform is effective in terms of improving student understanding and interest in the topics.

Both growth mindset and mentoring have been recognized as contributing toward student success. Grounded in theories of intelligence, a growth mindset asserts that intelligence is malleable and failures and struggles contribute to developing our skills and abilities, serving as stepping stones toward success. This contrasts with a fixed mindset where intelligence and abilities are considered to be set at birth, limiting success as challenges and hurdles become insurmountable experiences. While growth mindset has been addressed in the context of student and faculty activities in the classroom, this work is novel in that it examines the purposeful integration of growth mindset into faculty mentoring for computing students at the post-secondary level. In this innovative practice paper, we describe the growth mindset and mentoring experiences that faculty members participated in throughout the 2021 calendar year as well as how they prepared for the experience by developing and embracing their own growth mindset. Using qualitative data from semi-structured interviews, we share faculty members' perspectives on the novel endeavor of mentoring computing student scholars utilizing a growth mindset. Faculty mentors embraced the growth mindset mentoring and also revealed a variety of challenges in applying growth mindset to mentoring, along with how they tailored their growth mindset approaches throughout the semesters. Particular challenges centered around issues of race and other marginalized positions of the students. Faculty also described how the growth mindset mentoring experience positively shaped their work outside of the mentoring relationship, specifically in the classroom and other educational situations in addition to their family lives. As such, positive impacts of the interventions, which included the mentoring preparation, structure and implementation, may be seen beyond the set of student scholars who were being mentored. In the true spirit of embracing a growth mindset and continuously improving and building our own skillsets, both areas of success and areas for improvement are considered, particularly around using growth mindset mentoring to address issues students face regarding their race and marginalized positions. This work has implications for an overarching positive impact of establishing growth mindset mentoring ecosystems that may extend to a broader academic environment including many different programs as well as all faculty who impact and support (e.g. during instruction, office hours) a diverse student population.

Introductory assignments in CS Theory ask students to construct instances of various computational models (such as finite automata, regular expressions, context-free grammars, or push-down automata) for a given language. Verifying the correctness of their model instance is challenging for beginner CS Theory students since the concepts are abstract and there are infinitely many possible inputs. The popular JFLAP software allows students to visualize the running of their instance on a specific input. We recently developed a server extension to JFLAP which checks whether a student's instance is equivalent to the instructor's solution and, if not, it returns a "witness string,'' an input string on which the student's construction and the correct solution differ.

Computing theory is often perceived as challenging by students, and verifying the correctness of a student's automaton or grammar is time-consuming for instructors. Aiming to provide benefits to both students and instructors, we designed an automated feedback tool for assignments where students construct automata or grammars. Our tool, built as an extension to the widely popular JFLAP software, determines if a submission is correct, and for incorrect submissions it provides a "witness" string demonstrating the incorrectness.

Assignments based on meaningful real-world contexts have been shown to be valuable in introductory computing education. However, it can be difficult to distinguish the value of a broad context from the value of a particular instantiation of that context. In this work in progress, we report on our initial findings gathered from deployments of different pencil-puzzle-based assignments. Specifically, we have investigated the use of pencil puzzles as a contextual domain, working with instructors at eight institutions to deliver assignments appropriate to their situation and aligning with their existing materials. We then evaluate the assignments using student grades and survey responses regarding student perceptions of the assignments including self-assessed learning, given a wide array of demographic variables. Our initial results show that while there was some dependency of student responses on their prior programming experience, and female students' feedback were more positive about one aspect, overall these types of assignments do not appear to put particular groups of students at a strong (dis)advantage.

Computing theory analyzes abstract computational models to rigorously study the computational difficulty of various problems. Introductory computing theory can be challenging for undergraduate students, and the overarching goal of our research is to help students learn these computational models. The most common pedagogical tool for interacting with these models is the Java Formal Languages and Automata Package (JFLAP). We developed a JFLAP server extension, which accepts homework submissions from students, evaluates the submission as correct or incorrect, and provides a witness string when the submission is incorrect. Our extension currently provides witness feedback for deterministic finite automata, nondeterministic finite automata, regular expressions, context-free grammars, and pushdown automata.

