Value-Added Modeling for Teacher Effectiveness

[Excerpt] This report addresses issues associated with the evaluation of teacher effectiveness based on student growth in achievement. It focuses specifically on a method of evaluation referred to as value-added modeling (VAM). Although there are other methods for assessing teacher effectiveness, in the last decade, VAM has garnered increasing attention in education research and policy due to its promise as a more objective method of evaluation. The first section of this report describes what constitutes a VAM approach and how it estimates the so-called “teacher effect.” The second section identifies the components necessary to conduct VAM in education settings. Third, the report discusses current applications of VAM at the state and school district levels and what the research on these applications says about this method of evaluation. The fourth section of the report explains some of the implications these applications have for large-scale implementation of VAM. Finally, the report describes some of the federal policy options that might arise as Congress considers legislative action around these or related issues

Italian translation of the questionnaire for professional training evaluation

This works illustrates the psychometric properties of the Italian version of the Questionnaire for Professional Training Evaluation (Q4TE), validated by Grohmann and Kauffeld (2013). This 12-item questionnaire provides evaluation for different training outcomes, it is time efficient and applicable to several professional contexts, and it shows sound psychometric properties. In order to test the Italian form, we led two studies. In study 1 (N=125), an EFA led to a two-factor solution accounting for short and long-term training outcomes. In study 2 (N=122) a five-model comparison was performed. Although at a first stage a two factor solutions seemed to emerge, CFA found the best fit in a 6 inter-correlated first-order factors model (satisfaction, utility, knowledge, application to practice, individual organizational results and global organizational results). Relationships with learning transfer, transfer quantity, type of training, training methodologies, and individual variables (gender, age, tenure) are explored. Limitations, research and practical implications are discussed

Categorization of interestingness measures for knowledge extraction

Finding interesting association rules is an important and active research field in data mining. The algorithms of the Apriori family are based on two rule extraction measures, support and confidence. Although these two measures have the virtue of being algorithmically fast, they generate a prohibitive number of rules most of which are redundant and irrelevant. It is therefore necessary to use further measures which filter uninteresting rules. Many synthesis studies were then realized on the interestingness measures according to several points of view. Different reported studies have been carried out to identify "good" properties of rule extraction measures and these properties have been assessed on 61 measures. The purpose of this paper is twofold. First to extend the number of the measures and properties to be studied, in addition to the formalization of the properties proposed in the literature. Second, in the light of this formal study, to categorize the studied measures. This paper leads then to identify categories of measures in order to help the users to efficiently select an appropriate measure by choosing one or more measure(s) during the knowledge extraction process. The properties evaluation on the 61 measures has enabled us to identify 7 classes of measures, classes that we obtained using two different clustering techniques.Comment: 34 pages, 4 figure

Sparse Learning over Infinite Subgraph Features

We present a supervised-learning algorithm from graph data (a set of graphs) for arbitrary twice-differentiable loss functions and sparse linear models over all possible subgraph features. To date, it has been shown that under all possible subgraph features, several types of sparse learning, such as Adaboost, LPBoost, LARS/LASSO, and sparse PLS regression, can be performed. Particularly emphasis is placed on simultaneous learning of relevant features from an infinite set of candidates. We first generalize techniques used in all these preceding studies to derive an unifying bounding technique for arbitrary separable functions. We then carefully use this bounding to make block coordinate gradient descent feasible over infinite subgraph features, resulting in a fast converging algorithm that can solve a wider class of sparse learning problems over graph data. We also empirically study the differences from the existing approaches in convergence property, selected subgraph features, and search-space sizes. We further discuss several unnoticed issues in sparse learning over all possible subgraph features.Comment: 42 pages, 24 figures, 4 table