Background and Context. Code complexity measures have been used to guide the design of various activities within computing education, such as instructional sequencing and assessment. However, empirical evidence for the link of these measures to actual cognitive difficulties remains mixed, with studies suffering from small sample sizes and non-controlled experimental design.

eam-based capstone projects are vital in preparing computer science students for real-world challenges by fostering teamwork, communication, and industry-relevant technical skills. However, their assessment presents challenges, such as aligning academic criteria with other stakeholders' expectations, evaluating individual contributions within teams, fairly addressing the diverse skills required, and determining the appropriate level of external partners' involvement in the evaluation process. Moreover, the high stakes of these projects necessitate transparent and equitable assessment methods that all stakeholders perceive as fair. Our working group (WG) aims to address the challenges of assessing capstone projects by examining the perspectives of instructors, students, and other stakeholders to ensure fair and effective evaluation. Building on insights from our previous WG and a comprehensive review of the literature, we will employ a mixed-methods approach to explore the issues faced by various stakeholders in assessing capstone projects and to capture both common challenges experienced (quantitative), and delve into nuanced individual experiences (qualitative). By conducting this research in a multi-national, multi-institutional context, we aim to capture a diverse range of global perspectives while accounting for the variation in capstone courses. Our goal is to provide actionable recommendations that enhance assessment practices, improve learning outcomes, and foster effective team collaboration in team-based capstone courses, ultimately preparing students for real-world challenges.

Motivation and Objectives. Sense of belonging has been recognized as a construct that influences diversity, retention, and – to a lesser degree – learning outcomes in computer science classes. Prior studies on the construct of sense of belonging have shown that certain demographic groups, including but not limited to female students and first-generation academics, experience a low level of sense of belonging and consider it a barrier. On the other hand, previous research has also highlighted the importance of designing compiler error messages in a way that enhances their readability and, thus, improves effectiveness, particularly for novice programmers. In this study, we set out to investigate whether, and if so how, these two constructs interact.

Motivation and Objectives. Group work is an integral component of many job descriptions in the computing profession. To prepare students for this, and also to be able to scale course offerings, many instructors require students to work on homework assignments in small groups. The importance of group work has contributed to an increasing interest of computer science education researchers in regulatory strategies and processes occurring during collaborative learning situations. However, so far only limited work has been done on the concept of social regulation, or more precisely co-regulation and socially shared regulation. In this paper, we extend the nomological network of regulation in CS1 group work by incorporating the students’ self-efficacy and frequency of group work participation. We also report on barriers to groups work participation reported by the students.

Productive Failure is a variant of problem-based learning in which the order of the instruction and problem-solving phase is reversed. The effectiveness of Productive Failure with respect to conceptual knowledge has been demonstrated through a number of studies. The majority of these studies, however, took place in secondary Mathematics classrooms, whereas other studies resulting in less or no support of such an effectiveness were contextualized in other disciplines, including Computer Science, or in tertiary education. This has raised the question of which conditions support or hamper the use of Productive Failure. To deepen our understanding of such conditions, we designed and executed a Productive Failure intervention for a Pattern Recognition course, thus shifting the intervention context into a tertiary setting while maintaining proximity to Mathematics. In an experimental study, we compared the problem-solving progression of students in a Productive Failure setting with the progression of students in a traditional Direct Instruction setting. For this, we analyzed patterns of discourse arising among the participants as well as the longer-term retention of the concepts addressed. The results of our qualitative analysis suggest that, even in a short intervention, Productive Failure can be used to elicit a distinct pattern of progressing though the problem-solving process. At the same time, our study confirmed previous findings that the mode of instruction does not affect exam performance with respect to the specific topics addressed in the intervention. We discuss limitations of the study setting and possible implications for designing future research studies and teaching interventions.

Background and Context: Empirical researchers have a long-standing tradition of explicitly discussing the threats to and limitations of their research. In the past twenty years, these discussions emerged as a standard component of empirical research papers in computing education research (CER) as well.

