This article presents an intervention proposal that seeks to develop computational thinking in Early Childhood Education, using an educational ground robot with programmed directionality controls. Within the observational methodology, it has been possible to characterize computational thinking in children of Early Childhood Education -- 5 years…

In this paper we consider implications of the current world-wide inclusion of computational thinking in relation to children's development of algebraic thinking. Little is known about how newly developed visual programming environments such as Scratch could enhance early algebra learning. The study is based on examples of programming activities…

We investigate two non-iterative estimation procedures for Rasch models, the pair-wise estimation procedure (PAIR) and the Eigenvector method (EVM), and identify theoretical issues with EVM for rating scale model (RSM) threshold estimation. We develop a new procedure to resolve these issues--the conditional pairwise adjacent thresholds procedure…

In the multivariable calculus course, a standard application of triple integration is to find volumes of bounded regions in R[superscript 3]. In this article, we consider the problem of computing volumes, by way of examples, of regions bounded by planes and quadric surfaces. For illustration, we present the solution to two basic but non-standard…

We face complex global issues such as climate change that challenge our ability as humans to manage them. Models have been used as a pivotal science and engineering tool to investigate, represent, explain, and predict phenomena or solve problems that involve multi-faceted systems across many fields. To fully explain complex phenomena or solve…

Prior studies on game-based learning provide limited and mixed results in the transfer of skills learned during game play to contexts outside of the game. This study tested the effects of playing a blocked-based programming educational game implemented with in-game cognitive supports on students' ability to learn and apply computational thinking…

In this research, we analyzed the content of a practice-based research published in SSCI, ESCI and ERIC indexed journals related to Computational Thinking (CT) between 2019 and 2021. For this purpose, we searched Science Direct, Google Scholar and Web of Science databases and examined 97 papers. We evaluated the papers under the headings of…

Acknowledging evidence that the ability to estimate has major consequences for both later mathematics learning and real-world functionality, this paper examines the national mathematics curriculum for compulsory school for each of Denmark, Norway and Sweden for the estimation-related opportunities it offers children. Framed against four…

What is the processing cost of being garden-pathed by a temporary syntactic ambiguity? We argue that comparing average reading times in garden-path versus non-garden-path sentences is not enough to answer this question. Trial-level contaminants such as inattention, the fact that garden pathing may occur non-deterministically in the ambiguous…

Alpha-particle sources are widely used in industrial and medical applications. Such applications include smoke detectors, static charge eliminators, and radiation therapy. This paper is concerned with the detection of alpha particles. A number of techniques are known for the detection of alpha particles. Those techniques include the Geiger-Müller…

Social robots have the potential to play a vital role in education. Student technology acceptance may be considered as a driver for their successful integration in educational processes. Based on the ICAP (Interactive, Constructive, Active, Passive) framework and research on social robot roles, we have developed eight vignettes describing…

Randomized A/B tests allow causal estimation without confounding but are often under-powered. This paper uses a new dataset, including over 250 randomized comparisons conducted in an online learning platform, to illustrate a method combining data from A/B tests with log data from users who were not in the experiment. Inference remains exact and…

Since the phenomenal success of deep neural networks (DNNs) on image classification, the research community have been developing wider and deeper networks with complex components for a variety of visual understanding tasks. While such "heavy" models achieve excellent performance, they pose two main challenges: (1) the training requires a…

Multidimensional Item Response Theory (MIRT) is widely used in educational and psychological assessment and evaluation. With the increasing size of modern assessment data, many existing estimation methods become computationally demanding and hence they are not scalable to big data, especially for the multidimensional three-parameter and…

A wide range of machine-learning-based approaches have been developed in the past decade, increasing our ability to accurately model nonlinear and nonadditive response surfaces. This has improved performance for inferential tasks such as estimating average treatment effects in situations where standard parametric models may not fit the data well.…