Novice CS students can have a very wide range of starting knowledge. Bringing students to a professional level of practice requires an active process for students to develop their skills and knowledge, individually and collaboratively. Through the use of social constructivism, a theory of learning based on the benefits of collaborative social interactions between learners, these interactions allow learners to develop their knowledge by making sense of the interactions. This can take many forms, including discussing concepts, working on collaborative projects, and receiving timely and accurate feedback from their peer group and supervising instructors. The gap between what a learner is capable of doing without support and what the learner could perform with the assistance of the more knowledgeable other (MKO)---whether peer or teacher---is defined as the zone of proximal development (ZPD). Recent advances in chatbot technologies, especially those based on large language models (LLMs), and various institutional incentives naturally guide focus towards AI agents' potential as participants in collaborative social interactions. In this paper, we identify the characteristics and affordances of LLMs in how they might successfully support a computer science student through the ZPD, drawing on existing theory across key disciplines and knowledge of the characteristics of LLMs. We propose a group of models for constructive uses of this new technology, which will support social constructivism well, and provide more general guidelines for the use of LLMs.

Active Directory (AD), a directory service developed for Windows domain networks, is a frequent target for attackers due to its widespread adoption and the sensitive information it manages. Most existing attack path management solutions in ADs rely on simplistic node-to-node graphs, disregarding dependencies between edges. Specifically, in AD systems, there exists policy-defining edges that represent permissions on a set of objects. A single defensive action to remove such an edge can eliminate multiple permissions associated with all objects within the set - effectively removing all other edges connecting nodes in this set. In this paper, we propose a rigorous model that formalizes this concept of node-to-set mapping using $\alpha$-metagraph - a novel high-order graph model for capturing dependencies in AD systems. We present an algorithm for constructing the $\alpha$ metagraph from Active Directory data. Furthermore, we extend the current state-of-the-art AD defensive solution algorithm Spiral - to operate with the $\alpha$-metagraph model, taking advantage of policy-defining edges in AD network defense. Our extensive experiments demonstrate that the proposed $\alpha$-Spiral Algorithm, applied to $\alpha$-metagraphs, delivers timely and superior defense strategies, mitigating more attack sources within time and budget constraints than the original Spiral algorithm on node-to-node graphs.

Active Directory (AD), a directory service for Windows domain networks, is a common target for attackers due to its widespread use and the confidential data it contains. According to Microsoft, 95 million AD accounts are attacked every day and new attacks involving AD are a common occurrence. Despite frequent attacks against Active Directory and its critical role in network security, there are no publicly available datasets and tools for generating realistic AD graphs. This absence hinders the development and testing of novel methods for protecting AD systems. Realistic AD datasets are also essential for training and up-skilling human AD defenders. In this work, we develop ADSynth, a scalable and realistic AD attack graph generator. ADSynth uses metagraphs to model design principles of realistic AD systems, relying on three novel ideas: (1) metagraph abstractions of best practices in AD organizational design, (2) metagraph abstractions of security design principles in AD systems, and (3) a random metagraph model of common security misconfigurations. Our experiments demonstrate ADSynth's scalability in creating realistic AD graphs under various security settings. We apply ADSynth to some recent research on AD security and demonstrate that data from ADSynth significantly benefit these studies. ADSynth has been released to the community11https.z/adsynthcsizcr.github.io/22https://github.com/adsynthesizer/ADSynth.git.

This research paper examines the experience of Computer Science students during their period of study in higher education institutions, based on the surrounding ecological system, using Bronfenbrenner's theory of human development as a guiding principle. The objective of the study was to uncover the significant elements of the student experience when the student environment is regarded and categorised as an ecology. We focused on eight distinct student development lifestyle categories that could have an influence on their experience: the student's own awareness of their experience as a concept, their pre-university phase, their transition to the university phase, their university peers and colleagues, their social background outside the university, their hosting department, their extracurricular activities, and their post-university phase, if any. The study involved the participation of 206 computer science students, yielding a suitable population for analysis. The survey contained 42 questions to address the aspects of the eight categories discussed above, with a range of questions for each category. The questions employed a mixed-methods methodology using open-ended and closed questions. For the open-ended questions, we followed a grounded theory coding analysis technique to identify common codes in the student's responses. Word fre-quencies and figures were also analyzed to gain insights into students' perspectives using sentiment analysis techniques. For the quantitative questions, response rates and summary statistics were calculated. The findings shed light on the pivotal factors that influence the student experience of Computer Science students, offering valuable insights to students, educators, administrators, and decision-makers. The study highlights the significance of environmental factors in shaping student experience in the CS discipline and proposes potential directions for future research in this field. The implications of these findings for the creation of more supportive and effective learning environments for Computer Science students are also discussed.

