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An unplugged approach to teaching enables students to explore Computational Thinking without using a computer. It might appear that if students are to learn programming, they should focus on computer-based work; however, it appears that using "unplugged" activities before engaging in computer-based coding (programming) activities for each unit of work leads to better outcomes for students in the same amount of time. In this paper we explore why this could be the case, by reviewing literature that reports on these experiences, and also using different theoretical lenses (Notional Machines, Semantic Profiles, and the Zone of Proximal Development) to analyse how the combination of experiences can engage students. We also explore how the approach integrates with mathematics education.

This paper explores a major theoretical framework from psychology, Dual Process Theory (DPT), which has received surprisingly little attention in the computing education literature. DPT postulates the existence of two qualitatively different kinds of cognitive systems, a fast, intuitive "System 1" and a slow, reflective "System 2". System 1 is associated with cognitive factors such as crystallized intelligence, long-term memory and associative learning; System 2 with fluid intelligence, working memory, and rule learning. This paper summarizes DPT and the way it has been expressed and explored in literatures relating to intelligence, memory, learning, attention, cognitive load, and more. It proposes a summary model, the Dual Process Cycle (DPC). It then considers example concepts from computing education within the context of this model. Examples include programming expertise, mental models of programs, the notional machine, code reading and code writing, and the theory of Learning Edge Momentum (LEM). In conclusion, it is argued that the DPC (and the framework of DPTs in general) provides a useful context for defining such concepts more richly and exactly, and for generating interesting questions about them.

In this paper we present a novel neural network architecture that aims to combine the highly popular and successfulconvolutional neural network architecture with the learning mechanism of an unsupervised self-organizing map. The con-volutional self-organizing map (ConvSOM) is a hierarchical network consisting of several independent self-organizingmaps. It incorporates features associated with convolutional networks, such as weight sharing, spatial pooling, and hierar-chical abstraction, with the unsupervised, topographically organized self-organizing map. We will show that the resultingarchitecture performs poorly on the MNIST data set, but offers interesting avenues for further research.

Winter Storm Uri, one of the coldest in decades, brought snow and ice to Texas along with record subfreezing temperatures for 5 days February 13-17, 2021, and was followed by Winter Storm Viola, which brought more of the same February 18-19, 2021. Millions of Texans lost electricity and clean, running water for several days, which some suggest was due in part to a state-regulated energy market. Many Texas schools shut down for the entire week, as the death toll rose from these storms due to hypothermia and exposure, carbon monoxide poisoning, fire, drowning, and poor road conditions. Not only were COVID-19 vaccinations halted due to impassable roads but also Texas hospitals struggled to provide electricity and water pressure needed to perform life-saving medical treatments for their patients. The purpose of this article is to provide an overview of the historic winter storm event, identify vulnerable populations and key public health policies, and highlight the potential environmental public health risks associated with the storms., Introduction This article examines the impact of Winter Storms Uri and Viola on vulnerable populations in Texas as well as the impact of public utility policies, highlighting those related to [...]

As the healthcare industry becomes increasingly competitive, organizations must recognize the value of a diverse workforce. Future leaders in healthcare administration will play a vital role in shifting organizational perspectives and actively seeking out unique individuals from diverse backgrounds. By embracing diversity, equity, inclusion, and belonging (DEIB) principles, healthcare organizations can create a more inclusive and welcoming environment, which ultimately leads to improved patient care and outcomes. The integration of DEIB education in healthcare administration programs is a positive step toward addressing workforce challenges and cultivating a more inclusive healthcare system. Second year graduate students in their final course in a Masters Science in Health Services Administration (HSA) program were provided education and tools to develop DEIB plans. Exposure to university leadership and an external stakeholder with DEIB expertise and experiences further enhance the students' understanding of the Black, Indigenous, People of Color (BIPOC) population. The five-step DEIB plan template used by the students allows them to identify gaps in their own organizations and develop strategies to mitigate those gaps. This handson approach enables students to gain practical experience in fostering DEIB within healthcare settings. Addressing workforce issues such as staff shortages and turnover while promoting DEIB is crucial for creating a more inclusive and effective healthcare system. [ABSTRACT FROM AUTHOR]

Information present in any training set of vectors for machine learning can be interpreted in two different ways, either as whole states or as individual atomic units. In this paper, we show that these alternative information distributions are often inherently incongruent within the training set. When learning with a Boltzmann machine, modifications in the network architecture can select one type of distributional information over the other; favouring the activation of either state exemplar or atomic characteristics. This choice of distributional information is of relevance when considering the representation of knowledge in logic. Traditional logic only utilises preference that is the correlate of whole state exemplar frequency. We propose that knowledge representation derived from atomic characteristic activation frequencies is the correlate of compositional typicality, which currently has limited formal definition or application in logic. Further, we argue by counter-example, that any representation of typicality by 'most preferred model semantics' is inadequate. We provide a definition of typicality derived from the probability of characteristic features; based on neural network modelling. [ABSTRACT FROM AUTHOR]

