Pirate plunder: game-based computational thinking using scratch blocks

Policy makers worldwide argue that children should be taught how technology works, and that the ‘computational thinking’ skills developed through programming are useful in a wider context. This is causing an increased focus on computer science in primary and secondary education. Block-based programming tools, like Scratch, have become ubiquitous in primary education (5 to 11-years-old) throughout the UK. However, Scratch users often struggle to detect and correct ‘code smells’ (bad programming practices) such as duplicated blocks and large scripts, which can lead to programs that are difficult to understand. These ‘smells’ are caused by a lack of abstraction and decomposition in programs; skills that play a key role in computational thinking. In Scratch, repeats (loops), custom blocks (procedures) and clones (instances) can be used to correct these smells. Yet, custom blocks and clones are rarely taught to children under 11-years-old. We describe the design of a novel educational block-based programming game, Pirate Plunder, which aims to teach these skills to children aged 9-11. Players use Scratch blocks to navigate around a grid, collect items and interact with obstacles. Blocks are explained in ‘tutorials’; the player then completes a series of ‘challenges’ before attempting the next tutorial. A set of Scratch blocks, including repeats, custom blocks and clones, are introduced in a linear difficulty progression. There are two versions of Pirate Plunder; one that uses a debugging-first approach, where the player is given a program that is incomplete or incorrect, and one where each level begins with an empty program. The game design has been developed through iterative playtesting. The observations made during this process have influenced key design decisions such as Scratch integration, difficulty progression and reward system. In future, we will evaluate Pirate Plunder against a traditional Scratch curriculum and compare the debugging-first and non-debugging versions in a series of studies

Quickest Paths in Simulations of Pedestrians

This contribution proposes a method to make agents in a microscopic simulation of pedestrian traffic walk approximately along a path of estimated minimal remaining travel time to their destination. Usually models of pedestrian dynamics are (implicitly) built on the assumption that pedestrians walk along the shortest path. Model elements formulated to make pedestrians locally avoid collisions and intrusion into personal space do not produce motion on quickest paths. Therefore a special model element is needed, if one wants to model and simulate pedestrians for whom travel time matters most (e.g. travelers in a station hall who are late for a train). Here such a model element is proposed, discussed and used within the Social Force Model.Comment: revised version submitte

Squeaky Wheel Optimization

We describe a general approach to optimization which we term `Squeaky Wheel' Optimization (SWO). In SWO, a greedy algorithm is used to construct a solution which is then analyzed to find the trouble spots, i.e., those elements, that, if improved, are likely to improve the objective function score. The results of the analysis are used to generate new priorities that determine the order in which the greedy algorithm constructs the next solution. This Construct/Analyze/Prioritize cycle continues until some limit is reached, or an acceptable solution is found. SWO can be viewed as operating on two search spaces: solutions and prioritizations. Successive solutions are only indirectly related, via the re-prioritization that results from analyzing the prior solution. Similarly, successive prioritizations are generated by constructing and analyzing solutions. This `coupled search' has some interesting properties, which we discuss. We report encouraging experimental results on two domains, scheduling problems that arise in fiber-optic cable manufacturing, and graph coloring problems. The fact that these domains are very different supports our claim that SWO is a general technique for optimization

Bringing Computational Thinking to Nonengineering Students through a Capstone Course

Although the concept of computational thinking has flourished, little research has explored how to integrate various elements of computational thinking into an undergraduate classroom setting. Clarifying core concepts of computational thinking and providing empirical cases that apply computational thinking practices into a real-world educational setting is crucial to the success of software engineering education. In this article, we describe the development of a curriculum for a social innovation capstone course, using core concepts and elements of computational thinking. The course was designed for undergraduate students of a liberal arts college at a university in Korea. Students were asked to define a social problem and introduced to the core concepts and processes of computational thinking aided by Arduino and Raspberry Pi programming environments. After building a business model, they implemented a working prototype for their proposed solution. We document class project outcomes and student feedback to demonstrate the effectiveness of the approach

Bringing Computational Thinking to Nonengineering Students through a Capstone Course

Although the concept of computational thinking has flourished, little research has explored how to integrate various elements of computational thinking into an undergraduate classroom setting. Clarifying core concepts of computational thinking and providing empirical cases that apply computational thinking practices into a real-world educational setting is crucial to the success of software engineering education. In this article, we describe the development of a curriculum for a social innovation capstone course, using core concepts and elements of computational thinking. The course was designed for undergraduate students of a liberal arts college at a university in Korea. Students were asked to define a social problem and introduced to the core concepts and processes of computational thinking aided by Arduino and Raspberry Pi programming environments. After building a business model, they implemented a working prototype for their proposed solution. We document class project outcomes and student feedback to demonstrate the effectiveness of the approach

Professional Judgment in an Era of Artificial Intelligence and Machine Learning

Though artificial intelligence (AI) in healthcare and education now accomplishes diverse tasks, there are two features that tend to unite the information processing behind efforts to substitute it for professionals in these fields: reductionism and functionalism. True believers in substitutive automation tend to model work in human services by reducing the professional role to a set of behaviors initiated by some stimulus, which are intended to accomplish some predetermined goal, or maximize some measure of well-being. However, true professional judgment hinges on a way of knowing the world that is at odds with the epistemology of substitutive automation. Instead of reductionism, an encompassing holism is a hallmark of professional practice—an ability to integrate facts and values, the demands of the particular case and prerogatives of society, and the delicate balance between mission and margin. Any presently plausible vision of substituting AI for education and health-care professionals would necessitate a corrosive reductionism. The only way these sectors can progress is to maintain, at their core, autonomous professionals capable of carefully intermediating between technology and the patients it would help treat, or the students it would help learn

Teaching computational thinking to space science students

Computational thinking is a key skill for space science graduates, who must apply advanced problem-solving skills to model complex systems, analyse big data sets, and develop control software for mission-critical space systems. We describe our work using Design Thinking to understand the challenges that students face in learning these skills. In the MSc Space Science & Technology at University College Dublin, we have used insights from this process to develop new teaching strategies, including improved assessment rubrics, supported by workshops promoting collaborative programming techniques. We argue that postgraduate- level space science courses play a valuable role in developing more advanced computational skills in early-career space scientists

Berpikir komputasi di dalam kurikulum merdeka : analisis pada guru matematika

Tujuan dari artikel ini adalah menyoroti serta mendiskusikan aspek tentang persepsi awal terkait kemampuan berpikir komputasi pada pembelajaran matematika pada kurikulum merdeka. Selanjutnya kegiatan pelatihan  ini juga memiliki kontribusi ke dalam kegiatan penelitian sehingga harapannya membawa perubahan pada proses pembelajaran matematika. Metode penelitian yang digunakan adalah metode campuran yaitu penelitian kualitatif dan penelitian kuantitatif, Selanjutnya metode kualitatif yang digunakan adalah studi kasus. Selanjutnya pada proses pengambilan data menggunakan analisis data kuantitatif. Hasil dari kegiata penelitian ini adalah  adalah menunjukkan bahwa kurangnya pengetahuan awal terkait berpikir komputasi olwh guru. Serta kegiatan pelatihan ini mampu menghasilkan pembelajaran matematika yang terintegrasi matapelajaran matematika