This systematic literature review (SLR) critically examines the published literature since 2012 on the applications of machine learning (ML) in higher education. These applications include student performance classification, retention prediction, experience enhancement, and educational data mining. Machine Learning research has developed at a rapid pace to enhance different aspects of our lives. The educational sciences are at a pivotal point due to the potential influence that ML techniques have and will have on the field. We have aggregated the findings from a range of data sources, offering a thorough and objective perspective on the advancements, challenges, and future directions in this area. The volume of work that needed to be considered in the review clearly indicates the importance and relevance of this application of ML. In this SLR, we collect, analyse, and present the existing literature on ML applications in higher education. We utilise a comprehensive search strategy across multiple data sources, including 11 data sources, identifying all relevant studies published from 2012 to 2024. The inclusion criteria focused on peer-reviewed articles using ML-derived tools in areas such as student performance, retention, and experience. Exclusion criteria were rigorously applied to filter out studies that did not align with the focused domain of higher education and machine learning. The initial results yielded a pool of 16,581 articles, then refined to a substantive selection of 15,788 by removing duplicates, screening for false positives, and conducting quality assessment based on a structured quality control (QC) scoring system. The final set of papers was analysed to extract insights into ML applications' methodologies, effectiveness, and trends in higher education. The insights gained have allowed us to identify key trends, methodologies, and areas for future research. In addition, the surveying process will enable us to highlight publication patterns and critical research areas in the space.

Full research paper-Team configuration, work practices, and communication have a considerable impact on the outcomes of student software projects. This study observes 150 college students who first individually solve exercises and then carry out a class project in teams of three. All projects had the same requirements. We analyzed how students' behavior on individual pre-project exercises predict team project outcomes, investigated how students' time management practices affected other team members, and analyzed how students divided their work among peers. Our results indicate that teams consisting of only low-performing students were the most dysfunctional in terms of workload balance, whereas teams with both low-and high-performing students performed almost as well as teams consisting of only high-performing students. This suggests that teams should combine students of varying skill levels rather than allowing teams with only low performers or letting students to form teams without constraints. We also observed that individual students' poor time management practices impair their teammates' time management. This underlines the importance of encouraging good time management practices. Most teams reported that they divided tasks in a way that is beneficial for the acquisition of technical skills rather than collaboration and communication skills. Only a few teams assigned tasks so that students would have worked only on tasks they already knew and thus felt most comfortable to work with.

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.

Computing education research is built on the use of suitable methods within appropriate theoretical frameworks to provide guidance and solutions for our discipline, in a way that is rigorous and repeatable. However, the scale of theory covered extends well beyond the CS discipline and includes educational theory, behavioural psychology, statistics, economics, and game theory, among others. A computing education researcher's journey towards appropriate and discipline relevant theory can be challenging and, when a researcher has learned one area of theory, it can be easy to return to familiar theory, as it may not be clear what the next step could be. The periodic table is a visual arrangement of the elements to group like with like, providing insight into how families of elements will react. Could we do the same with learning theories located in the domain of computer science education, and would it be useful? The working group will identify and survey existing literature on relationships between key areas of theory in computing education, identify ways of organising these research areas to show how knowledge of one could assist another, and produce initial graphical representations of theory and their relationship groupings to assist researchers in understanding how computing theory is currently used in the discipline and what theories might become of interest.

Comparisons between community detection methods are mostly based on their accuracies in recovering the built-in community structure in artificial benchmark networks. Current community detection benchmarks assign a fixed fraction of inter-community links, referred to as the mixing fraction, for every community in the same network. We first show in this paper that the variation in community mixing fractions has different impacts on the performances of different community detection methods that could change the decision to select a particular detecting algorithm. To comprehensively compare community detection methods, we therefore need a benchmark that generates heterogeneous community mixing fractions, which is not currently available. We address this gap by generalizing the state-of-the-art Lancichinetti-Fortunato-Radicchi benchmark to generate networks with heterogeneous community mixing fractions. Using our new benchmark, we can quantify the impact of the variation in community mixing fractions on existing community detection methods and re- evaluate the performance of the detecting algorithms as a function of the heterogeneity among the mixing fractions. Furthermore, we show that the heterogeneous community mixing tests using our generalized benchmark reflect better the performance that would be expected on real networks than the homogeneous community mixing tests using the original benchmark.

