Educating students with FASD: linking policy,research and practice

Fetal alcohol spectrum disorder (FASD ) is a prevalent neurodevelopmental disability with significant implications for learning and behaviour. International research suggests that the prevalence of FASD in school‐aged children is 2.3–6.3%. In this paper, we address the questions: (1) what is FASD ; (2) what is the prevalence of FASD in schools; (3) what is the impact of FASD ; and (4) why develop special FASD education strategies and programmes? We summarise the 18‐year history of W innipeg S chool D ivision's development of its FASD P rogramme of services, describe the specialised FASD classrooms and then present the results from a consensus‐generating workshop comprised of 36 FASD education professionals, with over 209 years of collective FASD education programme experience, who were asked to identify and reach consensus on best strategies and lessons learned in FASD education programmes. We then suggest that effectively educating children with FASD is critical to get right if positive educational outcomes are to be realised.     

Enhancing children's literacy skills: designing the Q‐Tales ecosystem for children's e‐book design and publication

This article describes the conceptualisation and development of a pedagogical framework to support the design of e‐books for children to enhance literacy development. It emerged from research undertaken within the Q‐Tales international consortium project of the EU's Horizon 2020 Programme for Research and Innovation, where the aim was to facilitate key stakeholders to collaborate and participate in the online production and publication of high‐quality, educational e‐books for children. The pedagogical framework described here sought to answer the question “What concepts and principles undergird the effective design of pedagogically impactful e‐books for children?” It is grounded by the theoretical underpinnings of socio‐constructivism, constructionism and skill theory, and how they relate to children's literacy development. A framework describing different narrative forms and component features, key pedagogical activities appropriate for different stages of reading development and design recommendations regarding the integration of multimedia into e‐books are also central to the pedagogical framework. As well as informing the design of the Q‐Tales infrastructure for children's e‐book design and publication, we hope the guidelines and pedagogical activities enumerated here will be widely useful for those designing and developing digital, interactive narratives, particularly e‐books to enhance children's emerging literacy.     

Exploring the Robustness of a Unidimensional Item Response Theory Model With Empirically Multidimensional Data

Unidimensionality and local independence are two common assumptions of item response theory. The former implies that all items measure a common latent trait, while the latter implies that responses are independent, conditional on respondents’ location on the latent trait. Yet, few tests are truly unidimensional. Unmodeled dimensions may result in test items displaying dependencies, which can lead to misestimated parameters and inflated reliability estimates. In this article, we investigate the dimensionality of interim mathematics tests and evaluate the extent to which modeling minor dimensions in the data change model parameter estimates. We found evidence of minor dimensions, but parameter estimates across models were similar. Our results indicate that minor dimensions outside the primary trait have negligible consequences on parameter estimates. This finding was observed despite the ratio of multidimensional to unidimensional items being above previously recommended thresholds.     

Higher education for the elderly: Continuing in the mainstream of American life

Research in Higher Education - Do persons over age 62 enroll in universities to maintain their social interaction and stay in life's mainstream? This question was researched through a...     

Exploratory analyses to improve model fit: Errors due to misspecification and a strategy to reduce their occurrence

Two Lagrange multiplier (LM) methods may be used in specification searches for adding parameters to models: one based on univariate LM tests and respecification of the model (LM‐respecified method) and the other based on a partitioning of multivariate LM tests (LM‐incremental method). These methods may result in extraneous parameters being included in models due to either sampling error or the model being misspecified. A 2‐stage specification search may be used to reduce errors due to misspecification. In the 1st stage, parameters are added to models based on LM tests to maximize fit. Second, parameters added in the 1st stage are deleted if they are no longer necessary to maintain model fit. Illustrations are presented to demonstrate that errors due to misspecification occur with the LM‐respecified method and are even more likely with the LM‐incremental approach. These illustrations also show how the deletion stage can help eliminate some of these errors.