Motivation and Objectives. Productive Failure is a problem-based learning technique where students attempt to solve a problem before receiving instruction in the topic. By design, students may not find a satisfying solution. Prior studies of Productive Failure in STEM contexts have been conducted in secondary or introductory college settings. Focusing primarily on exploring appropriate analysis and modeling techniques, these studies showed that a Productive Failure approach can lead to greater conceptual knowledge acquisition and transfer capabilities compared to »traditional«Direct Instruction techniques. In this study, we build on these studies along two dimensions: First, we report on the design and evaluation of a Productive Failure intervention in a more advanced undergraduate class: third-year Operating Systems. Second, our intervention targeted a more advanced skill: applying synchronization primitives, rather than selecting appropriate modeling and analysis techniques.

This working group concerns the adoption of computing education (CE) in undergraduate computer science (CS) programmes. Such adoption requires both arguments sufficient to persuade our departmental colleagues and our education committees, and also curricular outlines to assist our colleagues in delivery. The goal of the group is to develop examples of both arguments and curricular outlines, drawing on any prior experience available.

Abstract We study Klee’s measure problem — computing the volume of the union of n axis-parallel hyperrectangles in $$\mathbb {R}^d$$ R d — in the oblivious RAM (ORAM) setting. For this, we modify Chan’s algorithm [12] to guarantee memory access patterns and control flow independent of the input; this makes the resulting algorithm applicable to privacy-preserving computation over outsourced data and (secure) multi-party computation. For $$d = 2$$ d = 2 , we develop an oblivious version of Chan’s algorithm that runs in expected $$\mathcal {O}(n \log ^{5/3} n)$$ O ( n log 5 / 3 n ) time for perfect security or $$\mathcal {O}(n \log ^{3/2} n)$$ O ( n log 3 / 2 n ) time for computational security, thus improving over optimal general transformations. For $$d \ge 3$$ d ≥ 3 , we obtain an oblivious version with perfect security while maintaining the $$\mathcal {O}(n^{d/2})$$ O ( n d / 2 ) runtime, i. e., without any overhead. Generalizing our approach, we derive a technique to transform divide-and-conquer algorithms that rely on linear-scan processing into oblivious counterparts. As such, our results are of independent interest for geometric divide-and-conquer algorithms that maintain an order over the input. We apply our technique to two such algorithms and obtain efficient oblivious counterparts of algorithms for inversion counting and computing a closest pair in two dimensions.

The session will present an overview of findings of a recently funded NSF workshop that set out to examine the pressing issues for computing education research for the next 15 years. Based on dialogs for scholars working in this area, the workshop participants developed a series of themes and topics that they felt should become the focus of computing education research efforts for the next 15 years. Main themes that emerged and will be discussed in this session include: diversity, equity, inclusion, ethics, broadening participation, teaching, learning, K-12, research to practice, computing's connection to other fields, and computing education research disciplinary issues. The session will focus on interesting research questions from each of these areas that are ripe to be explored as well as enablers and blockers to the progress of this work.

Previous studies on writing and understanding programs presented evidence that programmers beyond a novice stage utilize plans or plan-like structures. Other studies on code composition showed that learners have difficulties with writing, reading, and debugging code where interacting plans are merged into a short piece of code. In this article, we focus on the question of how different code-composition strategies and the familiarity with code affect program comprehension on a more abstract, i.e., algorithmic level. Using an eye-tracking setup, we explored how advanced students comprehend programs and their underlying algorithms written in either a merged or abutted (sequenced) composition of code blocks of varying familiarity. The effects of familiarity and code composition were studied both isolated and in combination. Our analysis of the quantitative data adds to our understanding of the behavior reported in previous studies and the effects of plans and their composition on the programs’ difficulty. Using this data along with retrospective interviews, we analyze students’ reading patterns and provide support that subjects were able to form mental models of program execution during task performance. Furthermore, our results suggest that subjects are able to retrieve and create schemata when the program is composed of familiar templates, which may improve their performance; we found indicators for a higher element-interactivity for programs with a merged code composition compared to abutted code composition.

