Do Politics in Our Democracy Prevent Schooling for Our Democracy? Civic Education in Highly Partisan Times

Amid hyper-partisanship, increasing critiques of civic education reform priorities from conservatives, and growing signs of democratic backsliding, can schools provide foundational support for democratic norms, commitments, and capacities? Drawing on a unique national survey of high school principals conducted in 2018, we examine how political context, district priorities, and principal beliefs and characteristics are related to support for civic education. We find that a school’s partisan context is unrelated to most supports for democratic education. Of note, however, support for the discussion of controversial issues is less common in conservative districts, raising important questions about why the discussion of controversial issues (a core building block of democratic societies) is less common in conservative settings. In addition, support for civic education at the school level is highest at schools led by principals who are civically active and in districts that are committed to democratic aims. At a time when school districts face highly contentious politics, these findings indicate that systemic district commitments can help strengthen our civic foundations and that principals and district leaders may be able to promote small-d democracy amid increasingly politicized school governance contexts

Is (poly-) substance use associated with impaired inhibitory control? A mega-analysis controlling for confounders.

Many studies have reported that heavy substance use is associated with impaired response inhibition. Studies typically focused on associations with a single substance, while polysubstance use is common. Further, most studies compared heavy users with light/non-users, though substance use occurs along a continuum. The current mega-analysis accounted for these issues by aggregating individual data from 43 studies (3610 adult participants) that used the Go/No-Go (GNG) or Stop-signal task (SST) to assess inhibition among mostly "recreational" substance users (i.e., the rate of substance use disorders was low). Main and interaction effects of substance use, demographics, and task-characteristics were entered in a linear mixed model. Contrary to many studies and reviews in the field, we found that only lifetime cannabis use was associated with impaired response inhibition in the SST. An interaction effect was also observed: the relationship between tobacco use and response inhibition (in the SST) differed between cannabis users and non-users, with a negative association between tobacco use and inhibition in the cannabis non-users. In addition, participants' age, education level, and some task characteristics influenced inhibition outcomes. Overall, we found limited support for impaired inhibition among substance users when controlling for demographics and task-characteristics

Mitigating Gender and L1 Biases in Automated English Speaking Assessment

Automated assessment using Natural Language Processing (NLP) has the potential to make English speaking assessments more reliable, authentic, and accessible. Yet without careful examination, NLP may exacerbate social prejudices based on gender or native language (L1). Current NLP-based assessments are prone to such biases, yet research and documentation are scarce. Considering the high stakes nature of English speaking assessment, it is imperative that tests are fair for all examinees, regardless of gender or L1 background. Through a series of three studies, this project addresses the need for more thorough investigations of bias in English speaking assessment. Study 1 examines biases in automated transcription, a key component of automated speaking assessment. Study 2 focuses on a specific type of bias known as differential item functioning (DIF), and determines which patterns of DIF are present in human rater scores and whether or not these patterns of DIF are exacerbated by a pretrained, large language model (LLM) known as BERT. Lastly, Study 3 presents a comparison of two approaches of mitigating DIF using LLMs. Results from Study 1 indicate that there are indeed biases in automated transcription, however these do not translate into biased speaking scores. In Study 2, it is shown that BERT does exacerbate human rater biases, although the effect size is small. Finally, Study 3 demonstrates that it is possible to debias human and automated scores; however, the two approaches have limitations, particularly when the source of DIF is unknown