Supporting Students with Math Anxiety

Math anxiety has been the focus of much research throughout the years. Math anxiety is defined as the feeling of discomfort and disturbance that is experienced when facing mathematical problems. Math anxiety causes students to avoid mathematics and learning of it because of the feeling of distress when confronted with a problem to complete. Math is studied so that students can learn about numbers in order to complete simple and complex calculations each and every day. The studying of mathematics has even impacted future career options for individuals. Career fields in the Science, Technology, Engineering, and Mathematics (STEM) have been on the decline because individuals have been avoiding taking classes in mathematics which results in fewer individuals pursuing such careers. Research has shown that beliefs about math are developed early on; once they have been established, they are hard to change. This study was conducted to determine how to support students with math anxiety. The study involved five math teachers, five science teachers, three special education teachers, and four administrators. Through the survey responses and the interviews, I found that educators need to support students with math anxiety. Educators need to make sure every student has opportunities to be successful in math

The Elementary and Secondary Education Act, as Amended by the No Child Left Behind Act: A Primer

[Excerpt] The primary source of federal aid to K-12 education is the Elementary and Secondary Education Act (ESEA), particularly its Title I, Part A program of Education for the Disadvantaged. The ESEA was initially enacted in 1965 (P.L. 89- 10), and was most recently amended and reauthorized by the No Child Left Behind Act of 2001 (NCLBA, P.L. 107-110). Virtually all ESEA programs are authorized through FY2008. During the current 110th Congress, congressional hearings are being conducted in anticipation of subsequent consideration of legislation to amend and extend the ESEA

Premise Selection for Mathematics by Corpus Analysis and Kernel Methods

Smart premise selection is essential when using automated reasoning as a tool for large-theory formal proof development. A good method for premise selection in complex mathematical libraries is the application of machine learning to large corpora of proofs. This work develops learning-based premise selection in two ways. First, a newly available minimal dependency analysis of existing high-level formal mathematical proofs is used to build a large knowledge base of proof dependencies, providing precise data for ATP-based re-verification and for training premise selection algorithms. Second, a new machine learning algorithm for premise selection based on kernel methods is proposed and implemented. To evaluate the impact of both techniques, a benchmark consisting of 2078 large-theory mathematical problems is constructed,extending the older MPTP Challenge benchmark. The combined effect of the techniques results in a 50% improvement on the benchmark over the Vampire/SInE state-of-the-art system for automated reasoning in large theories.Comment: 26 page

State-of-the-art on evolution and reactivity

This report starts by, in Chapter 1, outlining aspects of querying and updating resources on the Web and on the Semantic Web, including the development of query and update languages to be carried out within the Rewerse project. From this outline, it becomes clear that several existing research areas and topics are of interest for this work in Rewerse. In the remainder of this report we further present state of the art surveys in a selection of such areas and topics. More precisely: in Chapter 2 we give an overview of logics for reasoning about state change and updates; Chapter 3 is devoted to briefly describing existing update languages for the Web, and also for updating logic programs; in Chapter 4 event-condition-action rules, both in the context of active database systems and in the context of semistructured data, are surveyed; in Chapter 5 we give an overview of some relevant rule-based agents frameworks