At the beginning of their undergraduate studies, computer science students are exposed to introductory programming, discrete mathematics, and algorithms. A large body of literature has studied how such students comprehend programs. Much less attention has been paid to understanding how students comprehend algorithms, i.e., representations of problem-solving processes accompanied by proofs of their properties. To investigate whether algorithm comprehension is under-researched or whether we can simply transfer results from program comprehension (and, possibly, proof comprehension) to the domain of algorithms, we conducted a larger study following a Grounded-Theory approach. For this study, we worked with twelve participants with varying degrees of prior knowledge regarding algorithms, from technically no prior knowledge to advanced graduate students pursuing a doctoral degree in algorithms-related fields. In this paper, we report on the analysis and interpretation of interview data focused on exploring potential differences between program comprehension and algorithm comprehension. Our analyses and interpretations revealed both similarities and differences between program comprehension and algorithm comprehension: Unsurprisingly, we found that aspects known from program comprehension, e.g., using top-down approaches or utilizing prior knowledge of programming plans, can be found in algorithm comprehension as well. However, some of these aspects manifest themselves in notably different details or are influenced by, e.g., the wording of a proof. Most interesting, we found that novice and advanced students alike benefit from switching between reading the representation of the program-solving process and the accompanying proofs. We present qualitative results illustrating both similarities and differences between program comprehension and algorithm comprehensions and offer first explanations for these.

For many years, the C++-based Computer Programming Self-Efficacy Scale by Ramalingam and Wiedenbeck has been the de facto standard for assessing self-efficacy in introductory programming. Since the development of this instrument, however, both the landscape as well as the intended audience of introductory programming courses has changed beyond the use of a particular programming language. We revisit this instrument and its factorization in light of curricular developments and research results regarding concepts and competences taught in introductory courses. We report on the development and validation of a new instrument that covers most paradigms and languages used in CS1 and present exploratory and confirmatory factor analyses across different populations. Our validation and factor analyses suggest that the new instrument indeed measures self-efficacy with an acceptable fit of the model. In contrast, the factorization of the Computer Programming Self-Efficacy Scale was found to be less robust. Nonetheless, and in line with self-efficacy theory, our analyses suggest that researchers should take into account the educational context of the study population when reporting or comparing results at the level of factors.

Peer review is a mainstay of academic publication - indeed, it is the peer-review process that provides much of the publications' credibility. This working group is examining the ways peer review is used in various computing education venues and will use this examination to articulate community standards for peer review in this discipline.

The 32 chapters of the 2019 Cambridge Handbook of Computing Education Research synthesize the existing research in computing education and propose new directions for future research. An author from each chapter will summarize their chapter with auto-advancing slides. Attendees will be introduced to the breadth of content in the new handbook and can identify chapters of interest. This fits uniquely as a special session, and will likely be informative, inspiring, and overwhelming.

The use of vignettes has been shown to be an effective method both in teacher training and in the assessment of teachers' understanding of instructional strategies. In this paper, we report on a study in which we sought to identify real-world classroom situation that can be used as vignettes for K-12 Computer Science teacher education and professional development. Using semi-structured interviews with five expert teachers involved in in-service teacher training, we elicited a collection of twelve domain-specific critical incidents that can occur in K-12 Computer Science classrooms. These critical incidents were ranked by the experts with respect to their relevance using a Delphi process. As part of the semi-structured interviews, we also collected reactions to each of the critical incidents. We discuss the resulting critical incidents and the experts' comments in the light of the Computing Education Research literature.

While previous research has investigated psychological factors in introductory programming courses, only little is known about their impact in algorithms courses. Similarly, despite the importance of collaborative problem solving in both academic and non-academic settings, only a small number of studies reports on group work in domains other than programming. In our case study, we focused on the labs of an introductory algorithms course. We measured the cognitive load of the lab assignments as well as the students' emotional reaction to them. We connect these observations to self-efficacy, performance, psychological traits, and help-seeking behavior as well as to the insights gained from a comprehensive set of follow-up interviews. Even though our study is a small-scale study, the results from applying both quantitative and qualitative methods frame directions for both pedagogic interventions and further (revalidation) studies related to the connection of non-cognitive factors, learning experiences, and performance in collaborative algorithms labs.

