Toward a 5E-Based Flipped Classroom Model for Teaching Computational Thinking in Elementary School: Effects on Student Computational Thinking and Problem-Solving Performance

Computational thinking (CT) has attracted significant interest among many educators around the globe. Despite this growing interest, research on CT and programming education in elementary school remains at an initial stage. Many relevant studies have adopted only one type of method to assess students’ CT, which may lead to an incomplete view of student development on CT, while other studies employed small sample sizes, which may increase the chance of assuming a false premise to be true. Moreover, conventional programming courses typically have two limitations (e.g., limited student active learning and student low engagement). Given these gaps, this study investigates the effects of a theory-based (5E framework) flipped classroom model (FCM) on elementary school students’ understanding of CT concepts, computational problem-solving performance, and perceptions of flipped learning. To achieve this, a pretest-posttest quasi-experimental study was conducted in a rural elementary school, including 125 students in the experimental group and 122 students in the control group. The results showed that the 5E-based FCM significantly improved student understanding of CT concepts and computational problem-solving abilities. The results also revealed positive student perception toward the FCM. The benefits and challenges of the 5E-based FCM are discussed.

Promoting computational thinking through project-based learning

This paper introduces project-based learning (PBL) features for developing technological, curricular, and pedagogical supports to engage students in computational thinking (CT) through modeling. CT is recognized as the collection of approaches that involve people in computational problem solving. CT supports students in deconstructing and reformulating a phenomenon such that it can be resolved using an information-processing agent (human or machine) to reach a scientifically appropriate explanation of a phenomenon. PBL allows students to learn by doing, to apply ideas, figure out how phenomena occur and solve challenging, compelling and complex problems. In doing so, students take part in authentic science practices similar to those of professionals in science or engineering, such as computational thinking. This paper includes 1) CT and its associated aspects, 2) The foundation of PBL, 3) PBL design features to support CT through modeling, and 4) a curriculum example and associated student models to illustrate how particular design features can be used for developing high school physical science materials, such as an evaporative cooling unit to promote the teaching and learning of CT.

Machine Learning Optimization for Prostate Brachytherapy Treatment Planning

Prostate Low-Dose-Rate brachytherapy (LDR) is one of the most effective treatments for localized prostate cancer. Machine Learning (ML), the application of statistics to complex computational problem solving, was applied to prostate LDR brachytherapy treatment planning. Planning time, pre-implant dosimetry, and various measures of clinical implant quality for ML plans were compared against plans created by expert brachytherapists. The average planning time to create an ML plan was 0.84 _ 0.57 min compared to over 17.88 _ 8.76 min for an experienced brachytherapists. Dosimetry was not significantly different for ML and expert brachytherapist plans. Clinical implant quality for the ML plans were ranked as nearly equivalent to the brachytherapist treatment plans in all qualitative categories evaluated. The results of this thesis demonstrate that it is possible to generate high quality prostate brachytherapy treatment plans with comparable quality to those of a human expert using a custom ML algorithm.

Machine Learning Optimization for Prostate Brachytherapy Treatment Planning

Prostate Low-Dose-Rate brachytherapy (LDR) is one of the most effective treatments for localized prostate cancer. Machine Learning (ML), the application of statistics to complex computational problem solving, was applied to prostate LDR brachytherapy treatment planning. Planning time, pre-implant dosimetry, and various measures of clinical implant quality for ML plans were compared against plans created by expert brachytherapists. The average planning time to create an ML plan was 0.84 _ 0.57 min compared to over 17.88 _ 8.76 min for an experienced brachytherapists. Dosimetry was not significantly different for ML and expert brachytherapist plans. Clinical implant quality for the ML plans were ranked as nearly equivalent to the brachytherapist treatment plans in all qualitative categories evaluated. The results of this thesis demonstrate that it is possible to generate high quality prostate brachytherapy treatment plans with comparable quality to those of a human expert using a custom ML algorithm.

‘AI-Generated Inventions’: Time to Get the Record Straight?

This article attempts to clarify the notion of an ‘AI-generated’ invention, an issue which has triggered an intense debate on the future of patent law and policy. While there is a general consensus that such inventions are incompatible with the concept of human inventorship, it remains largely unclear to what extent concerns regarding ‘non-human’ ingenuity can be justified. Most uncertain is how AI ‘autonomously generates’ inventions, and in what way 'AI-generated' inventions differ from inventions developed with the aid of AI. Drawing on the extensive literature review, this article depicts AI techniques as methods of computational problem solving. It emphasises that such methods should not be equated with a computer’s ‘cognitive autonomy’. Further, it clarifies that the types of AI that have been most debated in the patent law literature ‒ artificial neural networks and evolutionary algorithms ‒ essentially require detailed instructions that determine how the relation between inputs and outputs is derived through computation. Accordingly, it is argued that, as long as computers rely on instructions defined by a human as to how solve a problem, the separation between human and non-human (algorithmic) ingenuity is, in itself, artificial. Ultimately, the article calls for a broader technical inquiry that would elucidate the relevance of the currently debated normative concerns over ‘non-human inventorship’ against the background of the technological state of the art.