Detailed in-depth feedback on programming assignments is beneficial because it identifies specific software design and development aspects that students can improve on. For the feedback to be effective, it is important that students are given the opportunity to address the feedback in a timely manner. However, detailed in-depth feedback often needs to be manually written by the lecturer or marker, especially for large and complex final year assignments where automated test suites are difficult to implement, introducing potential delay in providing the feedback. Following existing work, we propose a two-stage assignment design where students receive feedback on their final submission and are then given the opportunity to address the feedback. We analyse 147 assignment submissions and show that this assignment design improves assignment marks when compared to a single-stage submission, with failure rates dropping by up to 30%. To determine the impact of in-depth detailed feedback, we compare the learning outcomes across two years where students taking the two-stage assignment were given either detailed feedback or simple feedback consisting of component marks at the initial stage. We show the benefits of both approaches and analyse the potential advantages of providing more costly, manual feedback.

A key challenge for computer science educators worldwide is providing effective feedback and support to students, to ensure they are engaged with the course. This includes online feedback on discussion forums as well as feedback on programming assignments. Due to the significant problems of scale that need to be addressed, effective lecturer intervention is difficult, and at the same time the effect of intervention in online discussion forums is challenging to measure accurately. The same problem occurs when marking programming assignments, where detailed, in-depth feedback is often replaced with output from failed testcases, which the students sometimes proceed to address without giving thought to the quality of their overall solutions.

A key challenge for educators using online discussion forums is how to provide effective feedback and support to students, to ensure they are engaged with discussions on the forums, and do not disengage from the course. In addition, there is a significant problem of scale to address. Even relatively low population forums of less than a hundred students can generate thousands of posts, so it is infeasible for the lecturer to monitor every discussion to identify disengaging students. There is a need to understand the act of intervention, in order to provide automated tools to better assist teaching staff with this task. Measuring the impact of intervention can be challenging and requires us to, first, understand what ``standard'' behaviour looks like across different student groups and identify topics where intervention would be most effective. In this paper, we identify the impact of intervention on different groups of students, characterising their behaviour in terms of response time and activity, compare the different responses to programming-related questions and other questions, and identify the useful aspects of this study for computer science educators. We conduct an initial examination of the impact of the nature of the question on intervention effectiveness and propose an analysis method that can be applied to any computer science forum. we showcase the application of the method to three courses. Our results indicate that associating student activity with the number of forum posts is misleading, as students who are only reading the forums respond also to intervention.

It can be difficult for educators with limited resources to decide which queries need immediate attention when a high volume of questions arises on the discussion forum. This becomes increasingly complex as the educator aims to obtain the best outcome across all threads in a timely fashion. Existing approaches to automated forum analysis provide a useful grouping of messages and identify common discussions, but require additional attention towards effective intervention. Research has shown that the timing of messages relative to associated deadlines is a key indicator of priority. In this paper, we propose the formal representation of events within a discussion forum to facilitate the definition of potential and existing intervention strategies. We enhance forum events with information about teaching activities, such as assignment deadlines, and discuss intervention strategies.

Large volumes of Computer Science Educational (CS Ed) material are published every year but it is apparent that equally large volumes of this are not being read or having much impact on practice, or even available to the practitioners who could use it. How can we distribute CS Ed materials and information more effectively and in potentially innovative ways? This BOF will provide a platform for discussion on a selection of techniques that encourage discussion and dissemination of CS Ed techniques in the community. Is traditional publishing still a good approach or is it just part of a wider group of techniques?

A number of institutions and organisations provide online or face-to-face professional learning as part of outreach initiatives to increase skill levels and support for teachers in K-12 Computing education. With a number of countries introducing new K-12 Computer Science curricula around the globe, this provides a prime opportunity for the Computer Science education community to transform and develop models of teacher professional learning that address teachers' needs at-scale. This paper explores the theoretical underpinnings of a community-based professional learning MOOC for Australian teachers for K-6 Computer Science. This paper reflects on data collected from three offerings of the MOOC, presented in light of the theory and design considerations. This paper provides valuable insights of the design of community-centric MOOCs, and acts as a guide for the construction of online professional learning opportunities for Computer Science educators.

