Automatic grading and hinting in open-ended text questions

Open-ended text questions provide better assessment of learner’s knowledge, but analysing answers for this kind of questions, checking their correctness, and generating of detailed formative feedback about errors for the learner are more difficult and complex tasks than for closed-ended questions like multiple-choice.The analysis of answers for open-ended questions can be performed on different levels. Analysis on character level allows to find errors in characters’ placement inside a word or a token; it is typically used to detect and correct typos, allowing to differ typos from actual errors in the learner’s answer. The word-level or token-level analysis allows finding misplaced, extraneous, or missing words in the sentence. The semantic-level analysis is used to capture formally the meaning of the learner’s answer and compare it with the meaning of the correct answer that can be provided in a natural or formal language. Some systems and approaches use analysis on several levels.The variability of the answers for open-ended questions significantly increases the complexity of the error search and formative feedback generation tasks. Different types of patterns including regular expressions and their use in questions with patterned answers are discussed. The types of formative feedback and modern approaches and their capabilities to generate feedback on different levels are discussed too.Statistical approaches or loosely defined template rules are inclined to false-positive grading. They are generally lowering the workload of creating questions, but provide low feedback. Approaches based on strictly-defined sets of correct answers perform better in providing hinting and answer-until-correct feedback. They are characterised by a higher workload of creating questions because of the need to account for every possible correct answer by the teacher and fewer types of detected errors.The optimal choice for creating automatised e-learning courses are template-based open-ended question systems like OntoPeFeGe, Preg, METEOR, and CorrectWriting which allows answer-until-correct feedback and are able to find and report various types of errors. This approach requires more time to create questions, but less time to manage the learning process in the courses once they are run.     

Assessing Personality States: What to Consider when Constructing Personality State Measures

Repeated assessments of personality states in daily diary or experience sampling studies have become a more and more common tool in the psychologist's toolbox. However, and contrary to the widely available literature on personality traits, no best practices for the development of personality state measures exist, and personality state measures have been developed in many different ways. To address this, we first define what a personality state is and discuss important components. On the basis of this, we define what a personality state measure is and suggest a general guideline for the development of such measures. Following the ABC of test construction can then guide the strategy for obtaining validity and reliability evidence: (A) What is the construct being measured? (B) What is the intended purpose of the measure? And (C) What is the targeted population of persons and situations? We then conclude with an example by developing an initial item pool for the assessment of conscientiousness personality states. © 2020 The Authors. European Journal of Personality published by John Wiley & Sons Ltd on behalf of European Association of Personality Psychology     

Intuitions about sample size: the empirical law of large numbers

According to Jacob Bernoulli, even the ‘stupidest man’ knows that the larger one's sample of observations, the more confidence one can have in being close to the truth about the phenomenon observed. Two-and-a-half centuries later, psychologists empirically tested people's intuitions about sample size. One group of such studies found participants attentive to sample size; another found participants ignoring it. We suggest an explanation for a substantial part of these inconsistent findings. We propose the hypothesis that human intuition conforms to the ‘empirical law of large numbers’ and distinguish between two kinds of tasks–one that can be solved by this intuition (frequency distributions) and one for which it is not sufficient (sampling distributions). A review of the literature reveals that this distinction can explain a substantial part of the apparently inconsistent results. © 1997 John Wiley & Sons, Ltd.     

Consistency measures for pairwise comparison matrices

We propose new measures of consistency of additive and multiplicative pairwise comparison matrices. These measures, the relative consistency and relative error, are easy to compute and have clear and simple algebraic and geometric meaning, interpretation and properties. The correspondence between these measures in the additive and multiplicative cases reflects the same correspondence which underpins the algebraic structure of the problem and relates naturally to the corresponding optimization models and axiom systems. The relative consistency and relative error are related to one another by the theorem of Pythagoras through the decomposition of comparison matrices into their consistent and error components. One of the conclusions of our analysis is that inconsistency is not a sufficient reason for revision of judgements. © 1998 John Wiley & Sons, Ltd.     

