The rapid evolution of computing demands curricula that reflect modern practices, yet many CS1 and CS2 courses continue to emphasize only sequential programming. This NSF-funded project addresses that gap by designing and disseminating exemplar CS1 and CS2 courses that integrate parallel, distributed, and event-driven computing as core concepts. The materials include unplugged activities and programming labs for both C++ and Java. To ensure broad applicability and adoption, development occurred in collaboration with instructors from six diverse institutions who are now implementing the materials. Evaluation includes surveys, assignment-specific instruments, and cross-team analysis. This poster presents the project’s vision, methods, and resources, highlighting how others can adopt and adapt them to teach modern computing.

This work in progress innovative practice paper documents ways in which current introductory computing courses are designed for an earlier generation of computers. We describe our plans for updating these courses for modern systems and programming practices and share details of the development of exemplar courses that will be adoptable by diverse institutions and programs teaching introductory programming courses.

Engaging undergraduates in research has been shown to improve retention, increase students' sense of computer science identity, and increase their chances of continuing to graduate school. Yet research experiences at most universities are ad hoc, and many undergraduates-particularly those from groups underrepresented in computing-do not have the opportunity to participate. The Early Research Scholars Program (ERSP) is a structured, academic-year group-based undergraduate research program designed to help universities vastly increase participation in research for early computing undergraduates. ERSP launched at UC San Diego in 2014 where it now annually engages over 50 second-year undergraduates, 59% of whom are women, and 22% of whom are from underrepresented racial and ethnic groups. The program's portable design has enabled its expansion to 7 other colleges and universities. This workshop will train participants in launching ERSP (or any part of it) at their university to increase and diversify the undergraduates participating in research. Workshop leaders are the ERSP directors at four universities. They will address how to launch and run the program in different contexts. They will provide an interactive, hands-on experience of running the program covering the following topics: developing and teaching a research methods class, student application and selection to ensure a diverse and supportive cohort, and creating a dual-mentoring structure to engage and retain early undergraduates without overburdening faculty. Workshop participants will be invited to join the ERSP virtual community to get support launching their own version of ERSP.

This work contributes to a better understanding of computing teachers' perceptions of themselves as role models. Role models are described as important to address under-representation, yet there is little in-depth research on how role modeling works and what university teachers in computing can model to broaden participation in the discipline. We will analyze teachers' reflections on how they may, or want to, be perceived by their students, particularly in terms of professional competencies, emotions and attitudes towards well-being. We will use and further develop an already existing framework on role modeling in computing, and we will relate our findings to existing research on computing and science identities. Modeling aspects outside the computing norm can help provide students with a wider notion of what it means to be a computer scientist. Besides developing the theoretical understanding of computing teachers as role models , our work can support various ways of developing computing teachers' competences and departments' teaching culture. The results are one way to contribute to student diversity and equitable access, and more broadly increase the relevance of computing education for sustainability.

High Performance Computing (HPC) is the ability to process data and perform complex calculations at extremely high speeds. Current HPC platforms can achieve calculations on the order of quadrillions of calculations per second with quintillions on the horizon. The past three decades witnessed a vast increase in the use of HPC across different scientific, engineering and business communities, for example, sequencing the genome, predicting climate changes, designing modern aerodynamics, or establishing customer preferences. Although HPC has been well incorporated into science curricula such as bioinformatics, the same cannot be said for most computing programs. This working group will explore how HPC can make inroads into computer science education, from the undergraduate to postgraduate levels. The group will address research questions designed to investigate topics such as identifying and handling barriers that inhibit the adoption of HPC in educational environments, how to incorporate HPC into various curricula, and how HPC can be leveraged to enhance applied critical thinking and problem solving skills. Four deliverables include: (1) a catalog of core HPC educational concepts, (2) HPC curricula for contemporary computing needs, such as in artificial intelligence, cyberanalytics, data science and engineering, or internet of things, (3) possible infrastructures for implementing HPC coursework, and (4) HPC-related feedback to the CC2020 project.

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.

The performance of Computer Science (CS) on a range of international student engagement benchmarks, including the North American National Survey of Student Engagement (NSSE) in the USA and Canada, Student Experience Survey (SES) in Australia, and the Student Engagement Survey (SES) in the UK, has generally been poor over a number of years and unfortunately shows little sign of improvement. In this ITiCSE Working Group we propose to carry out an in-depth analysis of student perspectives and experiences regarding student engagement in their CS courses and to contrast these with the perspectives and experiences of CS academics. We hope this will allow us to better understand the alignment between CS student and CS academic perspectives on student engagement and obtain insight into possible reasons for the reported poor engagement performance.

There are several national benchmarks used to measure student engagement, including the National Survey of Student Engagement (NSSE) in the USA and Canada, the Student Experience Survey (SES) in Australia, and the UK Engagement Survey (UKES). For a number of years, the world-wide performance of Computer Science (CS) on these benchmarks and across a range of instruments has been weak and shows little sign of improvement. The weakness of CS ratings is apparent especially when compared to related STEM disciplines that consistently rate more highly on many measures.

Replication research is rare in CS education. For this reason, it is often unclear to what extent our findings generalize beyond the context of their generation. The present paper is a replication and extension of Achievement Goal Theory research on CS1 students. Achievement goals are cognitive representations of desired competence (e.g., topic mastery, outperforming peers) in achievement settings, and can predict outcomes such as grades and interest. We study achievement goals and their effects on CS1 students at six institutions in four countries. Broad patterns are maintained --- mastery goals are beneficial while appearance goals are not --- but our data additionally admits fine-grained analyses that nuance these findings. In particular, students' motivations for goal pursuit can clarify relationships between performance goals and outcomes.

