In many CS educational research studies, students are surveyed to understand their reactions to a particular pedagogical approach or tool. These surveys, as well as other types of evaluations, often invite students to provide open-ended feedback about their experiences. However, analyzing these comments can prove to be a challenge, especially to CS educators who may not have strong expertise in qualitative research methods. In addition, in a large study, evaluating all of the provided comments can consume a significant amount of researcher time. In this work, we undertook two separate conversations with ChatGPT in which we prompted it to perform qualitative analysis of a set of comments collected in an earlier study. This allowed us to begin to judge how effectively a modern large language model can serve as an assistant in qualitative analysis. We found that with the prompts we used, ChatGPT can reliably build a set of reasonable labels (codes) for a set of comments, but the application of its labels to specific comments may or may not be effective and human researchers still need to use care and their own understanding in interpreting its output.

Context-based assignments have been shown as effective and popular for introductory-level computing courses. We study the use of one such context, pencil puzzles (puzzles typically found in newspapers), in upper-level core computing courses. These puzzles are designed to inspire computational thinking, making them a great choice for introductory-level computing assignments, but their fit for upper-level courses is less clear. We collaborated with several instructors of upper-level courses at four institutions, who delivered a pencil-puzzle-based assignment in their course and allowed us to survey their students about their experience. Overall, the students indicated positive perceptions of the assignments. The most varied answers related to implementation aspects of the assignments. To analyze correlations between students' sentiments and their demographic and experiential background, we used mixed-effects regression modeling to analyze this heterogeneous data set. The survey responses were characterized by two dimensions, one roughly corresponding to students' sentiment about the assignment and the other to their technical assessment of the assignment. For the first dimension, we found that the students' self-reported level of preparedness from earlier courses positively correlated with their enjoyment of and satisfaction with the pencil puzzle assignment. The second dimension was correlated with both the level of preparedness as well as the students' self-reported problem solving type: Clarifier, Implementor, Ideator, and Developer. Somewhat surprisingly, the analysis indicated Ideator as being the most positively correlated with the technical aspects of the assignment. Notably, the analysis did not indicate any correlation with students' race or gender in either dimension.

We report on our experience using common natural language processing (NLP) tools to analyze two vastly different data sets of free-form responses collected during a study of assignments in introductory computing courses. Our first data set consists of typically short comments left by hundreds of students on assignment surveys. Our second data set is comprised of semi-structured individual interviews of eight instructors of up to an hour long each. We collected the data across several years as part of our investigation of the use of pencil puzzles as a context for introductory computer science. In an earlier work, we manually analyzed a fraction of the student comments (all data collected until that point), using grounded theory. The results were illuminating, but the process was very time consuming, consisting of manual assignment of a small number of codes to each comment. In this work, we investigate the usability of common NLP tools to speed up the process for the entire data set of student comments. We also applied these tools to the instructor interviews. The NLP tools do not appear to be effective to create the code base, but, once the code base was determined, they performed the actual coding (assignment of codes to each student comment) promisingly well. For the long-form instructor interviews, the situation was much more challenging, due to the wide-ranging nature of semi-structured interviews, interleaving discussion topics, and elements of natural speech. We report on the lessons learned while automatically analyzing these complex data sets.

The use of a well-chosen context for course assignments is widely regarded as motivating for students. However, it is challenging to study the utility of bringing a particular context to computing courses across different types of institutions and student demo- graphics. This is especially true in introductory computing since courses vary widely, for example, in topic order and depth of coverage. In this experience report, we present our approach to, and lessons learned from, studying the efficacy of a specific context for introductory computing assignments across a variety of environments. We focus on the context of pencil puzzles (puzzles like Sudoku or crosswords, designed to be solved on paper using a pencil) and the deployment and fit of pencil-puzzle-based assignments across different institutions' introductory curricula. We describe our overall process, including recruitment of instructors from a variety of institutions, development and deployment of assignments, and collection of student grade and survey data (including all necessary approvals). By design, we did not use the same assignment at each university, since we aimed to study the underlying context rather than a specific assignment, while also establishing the adoptability of the context to different circumstances. We discuss the heterogeneity of the resulting data set, how we chose to analyze it, and what conclusions can (and cannot) be drawn from such data. We conclude with lessons learned from this experience, with the hopes that they can help others who wish to propagate their innovations and study them in diverse situations.

