Integrating Timing Considerations to Improve Testing Practices

Integrating Timing Considerations to Improve Testing Practices synthesizes a wealth of theory and research on time issues in assessment into actionable advice for test development, administration, and scoring. One of the major advantages of computer-based testing is the capability to passively record test-taking metadata—including how examinees use time and how time affects testing outcomes. This has opened many questions for testing administrators. Is there a trade-off between speed and accuracy in test taking? What considerations should influence equitable decisions about extended-time accommodations? How can test administrators use timing data to balance the costs and resulting validity of tests administered at commercial testing centers? In this comprehensive volume, experts in the field discuss the impact of timing considerations, constraints, and policies on valid score interpretations; administrative accommodations, test construction, and examinees’ experiences and behaviors; and how to implement the findings into practice. These 12 chapters provide invaluable resources for testing professionals to better understand the inextricable links between effective time allocation and the purposes of high-stakes testing

What Can We Learn from a Semiparametric Factor Analysis of Item Responses and Response Time? An Illustration with the PISA 2015 Data

It is widely believed that a joint factor analysis of item responses and response time (RT) may yield more precise ability scores that are conventionally predicted from responses only. For this purpose, a simple-structure factor model is often preferred as it only requires specifying an additional measurement model for item-level RT while leaving the original item response theory (IRT) model for responses intact. The added speed factor indicated by item-level RT correlates with the ability factor in the IRT model, allowing RT data to carry additional information about respondents' ability. However, parametric simple-structure factor models are often restrictive and fit poorly to empirical data, which prompts under-confidence in the suitablity of a simple factor structure. In the present paper, we analyze the 2015 Programme for International Student Assessment (PISA) mathematics data using a semiparametric simple-structure model. We conclude that a simple factor structure attains a decent fit after further parametric assumptions in the measurement model are sufficiently relaxed. Furthermore, our semiparametric model implies that the association between latent ability and speed/slowness is strong in the population, but the form of association is nonlinear. It follows that scoring based on the fitted model can substantially improve the precision of ability scores

Integrating Timing Considerations to Improve Testing Practices

Integrating Timing Considerations to Improve Testing Practices synthesizes a wealth of theory and research on time issues in assessment into actionable advice for test development, administration, and scoring. One of the major advantages of computer-based testing is the capability to passively record test-taking metadata—including how examinees use time and how time affects testing outcomes. This has opened many questions for testing administrators. Is there a trade-off between speed and accuracy in test taking? What considerations should influence equitable decisions about extended-time accommodations? How can test administrators use timing data to balance the costs and resulting validity of tests administered at commercial testing centers? In this comprehensive volume, experts in the field discuss the impact of timing considerations, constraints, and policies on valid score interpretations; administrative accommodations, test construction, and examinees’ experiences and behaviors; and how to implement the findings into practice. These 12 chapters provide invaluable resources for testing professionals to better understand the inextricable links between effective time allocation and the purposes of high-stakes testing

Multivariate outlier detection in latent variable models

Outliers often pose serious problems for statistical models since they can distort the model fit and bias parameter estimation. Outliers are also worthy of attention in their own rights, as they are often informative of substructures of the data. This thesis aims to develop methods of detecting multivariate outliers in latent variable modelling contexts. Outliers are defined as data subsets deviating from a baseline model specified for the majority of the data. By this definition, we specify oneway outliers on the basis of atypical attributes of either individuals or variables and two-way outliers on the basis of atypical attributes of both individuals and variables. In this thesis, we develop the Forward Search (FS) procedures for detecting outlying individuals, latent groups of individuals and DIF variables. The FS does not examine just one subset of the data but instead fits a sequence of augmented subsets in order to decide which part of the data deviates from the baseline model. Outliers are identified through monitoring the effect of the sequential addition of individuals or items on the fitted model. The performance of the FS is assessed through simulated data and cross-national survey data under latent class models, factor mixture models and multiple-group latent variable models. To detect two-way outliers, the thesis proposes to impose a latent class model component for capturing two-way outliers upon a latent factor model component for capturing normal item response behaviour. Statistical inference is carried out under a fully Bayesian framework. The detection of two-way outliers is formulated based on the proposed Bayesian decision rules and compound decision rules that control local false discovery rate and local false non-discovery rate. The proposed method proves to be particularly useful in simultaneously detecting compromised items and test takers with item pre-knowledge in educational tests. To further improve two-way outlier detection, the two-way outlier detection model is extended in an explanatory framework by accounting for covariate effects and the relationships between latent variables

