This research aims to examine current practices in gifted and talented educator professional learning, as well as teacher attitudes, beliefs, and experiences towards gifted education in order to explore opportunities to further develop and improve professional learning structures. Through a qualitative methodology following the constructivist-interpretivist paradigm, this research utilizes a phenomenological interview design in which data from educator interviews are examined through thematic analysis. To support and further extrapolate on the feedback from the interviews, this research also includes a document analysis of the published descriptions of 30-hour educator training required for those providing GT services in the state of Texas. The thematic analysis of interviews identified three major themes and two minor themes after engaging in a deep analysis of the interview transcriptions. These major themes are the (1) utility of professional learning, (2) shared control of learning, and (3) understanding the whole student. Minor themes are (i) long-term career growth and (ii) role of professional support networks and connections. Results of the document analysis illustrate that the most frequent descriptions are associated with the abilities participants will take from the learning. Within this descriptive code, most of the language focused on learner competence, while few of the descriptions …

The present study provides an example of implementing the difference in differences (DD) estimator for a two-group, pretest-posttest design with K-12 educational intervention data. The goal is to explore the basis for causal inference via Rubin's potential outcomes framework. The DD method is introduced to educational researchers, as it is seldom implemented in educational research. DD analytic methods' mathematical formulae and assumptions are explored to understand the opportunity and the challenges of using the DD estimator for causal inference in educational research. For this example, the teacher intervention effect is estimated with multi-cohort student outcome data. First, the DD method is used to detect the average treatment effect (ATE) with linear regression as a baseline model. Second, the analysis is repeated using linear regression with cluster robust standard errors. Finally, a linear mixed effects analysis is provided with a random intercept model. Resulting standard errors, parameter estimates, and inferential statistics are compared among these three analyses to explore the best holistic analytic method for this context.

The age-cutoff regression discontinuity design (RDD) has emerged as one of the most rigorous quasi-experimental approaches to determining program effects of prekindergarten on literacy and numeracy outcomes for children at kindergarten entry. However, few pre-K meta-analyses have focused attention on validity threats. The current random-effects meta-regression tests the moderating effects of prominent threats to validity, selection bias and sensitivity, on impact estimates generated from age-cutoff regression discontinuity studies from large-scale programs. Results from averaging dependent standardized mean difference effects suggested small positive moderating effects of total attrition and robust 3-month bandwidths on reading effects, but not on math. However, these results were not statistically significant. In contrast, results generated from robust variance estimation yielded a small statistically significant association between total attrition and math effects. These mixed results may warrant further research on prekindergarten evaluation methodology, evaluation estimation methods, and the totality of evidence used to inform policy.