The Cost of Teacher Turnover in Alaska

Low teacher retention - high turnover - affects student learning. Teacher recruitment and retention are challenging issues in Alaska. Rates vary considerably from district to district and year to year, but between 2004 and 2014, district-level teacher turnover in rural Alaska averaged 20%, and about a dozen districts experienced annual turnover rates higher than 30%. High turnover rates in rural Alaska are often attributed to remoteness and a lack of amenities (including healthcare and transportation); teachers who move to these communities face additional challenges including finding adequate housing and adjusting to a new and unfamiliar culture and environment. Though urban districts have lower teacher turnover rates, they also have challenges with teacher recruitment and retention, particularly in hard-to-fill positions (such as special education and secondary mathematics) and in difficult-to-staff schools. Annually, Alaskan school districts hire about 1,000 teachers (500-600 are hired by its five largest districts), while Alaska’s teacher preparation programs graduate only around 200. The costs associated with teacher turnover in Alaska are considerable, but have never been systematically calculated,1 and this study emerged from interests among Alaska education researchers, policymakers, and stakeholders to better understand these costs. Using data collected from administrators in 37 of Alaska’s 54 districts, we describe teacher turnover and the costs associated with it in four key categories: separation, recruitment, hiring, and induction and training. Our calculations find that the total average cost of teacher turnover is $20,431.08 per teacher. Extrapolating this to Alaska’s 2008-2012 turnover data, this constitutes a cost to school districts of approximately$20 million per year. We focused on costs to Alaskan school districts, rather than costs to individual communities, schools, or the state. Our calculation is a conservative estimate, and reflects typical teacher turnover circumstances - retirement, leaving the profession, or moving to a new school district. We did not include unusual circumstances, such as mid-year departures or terminations. Our cost estimate includes costs of separation, recruitment, hiring, and orientation and training, and excludes the significant costs of teacher productivity and teacher preparation. We suggest that not all turnover is bad, nor are all turnover costs; and emphasize the need to focus on teacher retention as a goal, rather than reducing turnover costs. Even with conservative estimates, teacher turnover is a significant strain on districts’ personnel and resources, and in an era of shrinking budgets, teacher turnover diverts resources from teaching and learning to administrative processes of filling teacher vacancies. Our recommendations include: • Better track teacher turnover costs • Explore how to reduce teacher turnover costs • Support ongoing research around teacher turnover and its associated costs • Explore conditions driving high teacher turnover, and how to address themUniversity of Alaska FoundationExecutive Summary / Acknowledgements / Funding / Contact / What is teacher turnover? / What are the impacts of teacher turnover? / What factors are associated with teacher turnover? / What are the costs associated with teacher turnover? / Challenges in calculating turnover costs / Method / Analysis / Findings / Implications / Recommendations / Limitations / Conclusions / References / Appendix A: Detail costs of teacher turnover / Appendix B: Occupation codes & wages used for cost calculation

Familial aggregation of migraine and depression: Insights from a large Australian twin sample

