Automated Feedback as a Convergence Tools

This study evaluates two content delivery options for teaching a programming language to determine whether an asynchronous format can achieve the same learning efficacy as a traditional lecture (face-to-face) format. We use media synchronicity theory as a guide to choose media capabilities to incorporate into an asynchronous tutorial used asynchronously. We conducted an experiment with 49 students from three classes of a web development class at an American university. Our results suggest that an asynchronous tutorial can achieve the same learning outcomes as a traditional lecture format by using automated feedback for convergence. Somewhat surprisingly, we found that performance did not improve when students received both the tutorial and the lecture. Our results demonstrate that technical material can be effectively delivered asynchronously

Selection of Learning Algorithms for Trading Systems Based on Biased Estimators-- An Abstract

Zoran Obradovi'c 1 Tim Chenoweth 1;2;3 1 School of Electrical Engineering and Computer Science 2 Department of Management and Systems 3 Department of Economics Washington State University, Pullman WA 99164-2752 This is an extended abstract of a paper that will appear in (Obradovic, & Chenoweth in press). It extends our previous neural network based trading system described in (Chenoweth, Obradovic, & Lee in press; Chenoweth, & Obradovic in press). Our approach partitions the prediction problem into subproblems, attacks each subproblem separately, and combines the partial estimates into a final prediction. The trading system is composed of a preprocessing component, two prediction components, and a postprocessing component. The preprocessing is comprised of a feature selection process for selecting relevant features (Chenoweth, & Obradovic 1995), and a pattern partitioning process for separating the training patterns into three disjoint sets. One set is used to train an optimist..

Impact of Female and Male University Instructors Revealing Their High-Functioning Autism to Their Students

High-functioning autism, informally known as Asperger’s, involves diminished social skills considered important for university instruction. In an experiment, 370 freshmen and sophomores mostly Caucasian students from a western U. S. university responded to a survey to investigate whether male or female instructors might equally benefit from higher initial student impressions of their teaching ability. Role Congruity Theory suggests that individuals will be supported when their characteristics align with their group’s social roles. The theory implies that women should follow female stereotypes involving more empathy and social skills and less systemization. Examples of social skills include smooth back-and-forth conversations, appropriate eye contact, and interest in students. An example of systemization includes having a specific order in how things are done in a classroom. In contrast, men should follow male stereotypes involving more systemization and less empathy and social skills. As autism is more associated with male characteristics, female university instructors who reveal their male-oriented high-functioning autism might receive lower initial impressions. In contrast, men who reveal their high-functioning autism would receive higher impressions. Results showed that student impressions of female instructors were not significantly different when autism was revealed (p \u3c .26). In contrast, the results show that male instructors had higher student impressions if they reveal their autistic characteristics (p \u3c .01). This research is unique in its focus on the relationship between instructor gender, autism revelations, and student impressions of the instructors. Implications for future research and practice are provided

Selection of Learning Algorithms for Trading Systems Based on Biased Estimators

In our previous trading system for the S&P 500 index, promising results were obtained by partitioning the historic data into disjoint subsets used to design two biased local estimators whose partial estimates were combined into a trading recommendation [3, 4]. The objective of this study is to explore whether using cascade-correlation learning instead or in addition to the previously used back-propagation to train either one or both of the local estimators improves the trading system's performance. Several learning algorithm combinations were explored and tested using real financial data. The system yielding the best results used a mixture of learning algorithms (both back-propagation and cascade-correlation) and achieved an annual rate of return of 20.49% without a commission and 14.37% with a 0.05% commission over a five year trading period. This is significantly better than the annual rate of return achieved by both the buy and hold strategy (13.36%) and a system configuration that ..

A multi-component nonlinear prediction system for the S&P 500 index

The proposed stock market prediction system is comprised of two preprocessing components, two specialized neural networks, and a decision rule base. First, the preprocessing components determine the most relevant features for stock market prediction, remove the noise, and separate the remaining patterns into two disjoint sets. Next, the two neural networks predict the market’s rate of return, with one network trained to recognize positive and the other negative returns. Finally, the decision rule base takes both return predictions and determines a buy/sell recommendation. Daily and monthly experiments are conducted and performance measured by computing the annual rate of return and the return per trade. Comparison of the results achieved by the dual neural network system to that of the single neural network shows that the dual neural network system gives much larger returns with fewer trades. In addition, dual neural network experiments with the appropriately selected filtering and decision thresholds managed to achieve an almost twice larger annual rate of return when compared to that of the buy and hold strategy over a seventy month period. However, no claims are made that the proposed system is better than the buy and hold strategy when considering transaction costs

An Explicit Feature Selection Strategy for Predictive Models of the S&P 500 Index

The focus of this study is the selection of an appropriate set of features for a feed forward neural network model used to predict both future market direction and future returns for the S&P 500 Index. The experimental results provide evidence that the proposed feature selection process may result in a more successful prediction model. However, the study also indicates that the problem domain may need to be limited to predicting monthly instead of daily movements. In addition, the proposed process could be more useful for predicting the future market direction rather than actual returns. 1. Introduction While the application of neural networks to financial forecasting is beginning to receive academic attention [Freedman 1995], the issue of feature selection for financial forecasting problems has been largely ignored. Feature selection refers to choosing a subset of parameters (or features) from a larger pool of input information (technical and/or fundamental indicators) for designing a..

Dominance reversals and the maintenance of genetic variation for fitness

Antagonistic selection between different fitness components (e.g., survival versus fertility) or different types of individuals in a population (e.g., females versus males) can potentially maintain genetic diversity and thereby account for the high levels of fitness variation observed in natural populations. However, the degree to which antagonistic selection can maintain genetic variation critically depends on the dominance relations between antagonistically selected alleles in diploid individuals. Conditions for stable polymorphism of antagonistically selected alleles are narrow, particularly when selection is weak, unless the alleles exhibit "dominance reversals"-in which each allele is partially or completely dominant in selective contexts in which it is favored and recessive in contexts in which it is harmful. Although theory predicts that dominance reversals should emerge under biologically plausible conditions, evidence for dominance reversals is sparse. In this primer, we review theoretical arguments and data supporting a role for dominance reversals in the maintenance of genetic variation. We then highlight an illuminating new study by Grieshop and Arnqvist, which reports a genome-wide signal of dominance reversals between male and female fitness in seed beetles