How to accurately forecast seasonal time series is very important for many business area such as marketing decision, planning production and profit estimation. In this paper, we propose a weighted gradient Radial Basis Function Network based AutoRegressive (WGRBF-AR) model for modeling and predicting the nonlinear and non-stationary seasonal time series. This WGRBF-AR model is a synthesis of the weighted gradient RBF network and the functional-coefficient autoregressive (FAR) model through using the WGRBF networks to approximate varying coefficients of FAR model. It not only takes the advantages of the FAR model in nonlinear dynamics description but also inherits the capability of the WGRBF network to deal with non-stationarity. We test our model using ten-years retail sales data on five different commodity in US. The results demonstrate that the proposed WGRBF-AR model can achieve competitive prediction accuracy compared with the state-of-the-art.

In the era of the Internet of Things, enormous number of sensors have been deployed in different locations, generating massive time-series sensory data with geo-tags. However, such sensory readings are easily missing due to various reasons such as the hardware malfunction, connection errors, and data corruption. This paper focuses on this challenge--how to accurately yet efficiently recover the missing values for corrupted time-series sensor data with geo-stamps. In this paper, we formulate the time-series sensor data as a 3-order tensor that naturally preserves sensors' temporal and spatial dependencies. Then we exploit its low-rank and sparse-noise structures by drawing upon recent advances in Robust Principal Component Analysis (RPCA) and tensor completion theory. The main novelty of this paper lies in that, we design a highly efficient optimization method that combines the alternating direction method of multipliers and accelerated proximal gradient to recover the data tensor. Besides testing our method using the synthetic data, we also design a real-world testbed by passive RFID (RadioFrequency IDentification) sensors. The results demonstrate the effectiveness and accuracy of our approach.

Firewalls are a crucial element of any modern day business; they protect data and resources in a communications network from unauthorised access. In particular domains, such as SCADA networks, there are guidelines for firewall configuration, but currently there are no automated means to test compliance. Our research tackles this from first principles: we ask how firewall policies can be described at a high-level, independent of firewall-vendor and network minutiae. The semantic foundations we propose allow us to compare network-wide firewall policies and check if they are equivalent; or one is contained in the other in meaningful ways. These foundations also enable policy change-impact analysis and help identify functional discrepancies between multiple policy designs from users in distinct policy sub-domains (e.g., SCADA engineers, Corporate admins).

Significant advances have been made in the learning and teaching of Introductory Programming, including the integration of active and contextualised learning pedagogy. However, Massively Open Online Courses (MOOCs), where Computer Science and, more specifically, introductory programming courses dominate, do not typically adopt such pedagogies or lessons learned from more traditional learning environments. Moreover, the improvement of learning within the MOOC context in terms of discipline-specific pedagogy, and the improvement of student learning outcomes and processes have not been studied in depth.

A common requirement in policy specification languages is the ability to map policies to the underlying network devices. Doing so, in a provably correct way, is important in a security policy context, so administrators can be confident of the level of protection provided by the policies for their networks. Existing policy languages allow policy composition but lack formal semantics to allocate policy to network devices. Our research tackles this from first principles: we ask how network policies can be described at a high-level, independent of vendor and network minutiae. We identify the algebraic requirements of the policy-mapping process and propose semantic foundations to formally verify if a policy is implemented by the correct set of policy-arbiters. We show the value of our proposed algebras in maintaining concise network-device configurations by applying them to real-world networks.

A common goal in network-management is security. Reliable security requires confidence in the level of protection provided. But, many obstacles hinder reliable security management; most prominent is the lack of built-in verifiability in existing management paradigms. This shortfall makes it difficult to provide assurance that the expected security outcome is consistent pre- and post-deployment. Our research tackles the problem from first principles: we identify the verifiability requirements of robust security management, evaluate the limitations of existing paradigms and propose a new paradigm with verifi- ability built in: Formally-Verifiable Policy-Defined Networking (FV-PDN). In particular, we pay attention to firewalls which protect network data and resources from unauthorised access. We show how FV-PDN can be used to configure firewalls reliably in mission critical networks to protect them from cyber attacks.

