We present results of an in-depth survey of nearly 100 introductory programming (CS1) instructors in 18 countries spanning six continents. Although CS1 is well studied, relatively few broadly-scoped studies have been conducted, and none prior have exceeded regional scale. In addition, CS1 is a notoriously fickle and often changing course, and many might find it beneficial to know what other instructors are doing across the globe; perhaps more so as we continue to understand the impact of the COVID-19 pandemic on computing education and as the effects of Generative AI take hold. Expanding upon several surveys conducted in Australasia, the UK, and Ireland, this survey facilitates a direct comparison of global trends in CS1. The survey goes beyond environmental factors such as languages used, and examines why CS1 instructors teach what they do, in the ways they do. In total the survey spans 84 institutions and 91 courses in which a total of over 40,000 students are enrolled.

In this experience paper, we introduce the concept of 'diverging assessments', process-based assessments designed so that they become unique for each student while all students see a common skeleton. We present experiences with diverging assessments in the contexts of computer networks, operating systems, ethical hacking, and software development. All the given examples allow the use of generative-AI-based tools, are authentic, and are designed to generate learning opportunities that foster students' meta-cognition. Finally, we reflect upon these experiences in five different courses across four universities, showing how diverging assessments enhance students' learning while respecting academic integrity.

Much of the recent work investigating large language models and AI Code Generation tools in computing education has focused on assessing their capabilities for solving typical programming problems and for generating resources such as code explanations and exercises. If progress is to be made toward the inevitable lasting pedagogical change, there is a need for research that explores the instructor voice, seeking to understand how instructors with a range of experiences plan to adapt. In this paper, we report the results of an interview study involving 12 instructors from Australia, Finland and New Zealand, in which we investigate educators' current practices, concerns, and planned adaptations relating to these tools. Through this empirical study, our goal is to prompt dialogue between researchers and educators to inform new pedagogical strategies in response to the rapidly evolving landscape of AI code generation tools.

Several studies report code quality issues in academia by analysing student submissions. However, most of them focus on novices or a specific integrated development environment (IDE), and the findings might be less representative of code quality issues in general undergraduate computing. This study summarizes code quality issues from seven programming courses with various level of complexity. There are 931 assessment tasks with 15,323 Java/Python program files involved. The reported issues are specifically tailored to computing undergraduates and are selected with checkstyle (Java) and Flake8 (Python). Our study finds that students often neglect to use blank lines between code components, braces where they are optional, and a space after a comment marker. They also sometimes include too much code in one line. This initial study will be expanded via the development of a tool that can automatically summarize the code quality issues of each student submission.

To help address programming plagiarism and collusion, students should be informed about acceptable practices and about program similarity, both coincidental and non-coincidental. However, current approaches are usually manual, brief, and delivered well before students are in a situation where they might commit academic misconduct. This article presents an assessment submission system with automated, personalized, and timely formative feedback that can be used in institutions that apply some leniency in early instances of plagiarism and collusion. If a student’s submission shares coincidental or non-coincidental similarity with other submissions, then personalized similarity reports are generated for the involved submissions and the students are expected to explain the similarity and resubmit the work. Otherwise, a report simulating similarities is sent just to the author of the submitted program to enhance their knowledge. Results from two quasi-experiments involving two academic semesters suggest that students with our approach are more aware of programming plagiarism and collusion, including the futility of some program disguises. Further, their submitted programs have lower similarity even at the level of program flow, suggesting that they are less likely to have engaged in programming plagiarism and collusion. Student behavior while using the system is also analyzed based on the statistics of the generated reports and student justifications for the reported similarities.

When checking student programs for plagiarism and collusion, many similarity detectors aim to capture semantic similarity. However, they are not particularly effective for strongly directed assessments, in which the student programs are expected to be semantically similar. A detector focusing on syntactic similarity might be useful, and this paper reports its effectiveness on programming assessment tasks collected from algorithms and data structures courses in one academic semester. Our study shows that syntactic similarity detection is more effective than its semantic counterpart in strongly directed assessments, with some irregular similarity patterns being useful for raising suspicion. We also tested whether take-home assessments have higher similarity than in-class assessments, and confirmed that hypothesis. Consistency of the findings will be further validated on other courses with strongly directed assessments, and a syntactic similarity detector specifically tailored for strongly directed assessments will be proposed.

