Development of an Abbreviated Adult Reading History Questionnaire (ARHQ-Brief) Using a Machine Learning Approach.

Several crucial reasons exist to determine whether an adult has had a reading disorder (RD) and to predict a child's likelihood of developing RD. The Adult Reading History Questionnaire (ARHQ) is among the most commonly used self-reported questionnaires. High ARHQ scores indicate an increased likelihood that an adult had RD as a child and that their children may develop RD. This study focused on whether a subset of ARHQ items (ARHQ-Brief) could be equally effective in assessing adults' reading history as the full ARHQ. We used a machine learning approach, lasso (known as L1 regularization), and identified 6 of 23 items that resulted in the ARHQ-Brief. Data from 97 adults and 47 children were included. With the ARHQ-Brief, we report a threshold of 0.323 as suitable to identify past likelihood of RD in adults with a sensitivity of 72.4% and a specificity of 81.5%. Comparison of predictive performances between ARHQ-Brief and the full ARHQ showed that ARHQ-Brief explained an additional 10%-35.2% of the variance in adult and child reading. Furthermore, we validated ARHQ-Brief's superior ability to predict reading ability using an independent sample of 28 children. We close by discussing limitations and future directions.

Discovering the impact of reading coursework and discipline-specific mentorship on first-year teachers' self-efficacy: a latent class analysis.

Teacher self-efficacy is critical because it predicts teachers' future behavior and impacts teacher turnover. Most teachers begin their career with moderate to high self-efficacy for teaching, but often experience a sharp decline during the first year of teaching. After the first year, their self-efficacy begins to increase but rarely rises to the level it was prior to beginning teaching. Therefore, examining first-year teachers' self-efficacy is extremely important. Previous research generally depicts teachers as a homogeneous group, relying on variable-centered approaches and including self-efficacy as a scaling score, which may not be applicable at the individual level. Simply extending findings from the variable-centered analyses is insufficient. Therefore, the purpose of the present study is to examine the heterogeneous profiles of first-year teachers' self-efficacy from the 2011-2012 Schools and Staffing Survey and to investigate how self-efficacy profiles are related to teacher training at the individual level. Using latent class analyses, we found three statistically distinctive classes within self-efficacy: high, moderate, and low. Regardless of teaching assignments, teachers who completed reading content courses during preparation programs and received discipline-specific mentoring during their first year dominated a higher level of self-efficacy. We conclude that these two factors are essential to preparing and retaining high-quality teachers.