Free to read\ud \ud Objectives: This research examined the familial aggregation of migraine, depression, and their co-occurrence.\ud \ud Methods: Diagnoses of migraine and depression were determined in a sample of 5,319 Australian twins. Migraine was diagnosed by either self-report, the ID migraine™ Screener, or International Headache Society (IHS) criteria. Depression was defined by fulfilling either major depressive disorder (MDD) or minor depressive disorder (MiDD) based on the Diagnostic and Statistical Manual of Mental Disorders (DSM) criteria. The relative risks (RR) for migraine and depression were estimated in co-twins of twin probands reporting migraine or depression to evaluate their familial aggregation and co-occurrence.\ud \ud Results: An increased RR of both migraine and depression in co-twins of probands with the same trait was observed, with significantly higher estimates within monozygotic (MZ) twin pairs compared to dizygotic (DZ) twin pairs. For cross-trait analysis, the RR for migraine in co-twins of probands reporting depression was 1.36 (95% CI: 1.24–1.48) in MZ pairs and 1.04 (95% CI: 0.95–1.14) in DZ pairs; and the RR for depression in co-twins of probands reporting migraine was 1.26 (95% CI: 1.14–1.38) in MZ pairs and 1.02 (95% CI: 0.94–1.11) in DZ pairs. The RR for strict IHS migraine in co-twins of probands reporting MDD was 2.23 (95% CI: 1.81–2.75) in MZ pairs and 1.55 (95% CI: 1.34–1.79) in DZ pairs; and the RR for MDD in co-twins of probands reporting IHS migraine was 1.35 (95% CI: 1.13–1.62) in MZ pairs and 1.06 (95% CI: 0.93–1.22) in DZ pairs.\ud \ud Conclusions: We observed significant evidence for a genetic contribution to familial aggregation of migraine and depression. Our findings suggest a bi-directional association between migraine and depression, with an increased risk for depression in relatives of probands reporting migraine, and vice versa. However, the observed risk for migraine in relatives of probands reporting depression was considerably higher than the reverse. These results add further support to previous studies suggesting that patients with comorbid migraine and depression are genetically more similar to patients with only depression than patients with only migraine

Shared genetic factors underlie migraine and depression

Free to read\ud \ud Migraine frequently co-occurs with depression. Using a large sample of Australian twin pairs, we aimed to characterize the extent to which shared genetic factors underlie these two disorders. Migraine was classified using three diagnostic measures, including self-reported migraine, the ID migraine screening tool, or migraine without aura (MO) and migraine with aura (MA) based on International Headache Society (IHS) diagnostic criteria. Major depressive disorder (MDD) and minor depressive disorder (MiDD) were classified using the Diagnostic and Statistical Manual of Mental Disorders (DSM) criteria. Univariate and bivariate twin models, with and without sex-limitation, were constructed to estimate the univariate and bivariate variance components and genetic correlation for migraine and depression. The univariate heritability of broad migraine (self-reported, ID migraine, or IHS MO/MA) and broad depression (MiDD or MDD) was estimated at 56% (95% confidence interval [CI]: 53-60%) and 42% (95% CI: 37-46%), respectively. A significant additive genetic correlation (r G = 0.36, 95% CI: 0.29-0.43) and bivariate heritability (h 2 = 5.5%, 95% CI: 3.6-7.8%) was observed between broad migraine and depression using the bivariate Cholesky model. Notably, both the bivariate h 2 (13.3%, 95% CI: 7.0-24.5%) and r G (0.51, 95% CI: 0.37-0.69) estimates significantly increased when analyzing the more narrow clinically accepted diagnoses of IHS MO/MA and MDD. Our results indicate that for both broad and narrow definitions, the observed comorbidity between migraine and depression can be explained almost entirely by shared underlying genetically determined disease mechanisms

Understanding Teachers’ Cognitive Processes during Online Professional Learning: A Methodological Comparison

This study examined the effectiveness of three types of think aloud methods for understanding elementary teachers’ cognitive processes as they used a professional development website. A methodology combining a retrospective think aloud procedure with screen capture technology (referred to as the virtual revisit) was compared with concurrent and retrospective think aloud procedures. Elementary teachers from a large metropolitan area were assigned to one of the three think aloud conditions (N = 45). Participants in the concurrent condition verbalized their thoughts while simultaneously navigating a professional development website for 20 minutes. Participants in the retrospective condition verbalized their thoughts following their 20-minute website navigation without any aids. Finally, participants in the virtual revisit condition verbalized their thoughts while viewing a screen recording of their website navigation. Think aloud protocols were analyzed to determine the frequency of cognitive processes verbalized by participants in each condition. The findings of this study indicated significant differences in the types of verbalizations produced by participants across the three think aloud conditions. In addition, findings reveal benefits and limitations of employing each type of think aloud method in the context of a professional development website