Code reuse is a practice that may both support and hinder the learning process of programming students. To help offset the negative impacts of code reuse, many educators rely on code similarity detection tools as a first step in detecting code plagiarism. An alternative approach, often applied in parallel, is to help deter plagiarism by teaching students the importance of referencing externally sourced code. However, there are no broadly accepted standards for referencing in programming. We have found one standard proposed in the literature, and in this paper we explain the design of a system to help programmers apply that standard. The system we will build is a semi-automated code comment generation system that will rely on a code clone detection approach for detecting similarities between the student's code and the code at a website whose URL the student provides. This will assist the user to create appropriate references, in the form of inline comments, when they reuse code from websites or other sources. This work in progress paper explores the relevant literature, explains the design choices of the system and the plan for its evaluation, and presents a progress report on the work.

This research-to-practice work-in-progress paper presents an automated feedback tool that can be used in many teaching environments by integrating it with a web-based assessment submission system. Each time a student submits their work, they will automatically get feedback about aspects of the code quality. Automated feedback tools have been developed to educate students about code quality. However, integrating such a tool into an existing teaching environment can be challenging as these tools can depend on particular working environments, can be separate from the assessment submission system, or can require historical data. Our initial evaluation shows that the tool can be helpful as students do sometimes neglect to satisfy all code quality requirements. However, some false results are expected for spelling correction as student programs are not written in natural language. According to our quasi-experiments, the tool substantially reduces the number of word misspellings in comments due to their substantial frequency of occurrence.

Some code similarity detectors are designed to address academic integrity in early programming courses by recognising subtle variations, in the assumption that the code similarity in these courses is typically higher than that in later courses. Although the assumption is often used, it has no empirical evidence, and might be misleading. This study empirically investigates the assumption by examining the relationship between code similarity and course semester in seven programming courses with a total of 931 distinct assessment tasks. Our study shows that the argument is not necessarily true since in later courses, some assessment tasks require the students to follow a particular structure, to use external libraries, or to implement specific algorithms taught during the course.

The coronavirus (COVID-19) pandemic has forced an unprecedented global shift within higher education in the ways that we communicate with and educate students. This necessary paradigm shift has compelled educators to take a critical look at their teaching styles and use of technology. Computing education traditionally focuses on experiential, in-person activities. The pandemic has mandated that educators reconsider their use of student time and has catalysed overnight innovations in the educational setting. Even in the unlikely event that we return entirely to pre-COVID-19 norms, many new practices have emerged that offer valuable lessons to be carried forward into our post-COVID-19 teaching. This working group will explore what the post-COVID-19 academic landscape might look like, and how we can use lessons learned during this educational shift to improve our subsequent practice. The exploration will strive to identify practices within computing that appear to have been improved through exposure to online tools and technologies, and that should therefore continue to be used in the online space. In the broadest sense, our motivation is to explore what the post-COVID-19 educational landscape will look like for computing education.

There have been four principal studies on pass rates in introductory programming courses, in 2007, 2014, and two in 2019. These studies, the first two in particular, are extremely widely cited, but it appears that some of the citing papers misrepresent what was written in the original papers. We present an analysis of nearly 600 papers that cite one or more of these four papers, indicating what sorts of message they impute to the papers. We find that well over half of the citing papers fail to accurately represent the papers' findings, instead citing the papers as evidence either of a commonly accepted belief or of their own -- sometimes quite bizarre -- assertions.

For many decades the abstract has served as a standalone summary of an academic publication, one that succinctly informs readers of what they might expect to find upon reading the paper. While some publication venues require abstracts to conform with a specified structure, many others, including ITiCSE, leave the structure entirely to the paper's authors. In this paper we report on the components identified in the abstracts of ITiCSE's full papers and working group reports. We examine the abstracts of all 1496 of these publications from 25 years of ITiCSE to determine what structural elements they employ. We also construct something of an ethos of computing education by compiling assertions from the introductions of many abstracts. We find, among other things, that very few abstracts include all of the components that are recommended in a structured abstract; that a number of abstracts consist of nothing but background; that nearly half of abstracts do not include any results; and that nearly five percent of abstracts include references, despite often not having an associated reference list. As an example from the ethos, we find that industry wants people with soft skills, and it is important that we teach our students these skills. Our analysis will guide future ITiCSE authors as they consider how to formulate their own abstracts.

