Case studies of mental models in home heat control: searching for feedback, valve, timer and switch theories

An intergroup case study was undertaken to determine if: 1) There exist distinct mental models of home heating function, that differ significantly from the actual functioning of UK heating systems; and 2) Mental models of thermostat function can be categorized according to Kempton’s (1986) valve and feedback shared theories, and others from the literature. Distinct, inaccurate mental models of the heating system, as well as thermostat devices in isolation, were described. It was possible to categorise thermostat models by Kempton’s (1986) feedback shared theory, but other theories proved ambiguous. Alternate control devices could be categorized by Timer (Norman, 2002) and Switch (Peffer et al., 2011) theories. The need to consider the mental models of the heating system in terms of an integrated set of control devices, and to consider user’s goals and expectations of the system benefit, was highlighted. The value of discovering shared theories, and understanding user mental models, of home heating, are discussed with reference to their present day relevance for reducing energy consumption

PhotoRaptor - Photometric Research Application To Redshifts

Due to the necessity to evaluate photo-z for a variety of huge sky survey data sets, it seemed important to provide the astronomical community with an instrument able to fill this gap. Besides the problem of moving massive data sets over the network, another critical point is that a great part of astronomical data is stored in private archives that are not fully accessible on line. So, in order to evaluate photo-z it is needed a desktop application that can be downloaded and used by everyone locally, i.e. on his own personal computer or more in general within the local intranet hosted by a data center. The name chosen for the application is PhotoRApToR, i.e. Photometric Research Application To Redshift (Cavuoti et al. 2015, 2014; Brescia 2014b). It embeds a machine learning algorithm and special tools dedicated to preand post-processing data. The ML model is the MLPQNA (Multi Layer Perceptron trained by the Quasi Newton Algorithm), which has been revealed particularly powerful for the photo-z calculation on the base of a spectroscopic sample (Cavuoti et al. 2012; Brescia et al. 2013, 2014a; Biviano et al. 2013). The PhotoRApToR program package is available, for different platforms, at the official website (http://dame.dsf.unina.it/dame_photoz.html#photoraptor).Comment: User Manual of the PhotoRaptor tool, 54 pages. arXiv admin note: substantial text overlap with arXiv:1501.0650

Transportability of Trauma-Focused Cognitive Behavioral Therapy : A Case Study with Adolescents in a Residential Treatment Setting

Because of the increase in the numbers of adolescents presenting in residential care, the challenge and difficulty posed to therapists in treating this age-group, and the prevalence of chronic stress and complex trauma symptoms found in this population, the research conducted was a clinical case study investigating the transportability and effectiveness of using Trauma-Focused Cognitive Behavioral Therapy (TF-CBT), a manualized treatment format, with adolescent clients in a residential treatment setting. A doctoral candidate was trained to engage each of three residential clients in 12 individual sessions of TF-CBT. The Behavior Assessment System for Children (BASC), Children’s Depression Inventory (CDI), Jesness Behavior Checklist (JBC), Revised Children’s Manifest Anxiety Scale (RCMAS), Trauma Symptom Checklist for Children (TSCC), Working Alliance Inventory (WAI), were administered at pretest, mid-way through treatment, and at posttest to assess treatment outcome. A qualitative assessment of treatment outcome comparing pretest scores and posttest scores on measures were made, and a discussion of the treatment efficacy of TF-CBT with older adolescents in residential treatment was provided. Suggestions for future research of the application of TF-CBT with youth in a residential treatment setting are offered by the researcher. An empirical study proposal was devised to demonstrate the study\u27s application to a larger sample size

A Study Of Factors Contributing To Self-reported Anomalies In Civil Aviation

A study investigating what factors are present leading to pilots submitting voluntary anomaly reports regarding their flight performance was conducted. The study employed statistical methods, text mining, clustering, and dimensional reduction techniques in an effort to determine relationships between factors and anomalies. A review of the literature was conducted to determine what factors are contributing to these anomalous incidents, as well as what research exists on human error, its causes, and its management. Data from the NASA Aviation Safety Reporting System (ASRS) was analyzed using traditional statistical methods such as frequencies and multinomial logistic regression. Recently formalized approaches in text mining such as Knowledge Based Discovery (KBD) and Literature Based Discovery (LBD) were employed to create associations between factors and anomalies. These methods were also used to generate predictive models. Finally, advances in dimensional reduction techniques identified concepts or keywords within records, thus creating a framework for an unsupervised document classification system. Findings from this study reinforced established views on contributing factors to civil aviation anomalies. New associations between previously unrelated factors and conditions were also found. Dimensionality reduction also demonstrated the possibility of identifying salient factors from unstructured text records, and was able to classify these records using these identified features

