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Social Presence in Virtual World Collaboration: An Uncertainty Reduction Perspective Using a Mixed Methods Approach1
The life-like collaborative potential offered by virtual worlds (VWs) has sparked significant interest for companies to experiment with VWs in order to organize convenient, cost-effective virtual global workplaces. Despite the initial hype, recent years have witnessed a rather stagnant use of VWs for collaboration in organizations. Previous research recognizes that the inherent uncertainties within the VW environment are factors limiting their utilization by businesses. Hence, grounding this research in uncertainty reduction theory (URT), we aim to understand the modalities and mechanisms for mitigating the uncertainties and fostering user trust within VWs so that they can be effectively utilized as a workplace collaboration tool. With this end in view, we propose contextualizing and extending McKnight et al.’s (2002) institutional trust framework to the context of VWs by examining the significant role that social presence has in influencing the efficacy of the institution-based trust-building factors of situational normality and structural assurance in VWs. Using a sequential mixed methods approach (Venkatesh et al. 2013; Venkatesh, Brown, and Sullivan 2016), this research integrates results from a quantitative study with findings from a qualitative study to arrive at rich and robust inferences and meta-inferences, with the qualitative method first corroborating the inferences obtained from the quantitative research and then complementing them by identifying boundary conditions that may limit the use of VWs in organizations for workplace collaboration. The results together suggest not only the direct but also the interactional (complementary and substitutive) influences of social presence on the relationships of the two institutional trust-building factors to user trust in VWs.
Journal Article
Digital electronics. Volume 3, Finite-state machines
Presents the principles of combinational and sequential logic and the underlying techniques for the analysis and design of digital circuits. The approach is gradual and relatively independent of each other chapters. To facilitate the assimilation and practical implementation of various concepts, the book is complemented by a selection of practical exercises corrected.
Book
Lag Sequential Analysis for Identifying Blended Learners' Sequential Patterns of e-Book Note-taking for Self-Regulated Learning
Blended learning (BL) is regarded as an effective strategy for combining traditional face-to-face classroom activities with various types of online learning tools (e.g., e-books). An effective feature of e-books is the ability to use digital notes. When e-books are used in BL, the strategic adoption of note-taking provides benefits that influence the learners' progress for self-regulated learning (SRL) and course achievements. However, learners tend to be unsure about how note-taking is performed using online learning materials and lack knowledge of effective strategies for SRL. Furthermore, few studies have investigated blended learners' sequential patterns of e-book note-taking for SRL. Thus, in this paper, an exploratory study was conducted in an undergraduate course that implemented the BL design. The learning task for the blended learners in the present study was to study the learning material using BookRoll, an e-book system, during in-class and out-of-class learning sessions. Lag sequential analysis of the e-book learning behavior data was conducted to identify the blended learners' sequential behaviors of e-book note-taking for the cognitive strategy use of SRL. Moreover, the difference between higher- and lower-achievement blended learners in terms of their sequential behaviors of e-book note-taking for SRL was revealed. This study can help educators provide evidence-based educational feedback to learners regarding the identified sequential patterns of e-book note-taking that can be applied as effective strategies for promoting the cognitive strategy use of SRL and improvement of course achievement in BL.
Journal Article
Immediate Versus Delayed Sequential Bilateral Cataract Surgery: A Systematic Review
To compare the visual outcomes, safety, and patient-reported outcomes of ISBCS versus DSBCS in adults undergoing cataract surgery.
A systematic search of PubMed, Embase, CENTRAL, Web of Science, and gray literature (conference abstracts, registries) was conducted for studies published up to March 2023. Eligible studies directly compared ISBCS and DSBCS in adults, reporting best corrected visual acuity (BCVA), complications, or patient-reported outcomes. Two reviewers independently screened studies, extracted data, and resolved discrepancies by consensus.
Nine studies were included (3 randomized controlled trials, 5 retrospective cohorts, 1 review), encompassing sample sizes ranging from 298 patients to over 496,000 eyes. Both ISBCS and DSBCS resulted in significant improvements in BCVA, with comparable refractive accuracy. No significant differences were observed in serious complications such as endophthalmitis, cystoid macular edema, or posterior capsular rupture. Some studies reported a trend toward slightly higher patient satisfaction and faster rehabilitation in the ISBCS group.
ISBCS appears to be a safe and effective alternative to DSBCS in carefully selected patients, offering potential benefits in efficiency, recovery time, and patient satisfaction. Rigorous randomized controlled trials with standardized outcome measures and long-term follow-up are needed to further validate these findings.
