Employment profiles and compensation

Conclusions The authors’ expectations are that AECT members and other educational technologists will use the information presented to establish or increase their own levels of compensation (or the compensation levels of those they supervise). Knowledge of the education levels, experience, salaries, and work motivation factors of fellow educational technologists may enable you to negotiate more effectively and fairly. The AECT salary survey described in this article was commissioned and conducted prior to James A. Pershing becoming Editor-in-Chief of TechTrends.     

Neural-network-based decentralized fault-tolerant control for a class of nonlinear large-scale systems with unknown time-delayed interaction faults

This paper proposes an adaptive approximation design for the decentralized fault-tolerant control for a class of nonlinear large-scale systems with unknown multiple time-delayed interaction faults. The magnitude and occurrence time of the multiple faults are unknown. The function approximation technique using neural networks is employed to adaptively compensate for the unknown time-delayed nonlinear effects and changes in model dynamics due to the faults. A decentralized memoryless adaptive fault-tolerant (AFT) control system is designed with prescribed performance bounds. Therefore, the proposed controller guarantees the transient performance of tracking errors at the moments when unexpected changes of system dynamics occur. The weights for neural networks and the bounds of residual approximation errors are estimated by using adaptive laws derived from the Lyapunov stability theorem. It is also proved that all tracking errors are preserved within the prescribed performance bounds. A simulation example is provided to illustrate the effectiveness of the proposed AFT control scheme.     

Multiple Predictors of Asian American Children's School Achievement

Research Findings: A structural equation model (SEM) and multiple indicators and multiple causes (MIMIC) model were used to test family factors, parent psychological well-being, parent–child home activity, and parent school involvement in relation to children's school achievement. Data for this study were drawn from the Early Childhood Longitudinal Study–Kindergarten (ECLS-K), conducted by the U.S. Department of Education's National Center for Education Statistics (NCES). The sample for this study was 1,100 Asian American kindergartners and their parents. Practice or Policy: The results of this study are as follows: (a) Family factors, especially parental education levels and family income, were significantly associated with Asian American students' school achievement; (b) parent–child home activity was significantly related to students' school achievement but in a negative direction; (c) parental school involvement was not found to be significant in predicting students' school achievement; (d) parental psychological well-being was significantly associated with both parent–child home activity and students' school achievement; (e) family income was significantly associated with parental psychological well-being, parental school involvement, and children's school achievement; and (f) family structure was not significantly associated with school achievement.     

Online Versus Offline in the United States: Are the Media Shrinking?

We find that combined revenues for 10 major media in the United States have steadily declined as a proportion of overall economic activity or gross domestic product (GDP) from 1999 to at least 2009 (the latest year for which we have complete data). For individual media, we find a generally consistent pattern in which increasing revenues from Internet distribution are exceeded by declines in revenues from established distribution channels, with the exception of television and video games, whose revenues have so far kept pace with GDP. We also report a marked overall shift from advertiser to direct payment support for the media industries over this period. We consider four possible reasons for these revenue trends: shifts in consumer media usage, reduced appropriability due to more difficult copyright protection, inadequate advertising business models, and reduced costs due to more efficient Internet distribution. Our analysis suggests we may be entering an era of a declining size of media industries in terms of conventional measures, but not necessarily a falling supply of media products themselves.     

Influence of Type D personality on quality of life in university students: The mediating effect of health-promoting behavior and subjective health status

This study aimed to test a hypothetical path model estimating the influence of Type D personality on health-promoting behaviors (HPB), subjective health status, and quality of life (QoL) in university students, and to verify the mediating effects of HPB and subjective health status between Type D personality and QoL. A structural equation model involving 380 university students was used. Data were collected between November and December 2018 via structured questionnaires and analyzed using SPSS 25.0 and Amos 23.0. The model's fit indices were adequate. Type D personality had a direct effect on QoL and an indirect effect on QoL via HPB and subjective health status. Type D personality, HPB, and subjective health status explained 67.6% of the total variance in QoL. Intervention programs that consider Type D personality, HPB, and subjective health status may be useful to improve university students' QoL.     