When comparing student programs to check for evidence of plagiarism or collusion, the goal is to identify code segments that are common to two or more programs. Yet some code segments are common for reasons other than plagiarism or collusion, and so should not be considered. A few code similarity detection tools automatically remove very common segment, but they are prone to false results as no human validation is involved. This paper proposes a semi-automated approach for excluding common segments, where human validation is introduced before excluding the segments. As existing selection techniques are not detachable from their similarity detection tools, we propose a new tool to independently select the segments (C2S2), along with several adjustable selection constraints to keep the number of suggested segments reasonable for manual observation. In order to independently evaluate automated selection techniques, we propose and apply three metrics. The evaluation shows our selection technique to be more effective and efficient than the basis underlying existing selection techniques, and establishes the benefit of each of its selection features.

Research into the relationship between learning computing and students' attitudes, beliefs, and emotions often builds on theoretical frameworks from the social sciences in order to understand how these factors influence, for example, students' motivation, study practices, and learning results. In this paper we explore the computing education research literature to identify new theoretical constructs that have emerged from this research. We focus on empirical work in programming education that extends or adapts theories or instruments from the social sciences or that independently develops theories specific to programming. From an initial data set of more than 3800 papers published in the years 2010--2019, we identify 50 papers that present a range of domain-specific theoretical constructs addressing emotions, affect, beliefs, attitudes, and self-efficacy. They include 11 validated instruments and a number of statistical models, but also grounded theories and pedagogical models. We summarize the main results of many of these constructs and provide references for all of them. We also investigate how these constructs have informed further research by analysing over 850 papers that cite these 50 papers. We categorize the ways that theories can inform further research, and give examples of papers in each of these categories. Our findings indicate that among these categories, instruments have been most widely used in further research, thus affirming their value in the field.

Authors of academic papers are generally required to nominate several keywords that characterize the paper, but are rarely offered guidance on how to select those keywords. We analyzed the keywords in the past 15 years of selected computing education publications: the 1274 papers published in the proceedings of ICER and ITiCSE, including the ITiCSE working group reports. As well as the keywords assigned by the authors, we mined the abstracts of these papers to extract a separate list of keywords. Our work has two goals: to frame the thematic landscape of the field, using keywords that communicate the work conducted; and to detect differences between the human judgement and interpretation of keywords and the machine 'intelligence' on handling those keywords, with respect to the clusters of thematic topics identified in each case. The analysis shows that the field is dominated by learning approaches (e.g., active learning, collaborative learning), aspects of programming (e.g., debugging, misconceptions), computational thinking, feedback, and assessment, while other areas that have attracted attention include academic integrity (e.g., plagiarism) and diversity (e.g., female students, underrepresented groups). It was observed that the keywords chosen by authors are often too general to provide information about the paper (e.g., 'concerns', 'course', 'fun', 'justice'). We elaborate on the findings and begin a discussion on how authors can improve the communication of their research and make access to it more transparent.

Since 1996 the ITiCSE conference has provided a forum for academics with an interest in computing education scholarship and research. ITiCSE is often thought of as the European counterpart to northern America's SIGCSE Technical Symposium. Yet from its very beginning, ITiCSE has differed substantially from the Symposium. In addition to the many participants from Europe and North America, ITiCSE has also always hosted many participants from Australia and New Zealand, expanding its international perspective. Why is this? What is the appeal of ITiCSE to its participants, and what do its participants bring to ITiCSE? More specifically, what do Australians and New Zealanders bring to and take from the conference? Are there opportunities to involve computing educators from other regions? Judy Sheard and Simon will explore these questions and provide their perspectives. In this 25th year of ITiCSE, they will also speculate about the future of the conference and of the future role of Australia and New Zealand -- especially in light of the disruption in 2020.

The 25th ITiCSE might be considered an appropriate occasion for reflection on the history of the conference. In the 24 years since it began, ITiCSE has published more than 1400 full papers and working group reports. This paper presents an analysis of the authors of these papers and reports: who they are, where they come from, the numbers of authors per paper, and more. We highlight ITiCSE's most prolific authors, and note those with the greatest number of co-authors in ITiCSE publications. We apply Lotka's law to the population of authors: notwithstanding the handful of authors who have contributed to many ITiCSE papers, we find that the return rate for authors is not as high as Lotka's inverse-square law would suggest. We analyse the level of collaboration evident in papers with more than one author, and find a high level of collaboration between countries, presumably as a consequence of the nature of ITiCSE's working groups.

