Predicting student performance using educational data is a significant area of machine learning research. However, class imbalance in datasets and the challenge of developing interpretable models can hinder accuracy. This study compares different variations of the Synthetic Minority Oversampling Technique (SMOTE) combined with classification…

Teachers' data literacy and interpretive process are critical to understanding how they make sense of data. However, little is known about how mental representations shape and evolve in response to teachers' interpretive process. In the present study, I model and explore this recursive relationship between teachers' cognitive framing and…

Data collection and analyzation practices for English language development services are scarcely found in research, but needed in the subgroup of minority students commonly known as English language learners (Wiseman & Bell, 2021). Wiseman and Bell (2021) identified ELLs as one of the most under-documented student subgroups in the American…

With the passage of the 2015 Every Student Succeeds Act (ESSA), states were required to add a fifth indicator on "School Quality or Student Success" (SQSS) to their school accountability systems. An analysis of submitted ESSA state plans found that 13 states included measures of school climate as their SQSS indicator or incorporated…