Improving Computer Science (CS) education requires increasing the meaningful usage of research-supported pedagogy and curriculum. Studies on propagation have largely looked at dissemination and adoption from the perspective of adopters: what motivates them to discover, experiment with, and continue using innovative teaching. This study adds to a growing body of research on approaches to encourage adoption by examining the perspectives and advice of successful propagators-education researchers who have had their innovations widely adopted. Drawing on interviews with fourteen CS education researchers, this paper identifies both points of convergence and unique insights across several broad areas: barriers to adoption, the structure of academia, relevant principles of design and techniques for deployment, and strategies for propagation. Notable findings include: the structure of academia has aspects that both impede and facilitate successful propagation; traditional academic funding sources do not adequately support ongoing propagation; and some successful strategies for getting the word out involve oblique approaches for reaching potential users. This exploration of common successful approaches can serve as a guide for developers and educational advocates when working to attract new users and broaden impact.

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.

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.

Many great teaching techniques are presented every year at SIGCSE and other CS education conferences. Unfortunately, most of them achieve very limited adoption, with few instructors incorporating these ideas into their classrooms. There is significant literature on how to encourage instructors to adopt educational innovations in other STEM fields, but the CS education community has made only limited strides in this area. This session will feature an interactive discussion of some of the barriers that prevent the adoption of good ideas, what solutions are available, and a brief presentation of the results of an ITiCSE working group on this topic. Attendees will leave the session better equipped to promote the adoption of educational innovations, either their own or ones that they have decided to champion.

In computer science, educational innovation is constant, but many great ideas never achieve the type of widespread adoption necessary to make lasting and effective change to the way we teach and learn. Research across STEM education has shown that propagation planning is often an overlooked or undervalued part of educational innovation. Whether promoting our own projects or an outside innovation, barriers to success are more difficult to overcome when encountered without sufficient preparation. Plans for adoption and scaling of innovations are not one-size-fits-all, but there are lessons we can learn from both successful and unsuccessful previous projects. We will present a summary of these lessons based on our recent ITiCSE working group research experience on the topic. This writing workshop will focus on building propagation plans informed by best practices, within the context of individual project definitions of success. We will work in small groups to identify potential barriers to the success of our projects, learn about best practices for overcoming those barriers, and put in place a measurable and actionable plan for adoption and propagation. Participants will work toward a better plan for propagation while garnering advice from their peers, learning and generating new ideas about and methods for dissemination and adoption, and building a community of resources for future collaboration, champions for change, and peer feedback. Bringing a laptop is recommended.

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.

Most projects that create innovations in Computer Science education, whether they be changes to content or pedagogy, focus on first developing materials and then proving effectiveness. For educational innovations to have impact, however, they must be adopted by other instructors. Getting instructors to use new educational strategies is a significant challenge, with most new techniques never obtaining widespread adoption. Researchers who do consider dissemination of their research frequently use techniques such as publications and workshops, which are known to be insufficient.

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.

Problem solving is a powerful teaching methodology for computer science -- giving students a real problem to solve instead of simply discussing abstract concepts can motivate them and give them a path to better understanding. However, it is challenging to create novel example problems that are meaningful and engaging yet can be easily understood by all students. In this workshop, we will introduce participants to the vibrant world of pencil puzzles and show how many different types of puzzles can be used for a variety of topics throughout the introductory CS curriculum. Pencil puzzles are those designed to be solved by hand with pencil and paper (such as Sudoku, but including dozens of new types!) that have clear rules and are made to be solved deductively. As such, they are explicitly designed to be easy to understand and intriguing and naturally inspire algorithmic thought. We will explore a variety of on-line resources, including our own curated repository, to see how assignments throughout the introductory CS curriculum can be easily kept fresh. Participants will also experience a sample problem-solving session and collaboratively develop a new assignment for a topic of the group's choice. This workshop is intended for all teachers (late secondary and post-secondary) of introductory programming courses. Laptops are recommended.