When student programs are compared for similarity, certain segments of code are always sure to be similar. Some of these segments are boilerplate code -- public static void main String [] args and the like -- and some will be code that was provided to students as part of the assessment specification. The purpose of this working group is to explore what other code is expected to be reasonably common in student assessments, and should therefore be excluded from similarity checking. The answers will clearly vary with programming language, and perhaps with level of assessment item. Working group members will collect assessment submissions from their own or their colleagues' students, and it is hoped that these submissions will together encompass a wide variety of assessment tasks in a wide variety of programming languages. The working group aims to deliver clear guidelines as to what code can reasonably be excluded from automatic code similarity detection in various circumstances. It also aims to deliver a summary of what sort of code lecturers tend to provide for students when setting an assigned task, and why they provide that code.

This paper presents an analysis of all 1295 full papers and 129 working group reports presented and published at ITiCSE since the conference began in 1996. Working group reports are analysed separately from full papers, in recognition of the particular process by which they are created. The analysis shows that nearly 40% of ITiCSE's full papers concern programming education, and that more than half of them present work conducted in single courses. In contrast, most of the working group reports have a context that extends beyond specific topic areas, and report on work that was not conducted in any courses. About half of the full papers focus on techniques of teaching and learning or tools for teaching and learning, whereas two thirds of the working group reports focus on techniques of teaching and learning or curriculum. With both full papers and working group reports there has been a steady increase in the proportion of papers that clearly present educational research. The findings from our analysis provide valuable insights for potential authors, attendees, chairs, and the whole ITiCSE community.

In order to mature as a research field, computing education research (CER) seeks to build a better theoretical understanding of how students learn computing concepts and processes. Progress in this area depends on the development of computing-specific theories of learning to complement the general theoretical understanding of learning processes. In this paper we analyze the CER literature in three central publication venues -- ICER, ACM Transactions of Computing Education, and Computer Science Education -- over the period 2005--2015. Our findings identify new theoretical constructs of learning computing that have been published, and the research approaches that have been used in formulating these constructs. We identify 65 novel theoretical constructs in areas such as learning/understanding, learning behaviour/strategies, study choice/orientation, and performance/progression/retention. The most common research methods used to devise new constructs include grounded theory, phenomenography, and various statistical models. We further analyze how a number of these constructs, which arose in computing education, have been used in subsequent research, and present several examples to illustrate how theoretical constructs can guide and enrich further research. We discuss the implications for the whole field.

The goal of most computing education research is to effect positive change in how computing is taught and learned. Statistical techniques are one important tool for achieving this goal. In this paper we report on an analysis of ICER papers that use inferential statistics. We present the most commonly used techniques; an overview of the techniques the ICER community has used over its first 14 years of papers, grouped according to the purpose of the technique; and a detailed analysis of three of the most commonly used techniques (t-test, chi-squared test, and Mann-Whitney-Wilcoxon). We identify common flaws in reporting and give examples of papers where statistics are reported well. In sum, the paper draws a picture of the use of inferential statistics by the ICER community. This picture is intended to help orient researchers who are new to the use of statistics in computing education research and to encourage reflection by the ICER community on how it uses statistics and how it can improve that use.

We present an analysis of the visual portrayals of data (including results) in the full papers and working group reports of ITiCSE from 2013 to 2018. We find that tables are the most common visual portrayal of data in these publications, but that a number of graphical forms are also widely used. We examine the quality of the data portrayals for tables, graphs, and images using visual quality indicators derived from the literature. Overall, our findings are not positive. We find that many papers present data in such a way that it cannot be readily interpreted. The most common problem is captions that do not adequately describe the table, figure, or image. In tables, the main issues affecting readability of numeric data are poor alignment of numbers within a column, unnecessary notations, and unwarranted precision. In graphs and images, the prevalent problem is text that is too small to be read. We conclude with guidelines for future authors to ITiCSE and other computing education venues, in the hope that we can contribute to an improvement in the quality of computing education publications.

