Assessing the Early Impact of School of One: Evidence from Three School-Wide Pilots

For more than 150 years, education has been organized around classrooms in which one teacher attempts to meet the needs of a large group of students who have a wide range of prior experiences, knowledge, and ways of learning. This structure makes it exceedingly difficult to ensure that all students meet the same standards of performance. School of One (SO1) is an innovative, technology-enhanced math program that seeks to "meet students where they are," by creating individual learning plans, offering multiple teaching strategies, and using daily assessments to monitor progress and adapt lessons as needed. This report evaluates SO1's impact on students' state test scores during the first year of school-wide implementation in three New York City middle schools. It also presents exploratory analysis examining whether exposure to more SO1 material, or mastery of SO1 skills, is associated with improved math performance. Given the early stage of the program's development, the authors caution that the evaluation should not be interpreted as a definitive assessment of SO1's effectiveness. Rather, the findings provide a preliminary assessment of SO1's initial impact on students' math achievement and offer insights that may contribute to the program's development and inform future research

Clustering student skill set profiles in a unit hypercube using mixtures of multivariate betas

<br>This paper presents a finite mixture of multivariate betas as a new model-based clustering method tailored to applications where the feature space is constrained to the unit hypercube. The mixture component densities are taken to be conditionally independent, univariate unimodal beta densities (from the subclass of reparameterized beta densities given by Bagnato and Punzo 2013). The EM algorithm used to fit this mixture is discussed in detail, and results from both this beta mixture model and the more standard Gaussian model-based clustering are presented for simulated skill mastery data from a common cognitive diagnosis model and for real data from the Assistment System online mathematics tutor (Feng et al 2009). The multivariate beta mixture appears to outperform the standard Gaussian model-based clustering approach, as would be expected on the constrained space. Fewer components are selected (by BIC-ICL) in the beta mixture than in the Gaussian mixture, and the resulting clusters seem more reasonable and interpretable.</br> <br>This article is in technical report form, the final publication is available at http://www.springerlink.com/openurl.asp?genre=article &id=doi:10.1007/s11634-013-0149-z</br&gt

Innovative learning in action (ILIA) issue six: Innovative practice in assessment

Welcome to this, the sixth edition of Innovative Learning in Action (ILIA) which focuses our attention on the theme of innovative practice in assessment. On the face of it, innovative assessment may be regarded as any form of assessment which involves the application of a new technique, method or tool. However, to quote Graham Mohl (2007): ‘Innovative assessment is not just some trendy new technique dreamt-up purely to save on the amount of time teachers spend on marking, it is a genuine attempt to improve quality of learning in higher education. If we do save time in the process then all the better for our own learning.’ http://www.city.londonmet.ac.uk/deliberation s/assessment/mowl_index.html. The range of work in this edition of ILIA demonstrates how colleagues are readily embracing this fundamental principle. These papers and snapshots show us how contributors are actively exploring, reviewing and modifying their practice to address assessment principles and strategies helping to produce active learners who are reasoning, critical, highly motivated, capable of self-evaluation and equipped with transferable skills to enable them to flourish in the 21st century global economy. Whilst covering diverse and extensive territory both conceptually and practically, in their entirety these works share common ground in embracing the notion of ‘the redistribution of educational power’ (Heron, 1981). Assessment therefore becomes something which is not simply ‘done to’ students, but it is also ‘done by’ and ‘done with’ students (Harris and Bell, 1990) and is as much about enhancing the quality of their learning as it is about measuring their performance. Some of these works may challenge traditional positions and approaches and in so doing I hope they will provide you with a stimulating and thought-provoking opportunity to reflect on practice and student learning

Skill set profile clustering: the empty K-means algorithm with automatic specification of starting cluster centers

While students’ skill set profiles can be estimated with formal cognitive diagnosis models [8], their computational complexity makes simpler proxy skill estimates attractive [1, 4, 6]. These estimates can be clustered to generate groups of similar students. Often hierarchical agglomerative clustering or k-means clustering is utilized, requiring, for K skills, the specification of 2^K clusters. The number of skill set profiles/clusters can quickly become computationally intractable. Moreover, not all profiles may be present in the population. We present a flexible version of k-means that allows for empty clusters. We also specify a method to determine efficient starting centers based on the Q-matrix. Combining the two substantially improves the clustering results and allows for analysis of data sets previously thought impossible

Task-Specific Experience and Task-Specific Talent: Decomposing the Productivity of High School Teachers

We use administrative panel data to decompose worker performance into components relating to general talent, task-specific talent, general experience, and task-specific experience. We consider the context of high school teachers, in which tasks consist of teaching particular subjects in particular tracks. Using the timing of changes in the subjects and levels to which teachers are assigned to provide identifying variation, we show that much of the productivity gains to teacher experience estimated in the literature are actually subject-specific. By contrast, very little of the variation in the permanent component of productivity among teachers is subject-specific or level-specific. Counterfactual simulations suggest that maximizing the value of task-specific experience could produce nearly costless efficiency gains on the order of .02 test score standard deviations

Occupational Training in High School: When Does it Pay Off?

[Excerpt] About half of all youth either do not complete high school or end their formal education with the high school diploma. Even higher proportions of minority, disadvantaged and handicapped youth do not enter postsecondary education. Should public schools offer these youth occupationally specific education and training? If so, what form should this education take? Should the goal of the occupational component of high school vocational education be occupationally specific skills, career awareness, basic skills or something else? What should be the relationship between programs providing occupationally specific training and the employers who hire their graduates