Search

According to cognitive psychologists, consistent practice (i.e., tasks with a high degree of similarity) of a skill leads to the development of automaticity, with the degree of automatization being increased if the practice also involves exact repetition. Practice is skill specific, meaning that practicing one skill does not automatize related skills. Once achieved, automaticity has been characterized as the ability to perform a primary task with little interference from performing a concurrent secondary task. A second key characteristic of skill automatization is that it involves a gradual decrease in executive working memory (EWM) demands. The primary purpose of this study is to examine whether such claims put forth by cognitive psychologists are transferable to the automatization of second language (L2) bottom-up listening skills. The secondary purpose of this study is to explore whether reading while listening promotes listening automaticity to a greater extent than listening only, as has been argued in the L2 literature (Chang, 2011; Chang & Millett, 2014; Chang and Millett, 2016; Chang et al., 2018). The third purpose of this study is to examine whether visuospatial working memory (VWM) is a predictor of listening automaticity growth and to what degree it is related to EWM. Although L2 listening is regarded as a cognitive skill, the automatization of L2 listening has not been researched as described by cognitive psychologists. That is, using dual tasks and a reduction in EWM involvement as indices of automaticity, and providing consistent practice with an exact repetition component. In addition, the existing research into the question surrounding what kind of practice is more effective in bringing about L2 listening automaticity--listening while reading or listening only--has methodological flaws that have led to an inconclusive answer. To fill these gaps in the literature, I designed a listening-while-reading and a listening-only intervention that blended elements claimed to be conducive to automaticity development in cognitive psychology and L2 research, namely consistent training and exact repetition of stimuli and time pressure, while taking into account the methodological issues of previous L2 research. Data were collected from 290 Japanese high school students, who were assigned to the listening-while-reading, the listening-only, or a control group. Participants in the listening-only intervention listened to texts composed of a narrow range of vocabulary and grammar structures, which warranted consistency, whose speech rates increased by 10 words per minute (wpm) over the sessions from 100 wpm to 180 wpm. The listening-while-reading intervention involved listening to the same texts while reading the transcripts. Exact repetition of the stimuli was provided in both interventions by playing the texts two consecutive times in each intervention session. After the second listening, comprehension was assessed using a Japanese summary of the texts and a set of five multiple-choice questions. Degree of automatization was measured at three time points (i.e., Pretest, Posttest 1, and Posttest 2) separated by two cycles of training, through two dual tasks at each time point. The dual tasks, which consisted of listening to a text delivered at 160 wpm while drawing a series of vertical lines on blank paper and were followed by the abovementioned comprehension measures, were used to construct measures of automaticity. Dual-task data were linked through a Rasch longitudinal design, keeping text difficulty invariant across the dual tasks, with Rasch person measures for comprehension for subsequent analyses computed by combining scores on summaries and multiple-choice questions, while giving double weight to the summaries. Regarding the memory constructs, EWM was assessed through a speaking and a listening span task, whose scores were combined into a global EWM Rasch measure that was used in the analyses to test if the participants' EWM costs diminished over time. Similarly, a single VWM Rasch measure was calculated by combining the participants' scores on the Mr. Peanut task (DeAvila, 1974), which assesses visual memory, and the Corsi block span task (Corsi, 1972), which assesses spatial memory, as additional predictors of L2 listening automaticity. To my knowledge, measures of VWM have not previously been employed as predictors of listening ability. Results from a repeated-measures ANOVA and a multi-group latent growth model indicated that both intervention groups automatized their listening skills to a greater extent than the control group with the listening-only group developing automaticity to the largest extent. Although a reduction of EWM demands was not observed, possibly because the participants were not complete beginners and might have started the study with some degree of automaticity, the fact that they could perform the dual tasks less effortfully over time evidenced automatization. Interestingly, VWM significantly predicted growth in listening automaticity, which was interpreted to mean that as listeners automatize their listening skills, they become able to see what they hear. This result was explained in terms of attentional capacity limits, where higher-level processes, such as visualizing input, cannot be achieved until the lower-level processes such as word recognition or grammatical parsing have been automatized. Follow-up qualitative interviews corroborated the quantitative findings and extended them by suggesting that a small number of training sessions might be sufficient to develop listening automaticity. Results of an additional correlation analysis further showed that VWM and EWM are not highly related as has been shown by some experimental evidence in the working memory literature. The finding that listening only was superior to listening while reading is important because it contradicts previous research and suggests that to automatize listening, one needs to practice in real-operating conditions. Practical and theoretical implications are discussed. First, listening to texts whose speech rates increase over the practice sessions promotes listening automaticity. Next, claims by cognitive psychologists regarding the development of skill automaticity are largely generalizable to the natural conditions of classroom-based research focusing on the acquisition of real-world skills such as L2 listening. Last, the multi-component versus the unitary view of working memory. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

