A Bayesian-optimal principle for learner-friendly adaptation in learning games

Adaptive learning games should provide opportunities for the student to learn as well as motivate playing until goals have been reached. In this paper, we give a mathematically rigorous treatment of the problem in the framework of Bayesian decision theory. To quantify the opportunities for learning, we assume that the learning tasks that yield the most information about the current skills of the student, while being desirable for measurement in their own right, would also be among those that are efficient for learning. Indeed, optimization of the expected information gain appears to naturally avoid tasks that are exceedingly demanding or exceedingly easy as their results are predictable and thus uninformative. Still, tasks that are efficient for learning may be experienced as too challenging, and the resulting failures can lower motivation. Therefore, in addition to quantifying the expected informational benefit for learning of any prospective task to be presented next, we also model the expected motivational cost of its presentation, measured simply as the estimated probability of failure in our example model. We propose a “learner-friendly” adaptation algorithm that chooses the learning tasks by optimizing the expected benefit divided by the expected cost. We apply this algorithm to a Rasch-like student model implemented within a real-world application and present initial results of a pilot experiment.     

《心理统计学》教学内容的新探索

本文在对当前国内外主要心理统计学教材进行比较的基础上,指出与上个世纪八十年代的心理统计学教材内容相比较,在内容上的新探索主要体现在（1）由“假设检验”的内容中发展出“统计检验力”和“效果大小”的统计指标和估计方法;（2）引进一般线性模型来统合方差分析和回归分析这两种统计方法;（3）适度增加一些“多元统计分析”的内容等三个方面.本文对前两个方面的新内容作了简要评述,并对教材内容的编排方面提出了新的思路.     

Impact of genetic testing on causal models of heart disease and arthritis: An analogue study

Abstract

An analogue study investigated the impact of genetic testing on perceptions of disease. Using a 2 × 2 design, participants (n = 212) imagined receiving the information that they were at increased risk for either heart disease or arthritis. The type of risk information was either genetic or unspecified. Presentation of genetic risk information resulted in the condition being perceived as less preventable. Causal models of disease where investigated using principal components analysis. When hem disease was the stimulus condition, attributions to genes and chance were positively associated following unspecified risk information, and negatively associated following genetic risk information. When arthritis was the stimulus condition, presentation of genetic risk information was associated with attributions to genes becoming separated from the other attributions. One explanation for this is that providing genetic risk information may decrease perceptions of a sense of randomness or uncertainty in disease causation. The extent to which these effects occur in clinical populations. and their behavioural consequences. needs to be established.     

Handedness and Hemispheric Asymmetry in the Control Of Movements

This study investigated the influence of hemispheric specialization of function on the motor performance of the hands. Right-handed (n=17), ambidextral (n=21), and ambisinistral (n=12) subjects performed Fitts' (1954) reciprocal tapping task under two conditions with each hand. Conditions had the same index of difficulty but differed in movement precision. The left hand of righthanders was superior in the condition requiring the greatest amount of preprogramming. Conversely, the right hand was not superior in the condition having the greatest demand for feedback control. For ambidextrals, left-hand superiority in the relatively preprogrammed condition was also revealed. Ambisinistrals showed no significant difference between conditions with either hand. The results partially support the hypothesis that the motor performance of the hands mirrors the dominant processing mode of their contralateral hemisphere. Failure to find supportive evidence in ambisinistrals is consistent with the contention that they lack hemispheric specialization of function.     

Overcoming overconfidence in learning from video-recorded lectures: Implications of interpolated testing for online education

The video-recorded lecture represents a central feature of most online learning platforms. Nonetheless, little is known about how to best structure video-recorded lectures in order to optimize learning. Here, we focused on the tendency for high school and college students to be overconfident in their learning from video-recorded modules, and demonstrated that testing could be used to effectively improve the calibration between predicted and actual performance. Notably, interpolating a lecture with repeated tests helped to boost actual performance to the level of predicted performance, whereas a single test following the lecture served to lower unrealistic judgments of learning. The value of improving performance to match predictions of learning and other avenues for future research regarding meta-comprehension of video-recorded lectures is discussed.     

