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In this paper we have examined the position and roles of Computer Science in curricula in the light of recent calls for curriculum change and we have proposed principles and issues to consider in curriculum design as well as identifying priority areas for further research. The paper is based on discussions within and beyond the International Federation of Information Processing (IFIP) Education Community since 2012 as well as an analysis of curriculum developments in five different countries. Emerging themes have been discussed with reference to important perspectives from curriculum theory including "powerful knowledge" as a key element of entitlement and management of the growth of expertise. Based on this analysis we have identified areas of consensus as well as constraints, risks and issues that are still subject to controversy. There is an emerging consensus of the importance of Computer Science and the nature of its "powerful knowledge". Furthermore current understanding of the opportunities and benefits for starting to learn Computer Science early in primary schools has identified this early start as an entitlement and equity issue. There is a strong consensus that teacher professional development in Computer Science Education is critical for supporting curriculum change and is currently a major challenge in many countries. Other key issues include understanding how the growth of expertise affects potential structure and sequencing in the curriculum and the balance of content. Further considerations include how new technological opportunities interact with pedagogical approaches and can provide new potential for the growth of expertise.

Graduate employability has become an issue since there are broad mismatches between the acquired graduate skills from university and the required skills by employers. While previous researches have outlined the salient skills that need to be embedded in graduate education, to date no studies have attempted to methodically identify and synthesize the literature on graduate attributes. In this paper a total of 39 relevant studies on graduate skills and attributes in the subject areas of business and management, accounting, and computer science were extracted from Scopus® (database). This revealed a total of 53 graduate attributes, with some being highly used, such as communication, teamwork, problem solving, technological skills, creativity, interpersonal skills, leadership skills, self-management and flexibility/adaptability. The majority of studies used a quantitative survey method to collect and rank graduate attributes, and Australia emerged as the most active country in researching the domain.

Liquefaction-induced lateral spreading that has resulted in devastating damages to lifelines and buildings has been widely reported in recent earthquakes. Although it is impossible to preclude the occurrence of earthquakes, it is possible to predict its adverse effects through computer science such as machine learning (ML) algorithms. In this study, the ability of recently developed and powerful ML algorithms such as eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Light Gradient Boosting Machine (LightGBM) was investigated to predict the occurrence of liquefaction-induced lateral spreading. A relatively large dataset was used to develop ML models, including 6704 lateral spread observations from the 2011 Christchurch earthquake in New Zealand. The particle swarm optimization (PSO) algorithm is utilized for hyperparameter optimization of the gradient boosting models, called the PSO-XGBoost, PSO-LightGBM, and PSO-CatBoost. For comparison, the prediction results of the PSO optimized gradient boosting models were compared with that of the models using default parameters (i.e., XGBoost, CatBoost, and LightGBM). In addition, the SHapley Additive exPlanations approach is employed to explore the feature importance of the variables included in the dataset. The findings demonstrated that all the three gradient boosting algorithms performed well in predicting lateral spreading occurrence. Moreover, PSO-CatBoost outperformed other state-of-the-art models in terms of performance metrics. However, the PSO-LightGBM model may be considered the best choice for computers with older-gen hardware and important tasks that need to be completed in a short time. This study confirms the effectiveness of the proposed models, and the use of these boosting algorithms especially optimized with PSO is recommended for predicting the occurrence of liquefaction-induced lateral spreading. [ABSTRACT FROM AUTHOR]

We describe a collaboration between Māori (Indigenous people of Aotearoa/New Zealand) and Tauiwi (non-Māori) researchers on a software engineering project. Te Tiriti o Waitangi (The Treaty of Waitangi) provides the basis for Māori to lead research that involves Māori as participants or intends to impact Māori outcomes. Through collaboration, an extension of the traditional four-step software design process was created, culminating in a nine-step integrated process that included Kaupapa Māori (Māori ideology) principles. The collaboration experience for both Māori and Tauiwi highlighted areas of misunderstanding within the research context based on differing worldviews and our ability to navigate and work through this. This article provides context, guiding principles, and recommended research processes where Māori and Tauiwi aim to collaborate. [ABSTRACT FROM AUTHOR]

This opinion piece considers the relationship between computer science, industrial innovation and New Zealand’s research future, taking as its basis the case of Weta Digital and its continual development of computer-automated tools that replace cognitive labour. This process of computer automation offers a glimpse of the potential direction in which industrial and research production may move over the coming decades. [ABSTRACT FROM AUTHOR]