Teaching communication in sex education: Facilitating communication skills knowledge and ease of use

For the most part, sex education programs have sought to reduce the negative consequences of sexual behavior, such as pregnancy and sexuality transmitted infections (STIs). However, sex education programs also have the potential to encourage sexual behaviors that enhance relationships, such as communication with sexual partners. Higher levels of positive communication are related to higher relationship satisfaction and better decisions in regards to safe sex. However, communicating about sex can be uniquely challenging so specific instruction and practice may be necessary for this particular topic. One goal of this study was to experimentally examine the impact of teaching communication skills within the context of a sex education class, as compared to teaching them in a non-sexual context. Participants read an online educational module providing information about a variety of communication skills, using one of two sets of examples. It was expected that participants who read sexual examples would report that using communication skills to talk about sexual topics would be easier for them than those who read the non-sexual examples. Another goal of the study was to investigate the use of constructivism as a framework for teaching sex education classes, which holds that those learning contexts that result in personal engagement and connections with material are more effective than those that do not. Participants went through a series of writing exercises as they read the module, requiring them to either reflect on how the information could be useful in their own lives or simply summarize what they read. It was expected that participants who did the writing activity that requires personal engagement with the material will remember the material better, and will anticipate more ease using the skills presented than those who are asked to simply summarize the material they read. Additionally, it was expected that the participants in the personal engagement who also read the sexual examples would be especially at ease with using the skills to discuss sexual topics. Analyses of covariance were used to assess differences in knowledge and the ease of using the skills to talk about either general or sexual relationship topics, using participant pretest scores as covariates. Results indicated that going through the module did increase knowledge for all participants in all conditions, and that there was no difference in the learning that occurred as a result of the type of learning activity. For the ease of use across the two types of communication, sexual and general differing patterns of results were found. For the general topics, participants in the summarization condition thought suing the skills would be easier than the participants in the personal engagement condition, regardless of example type. For the sexual topics, participants in the summarization condition who also read the sexual examples thought that communicating would be easier than did participants in any of the other three groups, whose rating did not differ from each other. These results partially supported the hypotheses, contrary to expectations, the summarization activity facilitated greater ease instead of the personal engagement conditions; however, these results can still be understood in the context of constructivism. Instead of the personal engagement activity causing participants to see using the communication skills as easy, the connection and deeper processing of the material through the personal reflection may have caused participants to more clearly recognize the challenge inherent in relationship communication of all kinds. Future studies should expand this research to include both longitudinal and behavioral measures

Detecting and correcting errors in ruled-based expert systems : an integration of empirical and explanation-based learning

In this paper, we argue that techniques proposed for combining empirical and explanation-based learning methods can also be used to detect errors in rule-based expert systems, to isolate the blame for these errors to a small number of rules and suggest revisions to the rules to eliminate these errors. We demonstrate that FOCL, an extension to Quinlan's FOIL program, can learn in spite of an incorrect domain theory (e.g., a knowledge base of an expert system that contains some erroneous rules). A prototype knowledge acquisition tool, KR-FOCL, has been constructed that can utilize a trace of FOCL to suggest revisions to a rule base

Machine learning : techniques and foundations

The field of machine learning studies computational methods for acquiring new knowledge, new skills, and new ways to organize existing knowledge. In this paper we present some of the basic techniques and principles that underlie AI research on learning, including methods for learning from examples, learning in problem solving, learning by analogy, grammar acquisition, and machine discovery. In each case, we illustrate the techniques with paradigmatic examples

Convex Calibration Dimension for Multiclass Loss Matrices

We study consistency properties of surrogate loss functions for general multiclass learning problems, defined by a general multiclass loss matrix. We extend the notion of classification calibration, which has been studied for binary and multiclass 0-1 classification problems (and for certain other specific learning problems), to the general multiclass setting, and derive necessary and sufficient conditions for a surrogate loss to be calibrated with respect to a loss matrix in this setting. We then introduce the notion of convex calibration dimension of a multiclass loss matrix, which measures the smallest `size' of a prediction space in which it is possible to design a convex surrogate that is calibrated with respect to the loss matrix. We derive both upper and lower bounds on this quantity, and use these results to analyze various loss matrices. In particular, we apply our framework to study various subset ranking losses, and use the convex calibration dimension as a tool to show both the existence and non-existence of various types of convex calibrated surrogates for these losses. Our results strengthen recent results of Duchi et al. (2010) and Calauzenes et al. (2012) on the non-existence of certain types of convex calibrated surrogates in subset ranking. We anticipate the convex calibration dimension may prove to be a useful tool in the study and design of surrogate losses for general multiclass learning problems.Comment: Accepted to JMLR, pending editin

Storage capacity of a constructive learning algorithm

Upper and lower bounds for the typical storage capacity of a constructive algorithm, the Tilinglike Learning Algorithm for the Parity Machine [M. Biehl and M. Opper, Phys. Rev. A {\bf 44} 6888 (1991)], are determined in the asymptotic limit of large training set sizes. The properties of a perceptron with threshold, learning a training set of patterns having a biased distribution of targets, needed as an intermediate step in the capacity calculation, are determined analytically. The lower bound for the capacity, determined with a cavity method, is proportional to the number of hidden units. The upper bound, obtained with the hypothesis of replica symmetry, is close to the one predicted by Mitchinson and Durbin [Biol. Cyber. {\bf 60} 345 (1989)].Comment: 13 pages, 1 figur

Rules and principles in cognitive diagnoses

Cognitive simulation is concerned with constructing process models of human cognitive behavior. Our work on the ACM system (Automated Cognitive Modeler) is an attempt to automate this process. The basic assumption is that all goal-oriented cognitive behavior involves search through some problem space. Within this framework, the task of cognitive diagnosis is to identify the problem space in which the subject is operating, identify solution paths used by the subject, and find conditions on the operators that explain those solution paths and that predict the subject's behavior on new problems. The work presented in this paper uses techniques from machine learning to automate the tasks of finding solution paths and operator conditions. We apply this method to the domain of multi-column subtraction and present results that demonstrate ACM's ability to model incorrect subtraction strategies. Finally, we discuss the difference between procedural bugs and misconceptions, proposing that errors due to misconceptions can be viewed as violations of principles for the task domain