标准参照测验及其等级线信度的概化理论分析

在测量工作中,误用经典测验理论方法估计标准参照性测验的整体信度和等级线决策信度的情况非常突出。如,无论测量设计是交叉的还是嵌套的,也无论测验结果是做常模参照性解释,还是做标准参照性解释,测验工作者往往只报告克龙巴赫α系数或经典测验理论中的其它少数几个信度指标,而误把整体信度作为等级线信度的现象则更加普遍,这是十分不妥的。本文借用概化理论中的可靠性指数Φ和Φ(λ)公式,分别针对交叉设计和嵌套设计,就标准参照性测验的整体信度和等级分数线决策信度的估计问题进行了探讨。用数据演示的方法比较了交叉设计与嵌套设计在估计标准参照性测验整体信度方面的差异,展示了等级决策分数线决策信度的估计方法。     

Wofür sprechen die Daten?

What Do the Data Tell Us? Justification of scientific theories is a three-place relation between data, theories, and background knowledge. Though this should be a commonplace, many methodologies in science neglect it. The article will elucidate the significance and function of our background knowledge in epistemic justification and their consequences for different scientific methodologies. It is argued that there is no simple and at the same time acceptable statistical algorithm that justifies a given theory merely on the basis of certain data. And even if we think to know the probability of a theory, that does not decide whether we should accept it or not.     

Genetic Counseling for Fragile X Syndrome: Updated Recommendations of the National Society of Genetic Counselors

These recommendations describe the minimum standard criteria for genetic counseling and testing of individuals and families with fragile X syndrome, as well as carriers and potential carriers of a fragile X mutation. The original guidelines (published in 2000) have been revised, replacing a stratified pre- and full mutation model of fragile X syndrome with one based on a continuum of gene effects across the full spectrum of FMR1 CGG trinucleotide repeat expansion. This document reviews the molecular genetics of fragile X syndrome, clinical phenotype (including the spectrum of premature ovarian failure and fragile X-associated tremor-ataxia syndrome), indications for genetic testing and interpretation of results, risks of transmission, family planning options, psychosocial issues, and references for professional and patient resources. These recommendations are the opinions of a multicenter working group of genetic counselors with expertise in fragile X syndrome genetic counseling, and they are based on clinical experience, review of pertinent English language articles, and reports of expert committees. These recommendations should not be construed as dictating an exclusive course of management, nor does use of such recommendations guarantee a particular outcome. The professional judgment of a health care provider, familiar with the facts and circumstances of a specific case, will always supersede these recommendations.     

汉字的无线索回忆再认效应:重复学习和重复测验的作用

采用无线索回忆再认范式,对基于熟悉性的汉字语义特征再认进行了探索,考察了重复学习和重复测验对汉字语义特征的无线索回忆再认效应(RWCR效应)的影响。实验1采用即时测验,实验2采用延时测验,结果发现:(1)无论即时还是延时测验,汉字语义特征的再认均存在RWCR效应。(2)在即时测验时,重复学习对熟悉性有显著影响,重复测验对熟悉性没有影响;重复学习和重复测验均提高回想成绩,但二者无差异。(3)在延时测验时,重复学习组和重复测验组的熟悉性评分均下降,但前者下降快于后者;重复测验组回想的遗忘率较低,重复学习组回想的遗忘率较高。上述结果说明,汉字的语义特征存在稳定的RWCR效应,且重复学习主要影响熟悉性,重复测验主要影响回想。进一步证明了再认记忆的双加工理论。     

How experimental algorithmics can benefit from Mayo’s extensions to Neyman–Pearson theory of testing

Although theoretical results for several algorithms in many application domains were presented during the last decades, not all algorithms can be analyzed fully theoretically. Experimentation is necessary. The analysis of algorithms should follow the same principles and standards of other empirical sciences. This article focuses on stochastic search algorithms, such as evolutionary algorithms or particle swarm optimization. Stochastic search algorithms tackle hard real-world optimization problems, e.g., problems from chemical engineering, airfoil optimization, or bio-informatics, where classical methods from mathematical optimization fail. Nowadays statistical tools that are able to cope with problems like small sample sizes, non-normal distributions, noisy results, etc. are developed for the analysis of algorithms. Although there are adequate tools to discuss the statistical significance of experimental data, statistical significance is not scientifically meaningful per se. It is necessary to bridge the gap between the statistical significance of an experimental result and its scientific meaning. We will propose some ideas on how to accomplish this task based on Mayo’s learning model (NPT*).