Analyzing the discriminative attributes of products using text mining focused on cosmetic reviews

Consumers evaluate products through online reviews, in addition to sharing their product experiences. Online reviews affect product marketing, and companies use online reviews to investigate consumer attitudes and perceptions of their products. However, when analyzing a review, it is often the case that specific contexts are not taken into consideration and meaningful information is not obtained from the analysis results. This study suggests a methodology for analyzing reviews in the context of comparing two competing products. In addition, by analyzing the discriminative attributes of competing products, we were able to derive more specific information than an overall product analysis. Analyzing the discriminative attributes in the context of comparing competing products provides clarity on analyzing the strengths and weaknesses of competitive products and provides realistic information that can help the company's management activities. Considering this purpose, this study collected a review of the BB Cream product line in the cosmetics field. The analysis was sequentially carried out in three stages. First, we extracted words that represent discriminative attributes by analyzing the percentage difference of words. Second, different attribute words were classified according to the meaning used in the review by using latent semantic analysis. Finally, the polarity of discriminative attribute words was analyzed using Labeled-LDA. This analysis method can be used as a market research method as it can extract more information than a traditional survey or interview method, and can save cost and time through the automation of the program.     

A novel term weighting scheme based on discrimination power obtained from past retrieval results

Term weighting for document ranking and retrieval has been an important research topic in information retrieval for decades. We propose a novel term weighting method based on a hypothesis that a term’s role in accumulated retrieval sessions in the past affects its general importance regardless. It utilizes availability of past retrieval results consisting of the queries that contain a particular term, retrieved documents, and their relevance judgments. A term’s evidential weight, as we propose in this paper, depends on the degree to which the mean frequency values for the relevant and non-relevant document distributions in the past are different. More precisely, it takes into account the rankings and similarity values of the relevant and non-relevant documents. Our experimental result using standard test collections shows that the proposed term weighting scheme improves conventional TF^*IDF and language model based schemes. It indicates that evidential term weights bring in a new aspect of term importance and complement the collection statistics based on TF^*IDF. We also show how the proposed term weighting scheme based on the notion of evidential weights are related to the well-known weighting schemes based on language modeling and probabilistic models.     

Student–university relationships and reputation: a study of the links between key factors fostering students’ supportive behavioral intentions towards their university

Focusing on academic institutions in higher education as the research context, this study examined the relations of key factors affecting students’ supportive behavioral intentions toward the university (e.g., giving gifts as alumni, continuing education, and giving referrals regarding the university). Based on the literature from various disciplines, this study proposed four factors are critical influencing such intentions: (1) students’ communication behavior with the university, (2) perceived quality of educational experience, (3) evaluations of the relationship with the university, and (4) university reputation. The data in this study were collected using a survey of current undergraduate students (N = 336) who were attending a private university in Seoul during Fall 2005. Structural equation modeling (SEM) was used to analyze the proposed model. Regarding overall results, the proposed model can be retained as a valid model based on data-model fits: Eight hypotheses were supported in this study, but not the effect of students’ educational experience on university reputation. The findings suggest that to foster students’ supportive behavior, universities need to cultivate a good relationship with their students and to obtain favorable reputation held by students, while ensuring active communication behavior of students and the quality of students’ educational experience.

An improved design strategy for approximation-based adaptive event-triggered tracking of a class of uncertain nonlinear systems

This paper presents an improved adaptive design strategy for neural-network-based event-triggered tracking of uncertain strict-feedback nonlinear systems. An adaptive tracking scheme based on state variables transmitted from the sensor-to-controller channel is designed via only single neural network function approximator, regardless of unknown nonlinearities unmatched in the control input. Contrary to the existing multiple-function-approximators-based event-triggered backstepping control results with multiple triggering conditions dependent on all error surfaces, the proposed scheme only requires one triggering condition using a tracking error and thus can overcome the problem of the existing results that all virtual controllers with multiple function approximators should be computed in the sensor part. This leads to achieve the structural simplicity of the proposed event-triggered tracker in the presence of unmatched and unknown nonlinearities. Using the impulsive system approach and the error transformation technique, it is shown that all the signals of the closed-loop system are bounded and the tracking error is bounded within pre-designable time-varying bounds in the Lyapunov sense.     

Development of 1-mile walk tests to estimate aerobic fitness in children

To examine the reliability and validity of 1-mile walk tests for estimation of aerobic fitness (VO_2max) in 10- to 13-year-old children and to cross-validate previously published equations. Participants (n = 61) walked 1-mile on two different days. Self-reported physical activity, demographic variables, and aerobic fitness were used in multiple regression analyses. Eight models were developed with various combinations of predictors. The recommended model for fitness testing in schools was: VO_2max = 120.702 + (4.114 × Sex [F = 0, M = 1]) – (2.918 × 1-mile Walk Time [min]) – (2.841 × Age), R = .73, standard error of estimate = 6.36 mL·kg^?1·min^?1. Cross-validation of previously published equations demonstrated lower correlations with measured VO_2max than the newly developed walk tests. Evidence of reliability and validity for 1-mile walk tests to estimate VO_2max in young children was provided. The model that included 1-mile walk time, age, and sex may be appropriate for youth fitness testing in physical education, particularly for unmotivated or overweight young children.