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Simple Summary: Automatic segmentation of organs and other regions of interest is a promising approach for reducing the workload of doctors in radiotherapeutic planning, but it can be hard for doctors and researchers to keep up with current developments. This review evaluates 807 papers and reveals trends, commonalities, and gaps in the existing corpus. A set of recommendations for conducting effective segmentation studies is also provided. This review provides a formal overview of current automatic segmentation studies that use deep learning in radiotherapy. It covers 807 published papers and includes multiple cancer sites, image types (CT/MRI/PET), and segmentation methods. We collect key statistics about the papers to uncover commonalities, trends, and methods, and identify areas where more research might be needed. Moreover, we analyzed the corpus by posing explicit questions aimed at providing high-quality and actionable insights, including: "What should researchers think about when starting a segmentation study?", "How can research practices in medical image segmentation be improved?", "What is missing from the current corpus?", and more. This allowed us to provide practical guidelines on how to conduct a good segmentation study in today's competitive environment that will be useful for future research within the field, regardless of the specific radiotherapeutic subfield. To aid in our analysis, we used the large language model ChatGPT to condense information. [ABSTRACT FROM AUTHOR]

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The pandemic has accelerated technological advances in higher education. Learning management systems that make use of the persistent platform, a wide range of communication options, and profoundly immersive qualities quickly become the platform of choice for learning management systems. The blockchain enables non-fungible tokens (NFTs) and uses the blockchain architecture to validate college diplomas and transcripts, opening the way for a diverse variety of e-portfolios owned and shared by students and teachers. Another type of blockchain that is relatively new to the education scene is decentralized autonomous organizations (DAOs). Through this, technology courses, certificates, and more can become automated and authenticated on the blockchain. The current state of the web is rapidly evolving into what is known as Web 3.0. It is the emerging evolution of the internet to make it machine-readable, which includes leveraging technologies such as artificial intelligence (AI), the cloud, and distributed ledger technologies such as blockchain. From the perspectives of the university and the metaverse, this paper discusses the shift in education brought about by NFTs, DAOs, Web 3.0, and the metaverse. The powerful mix of Web 3.0 and the metaverse is about to revolutionize the way people learn and teach in the modern world.

Video is an increasingly important resource in higher education. A key limitation of lecture video is that it is fundamentally a sequential information stream. Quickly accessing the content aligned with specific learning objectives in a video recording of a classroom lecture is challenging. Recent research has enabled automatic reorganization of a lecture video into segments discussing different subtopics. This paper explores AI generation of visual and textual summaries of lecture video segments to improve navigation. A visual summary consists of a subset of images in the video segment that are considered the most unique and important by image analysis. A textual summary consists of a set of keywords selected from the screen text in the video segment by analyzing several factors including frequency, font size, time on screen, and existence in domain and language dictionaries. Evaluation was performed against keywords and summary images selected by human experts with the following results for the most relevant formulations. AI driven keyword selection yielded an F-1 score of 0.63 versus 0.26 for keywords sampled randomly from valid keyword candidates. AI driven visual summary yielded an F-1 score of 0.70 versus 0.59 for K-medoid clustering that is often employed for similar tasks. Surveys showed that 79% (72%) of the users agreed that a visual (textual) summary made a lecture video more useful. This framework is implemented in Videopoints, a real-world lecture video portal available to educational institutions.

