This study investigates a range of non-normative ideas that pre-service teachers (PSTs) employ in reasoning about sampling variability. This issue was studied in the context of a content course on statistics and probability for pre-service middle grade teachers at a Midwestern American university. Analysis of seven PSTs' video and screen records…

The sample mean in statistics is a concept of great importance, with its properties being extensively utilized in other areas, such as computer science. This research centers on the concept of the sample mean and its characteristics in a cohort of computer engineering students undertaking a required course in statistics at a university in the…

Traditionally, assessment professionals use analyses relying upon null hypothesis significance testing (NHST), but those tools have limitations when analyzing small samples or disaggregated data. This study used common NHST analytical techniques, compared their results, and then explored an alternative technique that perhaps allows for a more…

We present an instructional approach to teaching causal inference using Bayesian networks and "do"-Calculus, which requires less prerequisite knowledge of statistics than existing approaches and can be consistently implemented in beginner to advanced levels courses. Moreover, this approach aims to address the central question in causal…

Computational Thinking (CT) skills are increasingly recognized as essential for junior high school students, especially in addressing the demands of the digital era. This study explores how CT skills--decomposition, pattern recognition, abstraction, and algorithmic thinking--manifest in learning statistics based on students' cognitive abilities. A…

After encountering students who lost motivation throughout the semester, and finding most students' questions focused on how to improve their grade rather than on understanding the material, we developed a gamified grading structure hoping to remove students' barriers to motivation in the classroom. A brief review of the literature on known…

Predicting learner performance with precision is critical within educational systems, offering a basis for tailored interventions and instruction. The advent of big data analytics presents an opportunity to employ Machine Learning (ML) techniques to this end. Real-world data availability is often hampered by privacy concerns, prompting a shift…

This study introduces a novel Learner-Intention Continuum, spanning from curiosity and exploration to purposeful, goal-directed learning. This continuum fills a critical gap in understanding the diverse motivations of informal and semi-formal learners, specifically those who enroll in massive open online courses (MOOCs). Through latent class…

Background/purpose: This study examines whether Universal Design for Learning (UDL), based on the VARK model, can aid struggling students in mathematics in developing their statistical thinking skills. Additionally, the proposed study examines the relationship between learning preferences and the effect of these preferences on performance.…

Preparing the new generation to be data-literate citizens is a pressing challenge, and some explorations have been made to cultivate K-12 students' data science skills and attitudes. However, there is a lack of instructional models to guide the design of data science programs in K-12 due to its complex and interdisciplinary nature as well as the…

Based on the self-determination theory (SDT), this evaluates the effects of generative artificial intelligence (Gen-AI) on learning performance within China's education sector, emphasizing the roles of social interaction, utilitarian benefit, knowledge acquisition, and epistemic curiosity. The study employs a dual method, using PLS-SEM and fsQCA…

Students' reading ability affects their outcomes in learning software even outside of reading education, such as in math education, which can result in unexpected and inequitable outcomes. We analyze an adaptive learning software using Bayesian Knowledge Tracing (BKT) to understand how the fairness of the software is impacted when reading ability…

A hallmark of effective teaching is that it grants learners not just a collection of facts about the world, but also a toolkit of abstractions that can be applied to solve new problems. How do humans teach abstractions from examples? Here, we applied Bayesian models of pedagogy to a necklace-building task where teachers create necklaces to teach a…

Ineffective use of resources and suboptimal management at the school level can detrimentally affect human resource development and labour market outcomes. Therefore, efficiently managing human and physical resources is crucial to transforming school inputs into improved educational outcomes. This study assesses the effectiveness of educational…

To this date, few standardized tests measuring students' performance with regards to statistics exist. Only four tests have been proposed for college or university students. The goal of the present study is to investigate these tests. University professors or instructors experienced in teaching statistics were asked to list the concepts they think…