Vast numbers of publications in computing education begin with the premise that programming is hard to learn and hard to teach. Many papers note that failure rates in computing courses, and particularly in introductory programming courses, are higher than their institutions would like. Two highly distinct research projects have established that average success rates in introductory programming courses world-wide are in the region of 67%. However, there is little published work comparing pass rates in computing courses with those in other STEM disciplines. As institutions continually ask computing educators to justify the atypical failure rates in their courses, a thoroughly researched comparison of this sort could prove useful in demonstrating whether the phenomenon is real, and, if so, whether it extends somewhat beyond the boundaries of individual institutions. This working group will gather information on pass rates in computing courses, particularly introductory programming courses, and in courses at comparable levels in other STEM disciplines. Members of the group will be required to gather the information from their own institutions, and further data will be gathered by way of a broad survey. The data will be analysed to see whether global patterns can be established, and the group will survey the literature to gather and summarise postulated explanations for any difference between pass rates in computing and in other STEM disciplines.

A broad review of research on the teaching and learning of programming was conducted by Robins et al. in 2003. Since this work there have been several reviews of research concerned with the teaching and learning of programming, in particular introductory programming. However, these reviews have focused on highly specific aspects, such as student misconceptions, teaching approaches, program comprehension, potentially seminal papers, research methods applied, automated feedback for exercises, competency-enhancing games, and program visualisation. While these aspects encompass a wide range of issues, they do not cover the full scope of research into novice programming. Some notable areas that have not been reviewed are assessment, academic integrity, and novice student attitudes to programming. There does not appear to have been a comprehensive review of research into introductory programming since that of Robins et al. It is therefore timely to conduct and present such a review in order to gain an understanding of the research focuses, to highlight advances in knowledge since 2003, and to indicate possible future directions for research. The working group will conduct a systematic literature review based on the guidelines proposed by Kitchenham et al. This research project is well suited to an ITiCSE working group as the synthesis and discussion of the literature will benefit from input from a variety of researchers drawn from different backgrounds and countries.

Conferences such as ITiCSE have recently seen an increase in the number of papers presenting empirical research in computing education. While empirical research need not be quantitative, there has been a corresponding increase in the number of papers that present some level of statistical analysis to support their arguments. This poster introduces a project that is exploring how -- and how well -- statistics are used and reported in computing education research.

Parallel surveys of introductory programming courses were conducted in Australasia and the UK, with a view to examining the programming languages being used, the preferred integrated development environments (if any), and the reasons for these choices, alongside a number of other key aspects of these courses. This paper summarises some of the similarities and differences between the findings of the two surveys. In the UK, Java is clearly the dominant programming language in introductory programming courses, with Eclipse as the dominant environment. Java was also the dominant language in Australasia six years ago, but now shares the lead with Python; we speculate on the reasons for this. Other differences between the two surveys are equally interesting. Overall, however, there appears to be a reasonable similarity in the way these undergraduate courses are conducted in the UK and in Australasia. While the degree structures differ markedly between and within these regions -- a possible explanation for some of the differences -- some of the similarities are noteworthy and have the potential to provide insight into approaches in other regions and countries.

Current CS1 learning outcomes are relatively general, specifying tasks such as designing, implementing, testing and debugging programs that use some fundamental programming constructs. These outcomes impact what we teach, our expectations, and our assessments. Although prior work has demonstrated the utility of single concept assessments, most assessments used in formal examinations combine numerous heterogeneous concepts, resulting in complex and difficult tasks.

Safeguarding academic integrity is an issue of concern to all computing academics due to high and rising levels of plagiarism and other cheating in computing courses. There have been many studies of the cheating and plagiarism practices of computing students and the factors that can influence these practices, and a variety of strategies for reducing cheating have been proposed. This national study of first-year computing programs provides insights into what strategies computing academics use to discourage or prevent their students from cheating. Having interviewed 30 academics from 25 universities we found 21 different types of strategy, which we classified into five themes: education; discouraging cheating; making cheating difficult; and empowerment. We also found that academics often employ strategies across all of these themes.

Cognitive load theory is typically used to evaluate and improve learning materials, with the goal of optimising students' opportunity to acquire new knowledge and understanding. The cognitive load on a student is typically assessed either objectively, by taking physiological measurements while the student is learning, or subjectively, by asking the student to complete an appropriate questionnaire after the learning experience. However, there are circumstances in which a decision on learning materials must be made before those materials are developed and deployed, whereupon it is not helpful to measure the students during learning or to survey them after learning. Such circumstances necessitate a completely different approach, in which the assessment of the likely imposition of cognitive load is made by the instructors and informs the development of the learning materials.

