With the rise of generative AI performing tasks traditionally allocated to entry-level roles, and the current economic slowdown, recent graduates need to demonstrate non-technical skills and behaviours to stand out in the job market. Our study collects perspectives from academic and industry professionals on how graduates can demonstrate essential non-technical skills, also known as soft skills, and workplace behaviours. We used a mixed methods approach to analyse 168 survey responses and 34 interviews. Our findings indicate that communication and collaboration are crucial for entry-level professionals, whereas mentorship and a purpose-driven disposition are less important at this stage of their careers. Our findings suggest that asking questions and reflective thinking showcase non-technical skills and behaviours in recent graduates; these same approaches are also used by industry to assess such attributes, especially during interviews. We conclude with implications and practical advice for educators and researchers to integrate and advance authentic practice, helping students learn to apply and demonstrate non-technical skills and behaviours applicable in the workplace.

Previous research indicates that students in introductory programming courses show a preference for male instructors. However, existing studies are mostly based on natural experiments, where instructor gender is necessarily confounded with other elements such as teaching style and existing classroom dynamics.

Skills-based evaluation is an alternative evaluation model that is a variation of both mastery and specifications grading. Students are evaluated on the set of skills they have demonstrably acquired over the duration of a course and the level at which they are able to demonstrate those skills. This is in opposition to traditional models which evaluate performance at fixed time points. A growing body of research suggests that such alternative evaluation models are more equitable, motivating, and efficacious for students. However, adoption remains limited in part due to concerns about student acceptance, perceived lack of rigour, and the potential increase in workload due to repeated assessments.

Gender bias has long been recognized as a barrier to diversity and inclusion within computer science. Though gender bias has been heavily discussed, current research on the influence of instructor gender on student bias remains fragmented and diffuse. In this work, we systematically query and map the existing research on gender bias as it relates to perceptions of computer science instructors. This mapping serves as a resource to the community in its own right, and also allows the identification of areas of over/under analysis and avenues for further research.

Collaboration and Teamwork are some of the most important non-technical skills required by computer science graduates. However, the research on collaboration in CS education is diffuse and inconsistent. In this work, we set out to systematically catalog and map the research on collaboration as it pertains to computer science education. We produce a literature mapping of research on the axes of population being studied, intervention being researched, and method of evaluation. We find a large number of papers studying group projects or assessments, but very few with directly assigned roles or clear hierarchies, and much of the research is on the student experience, with relatively limited insight into the efficacy or learning outcomes. The full literature map, heat maps, and systematic details are made available for the community.

The widespread release of generative AI tools has led to a rapid rise in publications evaluating their impact on CS education. While there is no doubt that the area is new and rapidly evolving, it is important to begin to catalogue and map the literature at this early stage. In this work, we systematically search and map 82 papers evaluating the impact of generative AI tools on CS education. We then build a literature map of these papers using the axes of population, use of generative AI, and method of evaluation. This work will serve as both a snapshot of the first generation of generative AI papers in the field, and a road-map for further classification and literature review as the field develops.

Involving undergraduate students in research has a wide array of benefits, for the students themselves, for the research team, and for the community.

There is a wealth of evidence that involving undergraduate students in research has positive impacts in a variety of areas, from representation and retention to outcomes and self-efficacy. However, developing and growing an undergraduate research program can be daunting, especially for institutions that do not have a large existing research enterprise. In this work, we detail a program that revolves around student-developed literature maps to help students gain the ability to read and assess research papers in a way that is accessible, robust, and requires relatively little faculty overhead. We further detail how this program has been run through 4 iterations, with a total of 47 students producing 5 posters or short papers, and 3 full papers. In this work, we provide our experiences using literature mapping projects to boot-strap an undergraduate research program and provide quantitative and qualitative analysis of the students who have participated. All of the materials, including sample spreadsheets, and scripts to generate LaTeX tables and figures are included for anyone wishing to undertake a literature mapping project of their own.

The ITiCSE '23 final keynote raised teaching soft skills, or professional dispositions, to help students face challenges in modern programming. This project addresses helping computing students develop professional dispositions through collaborative learning (CL) since some in the industry observe entry-level engineers struggling due to their fragile professional dispositions. We are motivated to understand professional expectations from entry-level engineers and present the academia-industry gap to support practitioners and researchers in advancing CL in Computing Education, encouraging positive curricula and policy changes that promote DEIA. We will present CL practices alongside their supported professional dispositions to assist practitioners in adoption. We will present the academia-industry gap in CL for future research opportunities, helping researchers advance CL practices to integrate professional dispositions the industry expects from entry-level engineers.