This dissertation is comprised of three papers that propose and apply psychometric models to deal with complexities and challenges in large-scale assessments, focusing on modeling rater effects and complex learning progressions. In particular, three papers investigate extensions and applications of multilevel and multidimensional item response models, with a primary focus on (1) detecting rater effects in double-scored performance assessments, (2) monitoring human raters with automated scoring engine, and (3) developing measurement models for complicated learning progressions. The first paper applies and assesses the trifactor model for multiple ratings data in double-scored performance assessments, in which two different raters give independent scores for the same responses (e.g., the GRE essay). The trifactor model incorporates a cross-classified structure (e.g., items and raters) in addition to the general dimension (e.g., examinees). The paper includes a simulation design that follows the GRE example to reflect the incompleteness and imbalance in the real world assessments. The effect of the missingness rate in the data and ignoring the differences among the raters are investigated using the simulations. The use of the trifactor model is illustrated with empirical data. The second paper applies mixed-effects ordered probit models for the purpose of examining the effectiveness and efficiency of utilizing scores from automated scoring engines (AE) to monitor and provide diagnostic feedback to human raters under training compared to the scores from the human experts (HE). Using the real rater training study data, three types of rater effects--severity, accuracy, and centrality of each rater--are related with model parameters, and compared for cases (a) when the AE is considered as the true score and (b) when the HE is considered as the true score.The third paper proposes a structured constructs model based on change-point analysis to deal with complicated learning progressions, in which relations between levels across multiple constructs are assumed in advance. Based on the change-point analysis, and reparameterizations of the multidimensional Rasch model and partial credit model, cut score parameters and discontinuity parameters are incorporated to classify the examinees into the levels in the learning progressions, and to model the hypothesized relations as the advantage for examinees belonging to a certain level in one construct to reach a level in another construct. Parameter recovery of the proposed model and the consequences of ignoring the hypothesized relations are assessed using simulations. The use of the proposed model is illustrated with empirical data and interpreted as contributing to validity evidence for the hypothesized relations. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

To remain competitive in an increasingly complex manufacturing landscape, organisations are moving beyond a full automation narrative and considering the empowering role of augmentation. Although technology is an important pillar in industry, people remain essential on shop floors and will continue to be so in the future. Where total automation is not the preferred option, augmentation technologies and operator assistance systems (OAS) have the potential to realise an optimal combination of people and technology, resulting in human-technology integration (HTI). In this study, OAS are conceptualised as socio-technical systems that modify or complement an operator's capabilities. However, despite their promising potential for empowering the workforce, OAS are inadequately understood in industry. For example, there is a distinct lack of knowledge on the applicability of OAS, their value-added, and the most effective way of integrating OAS into production environments. The set of relevant technology management factors that need to be considered to understand and guide technology implementation projects concerning OAS is unknown. Understanding these factors is crucial as the successful adoption of OAS depends on how an application was developed and deployed. Focusing on OAS for execution support, this study therefore (a) explores the relevant technology management factors for integrating OAS into production systems, and (b) strives to understand how to systematically consider these factors during the integration of OAS into human-centric production systems. In technology management research, the use of multiple methods has been advocated to overcome individual methodological weaknesses and to allow for a richer approach to data collection, analysis and interpretation. Following pragmatism and engaged scholarship, this study applies procedural action research. Due to the contextual richness of OAS research, a mixed method research approach was selected, involving: (a) a systematic review of 2,928 papers; (b) 67 semi-structured expert interviews from 45 different manufacturing organisations; (c) 32 survey-guided industry case studies; (d) 108 structured industry workshops and working sessions; (e) ethnography and observations in ten different shop floor environments; (f) three industrial case studies; and (g) two in-depth evaluative industrial case studies over the course of three months each. As a result, this study identifies (a) 11 goal-based application areas for OAS, (b) 11 organisation-based application areas for OAS, (c) the value-added of OAS on shop floors, and (d) a set of 15 technology management factors that need to be considered when integrating OAS. An essential contribution emerging from these findings is the Augmented Workforce Canvas (Canvas) (Figure 1). The Canvas is a framework enabling practitioners to systematically understand and guide activities related to the integration of OAS. Evaluating the Canvas in two end-to-end industry case studies, this research provides evidence that the Canvas can be applied to guide OAS integration activities in industry. Overall, this study contributes to the understanding of industrial socio-technical systems and their integration into production systems by placing both people and the value-added of OAS at the heart of technology management decisions.

Recently, the first successful deployment of a fully automated commercial freight train operation was announced. This is the world's first automated heavy-duty and long-haul rail network. It's an impressive achievement, but why has it taken so long to achieve this when driverless urban metros have been in operation for more than 50 years? Although urban metros and freight trains are vehicles moved on rails, their operation and environment differ significantly. Metros operate in closed rail networks, while freight trains operate in open rail networks. However, the same taxonomy is often used to classify automation interchangeably in both environments. This paper provides context and an overview of driving automation in freight rail and reviews the existing taxonomies. This paper starts by providing context with an overview of the general process of driving a vehicle by delimiting its different stages. Next, we describe the overall process of driving a freight train to show the distinctive features of its setup and operation. In this analysis, we will point out the essential differences between open and closed rail networks, and the tasks that can potentially be automated. Additionally, we examine the evolution of level-based automation taxonomies and review those that have been proposed exclusively for driving automation in open and closed railway networks. Our objective is to provide a thorough summarization of the most relevant taxonomies to advance the definition of a suitable taxonomy and framework to classify automation capabilities in rail freight transport and identify some complex challenges ahead.

Every year infectious diseases are among the top major causes of death worldwide. Infectious diseases disproportionately impact resource-poor areas. While most developed countries have the ability to screen and quickly detect infectious diseases in humans, animals, and plants, developing countries lack this ability. In such areas, access to medical treatment and testing facilities is severely limited. Additionally, developing countries are more densely populated, which makes infectious disease surveillance exceedingly more difficult. As a result of this, infectious diseases tend to spread more quickly and outbreaks tend to occur more often in developing countries. Additionally, the increasing rate of antimicrobial resistant infections and emergence of novel infectious diseases increase the likelihood of global pandemic occurrence. It is evident that developing novel devices for detection of infectious diseases would be greatly beneficial to global health. It is important to note that developed countries would benefit equally from such a device. Pandemics tend to burden the economy and put strain on resources even in developed countries. The impact of such a device is determined by a set of criteria put forth by the World Health Organization (WHO). Based on the ASSURED criteria, an ideal device for point-of-care (POC) diagnostics must be affordable, sensitive, specific; and yet it must also be user-friendly, rapid and robust, equipment free, and deliverable to end-users. Additionally, this device must be easily adaptable for detection of novel pathogens. The nucleic acid amplification test (NAAT) meets many of these criteria. However, NAAT’s current workflow involves many user liquid handling steps as well as a thermocycler. This thesis aims to address these issues by first automating isothermal nucleic acid amplification, followed by coupling the amplification step with the lateral-flow immunoassay (LFA), which is a paper-based diagnostic device. Thus, taking advantage of the affordability and POC features of the LFA. The aim of these efforts is to work toward the ultimate goal of developing a device that receives the patient sample, then carries out all the steps in NAAT in an automated manner and produces results with just a push of a single button.

Abstract: In the current practice, cabinets (in kitchen, bathroom, and closet) are designed and manufactured using different software and tools. Sales use simple visualization tools (i.e. Sketchup) to communicate with their clients while the same cabinets are re-drafted for estimation, permits, and manufacturing in different tools. Information generated in each stage is re-built. However, due to the lack of BIM application in the cabinetry industry, the information gap between builder and cabinet manufacturer is still an issue that requires a quick solution because it causes cabinet drafting rework and waste in both the design and manufacturing phase. Extending manufacturing-centric BIM into cabinet design and manufacturing can address this issue and enhance the information exchange as well as enrich the information within the BIM model. Therefore, this paper presents an approach based on BIM to achieve automation in cabinet drafting, manufacturing, and production planning in order to improve design efficiency, reduce rework (I.e. redrafting), and reduce waste in both design and manufacturing. An application prototype is developed in the BIM environment in the form of an Autodesk Revit add-on to achieve the objectives through the automation of design and planning. A case study of residential cabinet design and production is subsequently presented to prove the feasibility of this prototype. As the main contribution of the proposed research, the in-depth integration of the BIM model with the automated design system, the optimized cutting stock algorithm to minimize waste, and the production process simulation together achieve full automation of cabinet design and of production planning for cabinet manufacturing.

The purpose of this research is to examine the ethical implications of automating education within a classroom. Automation is widespread throughout the workforce and expected to grow as technology continues to proliferate. From manufacturing cars to piloting planes, we have already seen automated systems take what was previously a human job. However, one job sector understudied for its potential use of automation is education. With the cost and access to education becoming a continued issue both domestically and internationally, corporations like Facebook, IBM, and Pearson have already begun experimenting with automated, computerized educators. To study the ethical implications of such automation, we will build a framework for the moral responsibilities of a teacher, examine the feasibility of automated systems meeting these guidelines, discuss the results of this analysis, and examine future implications. This paper will ultimately find that teacher's jobs are secure as education is a sector that, due to its complex social interactional requirements, is nearly impossible to fully automate. However, automation can play a vital role in pairing down the more menial tasks of an educator, therefore still having the ability to revolutionize the field

This paper aims to explain whether the great decoupling has influenced growing income inequality between high and low skilled labor. Recent findings have demonstrated that beginning in the 1980s there grew a gap between productivity and income, known as the great decoupling. This is due to growing technological advancements. Technology has contributed to inequality between high and low skilled labor by creating higher-skilled jobs that work with technology and automating "routine tasks" in low skilled jobs, such as manufacturing. A case study related to the rubber companies in Northeast Ohio- Firestone, Goodyear, B.F. Goodrich, and General Tire- will be conducted to analyze the effects of technology on the labor force. These claims will be further addressed with Regression Analysis to analyze the effect of technology on income inequality with other manufacturing states in the "Rust Belt". The results/findings will have practical implications for businesses in relation to automation.

The increased adoption of automation into various occupations strikes fear into many hearts, as people worry that robots will be able to perform their jobs more efficiently, leading to their inevitable unemployment. However, economists predict that this new technology will actually lead to a higher level of employment, although it will cause the aggregate labor share to decrease, forcing wages to decline. This paper focuses on ways in which we can recover a portion of the labor share and mitigate the decline of wages through the development of human capital via the educational system. Moreover, it seeks to understand ways in which we can exploit the jobs that will be created by automation by proposing methods to alter the educational structure to emphasize the new skills that are becoming more pertinent. My analysis provides evidence that by developing methods to encourage higher levels of educational attainment through dual degree and accelerated master’s programs, as well by decreasing the cost-exclusivity of higher education, we can both prepare future generations and reskill the current generation to succeed in this economic environment.

There is a need for advanced technology that can accurately measure the density and mass flow rate of drilling fluids at the high pressure well inlet in real-time. Current reliance on antiquated metering technologies such as the pressurized mud balance and the pump stroke counter to make these measurements greatly impedes our ability to accurately predict the near well bore pressure profile and measure the delta flow rate, which is one of the primary indicators for trouble events such as kicks or lost circulation. In order to address this gap in technology an X-ray sensor was developed to make real-time measurements at greater than 99% percent accuracy and 1 Hz measurement frequency. The X-ray sensor can measure the density of drilling fluids in the 8 ppg to 20 ppg range and with flow rates of up to 1200 gpm. These measurements are made using 320 kV/1500W polychromatic X-ray source, which is well within the range of readily available industrial X-ray tubes. In the past such measurements would require X-ray voltages that could only be achieved with linear accelerators thereby making the cost and size of equipment non-conducive to the drilling environment. However, recent advances in pipe manufacturing, particularly using a class of low density and high pressure materials known as carbon fiber reinforced polymers (CRPs) and, are now making it viable to re-visit relatively low cost X-rays systems for density and mass flowrate measurements. Windows constructed from CRPs allow us to bypass the high density carbon steel standpipe and make measurements at voltages that do not require a linear accelerator. In this paper we discuss the design and implementation of a CRP based X-ray sensor that is used to measure drilling mud density and mass flowrate at the high pressure well inlet.

Contemporary discourses and representations of automation stress the impending “autonomy” of automated technologies. From pop culture depictions to corporate white papers, the notion of autonomous technologies tends to enliven dystopic fears about the threat to human autonomy or utopian potentials to help humans experience unrealized forms of autonomy. This project offers a more nuanced perspective, rejecting contemporary notions of automation as inevitably vanquishing or enhancing human autonomy. Through a discursive analysis of industrial “deep texts” that offer considerable insights into the material development of automated media technologies, I argue for contemporary automation to be understood as a field for the exchange of autonomy, a human-machine autonomy in which autonomy is exchanged as cultural and economic value. Human-machine autonomy is a shared condition among humans and intelligent machines shaped by economic, legal, and political paradigms with a stake in the cultural uses of automated media technologies. By understanding human-machine autonomy, this project illuminates complications of autonomy emerging from interactions with automated media technologies across a range of cultural contexts.