This bibliometric study examines the authorship of the papers presented at ICER since the conference began in 2005. It finds that the pattern of authorship complies well with Lotka's law, an accepted model of author distribution within a discipline. ICER's most prolific authors are identified and their contributions quantified, along with measures of the collaborations between authors. New authors are found to be joining the community at a steady rate, some beginning as co-authors with established community members and some joining alone or with other new authors. The analysis extends to the contributions from different countries. The conclusion is that the community of ICER authors is truly international, has a solid core around which excellent growth is evident, displays strong collaboration, and has at least one of the characteristics of a full-fledged discipline.

The programming education literature includes many observations that pass rates are low in introductory programming courses, but few or no comparisons of student performance across courses. This paper addresses that shortcoming. Having included a small set of identical questions in the final examinations of a number of introductory programming courses, we illustrate the use of these questions to examine the relative performance of the students both across multiple institutions and within some institutions. We also use the questions to quantify the size and overall difficulty of each exam. We find substantial differences across the courses, and venture some possible explanations of the differences. We conclude by explaining the potential benefits to instructors of using the same questions in their own exams.

Ten selected questions have been included in 13 introductory programming exams at seven institutions in five countries. The students' results on these questions, and on the exams as a whole, lead to the development of a benchmark against which the exams in other introductory programming courses can be assessed. We illustrate some potential benefits of comparing exam performance against this benchmark, and show other uses to which it can be put, for example to assess the size and the overall difficulty of an exam. We invite others to apply the benchmark to their own courses and to share the results with us.

This panel presents varying global perspectives on the role of community colleges and 2- or 3-year technical schools (collectively called junior colleges here) in computing education. In some countries, students interested in a career in computing can obtain a 2- or 3-year degree instead of, or as a precursor to, a traditional Bachelor's degree. With representatives from five different countries and four different continents, the panel discusses the variety of pathways in computing education around the world, and in particular the role of community colleges and 2- or 3-year technical schools in these pathways.

There is widespread concern about lack of student engagement in Information and Communication Technology (ICT) courses and the influence of this on learning outcomes, retention, and the student experience overall. Lack of engagement is particularly concerning for first-year students, who are developing their study behaviours for the remainder of their degree programs. This study seeks to report on the myriad of current initiatives in Australian universities to increase ICT student engagement. This is explored for both in-class teaching innovations and the support structures with which students interface academically and administratively. The study draws upon data collected from interviews of 30 academics involved with the design and delivery of the first-year learning experience of ICT students in 25 Australian universities. Analysis of this data has provided a comprehensive overview of current initiatives to address student engagement. These covered a range of academic and non-academic aspects of the student experience. Our findings highlight the unique challenges that our first-year ICT students face and we recommend areas for further investigation.

Educational performance indicators are being considered or implemented in different ways by institutions and governments in different countries. What impact is this likely to have on computing education?

Certain practices, such as unauthorised collaboration with other students and unreferenced copying from external sources, are generally considered in the educational context to be breaches of academic integrity. This paper explores whether there are differences between the perceptions of the acceptability of these practices in the academic context and in the professional context. From focus groups of computing academics and students, and an online survey, we find that there are indeed differences in perceptions: that many practices considered unacceptable in the academic context are considered significantly more acceptable in the professional context. This raises questions concerning the roles of summative assessment and the possibilities of authentic assessment. The paper concludes that in much of programming education there is an unbreachable rift between the goal of authentic assessment, which necessarily entails collaborative work, and the need for summative assessment of individual effort, which typically requires work in isolation. The findings of our research have implications for computing education programs, particularly in regard to preparation of students for the workforce.

The methodology of eye tracking has been gradually making its way into various fields of science, assisted by the diminishing cost of the associated technology. In an international collaboration to open up the prospect of eye movement research for programming educators, we present a case study on program comprehension and preliminary analyses together with some useful tools.

We analyze the Computing Education Research (CER) literature to discover what theories, conceptual models and frameworks recent CER builds on. This gives rise to a broad understanding of the theoretical basis of CER that is useful for researchers working in that area, and has the potential to help CER develop its own identity as an independent field of study.

Student perceptions of academic integrity have been extensively researched in relation to text-based assessments, but there is rather less research relating to non-text-based assessments such as computer programs, databases, and spreadsheets. This paper reports the findings from a survey of computing students and academics to investigate perceptions of particular academic practices with regard to both essays and computing assessments. For each practice the research sought to discover whether it was perceived to constitute plagiarism or collusion and whether it was considered to be acceptable in an academic environment. While there was general agreement between academics and students regarding some practices, both groups displayed high levels of uncertainty about other practices. There was considerable variation between their attitudes to similar practices in the text and non-text environments, and between what was seen as plagiarism/collusion and perceptions of unacceptability. That is, there were practices that were perceived to be plagiarism or collusion but were considered acceptable, and others that were considered not to be plagiarism or collusion but were nevertheless thought unacceptable. These findings suggest a need for academic integrity policies and procedures specific to computing, accompanied by discipline-specific student education.