Although teamwork has been identified as an essential skill for Computer Science (CS) graduates, these skills are identified as lacking by industry employers, which suggests a need for more proactive measures to teach and assess teamwork. In one CS course, students worked in teams to create a wiki solution to problem-based questions. Through a case-study approach, we test a developed teamwork framework, using manual content analysis and sentiment analysis, to determine if the framework can provide insight into students’ teamwork behavior and to determine if the wiki task encouraged students to collaborate, share knowledge, and self-adopt teamwork roles. Analysis revealed the identification of both active and cohesive teams, disengaged students, and particular roles and behaviors that were lacking. Furthermore, sentiment analysis revealed that teams moved through positive and negative emotions over the course of developing their solution, toward satisfaction. The findings demonstrate the value of the detailed analysis of online teamwork. However, we propose the need for automated measures that provide real-time feedback to assist educators in the fair and efficient assessment of teamwork. We present a prototype system and recommendations, based on our analysis, for automated teamwork analysis tools.

Firewall configuration is an important activity for any modern day business. It is particularly a critical task for the supervisory control and data acquisition (SCADA) networks that control power stations, water distribution, factory automation, etc. Lack of automation tools to assist with this critical task has resulted in unoptimised, error prone configurations that expose these networks to cyber attacks. Automation can make designing firewall configurations more reliable and their deployment increasingly cost-effective. Best practices have been proposed by the industry for developing high-level security policy (e.g., ANSI/ISA 62443-1-1). But these best practices lack specification in several key aspects needed to allow a firewall to be automatically configured. For instance, the standards are vague on how firewall management policies should be captured at a high-level using its specifications. In this paper, we uncover these missing pieces and propose extensions. We apply our extended best-practice specification to real-world firewall case studies to achieve multiple objectives: 1) to evaluate the usefulness of the refined best-practice in the automated specification of firewalls and 2) to illustrate that even in simple cases, SCADA networks are often insecure due to their misconfigured firewalls.

The development of discipline-specific cognitive and meta-cognitive skills is fundamental to the successful mastery of software development skills and processes. This development happens over time and is influenced by many factors, however its understanding by teachers is crucial in order to develop activities and materials to transform students from novice to expert software engineers. In this paper, we analyse the evolution of learning strategies of novice, first year students, to expert, final year students. We analyse reflections on software development processes from students in an introductory software development course, and compare them to those of final year students, in a distributed systems development course. Our study shows that computer science - specific strategies evolve as expected, with the majority of final year students including design before coding in their software development process, but that several areas still require scaffolding activities to assist in learning development.

Many software development organisations today adopt global software engineering (GSE) and agile models, requiring software engineers to collaborate and develop software in flexible, distributed, online teams. However, many employers have expressed concern that graduates lack teamwork skills and one of the most commonly occurring problems with GSE models are issues with project management. Team managers and educators often oversee a number of teams and the large corpus of data, in combination with agile models, make it difficult to efficiently assess factors such as team role distribution and emotional climate. Current methods and tools for monitoring software engineering (SE) teamwork in both industry and education settings typically focus on member contributions, reflection, or product outcomes, which are limited in terms of real-time feedback and accurate behavioural analysis. We have created a dashboard that extracts and communicates team role distribution and team emotion information in real-time. Our proof of concept provides a real-time analysis of teamwork discussions and visualises team member emotions, the roles they have adopted and overall team sentiment during the course of a collaborative problem-solving project. We demonstrate and discuss how such a tool could be useful for SE team management and training and the development of teamwork skills in SE university courses.

This paper investigates the effect of different forms of concept maps as scaffolding techniques to support answering quizzes in an online learning environment. Concept maps which represent a course topic have being utilised as a scaffolding technique for learning the subject matters and problem solving. However, due to the typical amount of information presented in the topic concept maps, learners might feel overwhelmed, reducing their motivation and increasing the learners' disorientation. In order to overcome this issue, a study was conducted with 59 undergraduates of a Software Engineering course to measure the effect of different forms of concept maps on learning. The study obtained statistically significant results when using concept maps adapted to given quizzes (known as task-adapted concept maps). Students' reflections collected through a questionnaire were very positive towards task-adapted concept maps as a scaffolding technique.

