Impact Of Traditional Versus Alternative Assessment On Student Achievement

The focus of this study looks at the effect of alternative assessment on student achievement in a high school science classroom. Within this action research, students have choices and are able to demonstrate creativity in their alternative assessments. By comparing student results from traditional and alternative assessments, the impact of assessment showed students had an overwhelming preference for completing alternative assessments, and a majority of students did better on the alternative assessment when compared to the traditional assessment

SLIDES: ProPublica Coverage Pavillion, WY

Presenter: Abrahm Lustgarten, ProPublica 19 slide

Impact of Kinesthetic Learning on Student Knowledge Retention and Attitudes toward Mathematics

This research study examines the impact of kinesthetic learning on student knowledge retention and attitudes toward mathematics. Specifically, this study focuses on teaching mathematics to secondary students through dance. Quantitative data was collected and analyzed regarding student mathematics knowledge retention and student attitudes towards mathematics. Although student mathematics knowledge retention increased for both the control and treatment groups, student attitudes toward mathematics decreased after the intervention for the treatment group

SLIDES: ProPublica Coverage Pavillion, WY

Presenter: Abrahm Lustgarten, ProPublica 19 slide

Examining Number Talks with Secondary Preservice Teachers

This paper shares an investigation of the impact of Number Talks on secondary preservice teachers’ number sense. Quantitative data was collected and analyzed regarding preservice teachers’ number sense. Statistical analysis showed a significant increase in the number of strategies preservice teachers used to solve a mental mathematics problem. After experiencing Number Talks, nearly all of the preservice teachers responded that they would consider using Number Talks in their classrooms. Implications of the use of Number Talks with secondary preservice teachers are discussed

Are maintenance expert systems practical now?

This paper examines the question of whether Artificial Intelligence expert system technology can be effectively applied to real world maintenance problems. Expert maintenance systems Installed to date are reviewed and the ACE (Automated Cable Expertise) knowledge-based expert system presently marketed by AT&T is described in detail. ACE provides timely trouble-shooting reports and management analyses (or telephone cable maintenance). Many design decisions faced during the construction of ACE were guided by experience gained In the development of previous expert systems. The most significant departure from "standard" expert system architectures, however, is ACE's use of a conventional data base management system as its primary source of Information Its primary sources of knowledge are the expert maintenance personnel, who previously were users of the database system I and prImers on maintenance analysis strategy The coupling of "data base" and "knowledge-base" demonstrates In a forceful way the manner In which an expert system can significantly enhance the throughput and quality of automatic maintenance support systems. Based on the success of these maintenance expert systems, explosive growth of knowledge-based expert maintenance programs is predicted. Forthcoming maintenance expert systems will assist In the repair of high capital cost equipment, for which down time is expensive or unacceptable, or for which competent repair personnel are In short supply

Permanent His Bundle Pacing: Electrophysiological and Echocardiographic Observations From Long-Term Follow-Up

Background Permanent His bundle pacing (HBP) is a physiological alternative to right ventricular pacing. It is not known whether HBP can cause His-Purkinje conduction (HPC) disease. The aim of our study is to assess His bundle capture and its effect on left ventricular (LV) function in long-term follow-up and to determine HPC at the time of pulse generator change (GC) in patients with chronic HBP. Methods HB electrograms were recorded from the pacing lead at implant and GC. HBP QRS duration (QRSd), His-ventricular (HV) intervals, and HB pacing thresholds at GC were compared with implant measurements. HPC was assessed by pacing at cycle lengths of 700 ms, 600 ms, and 500 ms at GC. LV internal diameters, ejection fraction (EF), and valve dysfunction at baseline were compared with echocardiography during follow-up. Results GC was performed in 20 patients (men 13; age 74 ± 14 years) with HBP at 70 ± 24 months postimplant. HV intervals remained unchanged from initial implant (44 ± 4 ms vs 45 ± 4 ms). During HBP at 700 ms, 600 ms, and 500 ms (n = 17), consistent 1:1 HPC was present. HBP QRSd remained unchanged during follow-up (117 ± 20 ms vs 118 ± 23 ms). HBP threshold at implant and GC was 1.9 ± 1.1 V and 2.5 ± 1.2 V @ 0.5 ms. Despite high pacing burden (77 ± 13%), there was no significant change in LVEF (50 ± 14% at implant) during follow-up (55 ± 6%, P = 0.06). Conclusions HBP does not appear to cause new HPC abnormalities and is associated with stable HBP QRSd during long-term follow-up. Despite high pacing burden, HBP did not result in deterioration of left ventricular systolic function or cause new valve dysfunction

Race and space : mapping the construction of political identity

Identity construction is subject to pre-existing meaning systems and values attached to these meaning systems. Race, like class and gender is imbued with meaning which has been given to it through historical circumstance and event, global and local relations, and national policy and ideology. The impact of these forces gives meaning to the discourses which contribute to identity construction. These discourses, in recent critical analysis, have borrowed metaphors of geography, space in particular, in order to gain insight into the particularities of identity construction, how this identity is politicized and the tools necessary to subvert the essential categories of these politicized identities. This study argues that space, as a metaphorical tool, is indispensible in understanding the way in which political identity is constructed, particularly in relation to race and the way in which race is politicized

A Bayesian Rule Generation Framework for 'Omic' Biomedical Data Analysis

High-dimensional biomedical 'omic' datasets are accumulating rapidly from studies aimed at early detection and better management of human disease. These datasets pose tremendous challenges for analysis due to their large number of variables that represent measurements of biochemical molecules, such as proteins and mRNA, from bodily fluids or tissues extracted from a rather small cohort of samples. Machine learning methods have been applied to modeling these datasets including rule learning methods, which have been successful in generating models that are easily interpretable by the scientists. Rule learning methods have typically relied on a frequentist measure of certainty within IF-THEN (propositional) rules. In this dissertation, a Bayesian Rule Generation Framework (BRGF) is developed and tested that can produce rules with probabilities, thereby enabling a mathematically rigorous representation of uncertainty in rule models. The BRGF includes a novel Bayesian Discretization method combined with one or more search strategies for building constrained Bayesian Networks from data and converting them into probabilistic rules. Both global and local structures are built using different Bayesian Network generation algorithms and the rule models generated from the network are tested on public and private 'omic' datasets. We show that using a specific type of structure (Bayesian decision graphs) in tandem with a specific type of search method (parallel greedy) allows us to achieve statistically significant higher overall performance over current state of the art rule learning methods. Not only does using the BRGF boost performance on average on 'omic' biomedical data to a statistically significant point, but also provides the ability to incorporate prior information in a mathematically rigorous fashion for modeling purposes