Effective learning depends on effective feedback, which in turn requires a set of skills, dispositions and practices on the part of both students and teachers which have been termed "feedback literacy." A previously published teacher "feedback literacy competency framework" has identified what is needed by teachers to implement feedback well. While this framework refers in broad terms to the potential uses of educational technologies, it does not examine in detail the new possibilities of "automated feedback" (AF) tools, especially those that are open by offering varying degrees of transparency and control to teachers. Using analytics and artificial intelligence, open AF tools permit automated processing and feedback with a speed, precision and scale that exceeds that of humans. This raises important questions about how human and machine feedback can be combined optimally and what is now required of teachers to use such tools skillfully. The paper addresses two research questions: "Which teacher feedback competencies are necessary for the skilled use of open AF tools?" and "What does the skilled use of open AF tools add to our conceptions of teacher feedback competencies?" We conduct an analysis of published evidence concerning teachers' use of open AF tools through the lens of teacher feedback literacy, which produces summary matrices revealing relative strengths and weaknesses in the literature, and the relevance of the feedback literacy framework. We conclude firstly, that when used effectively, open AF tools exercise a range of teacher feedback competencies. The paper thus offers a detailed account of the nature of teachers' feedback literacy practices within this context. Secondly, this analysis reveals gaps in the literature, signalling opportunities for future work. Thirdly, we propose several examples of "automated feedback literacy," that is, distinctive teacher competencies linked to the skilled use of open AF tools.

The use of automated decision-making systems is increasing in education. While the potential impacts of ADM are becoming widely known amongst experts, the perspectives of those impacted by ADM remain peripheral. To broaden expertise and participation, this paper proposes that ADM needs to be considered as a sociotechnical controversy, as part of a technical democracy approach that utilises hybrid forums. Following Callon and colleagues, in this paper, technical democracy refers to the process of learning through uncertainty about sociotechnical controversies, and hybrid forums refer to the specific sites of democratisation. This paper first identifies key uses and concerns with ADM in education. Second, it proposes that restricted capacity for participation can be addressed through technical democracy. Last, it proposes that hybrid forums can create moments of democratisation through shared uncertainty, material politics, and collective experimentation.

In the recent Australian 2021 census, the socio-technical construct of algorithmically driven decision-making processes made LGBTQI+ data as a category of diversity, inclusion and belonging an absent presence. In this paper, we position the notion of 'data justice' in relation to the entrenchment of inequalities and exclusion of LGBTQI+ lives and consider the implications of LGBTQI+ data being missing in action. As we look at the notion of 'data justice', we consider five critical socio-technical imaginaries with different kinds of data to think through the implications of technical democracy, data justice and post-automation. Finally, we consider the imaginary of citizenship when LGBTQI+ data is habitually missing in action from systematic power integrated into forms of governance. This paper positions 'data' not as a static 'object or process' but as a dynamic ecology that carries with it a multi-faceted set of coded meanings requiring constant review and reconsideration.

In the market discourses of technological disruption, higher education institutions have routinely been positioned in deficit models and of anachronistic approaches to teaching at odds with the types of educational futures being presented by commercial organisations. Predominantly, automation technologies in the form of artificial intelligence are being promoted as the future of teaching. In this paper, on the other hand, we explore the prospects for using non-artificial intelligence automated agents in teaching and its impact on the teacher function at the University of Edinburgh. Through engagement with teachers, staff and students at the university, this research has identified use cases for bots, in what spaces they would be situated, and how they would supplement the teacher function. This paper argues that a community-driven approach combined with a sociomaterial conceptualisation can generate a shift from market discourses and to collaborative development of educational technologies.

Collaborative Problem-Solving Skills (CPS) have become increasingly important. Research into the development of CPS is still scarce, but there are several approaches that may be useful for its development. Specifically, providing feedback in collaborative contexts is key. In this paper, we study and develop a feedback system that uses Automated Planning techniques to promote communication among students. Our system is designed to be used in a real-world educational setting, considering the underpinning theory of when and how to give feedback. The system's frontend is a video game, which presents tasks that can only be solved when students collaborate. In the backend, the system computes the solution to the task in a partial-order plan using an automated planning engine. While it monitors the plan and provides feedback to students. We describe an experimental study involving 42 students aged between 10 and 13, in which we explore the effectiveness of the feedback that was given. We show that the feedback allowed the students to perform better in the game, improve their communication, and develop their collaborative problem-solving skills. We also describe a novel approach to monitoring multi-agent partial-order plans, specifically designed for plans with so-called independent chains, that is more efficient than previous approaches and therefore requires fewer computational resources in the classroom. This paper contributes to the literature in two ways. First, we propose a novel monitoring algorithm for partial-order plans that is better suited to educational settings. Second, we show that feedback extracted from a plan can promote reflection about collaborative problem-solving during a multi-agent activity.