Industry has called upon academia to better prepare Computer Science graduates for teamwork, especially in developing the soft skills necessary for collaborative work. However, the teaching and assessment of teamwork is not easy, with instructors being pressed for time and a lack of tools available to efficiently analyse student teamwork, where large cohorts are involved. We have developed a teamwork dashboard, founded on learning analytics, learning theory and teamwork models that analyses students' online teamwork discussion data and visualises the team mood, role distribution and emotional climate. This tool allows educators to easily monitor teams in real-time. Educators may use the tool to provide students with feedback about team interactions as well as to identify problematic teams. We present a case study, trialing the dashboard on one university Computer Science course and include reflections from the course lecturer to determine its utility in monitoring online student teamwork.

Falls are among the leading causes of hospitalization for the elderly and illness individuals. Considering that the elderly often live alone and receive only irregular visits, it is essential to develop such a system that can effectively detect a fall or abnormal activities. However, previous fall detection systems either require to wear sensors or are able to detect a fall but fail to provide fine-grained contextual information (e.g., what is the person doing before falling, falling directions). In this paper, we propose a device-free, fine-grained fall detection system based on pure passive UHF RFID tags, which not only is capable of sensing regular actions and fall events simultaneously, but also provide caregivers the contexts of fall orientations. We first augment the Angle-based Outlier Detection Method (ABOD) to classify normal actions (e.g., standing, sitting, lying and walking) and detect a fall event. Once a fall event is detected, we first segment a fix-length RSSI data stream generated by the fall and then utilize Dynamic Time Warping (DTW) based kNN to distinguish the falling direction. The experimental results demonstrate that our proposed approach can distinguish the living status before fall happening, as well as the fall orientations with a high accuracy. The experiments also show that our device-free, fine-grained fall detection system offers a good overall performance and has the potential to better support the assisted living of older people.

The Internet of Things (IoT) envisions smart objects collecting and sharing data at a global scale via the Internet. One challenging issue is how to disseminate data to relevant consumers efficiently. This paper leverages semantic technologies, such as Linked Data, which can facilitate machine-to-machine (M2M) communications to build an efficient information dissemination system for semantic IoT. The system integrates Linked Data streams generated from various data collectors and disseminates matched data to relevant data consumers based on conjunctive triple pattern queries registered in the system by the consumers. We also design a new data structure, CTP-automata, to meet the high performance needs of Linked Data dissemination. We evaluate our system using a real-world dataset generated from a Smart Building Project. With CTP-automata, the proposed system can disseminate Linked Data at a speed of an order of magnitude faster than the existing approach with thousands of registered conjunctive queries.

The Internet of Things (IoT) envisions smart objects collecting and sharing data at a global scale via the Internet. One challenging issue is how to disseminate data to relevant data consumers efficiently. In this paper, we leverage semantic technologies which can facilitate machine-to-machine communications, such as Linked Data, to build an efficient information dissemination system for semantic IoT. The system integrates Linked Data streams generated from various data collectors and disseminates matched data to relevant data consumers based on Basic Graph Patterns (BGPs) registered in the system by those consumers. To efficiently match BGPs against Linked Data streams, we introduce two types of matching, namely semantic matching and pattern matching, by considering whether the matching process supports semantic relatedness computation. Two new data structures, namely MVR-tree and TP-automata, are introduced to suit these types of matching respectively. Experiments show that an MVR-tree designed for semantic matching can achieve a twofold increase in throughput compared with the naive R-tree based method. TP-automata, as the first approach designed for pattern matching over Linked Data streams, also provides two to three orders of magnitude improvements on throughput compared with semantic matching approaches.

Semantic technologies aim to facilitate machine-to-machine communication and are attracting more and more interest from both academia and industry, especially in the emerging Internet of Things (IoT). In this paper, we consider large-scale information sharing scenarios among mobile objects in IoT by leveraging semantic techniques. We propose to broadcast Linked Data on-air using RDF format to allow simultaneous access to the information and to achieve better scalability. We introduce a novel air indexing method to reduce the information access latency and energy consumption. To build air indexes, we firstly map RDF triples in the Linked Data into points in a 3D space and build B+-trees based on 3D Hilbert curve mappings for all of the 3D points. We then convert these trees into linear sequences so that they can be broadcast over a wireless channel. A novel search algorithm is also designed to efficiently evaluate queries against the air indexes. Experiments show that our indexing method outperforms the air indexing method based on traditional 3D R-trees.