A library or just another information resource? A case study of users' mental models of traditional and digital libraries

A user's understanding of the libraries they work in, and hence of what they can do in those libraries, is encapsulated in their “mental models” of those libraries. In this article, we present a focused case study of users' mental models of traditional and digital libraries based on observations and interviews with eight participants. It was found that a poor understanding of access restrictions led to risk-averse behavior, whereas a poor understanding of search algorithms and relevance ranking resulted in trial-and-error behavior. This highlights the importance of rich feedback in helping users to construct useful mental models. Although the use of concrete analogies for digital libraries was not widespread, participants used their knowledge of Internet search engines to infer how searching might work in digital libraries. Indeed, most participants did not clearly distinguish between different kinds of digital resource, viewing the electronic library catalogue, abstracting services, digital libraries, and Internet search engines as variants on a theme

Cognitive load theory, educational research, and instructional design: some food for thought

Cognitive load is a theoretical notion with an increasingly central role in the educational research literature. The basic idea of cognitive load theory is that cognitive capacity in working memory is limited, so that if a learning task requires too much capacity, learning will be hampered. The recommended remedy is to design instructional systems that optimize the use of working memory capacity and avoid cognitive overload. Cognitive load theory has advanced educational research considerably and has been used to explain a large set of experimental findings. This article sets out to explore the open questions and the boundaries of cognitive load theory by identifying a number of problematic conceptual, methodological and application-related issues. It concludes by presenting a research agenda for future studies of cognitive load

Applying and Interpreting Mixture Distribution Latent State-Trait Models

Latent state-trait (LST) models are commonly applied to determine the extent to which observed variables reflect trait-like versus state-like constructs. Mixture distribution LST (M-LST) models relax the assumption of population homogeneity made in traditional LST models, allowing researchers to identify subpopulations (latent classes) with differing trait- and state-like attributes. Applications of M-LST models are scarce, presumably because of the analysis complexity. We present a step-by-step tutorial for evaluating M-LST models based on an application to mother, father, and teacher reports of children’s inattention (n = 811). In the application, we found three latent classes for mother and father reports and four classes for teacher reports. All reporter solutions contained classes with very low, low, and moderate levels of inattention. The teacher solution also contained a class with high inattention. Comparable mother and father (but not teacher) classes exhibited similar levels of trait and state variance

A Predictive Model of Nuclear Power Plant Crew Decision-Making and Performance in a Dynamic Simulation Environment

The safe operation of complex systems such as nuclear power plants requires close coordination between the human operators and plant systems. In order to maintain an adequate level of safety following an accident or other off-normal event, the operators often are called upon to perform complex tasks during dynamic situations with incomplete information. The safety of such complex systems can be greatly improved if the conditions that could lead operators to make poor decisions and commit erroneous actions during these situations can be predicted and mitigated. The primary goal of this research project was the development and validation of a cognitive model capable of simulating nuclear plant operator decision-making during accident conditions. Dynamic probabilistic risk assessment methods can improve the prediction of human error events by providing rich contextual information and an explicit consideration of feedback arising from man-machine interactions. The Accident Dynamics Simulator paired with the Information, Decision, and Action in a Crew context cognitive model (ADS-IDAC) shows promise for predicting situational contexts that might lead to human error events, particularly knowledge driven errors of commission. ADS-IDAC generates a discrete dynamic event tree (DDET) by applying simple branching rules that reflect variations in crew responses to plant events and system status changes. Branches can be generated to simulate slow or fast procedure execution speed, skipping of procedure steps, reliance on memorized information, activation of mental beliefs, variations in control inputs, and equipment failures. Complex operator mental models of plant behavior that guide crew actions can be represented within the ADS-IDAC mental belief framework and used to identify situational contexts that may lead to human error events. This research increased the capabilities of ADS-IDAC in several key areas. The ADS-IDAC computer code was improved to support additional branching events and provide a better representation of the IDAC cognitive model. An operator decision-making engine capable of responding to dynamic changes in situational context was implemented. The IDAC human performance model was fully integrated with a detailed nuclear plant model in order to realistically simulate plant accident scenarios. Finally, the improved ADS-IDAC model was calibrated, validated, and updated using actual nuclear plant crew performance data. This research led to the following general conclusions: (1) A relatively small number of branching rules are capable of efficiently capturing a wide spectrum of crew-to-crew variabilities. (2) Compared to traditional static risk assessment methods, ADS-IDAC can provide a more realistic and integrated assessment of human error events by directly determining the effect of operator behaviors on plant thermal hydraulic parameters. (3) The ADS-IDAC approach provides an efficient framework for capturing actual operator performance data such as timing of operator actions, mental models, and decision-making activities

Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA