The main purpose of this study is to develop a conceptual understanding of the irrational number of the square root of 2 ([square root]2 ). Participants in the study were 20 ninth-grade male students. Activity Theory was used as a framework to show the development of the conceptual understanding. Since this study was conducted during the COVID-19…

This paper combines a theoretical-based model with a data-driven approach to develop an Early Warning System that detects students who are more likely to dropout. The model uses innovative multilevel statistical and machine learning methods. The paper demonstrates the validity of the approach by applying it to administrative data from a leading…

To be literate in a society where the information shared online is often exploited, learners should be exposed to multiple aspects of contemporary predictive modeling. This article explores an activity in which grade 10 students learned how a famous AI algorithm (the Apriori algorithm) uses conditional probability to automate the process of…

Purpose: This study aims to analyse and evaluate the methodology followed by the Times Higher Education Impact Rankings (THE-IR), as well as the coverage obtained and the data offered by this ranking, to determine if its methodology reflects the degree of sustainability of universities, and whether their results are accurate enough to be used as a…

We developed a summer research experience program within a freestanding comprehensive cancer center to cultivate undergraduate students with an interest in and an aptitude for quantitative sciences focused on oncology. This unconventional location for an undergraduate program is an ideal setting for interdisciplinary training in the intersection…

Machine learning methods for predictive analytics have great potential for uncovering trends in educational data. However, simple linear models still appear to be most widely used, in part, because of their interpretability. This study aims to address the issues of interpretability of complex machine learning classifiers by conducting feature…

Within the last decade, different educational data mining techniques, particularly quantitative methods such as clustering, and regression analysis are widely used to analyze the data from educational games. In this research, we implemented a quantitative data mining technique (clustering) to further investigate students' feedback. Students played…

With the increased use of data in the governance of education, studies on how human beings interact with data become increasingly important. Recent scholarship has conceptualized human responses to data through the notion of affectivity. While this conceptualization offers a viable alternative to previous rational choice theorizations of human…

Teachers often use analogies to introduce unfamiliar or complex concepts. However, several studies have found that their knowledge on how to use them is limited or superficial. Similarly, the integration of technological resources in the teaching-learning processes is not always carried out efficiently, which evidences the need to improve…

This teaching brief describes an experiential project used in a graduate Principles of Management course for nonbusiness undergraduate students. Groups of four to six first-year MBA students interviewed a seasoned manager online twice over the 8-week course and discussed the applications of course material. Project subtopics included an…

This paper investigates students' mathematical modeling activity in data-rich modeling tasks. It aims at gaining insight into how students develop meaning when modeling data-rich situations and the mathematical models produced. A tendency to model a particular dataset, rather than the phenomenon that the dataset is a particular instance of, has…

The current data-centric business environment has seen an increasing demand for business students with knowledge and skills in the area of business analytics. This article presents the design and implementation of a foundation business analytics (BA) course for undergraduate business students who aspire to become data-literate professionals or…

In this comparative study, I examine principal data use in two sets of districts. One set of three districts has a district-wide focus on using data to improve instruction, and the other set, also with three districts, has a district-wide focus on analyzing disaggregated data by student demographic groups. Data sources include interviews with…

In addition to providing a set of techniques to analyze educational data, I claim that data science as a field can provide broader insights to education research. In particular, I show how the bias-variance tradeoff from machine learning can be formally generalized to be applicable to several prominent educational debates, including debates around…

The purpose of study was gain insight into pre-service teachers' experiences in using classroom data to make learning and teaching decisions. The qualitative study is based on the reflections and recommendations of three pre-service teachers' that participated in a data-driven decision-making intervention whilst on an immersive 10-week…