Recent years have seen an increase in activities geared towards making Computer Science courses available to all K-12 students. However, due to administrative regulations, such activities and their implementation often need to be localized on a national or even local context; these constraints, often paired with subtle but important terminology differences, hinder those wanting to compare the status quo across the boundaries of administrative units and to draw on experiences made elsewhere.

We report on the first steps of KETTI, a project that aims towards the development of a competence model for undergraduate teaching assistants (UTAs) in computer science. Using qualitative methods, we obtained a classification of existing designs for teaching that employ UTAs; some of the observed factors directly influence the methodological decision space of UTAs. We developed and implemented a UTA training scheme designed to foster student-oriented teaching in recitation sessions along with an instrument to gauge the effects of this instruction on a variety of psychometric scales. We report results from a small-scale pilot study at three institutions showing positive effects on teaching-related beliefs and self-efficacy.

We present an algorithm for the following problem: Given a dataset D: = {T1,..., Tn} of data trajectories and a set Q: = {Q1,..., Qm} of query trajectories, each of which with a distance parameter ϵi ≥ 0, report, for each query trajectory Qi, all data trajectories within a Fréchet distance of at most ϵi. As computing the Fréchet distance is known to be computationally demanding, our algorithm uses a filter-and-refinement approach to reduce the number of query/data candidate pairs for which the Fréchet distance needs to be computed exactly. As usually, we first use a hash-based range searching data structure to filter out candidate pairs whose minimum bounding rectangles are too far away. We then make extensive use of geometric properties of the Fréchet distance to prune further candidate pairs in a series of further steps of the filter phase. In the refinement phase, i.e., when exactly computing the Fréchet distance, we keep track of the boundary of the reachable space in the free space diagram to speed up the computation. Our algorithm is capable of using multiple threads in parallel; this is used to overlay the filter and refinement steps as well as the reporting of the output.

We report on the development and validation of an instrument to assess self-efficacy in an introductory algorithms course. The instrument was designed based upon previous work by Ramalingam and Wiedenbeck and evaluated in a multi-institutional setup. We performed statistical evaluations of the scores obtained using this instrument and compared our findings with validated psychometric measures. These analyses show our findings to be consistent with self-efficacy theory and thus suggest construct validity.

We consider the classic all-pairs-shortest-paths (APSP) problem in a three-dimensional environment where paths have to avoid a set of smooth obstacles whose surfaces are represented by discrete point sets with n sample points in total. We show that if the point sets represent epsilon-samples of the underlying surfaces, (1 ± O(sqrt{epsilon}))-approximations of the distances between all pairs of sample points can be computed in O(n^{5/2} log^2 n) time.

We report on case studies of using Computer Science Unplugged material as an alternative teaching method for computer science. The scope and target audiences for these studies were determined based upon reported classroom use of "unplugged" material by teachers. Our studies revalidate previous findings across multiple institutions and a broader student population and shows that, at least for the scenarios studied, "unplugged" activities are equally efficient compared to teaching using textbooks or interactive methods.

This paper reports on first steps towards identifying factors indicating students' performance in a CS2 course. We discuss a study undertaken to investigate the predictive and explanation power as well as the limits of weekly test items based on concept inventory questions, homework grades, and performance in a preceding CS1 course. We relate our findings for two subgroups to results on academic success in general and performance in a CS1 course in particular.