Journal Article
Local Gaussian Process Approximation for Large Computer Experiments
We provide a new approach to approximate emulation of large computer experiments. By focusing expressly on desirable properties of the predictive equations, we derive a family of local sequential design schemes that dynamically define the support of a Gaussian process predictor based on a local subset of the data. We further derive expressions for fast sequential updating of all needed quantities as the local designs are built up iteratively. Then we show how independent application of our local design strategy across the elements of a vast predictive grid facilitates a trivially parallel implementation. The end result is a global predictor able to take advantage of modern multicore architectures, providing a nonstationary modeling feature as a bonus. We demonstrate our method on two examples using designs with thousands of data points, and compare to the method of compactly supported covariances. Supplementary materials for this article are available online.
Journal Article
Trial Sequential Analysis in systematic reviews with meta-analysis
Background
Most meta-analyses in systematic reviews, including Cochrane ones, do not have sufficient statistical power to detect or refute even large intervention effects. This is why a meta-analysis ought to be regarded as an interim analysis on its way towards a required information size. The results of the meta-analyses should relate the total number of randomised participants to the estimated required meta-analytic information size accounting for statistical diversity. When the number of participants and the corresponding number of trials in a meta-analysis are insufficient, the use of the traditional 95% confidence interval or the 5% statistical significance threshold will lead to too many false positive conclusions (type I errors) and too many false negative conclusions (type II errors).
Methods
We developed a methodology for interpreting meta-analysis results, using generally accepted, valid evidence on how to adjust thresholds for significance in randomised clinical trials when the required sample size has not been reached.
Results
The Lan-DeMets trial sequential monitoring boundaries in Trial Sequential Analysis offer adjusted confidence intervals and restricted thresholds for statistical significance when the diversity-adjusted required information size and the corresponding number of required trials for the meta-analysis have not been reached. Trial Sequential Analysis provides a frequentistic approach to control both type I and type II errors. We define the required information size and the corresponding number of required trials in a meta-analysis and the diversity (D
2
) measure of heterogeneity. We explain the reasons for using Trial Sequential Analysis of meta-analysis when the actual information size fails to reach the required information size. We present examples drawn from traditional meta-analyses using unadjusted naïve 95% confidence intervals and 5% thresholds for statistical significance. Spurious conclusions in systematic reviews with traditional meta-analyses can be reduced using Trial Sequential Analysis. Several empirical studies have demonstrated that the Trial Sequential Analysis provides better control of type I errors and of type II errors than the traditional naïve meta-analysis.
Conclusions
Trial Sequential Analysis represents analysis of meta-analytic data, with transparent assumptions, and better control of type I and type II errors than the traditional meta-analysis using naïve unadjusted confidence intervals.
Journal Article
SURPRISED BY THE HOT HAND FALLACY? A TRUTH IN THE LAW OF SMALL NUMBERS
We prove that a subtle but substantial bias exists in a common measure of the conditional dependence of present outcomes on streaks of past outcomes in sequential data. The magnitude of this streak selection bias generally decreases as the sequence gets longer, but increases in streak length, and remains substantial for a range of sequence lengths often used in empirical work. We observe that the canonical study in the influential hot hand fallacy literature, along with replications, are vulnerable to the bias. Upon correcting for the bias, we find that the longstanding conclusions of the canonical study are reversed.
Journal Article
Sequential sampling strategy for extreme event statistics in nonlinear dynamical systems
We develop a method for the evaluation of extreme event statistics associated with nonlinear dynamical systems from a small number of samples. From an initial dataset of design points, we formulate a sequential strategy that provides the “next-best” data point (set of parameters) that when evaluated results in improved estimates of the probability density function (pdf) for a scalar quantity of interest. The approach uses Gaussian process regression to perform Bayesian inference on the parameter-to-observation map describing the quantity of interest. We then approximate the desired pdf along with uncertainty bounds using the posterior distribution of the inferred map. The next-best design point is sequentially determined through an optimization procedure that selects the point in parameter space that maximally reduces uncertainty between the estimated bounds of the pdf prediction. Since the optimization process uses only information from the inferred map, it has minimal computational cost. Moreover, the special form of the metric emphasizes the tails of the pdf. The method is practical for systems where the dimensionality of the parameter space is of moderate size and for problems where each sample is very expensive to obtain. We apply the method to estimate the extreme event statistics for a very high-dimensional system with millions of degrees of freedom: an offshore platform subjected to 3D irregular waves. It is demonstrated that the developed approach can accurately determine the extreme event statistics using a limited number of samples.
Journal Article