Robots are typically used at the college level either as a pedagogic platform for introductory programming or for more advanced courses in robotics. With robots becoming cheaper and more plentiful, personal interactions with them will become more commonplace. This project therefore takes the position that undergraduate computing students need the opportunity to explore core computing concepts in a robotics context. Specifically, we will give students the ability to work alongside teams of highly capable and easily programmable corobots, a term used to identify robots that work side by side with humans, rather than being completely autonomous and isolated. A modular approach is used to incorporate corobotics into various computer science (CS) courses such as first-year computing, networking, and data management, thus permitting the students to see these corobots in multiple contexts. This work-in-progress paper describes the corobotics infrastructure that has been developed, and outlines how this infrastructure can be used to support diverse courses in the CS curriculum.

Herds of livestock, when left to their own devices, will forage for food in predictable patterns, and will often overgraze preferred areas while leaving other areas untouched. The field of grazing management looks to improve the efficiency of land use by moving the animals through different pastures at regular intervals, akin to coverage algorithms used in robotics but with non-uniform coverage and additional constraints due to the animals' natural behaviors. The knowledge of the field of grazing management is largely in the hands of ranchers and other domain experts, and as such has not been the subject of much computational study. In this work, we have created novel learning techniques for expert systems that use both off-line and online learning to generate efficient performance. The rules of the expert systems are initially developed using an evolutionary algorithm, and after deployment, an adaptive algorithm tracks the state of the system and updates rule weights to improve both coverage efficiency and animal stress levels. We present a series of results based on increasingly complex versions of simulated herd models that show improvements over unconstrained motion in each case, and suggest how the algorithm can apply to robotic coverage problems.

For large self-reconfiguring robots, any algorithm that requires linear amounts of memory per module (with respect to the number of modules) or linear time for computation or communication per actuation is undesirable. While shape-forming may require linear amounts of memory, locomotion can be performed with simpler shape specifications, and therefore sublinear algorithms are possible. In this paper, we present a locomotion technique that performs both planning and actuation control in sublinear time and memory. The algorithm is inspired by reinforcement learning and uses dynamic programming to plan module paths in parallel. To ensure the physical integrity of the overall robot during motion, we have developed a novel localized cooperation scheme which may also be used with other self-reconfiguration algorithms. Our overall algorithm is able to direct locomotion over arbitrary obstacles, and the formulation of the goal used in the planning encourages dynamic stability

Certain agents and systems of agents, such as animals, possess innate path-planning capability, both locally and globally. If we wish to influence their motion, we may be able to take advantage of their local navigation ability and provide only some simple imposed constraints rather than computing and enforcing a detailed path. In this work, we consider the problem of generating a set of constraints, or "corridor", under which such an agent (or group of agents) produce an appropriate trajectory. The set of constraints should be both small in number and efficient in terms of length. When producing the corridor, we explicitly consider two different types of obstacles - those which the agents themselves would naturally avoid, such as dense vegetation, and those which they must be forced to avoid, such as sensitive stream environments. This allows both for simplification of the corridor and more natural use of the underlying navigation ability. An implementation and several examples of planned corridors are also presented

