QR decomposition is widely used for solving the least squares problem. However, existing materials about it may be too abstract for non-mathematicians, especially STEM students, and/or require serious background in linear algebra. The paper describes theoretical background and examples of GNU Octave compatible MATLAB scripts that give relatively…

Cognitive diagnosis models (CDMs) are the assessment tools that provide valuable formative feedback about skill mastery at both the individual and population level. Recent work has explored the performance of CDMs with small sample sizes but has focused solely on the estimates of individual profiles. The current research focuses on obtaining…

Recently, deep learning has become a pervasive tool in prediction problems for structured and/or unstructured big data in various areas including science and engineering. In particular, deep neural network models (i.e. a basic core model of deep learning) can be viewed as an extension of statistical models by going through the incorporation of…

Background and Context: Computational thinking (CT) is a fundamental skill and a new form of literacy that everyone should develop to participate in civic society. Sequencing and algorithmic thinking are at the core of CT. This study looked into how young children enrolled in a kindergarten in Colombia develop CT skills. Objective: This paper aims…

Higher education systems exhibit varying degrees of heterogeneity in approaches to undergraduate degree classification -- specifically for this Point of Departure: the wide variety of 'Degree Classification Algorithms' (DCAs) used to calculate students' final awards. To date, the impact of DCA variation remains an under-researched 'black box', and…

Human intelligence has the ability to capture abstract concepts from experience and utilize that learned knowledge for adaptation to new situations. Lifelong machine learning aims to achieve those same properties of human intelligence by designing algorithms to learn from a sequence of tasks, extract useful knowledge of previous tasks, and re-use…

In recent years, programming education has gained recognition at various educational levels due to its increasing importance. As the need for problem-solving skills becomes more vital, researchers have emphasized the significance of developing algorithmic thinking (AT) skills to help students in program development and error debugging. Despite the…

Background: Understanding algorithms is crucial for programming education, yet their abstract nature often challenges students. Algorithm visualisation (AV) has been proven effective in enhancing algorithmic thinking among university students. However, its efficacy for elementary school students and the optimal forms of AV tools remain unclear.…

Since 2018, institutions of higher education have been aware of the "enrollment cliff" which refers to expected declines in future enrollment. This paper attempts to describe how prepared institutions in Ohio are for this future by looking at trends leading up to the anticipated decline. Using IPEDS data from 2012-2022, we analyze trends…

Purpose: This study examined how physical education majors used computational thinking (CT) skills in a movement concept course. Method: Twenty-two physical education majors were tasked to create two gymnastics routines (i.e., algorithm design), analyze their routines (i.e., decomposition and abstraction), create and follow a personalized fitness…

The aim of this paper is to provide tools to teachers for monitoring student work and understanding practices in order to help student and possibly adapt exercises in the future. In the context of an online programming learning platform, we propose to study the attempts (i.e., submitted programs) of the students for each exercise by using…

This research works on creating a hybrid Knowledge Recommendation System (KRS) for an Entrepreneurship Course using the Knowledge Graph (KG) and Clustering Technologies (CTs). The system aims at improving students' learning experience by providing relevant learning materials and even focusing on learner preferences. These results are already part…

Open education systems play a significant role in providing flexible and accessible learning opportunities to large student populations, independent of time and location. These systems achieve cost efficiency through the effective implementation of economies of scale, reducing unit costs as student numbers increase. However, decision-making in the…

Collaborative learning in tertiary education faces challenges such as limited teacher intervention and effective student pairing. This study addresses these issues by proposing a data-driven peer recommendation approach enhanced with learner profile visualisation. The system dynamically matches students based on evolving learning profiles, using…

Consider the conventional multilevel model Y=C[gamma]+Zu+e where [gamma] represents fixed effects and (u,e) are multivariate normal random effects. The continuous outcomes Y and covariates C are fully observed with a subset Z of C. The parameters are [theta]=([gamma],var(u),var(e)). Dempster, Rubin and Tsutakawa (1981) framed the estimation as a…