An Examination of Research to Practice Gaps in Education: Whole Language, Learning Modalities, and Brain Gym

The purpose of this thesis is to examine the documented research to practice gap in education by discussing three recent trends in education that gained momentum with little to no empirical support: (a) whole language reading instruction; (b) modality-based instruction; and (c) Brain Gym. This thesis examines the trend in education toward embracing unsubstantiated strategies by spotlighting these three educational trends and offers recommendations for addressing the research to practice gap in the field of education

Methods for Teaching Hispanic English Language Learners

With the increase of diversity in American schools and the growing importance of English around the world, teaching English as a second language has become an essential aspect to education. It is difficult to have students of different cultural backgrounds in the classroom who are not proficient in English. A teacher cannot expect these students to succeed in an English only classroom setting. It is important that there be specialized teachers to address the needs of these students in order to reach all students where they are and help them accomplish all that they need to in order to succeed. Countless methods have been proposed for teaching students English. My thesis will examine the different methods and primarily focus on how they are used with Hispanic students and which ones seems to produce better results

Content-Based Weak Supervision for Ad-Hoc Re-Ranking

One challenge with neural ranking is the need for a large amount of manually-labeled relevance judgments for training. In contrast with prior work, we examine the use of weak supervision sources for training that yield pseudo query-document pairs that already exhibit relevance (e.g., newswire headline-content pairs and encyclopedic heading-paragraph pairs). We also propose filtering techniques to eliminate training samples that are too far out of domain using two techniques: a heuristic-based approach and novel supervised filter that re-purposes a neural ranker. Using several leading neural ranking architectures and multiple weak supervision datasets, we show that these sources of training pairs are effective on their own (outperforming prior weak supervision techniques), and that filtering can further improve performance.Comment: SIGIR 2019 (short paper

FPGA-Based Tracklet Approach to Level-1 Track Finding at CMS for the HL-LHC

During the High Luminosity LHC, the CMS detector will need charged particle tracking at the hardware trigger level to maintain a manageable trigger rate and achieve its physics goals. The tracklet approach is a track-finding algorithm based on a road-search algorithm that has been implemented on commercially available FPGA technology. The tracklet algorithm has achieved high performance in track-finding and completes tracking within 3.4 $\mu$s on a Xilinx Virtex-7 FPGA. An overview of the algorithm and its implementation on an FPGA is given, results are shown from a demonstrator test stand and system performance studies are presented.Comment: Submitted to proceedings of Connecting The Dots/Intelligent Trackers 2017, Orsay, Franc