Purpose: This study aims to report on an automatic deidentification system for labeling and obfuscating personally identifiable information (PII) in student-generated text. Design/methodology/approach: The authors evaluate the performance of their deidentification system on two data sets of student-generated text. Each data set was human-annotated for PII. The authors evaluate using two approaches: per-token PII classification accuracy and a simulated reidentification attack design. In the reidentification attack, two reviewers attempted to recover student identities from the data after PII was obfuscated by the authors' system. In both cases, results are reported in terms of recall and precision. Findings: The authors' deidentification system recalled 84% of student name tokens in their first data set (96% of full names). On the second data set, it achieved a recall of 74% for student name tokens (91% of full names) and 75% for all direct identifiers. After the second data set was obfuscated by the authors' system, two reviewers attempted to recover the identities of students from the obfuscated data. They performed below chance, indicating that the obfuscated data presents a low identity disclosure risk. Research limitations/implications: The two data sets used in this study are not representative of all forms of student-generated text, so further work is needed to evaluate performance on more data. Practical implications: This paper presents an open-source and automatic deidentification system appropriate for student-generated text with technical explanations and evaluations of performance. Originality/value: Previous study on text deidentification has shown success in the medical domain. This paper develops on these approaches and applies them to text in the educational domain.

Paper currency recognition (PCR) is an important area of pattern recognition. A system for the recognition of paper currency is one kind of intelligent system which is a very important need of the current automation systems in the modern world of today. It has various potential applications including electronic banking, currency monitoring systems, money exchange machines, etc. This paper proposes an automatic paper currency recognition system for paper currency. A method of recognizing paper currencies has been introduced. This is based on interesting features and correlation between images. It uses Radial Basis Function Network for classification. The method uses the case of Saudi Arabian paper currency as a model. The method is quite reasonable in terms of accuracy. The system deals with 110 images, 10 of which are tilted with an angle less than 15o. The rest of the currency images consist of mixed including noisy and normal images 50 each. It uses fourth series (1984–2007) of currency issued by Saudi Arabian Monetary Agency (SAMA) as a model currency under consideration. The system produces accuracy of recognition as 95.37%, 91.65%, and 87.5%, for the Normal Non-Tilted Images, Noisy Non-Tilted Images, and Tilted Images respectively. The overall Average Recognition Rate for the data of 110 images is computed as 91.51%. The proposed algorithm is fully automatic and requires no human intervention. The proposed technique produces quite satisfactory results in terms of recognition and efficiency. [ABSTRACT FROM AUTHOR]

This paper introduces constructivist dialogue mapping (CDM), a new type of concept mapping. CDM encodes what people learn during a non-goal directed learning activity. CDM is a practical means to outline the mini theories users fluidly construct as they explore open-ended learning environments. To demonstrate the method, in this paper we use CDM to track how two modelers elaborate understandings during use of a constructionist learning game, Ant Adaptation. Using the method, we show how two users contest and construct their idea of self-organization in ant colonies. The method is rooted in constructionism, constructivism, concept mapping, and conceptual change.

eLearning is fast progressing scientific field proposing novel and specific approaches in a range of domains. It is well established practice in universities, schools and organizations for delivering interactive, adaptive and flexible training, taking advantage of contemporary and emerging technologies. Informatics is a continuously evolving science presenting its theoretical and practical advances applicable in various research areas, including in eLearning. The paper presents an exploration focused on the symbiotic connection between Informatics and eLearning that leads to contemporary and innovative solutions, facilitating and automating a wide variety of activities at information processing. The term eLearning Informatics is conceptualized and explained as a scientific field outlining the current research achievements and further directions for development. The applied research methodology is based on outlining the main vision in the domain eLearning Informatics through utilization of bibliometric approach and construction of bibliometric networks as well as on detailed examination of topic-related scientific papers.