Technology and Testing

From early answer sheets filled in with number 2 pencils, to tests administered by mainframe computers, to assessments wholly constructed by computers, it is clear that technology is changing the field of educational and psychological measurement. The numerous and rapid advances have immediate impact on test creators, assessment professionals, and those who implement and analyze assessments. This comprehensive new volume brings together leading experts on the issues posed by technological applications in testing, with chapters on game-based assessment, testing with simulations, video assessment, computerized test development, large-scale test delivery, model choice, validity, and error issues. Including an overview of existing literature and ground-breaking research, each chapter considers the technological, practical, and ethical considerations of this rapidly-changing area. Ideal for researchers and professionals in testing and assessment, Technology and Testing provides a critical and in-depth look at one of the most pressing topics in educational testing today

To grow or to degrow? : discourse analysis on degrowth and socio-ecological justice in the futures of Arctic tourism

This study has explored the discourses related to degrowth and socio-ecological justice in the futures of Arctic tourism. Degrowth offers an alternative perspective for tourism development as it criticises growth ambition. Previous literature has addressed that degrowth has been mostly explored in overtourism literature but has gained little attention in the context of Arctic tourism. Degrowth should be practised in a way that is just and it increases the well-being of the local communities. Socio-ecological justice, which was the approach behind degrowth in this study, aims to address both human and nonhuman worlds’ relationality as a matter of justice. Nonhumans play an important part in tourism but often become marginalised due to anthropocentric actions in tourism development. The research data of this study consisted of ten interviews that were collected through the ArcticHubs project. As an analysis method, I utilized discourse analysis with a future-oriented approach. The underlying paradigm of this study was social constructionism. The main research question of this study was as follows: How are the discourses related to degrowth and socio-ecological justice constructed in the futures of Arctic tourism? The sub-questions were: What kinds of linguistic means these discourses are constructed through? and Who has the power to construct these discourses? One of the main results was that the tourism growth discourse was hegemonic which did not give space for the degrowth discourse. Tourism growth seemed to be taken for granted although previous literature had given a reason for rethinking tourism practices in the post-Covid-19 pandemic. The degrowth discourse was constructed through ways of speaking where tourism is only targeted for fewer and better-paying tourists or where tourism may need to be abandoned as travelling sustainably is not enough anymore. Luxury tourism, as addressed in the data, would be beneficial for the environment but at the same time, it increases injustices between tourists and locals. The socio-ecological discourse constructed a social reality where different stakeholders see nature in a different light: for some, nature can be utilised as a business environment and for others, it needs to be protected. The power relations of language use became apparent as they determine who can talk about tourism growth and how. In the futures, tourism degrowth needs more exploring as there are yet no strategies on how to degrow which is unjust for local communities who may want to practise it. Besides degrowth, also justice needs more exploration in tourism research, especially from the nonhuman point of view

Geographic Data Mining and Knowledge Discovery

Geographic data are information associated with a location on the surface of the Earth. They comprise spatial attributes (latitude, longitude, and altitude) and non-spatial attributes (facts related to a location). Traditionally, Physical Geography datasets were considered to be more valuable, thus attracted most research interest. But with the advancements in remote sensing technologies and widespread use of GPS enabled cellphones and IoT (Internet of Things) devices, recent years witnessed explosive growth in the amount of available Human Geography datasets. However, methods and tools that are capable of analyzing and modeling these datasets are very limited. This is because Human Geography data are inherently difficult to model due to its characteristics (non-stationarity, uneven distribution, etc.). Many algorithms were invented to solve these challenges -- especially non-stationarity -- in the past few years, like Geographically Weighted Regression, Multiscale GWR, Geographical Random Forest, etc. They were proven to be much more efficient than the general machine learning algorithms that are not specifically designed to deal with non-stationarity. However, such algorithms are far from perfect and have a lot of room for improvement. This dissertation proposed multiple algorithms for modeling non-stationary geographic data. The main contributions are: (1) designed a novel method to evaluate non-stationarity and its impact on regression models; (2) proposed the Geographic R-Partition tree for modeling non-stationary data; (3) proposed the IDW-RF algorithm, which uses the advantages of Random Forests to deal with extremely unevenly distributed geographic datasets; (4) proposed the LVRF algorithm, which models geographic data using a latent variable based method. Experiments show that these algorithms are very efficient and outperform other state-of-the-art algorithms in certain scenarios