This paper reports on research that used focus groups and a national online survey of computing students at Australian universities to investigate perceptions of acceptable academic practices in writing program code for assessment. The results indicate that computing students lack a comprehensive understanding of what constitutes acceptable academic practice with regard to writing program code. They are not clear on the need to reference code taken from other sources, or on how to do so. Where code from other sources is used, or inappropriate collaboration takes place between students, there appears to be a feeling that any academic misconduct is diminished or even nullified if the students subsequently work with the code to make it their own. These findings suggest a need for the development of standards that elucidate acceptable practices for computing, combined with ongoing education of computing students.

Previous studies of assessment of programming via written examination have focused on analysis of the examination papers and the questions they contain. This paper reports the results of a study that investigated how these final exam papers are developed, how students are prepared for these exams, and what pedagogical foundations underlie the exams. The study involved interviews of 11 programming lecturers. From our analysis of the interviews, we find that most exams are based on existing formulas that are believed to work; that the lecturers tend to trust in the validity of their exams for summative assessment; and that while there is variation in the approaches taken to writing the exams, all of the exam writers take a fairly standard approach to preparing their students to sit the exam. We found little evidence of explicit references to learning theories or models, indicating that the process is based largely on intuition and experience.

This paper describes the development of a classification scheme that can be used to investigate the characteristics of introductory programming examinations. We describe the process of developing the scheme, explain its categories, and present a taste of the results of a pilot analysis of a set of CS1 exam papers. This study is part of a project that aims to investigate the nature and composition of formal examination instruments used in the summative assessment of introductory programming students, and the pedagogical intentions of the educators who construct these instruments.

Of the students who pass introductory programming courses, many appear unable to explain the purpose of simple code fragments such as a loop to find the greatest element in an array. It has never been established whether this is because the students are unable to determine the purpose of the code or because they can determine the purpose but lack the ability to express that purpose. This study explores that question by comparing the answers of students in several offerings of an introductory programming course. In the earlier offerings students were asked to express the purpose in their own words; in the later offerings they were asked to choose the purpose from several options in a multiple-choice question. At an overseas campus, students performed significantly better on the multiple-choice version of the question; at a domestic campus, performance was better, but not significantly so. Many students were unable to identify the correct purpose of small fragments of code when given that purpose and some alternatives. The conclusion is that students' failure to perform well in code-explaining questions is not because they cannot express the purpose of the code, but because they are truly unable to determine the purpose of the code - or even to recognize it from a short list.

This paper presents a preliminary analysis of research papers in computing education. While previous analysis has explored what research is being done in computing education, this project explores how that research is being done. We present our classification system, then the results of applying it to the papers from all five years of ICER. We find that this subset of computing education research has more in common with research in information systems than with that in computer science or software engineering; and that the papers published at ICER generally appear to conform to the specified ICER requirements.

This paper presents an analysis of research papers about programming education that were published in computing education conferences in the years 2005 to 2008. We employed Simon's classification scheme to identify the papers of interest from the ICER, SIGCSE, ITiCSE, ACE, Koli Calling and NACCQ conferences. Having identified the papers, we analyzed the type of data collected, whether the analysis was qualitative, quantitative, or mixed, and the aims and outcomes being reported. The greatest number of papers employed quantitative research methods, investigated the ability, aptitude, or understanding of students, and were based in single courses. The theme of the research and the type of study conducted vary across the conferences, indicating the different nature and role of each conference. Papers that investigated student learning of programming in terms of established theories or models of learning were not common, indicating an area of research that deserves more attention.

We have applied Simon's system for classifying computing education publications to all three years of papers from ICER. We describe the process of assessing the inter-rater reliability of the system and fine-tuning it along the way. Our analysis of the ICER papers confirms that ICER is a research-intensive conference. It also indicates that the research is quite narrowly focused, with the majority of the papers set in the context of programming courses. In addition we find that ICER has a high proportion of papers involving more than one institution, and high proportions of papers on the themes of ability/aptitude and theories and models of teaching and learning.