Learning the first programming language is challenging for many students. High failure rates and bimodally distributed grades lead to a pedagogical interest in supporting students in first-year programming courses (CS1). In higher education, the important role of feedback for guiding the learning process and improving the learning outcome is widely acknowledged. This article introduces contemporary models of effective feedback practice as found in the higher education literature and offers an interpretation of those in the CS1 context. One particular CS1 course and typical course components are investigated to identify likely loci for feedback interventions and to connect related computer science education literature to these forms of feedback.

In higher education, quality feedback for students is regarded as one of the main contributors to improve student learning. Feedback to support students' development into self-regulated learners, who set their own goals, self-monitor their actual performance according to these goals, and adjust learning strategies if necessary, is seen as an important aspect of contemporary feedback practice. However, only those students who are aware of the course demands and the impact of certain study behaviors on their final achievement are in a position to self-regulate their learning on an informed basis. Learning analytics is an emerging field primarily concerned with using predictive models to inform educational instructors or learners about projected study outcomes. In a scoping study, over 200 students of an introductory programming course (CS1) were supplied with information revealing performance indicators for different stages on the course and projecting final performance for various achievement levels. The study was set out to explore the impact of this type of feedback in the confined context of a CS1 course as well as to learn about students' attitudes toward diagnostic course data in general. The results from the study suggest that students valued the information, but, despite high engagement with the information, students' study behavior and learning outcome remained rather unaffected for the aspects investigated. Given these multi-layered results, we suggest further exploration on the provision of feedback based on diagnostic course data--a vital step toward more transparency for students to foster their active role in the learning process.

In this paper we review three kinds of proposed solutions to the catastrophic forgetting problem in neural networks. The solutions are based on reducing hidden unit overlap, rehearsal, and pseudorehearsal mechanisms. We compare the methods and identify some underlying similarities. We then briefly note some potential implications of the rehearsal/pseudorehearsal based methods, including their application to sequential learning tasks.

For many years computing in New Zealand schools was focused on teaching students how to use computers, and there was little opportunity for students to learn about programming and computer science as formal subjects. In this article we review a series of initiatives that occurred from 2007 to 2009 that led to programming and computer science being made available formally as part of the National Certificate in Educational Achievement (NCEA), the main school-leaving assessment, in 2011. The changes were phased in from 2011 to 2013, and we review this process using the Darmstadt model, including describing the context of the school system, the socio-cultural factors in play before, during and after the changes, the nature of the new standards, the reactions and roles of the various stakeholders, and the teaching materials and methods that developed. The changes occurred very quickly, and we discuss the advantages and disadvantages of having such a rapid process. In all these changes, teachers have emerged as having a central role, as they have been key in instigating and implementing change.

We propose an expanded definition of Threshold Concepts (TCs) that requires the successful acquisition and internalisation not only of knowledge, but also its practical elaboration in the domains of applied strategies and mental models. This richer definition allows us to clarify the relationship between TCs and Fundamental Ideas, and to account for both the important and the problematic characteristics of TCs in terms of the Knowledge/Strategies/Mental Models Framework defined in previous work.

An unplugged approach to teaching enables students to explore Computational Thinking without using a computer. It might appear that if students are to learn programming, they should focus on computerbased work; however, it appears that using "unplugged" activities before engaging in computer-based coding (programming) activities for each unit of work leads to better outcomes for students in the same amount of time. In this paper we explore why this could be the case, by reviewing literature that reports on these experiences, and also using different theoretical lenses (Notional Machines, Semantic Profiles, and the Zone of Proximal Development) to analyse how the combination of experiences can engage students. We also explore how the approach integrates with mathematics education. [ABSTRACT FROM AUTHOR]

Compared to other subjects, the typical introductory programming (CS1) course has higher than usual rates of both failing and high grades, creating a characteristic bimodal grade distribution. In this article, I explore two possible explanations. The conventional explanation has been that learners naturally fall into populations of programmers and non-programmers. A review of decades of research, however, finds little or no evidence to support this account. I propose an alternative explanation, the learning edge momentum (LEM) effect. This hypothesis is introduced by way of a simulated model of grade distributions, and then grounded in the psychological and educational literature. LEM operates such that success in acquiring one concept makes learning other closely linked concepts easier (whereas failure makes it harder). This interaction between the way that people learn and the tightly integrated nature of the concepts comprising a programming language creates an inherent structural bias in CS1, which drives students towards extreme outcomes. (Contains 5 figures and 6 notes.)