The increasing prevalence of positioning and tracking systems has helped simplify tracking large amounts of, e.g., People moving through buildings or cars traveling on roads, over long periods of time. However, technical limitations of positioning algorithms and traditional sensing infrastructures are likely, especially indoors, to induce errors and biases in the resulting data. In particular, the resulting motion trajectories often do not conform perfectly to the underlying route network. As a consequence, analyses of trajectory sets are impeded by these phenomena, as it becomes hard to identify which route was taken in a particular travel instance or whether two travel instances followed the same route. In this paper, we present a bootstrapping approach and several algorithms to mitigate error biases and related phenomena, focusing on indoor scenarios. In particular, we are able to estimate and iteratively refine an underlying route network from a set of motion trajectories. Secondly, we represent sub trajectories, i.e., Movements on individual elements of the route network, by their median sub trajectory. The resulting aggregated and cleaned-up data set facilitates using further, domain-specific analysis tools. Additionally, it allows to predict the locally occurring expected positioning error biases. This in turn allows improved positioning, e.g., For real-time navigation assistance scenarios. We evaluate the proposed methods using trajectory data from employees at a large hospital complex. In particular, we show that we can reconstruct the hospital's route network accurately, and that we can furthermore extract median sub trajectories for almost all individual corridors. Finally, we illustrate that median trajectories deliver useful deviation maps to learn, and correct for, the expected local biases in positioning.

This session is a chance for researchers studying concept inventories (CIs)--low-cost assessments highlighting student misconceptions in a field--and CS education practitioners to communicate about advances in concept inventories in an engaging and utterly ridiculous way.

In North-Rhine Westphalia, the most populated state in Germany, Computer Science (CS) has been taught in secondary schools since the early 1970s. This article provides an overview of the past and current situation of CS education in North-Rhine Westphalia, including lessons learned through efforts to introduce and to maintain CS in secondary education. In particular, we focus on the differential school system and the educational landscape of CS education, the different facets of CS teacher education, and CS education research programs and directions that are directly connected with these aspects. In addition, this report offers a rationale for including CS education in general education, which includes the educational value of CS for students in today’s information and knowledge society. Through this article, we ultimately provide an overview of the significant elements that are crucial for the successful integration of CS as a compulsory subject within secondary schools.

We present the result of assessing first-year students' misconceptions related to algorithms and data structures. Our study confirms findings from previous small-scale studies but additionally broadens the scope of the topics and methods investigated. The evaluation of our experiments sheds light on dependencies between active and passive knowledge as well as on the instruments used; in particular, we conclude that there is no "one size fits all" instrument but that instruments should be selected depending on the topic at hand.

A variety of experience reports and studies has shown Computer Science Unplugged to be an effective resource for outreach, and it has been suggested to build upon these benefits to augment teaching in a regular classroom as well. Based upon an analysis of the learning objectives, "Unplugged" activities seem to be particularly well suited to serve as an introduction to Computer Science concepts and algorithms; whether or not the effectiveness of using these activities compares to that of traditional teaching methods, however, has remained an open question so far. We present the first experimental study of using Computer Science Unplugged material as part of a regular Computer Science class in lower secondary education. The evaluation of our study affirmatively answers the above question, i.e., teaching using "Unplugged" activities can be at least as effective as when following more conventional approaches.

In response to the increasing volume of trajectory data obtained, e.g., from tracking athletes, animals, or meteorological phenomena, we present a new space-efficient algorithm for the analysis of trajectory data. The algorithm combines techniques from computational geometry, data mining, and string processing and offers a modular design that allows for a user-guided exploration of trajectory data incorporating domain-specific constraints and objectives.

We describe the first results of our work towards a concept inventory for Algorithms and Data Structures. Based on expert interviews and the analysis of 400 exams we were able to identify several core topics which are prone to error. In a pilot study, we verified misconceptions known from the literature and identified previously unknown misconceptions related to Algorithms and Data Structures. In addition to this, we report on methodological issues and point out the importance of a two-pronged approach to data collection.

We report on the design and evaluation of the first year of a Computer Science course in lower secondary education that implements the concept of braided teaching. Besides being a proof-of-concept, our study demonstrates that students an indeed be taught Computer Science (as opposed to Information and Communication Technology) as early as in sixth grade while at the same time not falling behind with respect to Information Technology Literacy. We present quantitative and qualitative results and argue that Computer Science can be taught just like any other science subject worth full curriculum credit.