贵州省技区卫生服务机构准入抽查报告

深入了解我省社区卫生服务发展中存在的问题,随机抽样,现场调查。调查分析发现,社区卫生工作在我省呈现出蓬勃发展的趋势,但也暴露出了许多新的问题。应继续加强人才队伍建设,加强对社区卫生服务机构的监管,依法严格社区卫生服务机构、从业人员和技术的准入管理,制定细化的技术规范、操作规程,建立系统的考核标准、评价体系。     

人脸图形识别取样的眼动特征

通过眼动数据的分析,探讨人脸图形识别的取样特征。实验为2×2的被试内设计,自变量为显示条件（良好显示和不良显示）和显示方式（旋转显示和非旋转显示）。实验的因变量有反应时、总注视时间、注视比率、首视点注视时间、注视点数量、扫视总距离、扫视平均距离、正确率等8个指标。被试为12名大学生,刺激材料为10幅中国人脸部图形,主要实验设备是一台Eyelinkll型服动仪。结果发现,显示条件和显示方式对人脸图形识别绩效的影响显著;随着作业难度加大,被试图形识别的首视点注视时间显著增加;人脸图形识别中,显示条件对信息取样注视比率影响显著．而显示方式显著影响信息取样点数量．     

Independence of sampling of motion parallax and binocular disparity in discrimination of depth1

Abstract: The sampling strategy of the visual system in binocular disparity and motion parallax to discriminate depth was investigated. Human observers were asked to discriminate between the depths of two surfaces defined by both cues. Gaussian noise was added to the depths represented by each cue, and the correlation in noise was manipulated. Human performance was compared with two types of likelihood models. The first was based on independent sampling, in which data from the two cues were gathered from independent sets of points in the display. The second was based on paired sampling, in which data from these cues were gathered from the same set of points. The former model yielded a better fit with human performance. This suggests that the visual system is more likely to adopt independent sampling.     

Influence of dysphoria on positive and negative cognitive reactivity to daily mood fluctuations

We used an experience sampling design to investigate the influence of dysphoria on positive and negative cognitive reactivity. Participants recorded their thoughts and mood four times per day on PDA devices for one week. We hypothesized that those higher in dysphoria would demonstrate a greater increase in negative thinking in response to negative mood, and a weaker increase in positive cognitions in response to positive mood. These hypotheses were largely supported. For those participants who reported higher initial dysphoria, there was a stronger association between negative mood and thinking and a weaker link between positive mood and thinking. Regression analyses indicated that positive and negative cognitive reactivity were independently related to dysphoria, suggesting that they represent distinct processes. Our results highlight the importance of understanding levels of both negative and positive cognitive reactivity and underscore the benefits of assessing mood and cognition with repeated measurements in "real-time," in order to better understand the antecedent effects of mood on thinking.     

Stochastic search item selection for factor analytic models

In this paper we implement a Markov chain Monte Carlo algorithm based on the stochastic search variable selection method of George and McCulloch (1993) for identifying promising subsets of manifest variables (items) for factor analysis models. The suggested algorithm is constructed by embedding in the usual factor analysis model a normal mixture prior for the model loadings with latent indicators used to identify not only which manifest variables should be included in the model but also how each manifest variable is associated with each factor. We further extend the suggested algorithm to allow for factor selection. We also develop a detailed procedure for the specification of the prior parameters values based on the practical significance of factor loadings using ideas from the original work of George and McCulloch (1993). A straightforward Gibbs sampler is used to simulate from the joint posterior distribution of all unknown parameters and the subset of variables with the highest posterior probability is selected. The proposed method is illustrated using real and simulated data sets.     

Repetitive Thought Impairs Sleep Quality: An Experience Sampling Study

Although previous research has suggested that presleep negative cognitive activities are associated with poor sleep quality, there is little evidence regarding the association between negative thoughts and sleep in real-life settings. The present study used experience sampling and long-term sleep monitoring with actigraphy to investigate the relationships among negative repetitive thought, mood, and sleep problems. During a 1-week sampling period, 43 undergraduate students recorded their thought content and mood eight times a day at semirandom intervals. In addition to these subjective reports, participants wore actigraphs on their wrists in order to measure sleep parameters. Analyses using multilevel modeling showed that repetitive thought in the evening was significantly associated with longer sleep-onset latency, decreased sleep efficiency, and reduced total sleep time. Furthermore, impaired sleep quality was significantly associated with reduced positive affect the next morning, and decreased positive affect was indirectly associated with increased repetitive thought in the evening. These findings suggest the existence of a self-reinforcing cycle involving repetitive thought, mood, and impaired sleep quality, highlighting the importance of cognitive and emotional factors in enhancement and maintenance of good-quality sleep.