Problematic Nature of School Purposes: Implications for pre‐service teacher training programmes     

Harry M. Teitelbaum 《Journal of educational administration and history》1997,29(2):142-157

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On the racism industry     

Harry V. Jaffa 《Academic Questions》1996,9(4):83-85

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Some economic aspects of science     

Harry G. Johnson 《Minerva》1972,10(1):10-18

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U Crazy     

Harry Stein 《Academic Questions》2011,24(4):489-493

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DEVELOPING TRAITS OF CHARACTER IN LIFE SITUATIONS     

W. W. Charters∗ 《Religious education (Chicago, Ill.)》2013,108(7):753-757

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Predicaments,Problems, Puzzles,and Paradoxes in Educational Research*     

Harry S. Broudy 《Educational theory》1983,33(1):33-39

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Problems Associated with Multi-Media Data Bases     

Philip G Barker  Harry Yeates 《British journal of educational technology : journal of the Council for Educational Technology》1981,12(2):158-175

Education is intimately associated with communication, and so may utilize any technology that is able to support a process that enables information to be transferred from one location to another. This paper describes the meaning of the term multi-media data base and presents a model of its potential role in aiding message creation for the purposes of communicating educational material using many parallel channels. Some of the problems associated with the implementation of such systems are outlined.  相似文献   

A success story: A large urban district offers a working model for implementing multisensory teaching into the resource and regular classroom     

Lenox Hutcheson  Harry Selig  Norma Young 《Annals of dyslexia》1990,40(1):77-96

A large urban school district contracted with a private nonprofit educational foundation to train 126 special education resource teachers in the last three years in an Orton-Gillingham-based program. These teachers are currently teaching learning-disabled students in groups of 8–10 at the elementary level and 10–13 students at the secondary level. Learning-disabled students who qualify for Special Education, either in reading or spelling, or both, are receiving the instruction. The teachers took a Basic Introductory Class (90 hours of Advanced Academic Credit offered by the Texas Education Agency, or six hours of graduate credit at a local university) in order to teach the program in the resource setting. A two year Advanced Training included annual on-site observations, two half-day workshops each fall and spring, and a two-day advanced workshop in the second summer. First grade teachers, one selected from each of the 164 campuses, supervisors, and principals attended a 25-hour course on “Recognizing Dyslexia: Using Multisensory Teaching and Discovery Techniques.” The first grade teachers and special education resource teachers collaborated to provide inservice training for their colleagues. Research, conducted by the district’s Research Department, reveals statistically significant gains in reading and spelling ability for the learning-disabled resource students as measured by the Woodcock Reading Mastery Test-Revised, and the Test of Written Spelling.  相似文献   

Obituary: Bernard Theodoor Marie Tervoort: May 29, 1920,Groesbeek, the Netherlands--August 17, 2006, Bussum, the Netherlands     

Knoors H 《Journal of deaf studies and deaf education》2007,12(2):242

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Machine learning-enabled multiplexed microfluidic sensors     

Sajjad Rahmani Dabbagh  Fazle Rabbi  Zafer Doan  Ali Kemal Yetisen  Savas Tasoglu 《Biomicrofluidics》2020,14(6)

High-throughput, cost-effective, and portable devices can enhance the performance of point-of-care tests. Such devices are able to acquire images from samples at a high rate in combination with microfluidic chips in point-of-care applications. However, interpreting and analyzing the large amount of acquired data is not only a labor-intensive and time-consuming process, but also prone to the bias of the user and low accuracy. Integrating machine learning (ML) with the image acquisition capability of smartphones as well as increasing computing power could address the need for high-throughput, accurate, and automatized detection, data processing, and quantification of results. Here, ML-supported diagnostic technologies are presented. These technologies include quantification of colorimetric tests, classification of biological samples (cells and sperms), soft sensors, assay type detection, and recognition of the fluid properties. Challenges regarding the implementation of ML methods, including the required number of data points, image acquisition prerequisites, and execution of data-limited experiments are also discussed.  相似文献