To provide a unified view of any scientific field, outreach programs need to realistically portray the subject in question. Consequently, topics and methods actually taught in Computer Science courses should to be touched upon in Computer Science outreach programs or, conversely, elements from successful Computer Science outreach programs can be used to enrich established courses in Computer Science. We follow up on the latter aspect and investigate how outreach material might be used as a teaching resource in lower secondary Computer Science. In particular, we extract and classify learning objectives from the activities of the well-received Computer Science Unplugged program. Based upon this classification, we comment on where and to which extent these activities can be used to enrich teaching Computer Science in secondary education.

We develop a k-d tree variant that is resilient to a pre-described number of memory corruptions while still using only linear space. We show how to use this data structure in the context of clustering in high-radiation environments and demonstrate that our approach leads to a significantly higher resiliency rate compared to previous results.

We present a classification-based approach to identify quasi-stellar radio sources (quasars) in the Sloan Digital Sky Survey and evaluate its performance on a manually labeled training set. While reasonable results can already be obtained via approaches working only on photometric data, our experiments indicate that simple but problem-specific features extracted from spectroscopic data can significantly improve the classification performance. Since our approach works orthogonal to existing classification schemes used for building the spectroscopic catalogs, our classification results are well suited for a mutual assessment of the approaches' accuracies.

This panel will examine the challenges associated with establishing and maintaining computer science subject associations and ensuring their potential for improving all aspects of teaching and learning. The members of this panel have a wealth of experience, working both nationally and internationally to support pre-college computer science education.Each provides a unique perspective on the current state of computer science education, the organizations that have evolved ed to support it, and the challenges these organizations face in their efforts to support a discipline that is both misunderstood and undervalued.

In this paper, we propose a new approach to thinking about and implementing Computer Science curricula in secondary education. The characteristic feature is to organize the items to be taught into what we call "strands" which then can be interlaced during the course. This naturally leads to a spiral curriculum in secondary Computer Science education. In the view of our proposed approach, we also comment on the role of programming in secondary education.

We describe space-efficient algorithms for solving problems related to finding maxima among points in two and three dimensions. Our algorithms run in optimal $\mathcal{O}(n\log n)$ time and occupy only constant extra space in addition to the space needed for representing the input.

The goal of multiobjective optimization is to find a set of best compromise solutions for typically conflicting objectives. Due to the complex nature of most real-life problems, only an approximation to such an optimal set can be obtained within reasonable (computing) time. To compare such approximations, and thereby the performance of multiobjective optimizers providing them, unary quality measures are usually applied. Among these, thehypervolume indicator(orS-metric) is of particular relevance due to its favorable properties. Moreover, this indicator has been successfully integrated into stochastic optimizers, such as evolutionary algorithms, where it serves as a guidance criterion for finding good approximations to the Pareto front. Recent results show that computing the hypervolume indicator can be seen as solving a specialized version of Klee's Measure Problem. In general, Klee's Measure Problem can be solved with${\cal O}(n \log n + n^{d/2}\log n)$comparisons for an input instance of size$n$in$d$dimensions; as of this writing, it is unknown whether a lower bound higher than$\Omega (n \log n)$can be proven. In this paper, we derive a lower bound of$\Omega (n\log n)$for the complexity of computing the hypervolume indicator in any number of dimensions$d≫1$by reducing the so-calleduniformgapproblem to it. For the 3-D case, we also present a matching upper bound of${\cal O}(n\log n)$comparisons that is obtained by extending an algorithm for finding the maxima of a point set.

We present the first provably I/O-efficient dynamic data structure for point location in a general planar subdivision.Our structure uses O(N/B) disk blocks to store a subdivision of size N, where B is the disk block size.Queries can be answered in 0(log~ N) I/Os in the worst-case, and insertions and deletions can be performed in O(log 2 N) and O(10g B N) I/Os amortized, respectively.Previously, an I/Oefficient dynamic point location structure was only known for monotone subdivisions.Part of our data structure is based on a new external version of the so-called logarithmic method which allows for efficient dynamization of static external-memory data structures with certain characteristics.We believe that this method could prove helpful in the dynamization of other external memory structures.