RFID-based localization and tracking has some promising potentials. By combining localization with its identification capability, existing applications can be enhanced and new applications can be developed. In this paper, we investigate a tag-free indoor localizing and tracking problem (e.g., people tracking) without requiring subjects to carry any tags or devices in a pure passive environment. We formulate localization as a classification task. In particular, we model the received signal strength indicator (RSSI) of passive tags using multivariate Gaussian Mixture Model (GMM), and use the Expectation Maximization (EM) to learn the maximum likelihood estimates of the model parameters. Several other learning-based probabilistic approaches are also explored in the localization problem. To track a moving subject, we propose GMM based Hidden Markov Model (HMM) and k Nearest Neighbor (kNN) based HMM approaches. We conduct extensive experiments in a testbed formed by passive RFID tags, and the experimental results demonstrate the effectiveness and accuracy of our approach.

Concept maps are commonly used tools for organising and representing knowledge in order to assist meaningful learning. Although the process of constructing concept maps improves learners’ cognitive structures, novice students typically need substantial assistance from experts. Alternatively, expert-constructed maps may be given to students, which increase the workload of academics. To overcome this issue, automated concept map extraction has been introduced. One of the key limitations is the lack of an evaluation framework to measure the quality of machine-extracted concept maps. At present, researchers in this area utilise human experts’ judgement or expert-constructed maps as the gold standard to measure the relevancy of extracted knowledge components. However, in the educational context, particularly in course materials, the majority of knowledge presented is relevant to the learner, resulting in a large amount of information that has to be organised. Therefore, this paper introduces a machine-based approach which studies the relative importance of knowledge components and organises them hierarchically. We compare machine-extracted maps with human judgment, based on expert knowledge and perception. This paper describes three ranking models to organise domain concepts. The results show that the auto-generated map positively correlates with human judgment (rs~1) for well-structured courses with rich grammar (well-fitted contents).

Computer Science students struggle to develop fundamental programming skills and software development processes. Crucial to successful mastery is the development of discipline specific cognitive and metacognitive skills, including self-regulation. We can assist our students in the process of reflection and self-regulation by identifying and articulating successful self-regulated learning strategies for specific discipline contexts. However, in order to do so, we must develop an understanding of those discipline-specific strategies that are successful and can be readily adopted by students.

Device-free passive localization aims to localize or track targets without requiring them to carry any devices or to be actively involved with the localization process. This technique has received much attention recently in a wide range of applications including elderly people surveillance, intrud

With the fast emerging Internet of Things (IoT), effectively and efficiently searching and selecting the most related things of a user’s interest is becoming a crucial challenge. In the IoT era, human interactions with things are taking place at a new level in ubiquitous computing. These interaction

Computer-based assessment is a useful tool for handling large-scale classes and is extensively used in the automated assessment of student programming assignments in Computer Science. The forms that this assessment takes, however, can vary widely from simple acknowledgement to a detailed analysis of output, structure and code. This study focusses on output analysis of submitted student assignment code and the degree to which changes in automated feedback influence student marks and persistence in submission. Data was collected over a four year period, over 22 courses but we focus on one course for this paper. Assignments were grouped by the number of different units of automated feedback that were delivered per assignment to investigate if students changed their submission behaviour or performance as the possible set of marks, that a student could achieve, changed. We discovered that pre-deadline results improved as the number of feedback units increase and that post-deadline activity was also improved as more feedback units were available.

Emulated networks and systems, where router and server software are run in virtual environments, allow network operators and researchers to perform experiments at large scale more economically than in testbeds. Running real code provides a greater level of realism than simulation.

Autonomous defence systems are typically characterised by hard constraints on space, weight and power. These constraints have a strong impact on the non-functional properties, and performance, of the final system. System execution modelling tools permit early prediction of the performance of model driven systems, however the focus to date has been on understanding the performance of a model rather than determining if it meets performance requirements, and subsequently carrying out analysis to reveal the causes of any requirement violations. In this paper, we propose an integrated approach to performance prediction of model-driven distributed real time embedded defence systems. Our architectural prototyping system supports a scenario-driven experimental platform for evaluating model suitability within a set of deployment and real-time performance constraints. We present an overview of our performance prediction system, demonstrating the integration of modelling, execution and visualisation, and discuss a case study to illustrate our approach.