As one develops instructional innovations, it is important not only to propagate them into new and different environments, but also to study their efficacy in these new locations with different demographics of students. Previously, we showed that introductory CS assignments based on various pencil-and-paper puzzles are valuable, but this study was done at a single university. In this poster, we report on propagation of puzzle-based assignments to many universities and collection of the resulting data from these different contexts. This allows us to study the efficacy of the assignments in these disparate environments. In order to ease adoption at other universities, we are also interested in the experience of the instructors in implementing the assignments in their courses. Our overall goal is to "close the feedback loop" by collecting and analyzing all of this data to improve both their effectiveness and adoptability. This poster presents details of the deployment and data collection process, including working with the respective IRBs, selecting and implementing the various assignments, collecting student grades and survey responses, and conducting instructor interviews, in the hopes that it will help other educators to more efficiently and effectively close the feedback loop for their own innovations.

Pencil puzzles (puzzles such as sudoku and many others that are designed to be solved by humans, promoting computational thinking) provide a natural context for CS1/2 assignments. In a prior work we analyzed Likert-scaled student responses and assignment/course grades to show that not only are such assignments effective but are also largely independent of gender and prior computing experience. This paper focuses on open-ended student comments, both to see if they provide additional insights about the assignments and student perceptions not apparent from the Likert-scaled responses, and to see if these comments are consistent with the results from the prior work. We surveyed over 1000 students who had used pencil-puzzle-based assignments and invited them to make open-ended comments in their survey responses. We used grounded theory to develop codes for the large volume of student survey comments, as well as for semi-structured interviews with the instructors and focus groups with student TAs. Statistical analysis of the coded comments identified several interesting relationships, such as students being appreciative of their learning even when they perceived the assignments as difficult, which were not available from the Likert-scaled data. The analysis also confirmed that these assignments are largely gender- and experience-neutral. We conclude by discussing how these results and the coding process lead to improvements in assignment development and inform future research directions.

End-of-course evaluations and other student surveys typically include the opportunity for students to provide free-form comments. These are rich sources of data but are often only subjectively taken into account to further improve course delivery or analyze the effectiveness of assignments. We designed several puzzle-based assignments for typical CS1/2 topics and surveyed students as part of our efforts to analyze the assignments' efficacy and improve them over time. The surveys included traditional measures such as demographic data, Likert-scaled questions about assignment perceptions, and open-ended comments. With thousands of survey responses, we wanted to see if the open-ended comments yield additional, statistically significant, insights on either the assignments or students' learning. We developed a coding scheme for the comments using grounded theory analysis to represent patterns among the data. After refining the coding scheme we statistically analyzed the comments and found some interesting relationships, not apparent from the Likert-scaled questions, among certain codes. We also conducted extensive semi-structured interviews with instructors and student teaching assistants, also using grounded theory analysis to develop a set of codes for these different perspectives. The coding processes themselves allowed for a deeper understanding of the concerns about and appreciation for the assignments from both groups of participants. This poster reports on how the statistical results and the coding schemes, including the overlap and dissonance between the two coding schemes, inform our continued efforts to improve both assignment development and future research on the teaching and learning of CS concepts.

The teaching of introductory computer science can benefit from the use of real-world context to ground the abstract programming concepts. We present the domain of pencil puzzles as a context for a variety of introductory CS topics. Pencil puzzles are puzzles typically found in newspapers and magazines, intended to be solved by the reader through the means of deduction, using only a pencil. A well-known example of a pencil puzzle is Sudoku, which has been widely used as a typical backtracking assignment. However, there are dozens of other well-tried and liked pencil puzzles available that naturally induce computational thinking and can be used as context for many CS topics such as arrays, loops, recursion, GUIs, inheritance and graph traversal. Our contributions in this paper are two-fold. First, we present a few pencil puzzles and map them to introductory CS concepts that the puzzles can target in an assignment, and point the reader to other puzzle repositories which provide the potential to lead to an almost limitless set of introductory CS assignments. Second, we have formally evaluated the effectiveness of such assignments used at our institution over the past three years. Students reported that they have learned the material, believe they can tackle similar problems, and have improved their coding skills. The assignments also led to a significantly higher proportion of unsolicited statements of enjoyment, as well as metacognition, when compared to a traditional assignment for the same topic. Lastly, for all but one assignment, the student's gender or prior programming experience was independent of their grade, their perceptions of and reflection on the assignment.