There is evidence that data-based decision-making (DBDM) can improve outcomes for a wide range of students. However, less is known about how special education teachers are trained to use data to inform instruction that targets academic progress for students with extensive support needs (ESN). The purpose of this systematic literature review was to…

There is a need for public health undergraduates to acquire skills in data collection, statistical programming, and infectious diseases modeling. Public health officials and accreditation bodies underline the importance of a cumulative, "real-world" experience as part of a student's education. The Watermelon Meow Meow (WMM) outbreak is a…

A significant activity in the business analytics process is enrichment, which deals with acquiring and combining data from external sources. While different strategies for enrichment are possible, it can be accomplished more efficiently through automation using Python scripts. Since business students may not be immersed in technology skills and…

Through the sports group teaching of school students, this paper uses the evaluation method to construct the ability evaluation model, collects the changes of language ability, and tests the students' understanding ability, expression ability, action language, and group cooperation. Following the teaching for three months, the average value of the…

The generation, use, and analysis of educational data comes with many promises and opportunities, especially where digital materials allow usage of learning analytics (LA) as a tool in data-based decision-making (DBDM). However, there are questions about the interplay between teachers, students, context, and technology. Therefore, this paper…

Background: With around 200,000 autistic students set to enter universities and the workforce in the next decade, inclusive programs are receiving increased attention to helping educational systems carefully integrate these students' needs. Moreover, educational programs recognize that problem-solving is critical for school success and optimal…

This study was grounded in the spatial computational thinking model developed by the "3D Weather" project funded by the NSF STEM+C program. The model reflects a discipline-based perspective towards computational thinking and captures the spatial nature of computational thinking in meteorology and the reliance of computational thinking on…

Problem decomposition into sub-problems or subgoals and recomposition of the solutions to the subgoals into one complete solution is a common strategy to reduce difficulties in structured problem solving. In this study, we use a datadriven graph-mining-based method to decompose historical student solutions of logic-proof problems into Chunks. We…

Real-world data are fundamental to modern teaching methodologies that aim to improve statistical knowledge and reasoning in students. Statistical information is encountered in everyday life, such as media articles and involves real-world contexts. However, information could be biased or (mis)represented and students should be concerned about the…

This research explores the perspectives of marketing students and educators about the Big Data courses in marketing education programs. It also examines drivers that predict interest on the part of marketing students in taking Big Data courses. Data was collected through interviews with 20 marketing educators, and a survey was completed by 480…

A linked data approach provides new opportunities for annotating, interlinking, sharing and enriching massive open online educational resources. However, it can be difficult for non-expert users to build and utilize the educational linked data in educational settings. Thus, flexible and user-friendly ways to represent, interlink, visualize and…

This study investigates the extent to which paper-based data-driven learning (DDL) activities can improve Thai EFL students' grammar learning of conditional sentences (the second condition), as well as the participants' attitudes toward learning through the DDL approach. This was a two-week research using a one-group pre-test and post-test design.…

Contribution: This article presents evidence that electrical engineering, computer science, and data science students, participating in introduction to machine learning (ML) courses, fail to interpret the performance of ML algorithms correctly, since they fail to consider the application domain. This phenomenon is referred to as the domain neglect…

Students at all levels of education experience difficulties with the concepts of measurement uncertainties. One task that includes concepts of measurement uncertainties is a data comparison problem where students decide whether two datasets are in agreement or not--an authentic scientific practice. To aid students with these concepts and tasks,…

The purpose of this process evaluation was to evaluate how classroom teachers, campus administrators, and instructional staff differ in their perception of data usage for educational improvement in a large suburban Independent School District (ISD) in Southeast Texas. The researcher was granted permission by the superintendent of the school…