The Comprehensive Assessment of Leadership for Learning: A Next Generation Formative Evaluation and Feedback System

Recent developments in federal policy have prompted the creation of state evaluation frameworks for principals and teachers that hold educators accountable for effective practices and student outcomes. These changes have created a demand for formative evaluation instruments that reflect current accountability pressures and can be used by schools to focus school improvement and leadership development efforts. The Comprehensive Assessment of Leadership for Learning (CALL) is a next generation, 360-degree on-line assessment and feedback system that reflect best practices in feedback design. Some unique characteristics of CALL include a focus on: leadership distributed throughout the school rather than as carried out by an individual leader; assessment of leadership tasks rather than perceptions of leadership practice; a focus on larger complex systems of middle and high school; and transparency of assessment design. This paper describes research contributing to the design and validation of the CALL survey instrument.

Expanding Learning Time in Schools: Considering the Challenges of Implementation and the Potential Impact

Considering the broader context of school reform that is seeking education strategies that might deliver substantial impact, this article examines four questions related to the policy and practice of expanding learning time: (a) why do educators find the standard American school calendar insufficient to meet students’ educational needs, especially those of disadvantaged students? (b) how do educators implement a longer day and/or year, addressing concerns about both educational quality and costs? (c) what does research report about outcomes of expanding time in schools? and (d) what are the future prospects for increasing the number of expanded-time schools? The paper examines these questions by considering research, policy, and practice at the national level and, throughout, by drawing upon additional evidence from Massachusetts, one of the leading states in the expanded-time movement. In considering the latter two questions, the article explores the knowns and unknowns related to expanded learning time and offers suggestions for further research.

Assessing the Influence of Brain-based Instructional Methods and Its Impact on Systemic Change for Learning Communities

Background: As scholars who prepare future school leaders to be innovative instructional leaders for their learning communities, we are on the verge of a curriculum design revolution. The application of brain research findings promotes educational reform efforts to systemically change the way in which children experience school. However, most educators, school leaders, board members, and policy makers are ill prepared to reconsider the implications for assessment, pedagogy, school climate, daily schedules, and use of technology. This qualitative study asked future school leaders to reconsider how school leadership preparedness programs prepared them to become instructional leaders for the 21st century. The findings from this study will enhance the field of school leadership, challenging the current emphasis placed on standardized testing, traditional school calendars, assessments, monocultural instructional methods, and meeting the needs of diverse learning communities. [See PDF for complete abstract]

Teaching Tuberculosis Content: Web-Based Resources for Nurse Educators

Introduction: Tuberculosis (TB) is a global health concern with one-third of the world’s population infected. With the goal of eliminating TB, a component of appropriate management of the disease is ensuring baccalaureate nursing students receive current and consistent TB education. [See PDF for complete abstract]

Commentary on "Expanded Learning Time in Schools"

As co-founder of KIPP, I know from experience and research that more time in school works. A well-designed extended-time program can help underserved students catch up academically, and prepare them for the rigors of higher education. Implementing extended time more widely poses challenges, but there are also creative solutions to these challenges.

A Step in the Right Direction: Development of the Computerized Assessment for Preschool Social Emotional Learning (CAPSEL)

Invited commentary on "Computerizing Social-Emotional Assessment for School Readiness".

Computerizing Social-Emotional Assessment for School Readiness: First Steps toward an Assessment Battery for Early Childhood Settings

The transition into formal schooling is a crucial foundation that can set children on a cycle of success or failure in both academic and social domains. A child’s abilities to express healthy emotions, understand emotions of self and others, regulate emotion, attention, and behavior, make good decisions regarding social problems, and engage in a range of prosocial behaviors, all work together to promote a successful school experience. However, many children have deficits in these skills by school entry, and educators lack the requisite tools to identify, track and assess skills these children need to learn. Thus, because social-emotional learning (SEL) is so crucial, assessment tools to pinpoint children’s skills and progress are vitally necessary. Previous work by the authors and other researchers has led to the development of strong assessment tools; however, these tools are often developed solely for research use, not practitioner application. In the following, using our assessment battery as an example, we will discuss the steps necessary to adapt SEL assessment for computer-based administration and optimal utility in early childhood education programs.

Comparison of machine learning approaches and statistical methods for estimation of airborne chemical exposures

Accurate quantitative estimation of exposure using retrospective data has been one of the most challenging tasks in the exposure assessment field. To improve these estimates, some models have been developed using published exposure databases with their corresponding exposure determinants. These models are designed to be applied to reported exposure determinants obtained from study subjects or exposure levels assigned by an industrial hygienist, so quantitative exposure estimates can be obtained. ^ In an effort to improve the prediction accuracy and generalizability of these models, and taking into account that the limitations encountered in previous studies might be due to limitations in the applicability of traditional statistical methods and concepts, the use of computer science- derived data analysis methods, predominantly machine learning approaches, were proposed and explored in this study. ^ The goal of this study was to develop a set of models using decision trees/ensemble and neural networks methods to predict occupational outcomes based on literature-derived databases, and compare, using cross-validation and data splitting techniques, the resulting prediction capacity to that of traditional regression models. Two cases were addressed: the categorical case, where the exposure level was measured as an exposure rating following the American Industrial Hygiene Association guidelines and the continuous case, where the result of the exposure is expressed as a concentration value. Previously developed literature-based exposure databases for 1,1,1 trichloroethane, methylene dichloride and, trichloroethylene were used. ^ When compared to regression estimations, results showed better accuracy of decision trees/ensemble techniques for the categorical case while neural networks were better for estimation of continuous exposure values. Overrepresentation of classes and overfitting were the main causes for poor neural network performance and accuracy. Estimations based on literature-based databases using machine learning techniques might provide an advantage when they are applied to other methodologies that combine `expert inputs' with current exposure measurements, like the Bayesian Decision Analysis tool. The use of machine learning techniques to more accurately estimate exposures from literature-based exposure databases might represent the starting point for the independence from the expert judgment.^