Computing educators strive to align curricula with workforce needs yet rarely articulate how professional competencies, behaviors, and responsibilities develop across different educational pathways. As the community moves beyond the 2027 curriculum revision cycle, bridge pathways spanning vocational, undergraduate, graduate, and non-computing entry routes are rapidly expanding. At the same time, widespread AI use requires curricula to more explicitly address professional judgment, accountability, and ethical reasoning, in addition to technical skills. This working group brings participants together to map, compare, and synthesize real curriculum and pathway artifacts using shared academic and workforce frameworks. Participants will build a competency-oriented pathway framework, identify recurring bridge pathway designs and gaps, and produce practical, post-2027 guidance that supports coherent, scalable, and internationally transferable computing curricula.

Allocating students to projects is a commonplace task in computing education. These decisions underpin student-supervisor allocation, the formation of tutee and capstone groups, and pair programming. These allocations play a critical role for individual learner outcomes and the success of collaborative interventions. For example, imbalance in either gender, ethnicity, or nationality can negatively impact learner outcomes. Despite the critical importance of these allocation choices, we see little consensus on how these are implemented. The allocation task can be challenging and time-consuming for instructors of even moderately-sized classes, and the fairness implications can be difficult to assess. Inadvertently, an instructor may allocate in a way that amplifies existing biases or disproportionately harms those from disadvantaged or protected groups. From students' perspectives, a lack of transparency on the allocation process may also lead to issues of trust. The Working Group will undertake a study of allocation practices by bringing together educational and ML literature to develop and evaluate the fairness of allocation methods, and develop educator guidelines to promote pedagogically grounded allocation practices.

The field of data systems has seen quick advances due to the popularization of data science, machine learning, and real-time analytics. In industry contexts, system features such as recommendation systems, chatbots and reverse image search require efficient infrastructure and data management solutions. Due to recent advances, it remains unclear (i) which topics are recommended to be included in data systems studies in higher education, (ii) which topics are a part of data systems courses and how they are taught, and (iii) which data-related skills are valued for roles such as software developers, data engineers, and data scientists. This working group aims to answer these points to explain the state of data systems education today and to uncover knowledge gaps and possible discrepancies between recommendations, course implementations, and industry needs. We expect the results to be applicable in tailoring various data systems courses to better cater to the needs of industry, and for teachers to share best practices.

Many educational institutions strive for constant evolution of computer science (CS) study programs to parallel the global technological development. As developments pick-up speed and curricula reconstructions happen ever more often, the institutions can not wait for an entire generation of students to have finished, to be able to assess the results and analyze the process of transition. This paper presents a set of tools as part of an open-sourced system for curriculum management, useful in the analysis of the evolution of curricula across time, tracking both structural and topical changes in reference to computing curricula guidelines.

Increased opportunities for online learning, including growth in Massive Open Online Courses (MOOCS), are changing our education environments, increasing access and flexibility in how students engage with education. However, there are still many questions regarding how we engage with students effectively in these environments, in particular through assessment.

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.

Estimating grades for courses that are yet to be enrolled by students can help them in making decisions towards timely graduation and achieving better overall results. This paper presents an evaluation of grade prediction for future courses using the model-based collaborative filtering methods: Probabilistic Matrix Factorization and Bayesian Probabilistic Matrix Factorization using Markov Chain Monte Carlo. The prediction model was evaluated in a simulated scenario of an enrollment cycle in a winter and summer semester, based on a real data-set of enrollments and grades over several years at the authors' institution. Several evaluation metrics were used in order to assess the accuracy of predictions and analyze the distribution of the prediction deviation across study programs and grades. Beside the standard approach in predicting the final grade that is to be achieved by a student in a future course, we have also devised a method to estimate if the student will fail the course, so that he will have to re-enroll it at least once. The results showed that the predicted grades were in the range ±1 compared to the actual grades in more than 80% of the records.

Since computing education began, we have sought to learn why students struggle in computer science and how to identify these at-risk students as early as possible. Due to the increasing availability of instrumented coding tools in introductory CS courses, the amount of direct observational data of student working patterns has increased significantly in the past decade, leading to a flurry of attempts to identify at-risk students using data mining techniques on code artifacts. The goal of this work is to produce a systematic literature review to describe the breadth of work being done on the identification of at-risk students in computing courses. In addition to the review itself, which will summarize key areas of work being completed in the field, we will present a taxonomy (based on data sources, methods, and contexts) to classify work in the area.

Computing is a field that is constantly evolving and as a result curricula at the universities are frequently under reconstruction. The number of curricula reconstructions at the author's institution produced multitude of options and sometimes confusingly similar choices that the students can have. This is considered as one of the main problems in improving student guidance and the primary objective behind the structured solution presented in this paper that enables the tracking of all the variances and inter-dependencies produced by all the curricula changes and helps the students make the most relevant choices towards specialization.