This work reports on a pilot study that examines the impact of production quality of coding demonstration videos on student learning. With an increase in demand for asynchronous content, many instructors are finding the scripting and development of coding demonstration videos to be cumbersome and time consuming. However, it is an open question whether it is necessary to carefully script and produce videos to satisfy student desires or provide positive learning outcomes. In this study, participants watched coding demonstration videos, and the impact was measured on attitude surveys and content questions. Students were randomly assigned to watch either high production quality videos which were carefully scripted and edited for concision and accuracy, or low production quality videos which were created on-the-fly by teaching assistants with no preparation time or post video editing. In this pilot study, no statistical significance in test performance was found based on the production quality of the videos. There was an impact on responses to a single question on the CAS. This work develops a methodology for further evaluation of the impact of production quality of coding demonstration videos that will eventually provide guidance to practitioners as to the level of time and effort required to maximize student benefit.

There is a growing movement in the education community to re-evaluate grading practices and find ways of evaluating students that are more flexible, equitable, and focused on student growth. A wide variety of alternative evaluation systems have been developed, including mastery grading, specifications grading, contract grading and ungrading.

The COVID-19 pandemic caused many institutions to radically alter the way in which they delivered and evaluated their educational models and frameworks. In the subsequent 3 years, a great deal of research has been conducted in the CS Education community as to the changes that were made and how they impacted learners, educators, and institutions.

With the recent growth in popularity of alternative evaluation methods, two methodologies have become particularly prevalent in CS education literature: Specifications grading and contract grading. Recent work has shown that these novel evaluation approaches can have positive impacts in the classroom and lead to more equitable outcomes for students. However, there is not yet a consensus on terminology, implementation details and best practices. In this work, we review the literature on the use of specifications and contract grading in CS education. We find that while there is a good deal of promising research, there is a great deal of variation in methodologies and a sparsity of evaluation of the efficacy of learning outcomes.

Recent work has confirmed that computing students experience the Imposter Phenomenon (IP) at higher rates than reported in other disciplines. However, no work has examined what aspects of the university computing experience might lead to a higher rate of IP experiences. We aim to illustrate the IP experiences students have, identify common sources of these experiences, and document the effects of these experiences and how students respond to them. We asked undergraduate students to share recent experiences that illustrate their experiences with the IP. We conducted an inductive thematic analysis on these open-ended responses, resulting in a set of inter-connected themes. A significant fraction of students related stories about making comparisons with peers or observing peer behaviour that made them question their abilities. Students also spoke about holding unrealistic expectations learned from their peers or imposed by the environment. These experiences may be particularly acute for minority-affiliated students who may come to feel they do not belong. Ultimately, these IP experiences can lead to a loss of motivation or a cycle of failure that leads students to leave computing. The central role social comparisons play in IP experiences suggests that it is particularly important to foster communities where opportunities for comparison are reduced and where realistic expectations are explicitly set.

In the first 2 years following the outbreak of COVID-19, many papers have been published regarding the impacts and adaptations of the pandemic on computer science education. As a first step towards a systematic literature mapping, this study attempts to develop a process for searching and a categorization schema for papers. The goal of this project is to produce a literature map which will be used to provide an initial assessment of the state of research, as well as a framework for future research directions.

Evidence for the efficacy of exam wrappers has been varied, with some studies showing an improvement in grades and metacognition and others showing no evidence of improvement. The physical nature of wrappers makes implementation and study replication difficult. In this work, we develop a methodology for the deployment and evaluation of virtual exam wrappers. We then validate our model by replicating existing exam wrapper studies.

Many studies have shown the efficacy of pair programming for students learning to program. However, most of these studies have taken place in an in-person environment, where the driver and navigator are physically sharing a keyboard and screen and can communicate verbally and non-verbally. With the increase in online learning, especially during the COVID-19 pandemic. It is important to know whether these results generalize to an online environment.

Motivation Despite the widespread belief that computing practitioners frequently experience the Imposter Phenomenon (IP), little formal work has measured the prevalence of IP in the computing community despite its negative effect on achievement.

Learning is a social and emotional activity. Unfortunately, most instructors find that they lack the time and resources to focus on student affect during lectures, and student well-being is consigned to activities outside the lecture hall. As we see a rise in mental health issues in university students, there is a need for integrated approaches to support student well-being. Practices that are brief and easy to implement can be integrated into lecture sessions to help students, without detracting from learning outcomes or placing undue burden on course instructors. In this session, we will first make a case for lightweight practices, and then present and demonstrate some simple practices that can be used to support students' emotional engagement. The practices that will be presented are evidence-based and have been used in a variety of computing courses.

