School teachers around the world use online resources in their teaching.The education systems in which teachers work directly impact their freedom to choose, adapt, and use resources in their classes.This, in turn, influences teachers' information interactions involved in lesson planning and resource preparation, but few studies have investigated this relationship.Based on a semi-structured interview and observation study with 15 school teachers working with the Scottish Curriculum for Excellence, a process model is proposed to describe teacher tasks and strategies for finding, using, and sharing educational resources.This study addresses the influence of the education system context and considers implications for design of search systems that support online educational resource search by school teachers.A supplementary open licensed dataset can be found at https://osf.io/a2xs8/. CCS Concepts• Information

One of the less explored approaches to foster equity, diversity, and inclusion (EDI) in Computer Science (CS) is through changes to the curriculum. Despite sporadic work on the adoption of Culturally Responsive Computing (CRC) and Universal Design for Learning (UDL), the inclusion of equity-minded courses, or modifications on specific elements of the curriculum such as introductory programming courses, there has never been a wide exploration or adoption of a successful equity-minded undergraduate CS curriculum.

Over the last two years in the School of Informatics at the University of Edinburgh, we have implemented a new approach to student support for our undergraduate and masters students, integrating new approaches to practical support and well-being with a range of co- and extra-curricula events, designed to help computing students develop more completely as future employees and citizens. In this paper, we outline the new approach, comparing it with our traditional approach to student support in our department, and consider how successful this switch has been through interviews with twenty-six students. Our research indicates that the key things that students value in student support are reliability and consistency, and that whilst engaging computing students in non-core activities is challenging, there are approaches that can help - in particular, being very specific how students will benefit through attending and allowing flexibility in routes to engagement.

Background and Context. The relationship between spatial skills and computing science success has been demonstrated at multiple institutions. ICER has reacted positively to two theories for why this relationship exists, by both Parkinson & Cutts and Margulieux. However, only limited work has been done to validate these theories, and more confirmatory research about the relationship between spatial skills and module grades in CS is necessary.

At ITiCSE 2021, Working Group 3 examined the evidence for teaching practices that broaden participation for women in computing, based on the National Center for Women & Information Technology (NCWIT) Engagement Practices framework. One of the report's recommendations was "Make connections from computing to your students' lives and interests (Make it Matter) but don't assume you know what those interests are; find out! " The goal of this 2023 working group is to find out what interests women students by bringing together data from our institutions on undergraduate module enrollment, seeing how they differ for women and men, and what drives those choices. We will code published module content based on ACM curriculum guidelines and combine these data to build a hierarchical statistical model of factors affecting student choice. This model should be able to tell us how interesting or valuable different topics are to women, and to what extent topic affects choice of module - as opposed to other factors such as the instructor, the timetable, or the mode of assessment. Equipped with this knowledge we can advise departments how to focus curriculum development on areas that are of value to women, and hence work towards making the discipline more inclusive.

Uptake of Computing Science (CS) in schools in Scotland is far lower than desired, because of young people both not being able to access the subject and not choosing to study it. Moreover, over the last two decades, uptake across the country has been dropping, and gender balance in uptake is not only poor but worsening. As a first step to gaining insight into how we could work to improve uptake of CS, we have analysed data for secondary schools in Scotland over the last three years, with a focus on publicly-funded schools, to explore where inequalities and context-specific drivers have an impact. In this paper, we discuss how the location of a school in a certain socio-economic area and in an urban/rural/remote location impact the chances of young people studying CS. The data indicates that socio-economic advantage is a positive factor in accessing CS. It also indicates that urban locations tend to be advantageous in this respect, though the data around this is more complicated.

There are many researchers in the Computer Science Education field who have moved into this area from a different field of research. This can present challenges for these researchers, who have not come from, and may not currently be in, CSEd research groups where knowledge of the area is plentiful. Key challenges are building networks and getting to know people, conventions, conferences and so on in the field. The proposed session is designed to help those who have moved into the field - whether recently or some years ago - to build connections and share knowledge about how to thrive and progress in the field. Our intention is to build this into an ongoing network that will support those currently in the field with this background as well as those wanting to move into the field in the future.

