Flipped classroom teaching methods in medical education

BACKGROUND: Recent rapid increases in technology development have become increasingly prevalent in education. As a result of online education resources, classroom teaching dynamics have begun to shift away from traditional lecture. In particular, flipping the classroom has become popular in higher education. Flipping the classroom consolidates standard lectures into at-home self-study modules, and utilizes class time for engaging students in critical thinking exercises. Some research suggests that this style of teaching has led to increased student satisfaction and higher exam scores. OBJECTIVES: The objective of this study is to compare flipped classroom modules to traditional lecture in PA and medical student didactic education. METHODS: This study is a crossover interventional study that includes first year PA students and second year medical students from Boston University. Students will be randomly assigned to either a control group or experimental group. Both groups will take three tests throughout the study: a pre-test prior to intervention, a test following the first week prior to crossover, and a final exam after crossover completion. Each exam will consist of 30 multiple choice questions and a Likert scale questionnaire assessing student satisfaction. The control group will be exposed to traditional lecture while the experimental group will be exposed to a flipped classroom module. Content will be identical between groups, and following module completion, the groups will crossover for exposure to opposing treatment. RESULTS: Each cohort’s exam scores will be evaluated based on mean score and standard deviation at all three time points. Additionally, Likert scale responses will be evaluated at all three time points. Values will be assessed to determine if a relationship between lecture style, exam scores, and student satisfaction exist. DISCUSSION: Results from this study will help to determine the significance of flipped classroom learning in medical education

Physical properties of 6dF dwarf galaxies

Spectral synthesis is basically the decomposition of an observed spectrum in terms of the superposition of a base of simple stellar populations of various ages and metallicities, producing as output the star formation and chemical histories of a galaxy, its extinction and velocity dispersion. The STARLIGHT code provides one of the most powerful spectral synthesis tools presently available. We have applied this code to the entire Six-Degree-Field Survey (6dF) sample of nearby star-forming galaxies, selecting dwarf galaxy candidates with the goal of: (1) deriving the age and metallicity of their stellar populations and (2) creating a database with the physical properties of our sample galaxies together with the FITS files of pure emission line spectra (i.e. the observed spectra after subtraction of the best-fitting synthetic stellar spectrum). Our results yield a good qualitative and quantitative agreement with previous studies based on the Sloan Digital Sky Survey (SDSS). However, an advantage of 6dF spectra is that they are taken within a twice as large fiber aperture, much reducing aperture effects in studies of nearby dwarf galaxies.Comment: To appear in JENAM Symposium "Dwarf Galaxies: Keys to Galaxy Formation and Evolution", P. Papaderos, S. Recchi, G. Hensler (eds.). Lisbon, September 2010, Springer Verlag, in pres

Latent Replay for Continual Learning on Edge devices with Efficient Architectures

openDue to the limited computational capabilities, low memory and limited energy budget, training deep neural networks on edge devices is very challenging. On the other hand, privacy and data limitations, lack of network connection, as well as the need for rapid model adaptation, make real-time training on the device crucial. Standard artificial neural networks suffer from the issue of catastrophic forgetting, making learning difficult. Continual learning shifts this paradigm to networks that can continuously accumulate knowledge on different tasks without the need to retrain from scratch. In this work, a Continual Learning technique called Latent Replay is employed, in which the activations of intermediate layers are stored and used to integrate training data for each new task. This approach reduces the computation time and memory required, facilitating training on the limited resources of edge devices. In addition, a new efficient architecture, known as PhiNets, was used for the first time in the context of Continual Learning. An intensive study was conducted to compare PhiNets with efficient architectures already tested in this context, such as MobileNet. Several metrics were considered, such as computation time, inference time, memory used, and accuracy. In addition, the variation of these metrics based on factors such as the layer at which Latent Replay is applied was analysed. Tests were performed on well-known computer vision datasets, evaluating them as a stream of classes.Due to the limited computational capabilities, low memory and limited energy budget, training deep neural networks on edge devices is very challenging. On the other hand, privacy and data limitations, lack of network connection, as well as the need for rapid model adaptation, make real-time training on the device crucial. Standard artificial neural networks suffer from the issue of catastrophic forgetting, making learning difficult. Continual learning shifts this paradigm to networks that can continuously accumulate knowledge on different tasks without the need to retrain from scratch. In this work, a Continual Learning technique called Latent Replay is employed, in which the activations of intermediate layers are stored and used to integrate training data for each new task. This approach reduces the computation time and memory required, facilitating training on the limited resources of edge devices. In addition, a new efficient architecture, known as PhiNets, was used for the first time in the context of Continual Learning. An intensive study was conducted to compare PhiNets with efficient architectures already tested in this context, such as MobileNet. Several metrics were considered, such as computation time, inference time, memory used, and accuracy. In addition, the variation of these metrics based on factors such as the layer at which Latent Replay is applied was analysed. Tests were performed on well-known computer vision datasets, evaluating them as a stream of classes

The Preparation of Reading Teachers for the Disadvantaged

From September 1969 to September 1970 Loyola University of New Orleans conducted an EPDA Experienced Teachers Fellowship Program in Reading Instruction for Elementary and Junior High School Teachers. The purpose of the program, funded by the United States Office of Education, was to prepare 20 teachers chosen from Louisiana Public and Private schools to teach reading to disadvantaged youth

The Disadvantaged Child and His Problems With Regard to Reading

One of the most fashionable topics for discussion among educators today is the problem of the education of the culturally deprived child. The few people trying to understand this child have given him a name, not a satisfactory name, but a name; they call him C( culturally deprived. What defines him is not an absence of money or nice clothes or good furniture or cars or food, although all these objects are usually lacking. These children suffer from poverty of experience. Perhaps their lives are rich with experience their teachers know nothing about. But they are growing up unequipped to live in an urban, primarily middle-class world of papers and pens, books and conversations, machines and desks and time clocks