Computing has, for many years, been one of the least demographically diverse STEM fields, particularly in terms of women's participation [2] and those from minoritized racial and ethnic groups. In the case of higher education, one of the most powerful sites of intervention is the classroom. The last decade has seen a proliferation of research exploring new teaching techniques and course sequencing and their effect on the retention of students who have historically been excluded from computing. This research suggests interventions and practices that can affect the inclusiveness of the computer science classroom and potentially improve learning outcomes for all students. But research needs to be translated into practice, and practices need to be taken up in real classrooms. The goal of this working group (WG) is to conduct a systemic "state-of-the-art" review of recent empirical studies of teaching practices that have some explicit test of the impact on women (or other under-represented groups) in computing. The WG will produce an annotated bibliography and a report that distills the research into specific, actionable practices.

Several studies have shown mixed results when presenting enhanced (simplified or extended) error messages to introductory programming students. In this work, we detail a pilot study using a tool specifically designed to capture data about students presented with different error messages. Initial data indicates that the tool is capable of capturing relevant data, and that future studies may show an impact of modifying error messages.

Many research papers pull data from student surveys. But are those surveys well designed? Are the questions used validated? Are the results comparable across studies? What exactly are we asking our students? In this work, we performed a systematic literature map of the past 15 years of papers in the three main conferences sponsored by the ACM Special Interest Group on Computer Science Education: International Computing Education Research (ICER), Innovation and Technology in Computer Science Education (ITiCSE), and the Special Interest Group on Computer Science Education Technical Symposium (SIGCSE). We search for all papers referring to student surveys or questionnaires. Out of 1313 papers analyzed, 42 papers referred to surveys containing general questions applicable to many or all computer science students. Our analysis showed that many papers were using surveys to extract similar types of information, such as demographics, prior experience or motivation to study computer science. However, the questions were being asked in different ways, using different scales, thus making it difficult or impossible to compare survey results between studies. We further found that while some studies based their questions on well-validated surveys, or at least shared their questions for possible later validation, approximately half of the papers found neither validated their questions, nor shared them to allow for post-hoc validation.

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.

Diagnostic messages generated by compilers and interpreters such as syntax error messages have been researched for decades. Unfortunately these messages which include error, warning, and runtime messages, present substantial difficulty and could be more effective, particularly for novices. Recent years have seen increased number of papers in the area including studies on the effectiveness of these messages, improving or enhancing them, and their usefulness as a part of programming process data that can be used to predict student performance. Despite this increased interest, the long history of literature is quite scattered and has not been brought together in any digestible form. We argue that in order to help the community proceed with more work on diagnostic messages, the literature needs to be presented in a state-of-the-art report. In addition we will synthesize and present the existing evidence for these messages including the difficulties they present and their effectiveness. We will also formulate a set of guidelines based on this evidence that can be used when designing or enhancing diagnostic messages. This work can serve as a starting point for those who wish to conduct research on such messages, those who wish to design better messages or those that aim to measure their effectiveness, more effectively.

Most computer science examinations tend to start with the easiest questions and progress towards the more difficult material. Whether this is because of the highly scaffolded nature of the course, an attempt to 'ease students in', or simply by convention, is unclear. However, there is a great deal of data from the psychology literature to suggest that human perception of the difficulty or discomfort of a task is disproportionately affected by the last part of the task completed. Therefore, is it possible that by structuring our exams in an easy-to-hard fashion, we are causing students to perceive the test as more difficult than it actually is? Could changing the question order allow us to change students' perception of their own achievement? What effect could this have on actual performance? This paper attempts to answer these questions by randomly assigning students to write exams ordered either easy-to-hard (referred as 'Easy-Difficult') or hard-to-easy ('Difficult-Easy), then ask them to predict their marks on per-question basis. We find that the question ordering has a small but not statistically significant effect on the performance, and virtually no effect on predicted marks when treating the entire class as one unstratified sample. However, the effect was significant for certain subgroups created via stratification. In particular, swapping the order of the questions may have hurt the performance of international students, but significantly raised both the performance and confidence of female students.

A common refrain heard by instructors of CS1 courses is ``I'm sure I did better than that" or ``I have no idea how I got that mark". Sometimes differences in a student's expected and reported marks may be due to a mistake on the part of the grader of the work, but more often than not this is an indicator that a student is not accurately assessing their own level of achievement on a piece of work. This may be an issue of capability (some students may lack the tools to assess what they have done correctly or incorrectly?) or one of confidence (some students are certain they are making mistakes even if they have done everything correctly). Regardless of the source of the gap between predicted and reported grades, it is an important skill for students to be able to accurately assess their own capabilities and performance.

University can be a difficult, stressful time for students. This stress causes problems ranging from academic difficulties and poor performance, to serious mental and physical health issues. Studies have shown that physical activity can help reduce stress, improve academic performance and contribute to a healthier campus atmosphere physically, mentally, and emotionally. Computer science is often considered among the most difficult and stressful programs offered at academic institutions. Yet the current stereotype of computer scientists includes unhealthy lifestyle choices and de-emphasizes physical activity.