Most reconfiguration planners for self-reconfiguring robots do not consider the placement of specific modules within the configuration. Recently, we have begun to investigate heterogeneous reconfiguration planning in lattice-based systems, in which there are various classes of modules. The start and goal configurations specify the class of each module, in addition to placement. Our previous work presents solutions for this problem with unrestricted free space available to the robot during reconfiguration, and also free space limited to a thin connected region over the entire surface of the configuration. In this paper, we further this restriction and define free space by an arbitrarily-shaped bounding region. This addresses the important problem of reconfiguration among obstacles, and reconfiguration over a rigid surface. Our algorithm plans module trajectories through the volume of the structure, and is divided into two phases: shape-forming, and sorting the goal configuration to correctly position modules by class. The worst-case running time for the first phase is O(n2) with O(n2) moves for an n-module robot, and a loose upper bound for the second phase is O(n4) time and moves. However, we show this bound to be Θ (n2)time and moves in common instances.

We describe a moving virtual fence algorithm for herding cows. Each animal in the herd is given a smart collar consisting of a GPS, PDA, wireless networking and a sound amplifier. Using the GPS, the animal's location can be verified relative to the fence boundary. When approaching the perimeter, the animal is presented with a sound stimulus whose effect is to move away. We have developed the virtual fence control algorithm for moving a herd. We present simulation results and data from experiments with 8 cows equipped with smart collars.

Sensor networks are systems of many small units that work together to monitor a given environment. Endowing such sensor units with mobility can allow them to reactively converge on more interesting portions of their environment. This enables the concentration of sensing resources where they are most useful and provides robustness by delivering redundancy at the point of interest. However, when converging, in general the sensors should not leave any portion of the environment unsensed. In this paper, we review distributed methods for controlling the sensors and describe a family of distributed methods for retaining coverage while allowing the convergence to proceed where possible. The coverage methods are based on the Voronoi diagram of the sensors' positions, and can use different amounts of communication and computation to produce complete coverage of the environment. We also describe extensions that serve to make coverage more uniform or allow specific areas to be left uncovered. We present implementations of these algorithms in simulation and describe results and avenues of future work.

Current research in self-reconfiguring robots focuses predominantly on systems of identical modules. However, allowing modules of varying types, with different sensors, for example, is of practical interest. In this paper, we propose the development of an algorithmic basis for heterogeneous self-reconfiguring systems. We demonstrate algorithmic feasibility by presenting O(n/sup 2/) time centralized and O(n/sup 3/) time decentralized solutions to the reconfiguration problem for n non-identical modules. As our centralized time bound is equal to the best published homogeneous solution, we argue that space, as opposed to time, is the critical resource in the reconfiguration problem. Our results encourage the development both of applications that use heterogeneous self-reconfiguration, and also heterogeneous hardware systems.

In this paper we examine the role of very simple and noisy sensors for the tracking problem. We propose a binary sensor model, where each sensor's value is converted reliably to one bit of information only: whether the object is moving toward the sensor or away from the sensor. We show that a network of binary sensors has geometric properties that can be used to develop a solution for tracking with binary sensors and present resulting algorithms and simulation experiments. We develop a particle filtering style algorithm for target tracking using such minimalist sensors. We present an analysis of a fundamental tracking limitation under this sensor model, and show how this limitation can be overcome through the use of a single bit of proximity information at each sensor node. Our extensive simulations show low error that decreases with sensor density.

Modular robots are systems composed of a number of independent units that can be reconfigured to fit the task at hand. When the modules are computationally independent, they form a large distributed system with no central controller. We are concerned with the ability of such modular robots to easily recognize the achievement (or lack thereof) of a given goal configuration. We present algorithms for a class of 2D and 3D modular robots, along with correctness and running time analysis. We have successfully implemented the 2D algorithm on the second-generation Crystalline Atomic robot, a self-reconfigurable modular robot under development in our laboratory and we present implementation details and experimental results.

