Using data mining to predict secondary school student performance

Although the educational level of the Portuguese population has improved in the last decades, the statistics keep Portugal at Europe’s tail end due to its high student failure rates. In particular, lack of success in the core classes of Mathematics and the Portuguese language is extremely serious. On the other hand, the fields of Business Intelligence (BI)/Data Mining (DM), which aim at extracting high-level knowledge from raw data, offer interesting automated tools that can aid the education domain. The present work intends to approach student achievement in secondary education using BI/DM techniques. Recent real-world data (e.g. student grades, demographic, social and school related features) was collected by using school reports and questionnaires. The two core classes (i.e. Mathematics and Portuguese) were modeled under binary/five-level classification and regression tasks. Also, four DM models (i.e. Decision Trees, Random Forest, Neural Networks and Support Vector Machines) and three input selections (e.g. with and without previous grades) were tested. The results show that a good predictive accuracy can be achieved, provided that the first and/or second school period grades are available. Although student achievement is highly influenced by past evaluations, an explanatory analysis has shown that there are also other relevant features (e.g. number of absences, parent’s job and education, alcohol consumption). As a direct outcome of this research, more efficient student prediction tools can be be developed, improving the quality of education and enhancing school resource management

Adjusting for academic preparedness when estimating enrollment disparities in advanced high school coursework

Whether racial disparities in enrollment in advanced high school coursework can be attributed to differences in prior academic preparation is a central empirical question in sociological research, with important implications for education policy. However, the regression-based approaches to this question that are predominant in the literature suffer significant methodological limitations by implicitly assuming that students are similarly prepared if and only if they have similar values on a selected set of academic background measures. Here, we provide a general technique to estimate enrollment disparities in advanced coursework between similarly-prepared students of different races that is less vulnerable to these limitations. We introduce a novel measure of academic preparedness, a student's ex-ante probability of "success" in the course, directly adjust for this single measure in a regression model of enrollment on race, and assess the robustness of estimated disparities to potential unmeasured confounding. We illustrate this approach by analyzing Black-White disparities in AP mathematics enrollment in a large, urban, public school system in the United States. We find that preexisting differences in academic preparation do not fully explain the under-representation of Black students relative to White students in AP mathematics, and contrast our results with those from traditional approaches

Student-Centered Learning: Dozier-Libbey Medical High School

This case study is one of four written by SCOPE about student-centered practices in schools. The case studies address the following questions:1. What are the effects of student-centered learning approaches on student engagement, achievement of knowledge and skills, and attainment (high school graduation, college admission, and college continuation and success), in particular for underserved students?2. What specific practices, approaches, and contextual factors result in these outcomes?The cases focus on the structures, practices, and conditions in the four schools that enable students to experience positive outcomes and consider the ways in which these factors are interrelated and work to reinforce each other

2012-2013 Graduate Catalog

2012-2013 graduate catalog for Morehead State University

The Bottom Line Limitation to the Rule of \u3cem\u3eGriggs v. Duke Power Company\u3c/em\u3e

Part I of this article analyzes the background to the Teal decision and the treatment by the majority and dissent of the issue known in employment discrimination law as the bottom line limitation to the disparate impact theory of employment discrimination. Part II explains why, for reasons beyond those considered by the Teal majority, not only was the Court\u27s rejection of the bottom line theory manifestly correct, but a contrary result would have had grievous consequences. Part III then argues for a similar rejection of the bottom line limitation in those situations where most observers have taken for granted that the bottom line limitation would apply