This session will help participants understand the importance of, and challenges in, introducing collaborative learning within introductory Computer Science curricula. At the University of Adelaide, we have designed our first year curriculum, a sequence of three courses, around collaborative learning - in this session we explore our experiences in developing collaborative activities, collaborative assessment and appropriate training for staff, academic and sessional, involved in collaborative sessions. Based on a seven year programme of designing and undertaking collaborative activities, we will discuss our successes, and our failures, in the use of collaborative learning techniques.

Collaborative learning encourages deeper learning, producing significant benefit in learning outcomes. There has been an increasing trend to adopt collaborative activities, due to the expected learning benefits but also because of the expected social benefits and their impact on transition concerns. However, collaborative activities may also introduce additional stress and anxiety for students as they cope with altered participation expectations, and the need to develop collaboration, communication and management skills concurrently with their discipline skills. In this paper we describe a phenomenographic analysis of student's reflections on collaborative activities, including their perceptions of the purpose of such activities, and corresponding behaviours.

How do we identify students who are at risk of failing our courses? Waiting to accumulate sufficient assessed work incurs a substantial lag in identifying students who need assistance. We want to provide students with support and guidance as soon as possible to reduce the risk of failure or disengagement. In small classes we can monitor students more directly and mark graded assessments to provide feedback in a relatively short time but large class sizes, where it is most easy for students to disappear and ultimately drop out, pose a much greater challenge. We need reliable and scalable mechanisms for identifying at-risk students as quickly as possible, before they disengage, drop out or fail. The volumes of student information retained in data warehouse and business intelligence systems are often not available to lecturing staff, who can only observe the course-level marks for previous study and participation behaviour in the current course, based on attendance and assignment submission.

Computer Science majors must be able to communicate effectively. Industry surveys identify the development of communication and critical thinking skills as key to the reform of the higher education sector. However, academics are challenged by time and discipline content pressures, as well as a lack of familiarity with the teaching and assessment of communication skills content. There is considerable existing work in the area of communication skills development, positioned both in terms of curriculum guidelines for effective communication skills development, and example communication skills activities. However, this research is deficient in detailed, contextualised methodologies and frameworks for the development of communication skills within the Computer Science curriculum. We present a new methodology, building upon well established theoretical frameworks, designed to assist academics in the development of communication skills activities integrated with discipline content across the curriculum. We illustrate this methodology in the design of a CS1/CS2 communication skills course.

Puzzle-based learning (PBL) is a new and emerging model of teaching critical thinking and problem solving. Today's market place needs skilled graduates capable of solving real problems of innovation in a changing environment. A learning goal of PBL is to distill domain independent transferable heuristics for tackling problems. While solving puzzles is innately fun, companies such as Google and Yahoo also use puzzles to assess the creative problem solving skills of potential employees. In this interactive workshop we will examine a range of puzzles and games. What general problem solving strategies can we learn from the way we solve these examples? Participants will emerge with the needed pedagogical foundation to offer a full course on PBL or to include it as part of another course.

Network design, as it is currently practiced, involves putting devices together to create a network. However, a network is more than the sum of its parts, both in terms of the services it provides, and the potential for bugs. Devices are important, but their combination into a network should follow from expression of high-level policy, not the minutiae of network device configuration. Ideally we want to consider the network as a whole object. In this paper we develop generalized graph products that allow the mathematical design of a network in terms of small subgraphs that directly express business policy. The result is a flexible algebraic description of networks suitable for manipulation and proof. The approach is more than just design - it allows for analysis of existing networks providing an understanding of the policies used in their construction, something which can be difficult if the original designers no longer work on that network. We apply the approach to several real world networks to demonstrate how it can provide insight, and improve design.

The study of network topology has attracted a great deal of attention in the last decade, but has been hampered by a lack of accurate data. Existing methods for measuring topology have flaws, and arguments about the importance of these have overshadowed the more interesting questions about network structure. The Internet Topology Zoo is a store of network data created from the information that network operators make public. As such it is the most accurate large-scale collection of network topologies available, and includes meta-data that couldn't have been measured. With this data we can answer questions about network structure with more certainty than ever before - we illustrate its power through a preliminary analysis of the PoP-level topology of over 140 networks. We find a wide range of network designs not conforming as a whole to any obvious model.