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Lecture videos are amongst the most widely used instructional methods within present Massive Open Online Courses (MOOCs) and other digital educational platforms. As the main form of instruction, student engagement behaviour, including interaction with videos, directly impacts the student success or failure and accordingly, in-video dropouts positively correlate with dropout from MOOCs. The primary focus of previous video learning analytics studies is on analyzing video interaction behaviour using "explicit" factors (i.e., views). Limited research studies focus on "implicit" video learning analytics (e.g., pause, seek, content type) and their impact on student success, with existing studies addressing video interactions and their relationship with visual transitions. We aim to explore the association between video interactions and non-visual (i.e., verbal) content. This research focuses on language and discourse features of lecture video contents. We conduct a fine-grained analysis of 3.4 million video interactions across two AdelaideX MOOCs -- Programming (Code101x) and Cyberwar, Surveillance and Security (Cyber101x). According to our results, a number of discourse features (e.g., lexical diversity and causal connectives) demonstrate a statistically significant correlation with video interactions. We present insights regarding educational video design implications based on discourse processing theories.

The substantial growth of online learning, and in particular, through massively open online courses (MOOCs), supports research into nontraditional learning contexts. Learners' confusion is one of the identified aspects which impact the overall learning process, and ultimately, course attrition. Confusion for a learner is an individual state of bewilderment and uncertainty as to how to move forward. The majority of recent works in MOOCs neglects the individual factor and measure the influence of community-related aspects (e.g., votes, views) for confusion classification. While these are useful measures, these models neglect the personalized context, such as an individual's affect or emotions. Toward this, and within the MOOC context, we propose machine learning models based solely on language and discourse features extracted from learners' discussion posts. With over 83% F1-score in all domains, it is evident that linguistic-only features are highly effective in confusion classification, with our models outperforming previous models developed for confusion classification. In this article, we also obtain good performance for cross-domain confusion classification between 70.7% and 84.5% F1-score for all domain pairs (i.e., training on one domain and testing on another domain). Primarily, this article contributes through the development of a novel linguistic feature set that is predictive for effective confusion classification.

Computer Science (CS) curricula are being adopted worldwide. However, the lack of prior teaching training within this area means that we have an urgent need to provide teacher professional development. Best practice teacher professional development motivates us to construct interactive and sustainable models of professional development. However, there are challenges in doing this at the scale that we need and with the resources that are available. In this paper, we describe an ecosystem-based approach to supporting teacher professional development, aligned with best practices and with teachers' and schools' needs. An ecosystem approach operates under the coordination of a governance model, features a central hub and conceptualises the relationships of the organisations as a network. Ecosystems are underpinned by value co-creation, shared logic and can be non-geographical. We believe an ecosystem view of a teacher professional development program supports sustainability, authenticity and flexibility in how teachers engage with professional development, positioning teachers as co-contributors.

Radio tagging and tracking are fundamental to understanding the movements and habitats of wildlife in their natural environments. Today, the most widespread, widely applicable technology for gathering data relies on experienced scientists armed with handheld radio‐telemetry equipment to locate low‐power radio transmitters attached to wildlife from the ground. Although aerial robots can transform labor‐intensive conservation tasks, the realization of autonomous systems for tackling task complexities under real‐world conditions remains a challenge. We developed ConservationBots—small aerial robots for tracking multiple, dynamic, radio‐tagged wildlife. The aerial robot achieves robust localization performance and fast task completion times—significant for energy‐limited aerial systems while avoiding close encounters with potential, counterproductive disturbances to wildlife. Our approach overcomes the technical and practical problems posed by combining a lightweight sensor with new concepts: (i) planning to determine both trajectory and measurement actions guided by an information‐theoretic objective, which allows the robot to strategically select near‐instantaneous range‐only measurements to achieve faster localization, and time‐consuming sensor rotation actions to acquire bearing measurements and achieve robust tracking performance; (ii) a bearing detector more robust to noise; and (iii) a tracking algorithm formulation robust to missed and false detections experienced in real‐world conditions. We conducted extensive studies: simulations built upon complex signal propagation over high‐resolution elevation data on diverse geographical terrains; field testing; studies with wombats (Lasiorhinus latifrons; nocturnal, vulnerable species dwelling in underground warrens) and tracking comparisons with a highly experienced biologist to validate the effectiveness of our aerial robot and demonstrate the significant advantages over the manual method. [ABSTRACT FROM AUTHOR]

Computer science, like technology in general, is seen as a masculine field and the under-representation of women an intransigent problem. In this paper, we argue that the cultural belief in Australia that computer science is a domain for men results in many girls and women being chased away from that field as part of a border protection campaign by some males--secondary school teachers, boys and men playing games online and young men on campus at university. We draw on American feminist philosopher, Iris Marion Young's analysis of the "five faces" of oppression to suggest strategies whereby Australian universities could support women in computer science and educate men about respectful behaviour and gender equity.

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.

To support teachers to implement Computer Science curricula into classrooms from the very first year of school, teachers, schools and organisations seek quality curriculum resources to support implementation and teacher professional development. Until now, many Computer Science resources and outreach initiatives have targeted K-12 school-age children, with the intention to engage children and increase interest, rather than to formally teach concepts and skills. What is the educational quality of existing Computer Science resources and to what extent are they suitable for classroom learning and teaching? In this paper, an assessment framework is presented to evaluate the quality of online Computer Science resources. Further, a semi-systematic review of available online Computer Science resources was conducted to evaluate resources available for classroom learning and teaching and to identify gaps in resource availability, using the Australian curriculum as a case study analysis. The findings reveal a predominance of quality resources, however, a number of critical gaps were identified. This paper provides recommendations and guidance for the development of new and supplementary resources and future research.

England and Australia have introduced new learning areas, teaching computer science to children from the first year of school. This is a significant milestone that also raises a number of big challenges: the preparation of teachers and the development of resources" at a national scale." Curriculum change is not easy for teachers, in any context, and to ensure teachers are supported, scaled solutions are required. One educational approach that has gained traction for delivering content to large-scale audiences are massively open online courses (MOOCs); however, little is known about what constitutes effective MOOC design, particularly within professional development contexts. To prepare teachers in Australia, we decided to ride the wave of MOOCs, developing a MOOC to deliver free computing content and pedagogy to teachers with the integration of social media to support knowledge exchange and resource building. The MOOC was designed to meet teacher needs, allowing for flexibility, ad-hoc interactions, support and the open sharing of resources. In this paper, we describe the process of developing our initiative, participant engagement and experiences, so that others encountering similar changes and reforms may learn from our experience.

Contributing student pedagogy (CSP) builds upon social constructivist and community-based learning principles to create engaging and productive learning experiences. What makes CSP different from other, related, learning approaches is that it involves students both learning from and also explicitly valuing the contributions of other students. The creation of such a learning community builds upon established educational psychology that encourages deep learning, reflection and engagement. Our school has recently completed a review and update of its curriculum, incorporating student content-creation and collaboration into the design of key courses across the curriculum. Our experiences, based on several years of experimentation and development, support CSP-based curriculum design to reinforce the value of the student perspective, the clear description of their own transformative pathway to knowledge and the importance of establishing student-to-student networks in which students are active and willing participants. In this paper, we discuss the tools and approaches that we have employed to guide, support and structure student collaboration across a range of courses and year levels. By providing an account of our intentions, our approaches and tools, we hope to provide useful and transferrable knowledge that can be readily used by other academics who are considering this approach. (Contains 6 tables and 5 figures.)