Previous work on self-reconfiguring modular robots has concentrated primarily on hardware and reconfiguration algorithms for particular systems. We introduce a type of generic locomotion algorithm for self-reconfigurable robots. The algorithms presented are inspired by cellular automata, using geometric rules to control module actions. The actuation model used is a general one, presuming that modules can generally move over the surface of a group of modules. These algorithms can then be instantiated on to a variety of particular systems. Correctness proofs of the rule sets are also given for the generic geometry, with the intent that this analysis can carry over to the instantiated algorithms to provide different systems with correct locomotion algorithms.

Effective algorithms for modular self-reconfiguring robots should be distributed and parallel. In previous work, we explored general algorithms for locomotion and self-replication and their instantiations to systems in which modules move over the surface of the robot. In this work, we present several algorithms applied to the Crystal robot-two new distributed locomotion algorithms designed specifically for unit-compressible actuation, as well as the adaptation of a generic division algorithm to the Crystal. We also present the integration of a locomotion algorithm with a distributed goal recognition algorithm developed previously. This allows the robot to reconfigure and recognize the achievement of its goal, all without the use of a central controller. We have instantiated all of these algorithms on the Crystal hardware, and we present results of our experiments. These experiments empirically verify the utility of our distributed algorithms on a self-reconfiguring system.

The ability of self-reconfigurable robots to solve a variety of robot tasks comes in part from their use of a large number of modules. Effective use of these systems requires parallel actuation and planning, both for efficiency and independence from a central controller. This paper presents the PacMan algorithm, a technique for distributed actuation and planning. This algorithm was developed for systems with unit-compressible modules, such as the crystalline robot. We also describe some analytical properties of the PacMan planning and actuation, and discuss simulation and hardware experiments.

Computing rectilinear shortest paths in two dimensions has been solved optimally using a number of different techniques. A variety of related problems have been solved, including minimizing the number of bends in the path, the total rectilinear distance, or some combination of both. However, solutions to the 3D versions of these problems are less common. We propose a solution to the 3D minimum-bend path problem, which has theoretical as well as practical interest. Applications include motion planning problems where straight line motion is preferred over taking arbitrary turns. We employ our results in motion planning for self-repair in self-reconfigurable robots.

A distributed cooperative coverage algorithm DC/sub R/ is presented, which is derived from an earlier complete single-robot algorithm, CC/sub R/. DC/sub R/ executes independently on each robot in a team where the individual robots do not know the initial locations of their peers and applies to systems of robots operating in a rectilinear environment that use only intrinsic contact sensing to determine the boundaries of the environment. Due to the reactive nature of CC/sub R/, the natural extension to DC/sub R/ preserves the completeness properties of the single-robot algorithm, and the outline of a completeness proof of DC/sub R/ is also presented. DC/sub R/ has been implemented in simulation, and directions for future work are presented which will make the algorithm more suited to physical robot systems.

Developing automated assembly systems normally happens in two distinct stages: first an "off-line" stage in which the system is designed and programmed in simulated and then an "online" stage in which the simulation results are used to minimize the deployment and integration time of the physical machines. The distinction is so great that usually completely different software environments are used in the design phase than are used in the deployment and operation phase. We present the architecture for agile assembly (AAA), a comprehensive integrated framework that is designed to blur these stages together and ease the transitions between them. We have used the protocols of AAA to create an integrated interface tool which can be used throughout the life-cycle of a developing AAA factory, from its design to its operation. We have tested the integrated interface tool both in simulation and with our prototype hardware, which is designed for high precision four-degree-of-freedom assembly.

Planar linear motors have been shown to be capable of fast accurate 2-DOF motions, making them useful for assembly tasks. However, the lack of sensing capability has limited their applications. Previous planar motor sensors were precise but bulky and sensed only a single direction of motion. We present a small integrated 3-DOF sensor capable of 0.2 /spl mu/m position resolution (1/spl sigma/) which has been integrated with an existing planar linear motor without changing its overall size. Physical design of the sensor is presented as well as the custom electronics used to produce a low-noise DC output from the small signal levels of the sensor. Finally, we present results that demonstrate the resolution and accuracy of the complete sensor system.