There is an increasing trend to use national benchmarks to measure student engagement, with instruments such as North American National Survey of Student Engagement (NSSE) in the USA and Canada, Student Experience Survey (SES) in Australia and NZ (previously known as the University Experience Survey UES), and Student Engagement Survey (SES) in the UK. Unfortunately, Computer Science (CS) rates fairly poorly on a number of measures in these surveys, even when compared to related STEM disciplines. Initial research suggests reasons for this poor performance may include a lack of awareness by CS academics of these instruments and the student engagement measures they are based on, and a misalignment between these instruments and the research focus of computing educators, leading to misdirected efforts in research and teaching practice. In this working group we carry out an in-depth analysis of international student engagement instruments to facilitate a greater awareness of the international benchmarks and what aspects of student engagement they measure. The working group also examine the focus of current computing education research and its alignment to student engagement measures on which these instruments are based. Armed with this knowledge, the computing education community can make informed decisions on how best to respond to these measures and consider ways to improve our performance in relation to other disciplines. In particular it is important to understand why certain measures of student engagement are built into these instruments, how these align to our current research practice or even to provide feedback to the designers of these instruments from a CS perspective.

An Open Ended Group Project (OEGP) is a distinguishable pedagogical tool, used by teachers in computing, engineering, and information technology courses. The tool contributes to the development of ‘soft’ skills essential for students' future career needs. This paper reports on a phenomenographic study that investigates the research question: What are the ways in which academics teaching Open Ended Group Projects experience teaching the course? Previous studies, using the phenomenographic research approach, have offered insights into academics' conceptions of science learning and teaching. However, there are no studies that investigate the experiences of teachers who use OEGP in their classes. This is the first study, that asks academics using an OEGP how they experience teaching these courses. Students enrolled in a course on computing education research conducted the small-scale study at Uppsala University, Sweden. In order to answer the research question, a theoretical sample was selected with a wide range of relevant population characteristics (e.g. background, prior experience, gender, and age). The semi-structured interview questions focused on understanding of OEGP, the learning objectives of OEGP, strategies for teaching these learning objectives, and the teacher's experience in teaching through OEGP. The results indicate that teachers see their role within OEGP as a coach and that the variation between experiences lies in what is intended to be coached. This variation is presented in a hierarchy (the outcome space). Categories focus on: the team, discipline, problem solving skills, learning and motivation. We also look at the first-hand experience of a student in an OEGP course and discuss the teachers' perceptions of students' experiences of OEGP. The implication for teaching is that a teacher needs to reconsider the way he or she teaches more often in OEGP than in regular courses. There are two reasons for this. First, teaching OEGP is based less on teaching content knowledge and more on teaching skills. Secondly, OEGP deals with a ‘real problem’, and aspects of the problem continuously change. Teachers are recommended to aim for reaching a higher category, indicating a deeper level of experience, and to use the experience of coaching OEGP in other courses as well. The importance of metacognition (reflection on action) and discussion with other teachers who have experience in teaching OEGP is highlighted.

We know from research that there is an intimate relationship between student learning and the context of learning. What is not known or understood well enough is the relationship of the students’ background and previous studies to the understanding and learning of the subject area—here, computer science (CS). To show the contextual influences on learning CS, we present empirical data from a qualitative investigation of the experiences of Chinese students studying for a master degree at Sweden's Uppsala University. Data were collected of the students’ understanding and learning of CS, their experience of the teaching and their own studies, and of their personal development in Sweden. Using an analysis framework grounded in phenomenography, we analytically separated the what and how aspects of learning. In this article, we describe the what , or the content of the students’ learning, and identify dimensions of variation in the experiences of students. These dimensions relate to the foci of the CS programs, the learning outcomes, and the impact of the studies. The findings from the analyses indicate pedagogical and pragmatic implications for teaching and learning CS in higher education institutions. The study extends the traditional use of phenomenography through the discussion of the dimensions of variation in the experiences and the values within the dimensions. It opens the way for understanding the relational nature of learning in computing education.

During the past two years, Massive Open Online Courses (MOOCs) have created wide interest in the academic world raising both enthusiasm for new opportunities for universities and many concerns for the future of university education. The discussion has mainly appeared in non-scientific forums, such as magazine articles, columns and blogs, making it difficult to judge wider opinions within academia. To collect more rigorous data we surveyed teachers, researchers, and academic managers on their opinions and experiences of MOOCs. In this paper, we present our analysis of responses from the computer science academic community (n=137). Their feelings about MOOCs are highly mixed. Content analysis of open-ended questions revealed that the most often mentioned positive aspects included affordances of MOOCs, freedom of time and location for studying, and the possibility to experience teaching from top-level international teachers/experts. The most common negative aspects included concerns about pedagogical designs of MOOCs, assessment practices, and lack of interaction with the teacher. About half the respondents claimed they had not changed their teaching as a result of MOOCs, a small number used MOOCs as learning resources and very few were engaging with MOOCs in any significant way.

Research questions in STEM disciplines are frequently strongly contextualised in the teaching and learning practice of the researcher. In this paper we chart a number of possible paths a researcher can follow from a single research proposition, or fundamental research question, to results which can vary significantly in nature. In order to do this, we establish a theoretical framework for research activity and examine the meaning of “theory” as a cognitive and research tool that helps engineering education practitioners and researchers. The paper reflects on the nature and role of different types of theory at four distinct stages of engineering education research: disciplinary, methodological, analytical, and interpretive. We illustrate how theory applies to the framing and integration of study results, and assists in the process of relating theories of learner development and learning to results of empirical data analysis.