Students have experienced incredible shifts in the in their learning environments, brought about by the response of universities to the ever-changing public health mandates driven by waves and stages of the coronavirus pandemic (COVID-19). Initially, these shifts in learning (mode of course delivery, course availability, etc.) were considered emergency responses. However, as the pandemic presses on, students have had to repeatedly adapt to the continuously evolving educational landscape as this global health crisis forced an "unprecedented global shift within higher education in the ways that we communicate with and educate students". This working group builds upon foundations and structure created by a 2021 ITICSE Working Group exploring the effects of COVID-19 on teaching and learning from a faculty perspective. That Working Group identified the incorporation of some pandemic-induced changes into future teaching practices. In this Working Group, we explore existing literature regarding the student experience in response to the evolving teaching practices catalyzed by COVID-19). Traditionally, computing is a subject full of experiential learning opportunities, rich with in-person labs and exercises. We explore how the changes within the COVID-affected academic landscape have altered that student experience. The current group of computing students will have had experiences under both typical (i.e. pre-pandemic) and COVID-affected teaching practices. It is, therefore, timely that we understand how each has impacted how they perceive their learning environment and educational experience. In turn, identifying those practices that have most benefited the student learning experience will help computing faculty improve their practices going forward.

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.

We present a new wordnet resource for Scottish Gaelic, a Celtic minority language spoken by about 60,000 speakers, most of whom live in Northwestern Scotland. The wordnet contains over 15 thousand word senses and was constructed by merging ten thousand new, high-quality translations, provided and validated by language experts, with an existing wordnet derived from Wiktionary. This new, considerably extended wordnet—currently among the 30 largest in the world—targets multiple communities: language speakers and learners; linguists; computer scientists solving problems related to natural language processing. By publishing it as a freely downloadable resource, we hope to contribute to the long-term preservation of Scottish Gaelic as a living language, both offline and on the Web.

Smart-cards are considered to be one of the most secure, tamper-resistant, and trusted devices for implementing confidential operations, such as authentication, key management, encryption and decryption for financial, communication, security and data management purposes. The commonly used RSA PKCS#11 standard defines the Application Programming Interface for cryptographic devices such as smart-cards. Though there has been work on formally verifying the correctness of the implementation of PKCS#11 in the API level, little attention has been paid to the low-level cryptographic protocols that implement it.

This article addresses the problem of finding suitable agents to collaborate with for a given interaction in distributed open systems, such as multiagent and P2P systems. The agent in question is given the chance to describe its confidence in its own capabilities. However, since agents may be malicious, misinformed, suffer from miscommunication, and so on, one also needs to calculate how much trusted is that agent. This article proposes a novel trust model that calculates the expectation about an agent's future performance in a given context by assessing both the agent's willingness and capability through the semantic comparison of the current context in question with the agent's performance in past similar experiences. The proposed mechanism for assessing trust may be applied to any real world application where past commitments are recorded and observations are made that assess these commitments, and the model can then calculate one's trust in another with respect to a future commitment by assessing the other's past performance.

Typical ontology matching applications, such as ontology integration, focus on the computation of correspondences holding between the nodes of two graph-like structures, e.g., between concepts in two ontologies. However, there are applications, such as web service integration, where we may need to establish whether full graph structures correspond to one another globally, preserving certain structural properties of the graphs being considered. The goal of this paper is to provide a new matching operation, called structure preserving matching. This operation takes two graph-like structures and produces a set of correspondences between those nodes of the graphs that correspond semantically to one another, (i) still preserving a set of structural properties of the graphs being matched, (ii) only in the case if the graphs are globally similar to one another. We present a novel approximate structure preserving matching approach that implements this operation. It is based on a formal theory of abstraction and on a tree edit distance measure. We have evaluated our solution with encouraging results.

We describe ORS, an ontology repair system. In contrast to most ontology matching systems, ORS is designed to repair an ontology that does not accurately model its domain. ORS’s ontology repairs include belief revisions, but more often makes signature repairs. It does not require full access to the ontologies of other agents and works entirely automatically and dynamically. ORS is the first example of a new breed of dynamic, automatic ontology-repair mechanisms, which we believe will be essential to realise the vision of autonomous, interacting agents, such as envisaged in the emantic Web. Full access to another (potentially rival) agent’s ontology is unrealistic; static and interactive matching mechanisms are unrealistic in the context of huge, dynamic populations of agents and full ontological agreement is pragmatically unrealistic. We present encouraging experimental results, plus an analysis of current limitations to be addressed in future work.