Much work has been done on the achievement gap between code tracing and code writing in CS1 students. The generally accepted explanation for this gap is that tracing and writing form separate steps in a learning scaffolding; students must first learn to trace code before they can be expected to write code. The expectation is that once students have mastered these skills, future grades will be driven by their ability to understand the deeper learning concepts, and so the gap between tracing and writing should disappear. In this paper, we detail and evaluate a study on 384 CS2 students to evaluate whether a tracing-writing gap still exists, and assess whether anything can be deduced about students who continue to exhibit such a gap. We find that not only does the gap seem to have closed by CS2, students are equally likely to show a reverse gap in the writing-tracing direction. However, further analysis shows a strong correlation between students who do continue to have a gap (in either direction) and poor overall performance in the course.

Practice is an important part of introductory CS courses, and practical sessions are a student's best opportunity for hands-on experience with the material covered in the course in a supervised, supportive environment. However, finding a balance between challenging more experienced students and alienating newcomers can be difficult and frustrating. One possible solution is to let the students self-select the problems they wish to attempt. The difficulty then becomes one of motivation and administration.

Marking coding exam questions for introductory computer science courses is notoriously resource intensive and difficult to perform consistently. Students can be easily led astray by minor misunderstandings in the wording of questions, and graders often find it difficult to decide whether mistakes are attributable to simple misinterpretations, minor memory errors, or major lack of ability/understanding of the core concepts being evaluated. In this paper we detail and evaluate "Code Mangler" questions. The "Code Mangler" is a fictitious character who manipulates code; removing commenting, changing the order of lines, adding bugs, and otherwise breaking perfectly good code. The role of the student on the exam is then to use the mangled results to reverse engineer the original code. We discuss the benefits of this style of question, and perform an evaluation on a large (475 student) CS1 course, demonstrating that these questions are less resource intensive to mark than traditional coding questions, improve the confidence of the graders, and correlate strongly with student ability as assessed in traditional question styles.

OBJECTIVE: Relation extraction in biomedical text mining systems has largely focused on identifying clause-level relations, but increasing sophistication demands the recognition of relations at discourse level. A first step in identifying discourse relations involves the detection of discourse connectives: words or phrases used in text to express discourse relations. In this study supervised machine-learning approaches were developed and evaluated for automatically identifying discourse connectives in biomedical text. MATERIALS AND METHODS: Two supervised machine-learning models (support vector machines and conditional random fields) were explored for identifying discourse connectives in biomedical literature. In-domain supervised machine-learning classifiers were trained on the Biomedical Discourse Relation Bank, an annotated corpus of discourse relations over 24 full-text biomedical articles (~112,000 word tokens), a subset of the GENIA corpus. Novel domain adaptation techniques were also explored to leverage the larger open-domain Penn Discourse Treebank (~1 million word tokens). The models were evaluated using the standard evaluation metrics of precision, recall and F1 scores. RESULTS AND CONCLUSION: Supervised machine-learning approaches can automatically identify discourse connectives in biomedical text, and the novel domain adaptation techniques yielded the best performance: 0.761 F1 score. A demonstration version of the fully implemented classifier BioConn is available at: http://bioconn.askhermes.org.

We propose a spatial autocorrelation aware, energy efficient, and error bounded framework for interpolating maps from sensor fields. Specifically, we propose an iterative reporting framework that utilizes spatial interpolation models to reduce communication costs and enforce error control. The framework employs a simple and low overhead in-network coordination among sensors for selecting reporting sensors so that the coordination overhead does not eclipse the communication savings. Due to the probabilistic nature of the first round reporting, the framework is less sensitive to sensor failures and guarantees an error bound for all functional sensors for each epoch. We then propose a graceful integration of temporal data suppression models with our framework. This allows an adaptive utilization of spatial or temporal autocorrelation based on whichever is stronger in different regions of the sensor field. We conducted extensive experiments using data from a real-world sensor network deployment and a large Asian temperature dataset to show that the proposed framework significantly reduces messaging costs and is more resilient to sensor failures. We also implemented our proposed algorithms on a sensor network of MICAz motes. The results show that our algorithms save significant energy and the out of bound errors due to packet loss are below 5%.

Given that outsourcing has become a fact of life, it is becoming increasingly obvious that we need to ensure that computer science students are taught the necessary skills to cope with global software development. Unfortunately, the enormous amount of time that it takes to coordinate and support such activities can deter even the most devoted educator. This paper describes a course that used a computer supported collaborative tool help teach distributed teams from Turkey and the US how to work together to solve programming problems. The system contains both collaborative tools that support groups, as well as course management software for helping instructors with administrative tasks. Examples of the usage of the system and data collected from the undergraduate computer science course that used the software are presented. Based on that experience, future plans to refine the system for early detection of problem teams, and the advantages of implementing the software as a Web service are also discussed. This information is designed to provide support for effective multi-institutional learning courses