The structure of Chinese beginning online instructors’ competencies: evidence from Bayesian factor analysis

Wang, Yixi; Wang, Yang; Stein, David; Liu, Qingtang; Chen, Wenli

doi:10.1007/s40692-021-00186-9

Cited by 7 publications

(5 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Online learning has brought convenience to teaching and learning without the restraint of time and space. However, the quality of online learning needs to be improved ( Chen et al, 2021 ; Wang et al, 2021 ). Lee and Recker (2021) proposed that online learning quality depended not only on online learning resources but also on instructors’ teaching presence.…”

Section: Introductionmentioning

confidence: 99%

Constructing a Teaching Presence Measurement Framework Based on the Community of Inquiry Theory

et al. 2021

Self Cite

View full text Add to dashboard Cite

Given that there is no consensus on a framework for measuring presence in online teaching, this paper focuses on the construction of a reliable measurement framework of teaching presence based on the Community of Inquiry theory. In this study, 408 questionnaires were collected from college students who had online learning experience. Item analysis, exploratory factor analysis, and confirmatory factor analysis were used to analyze the results, which showed that the five-factor framework is in good agreement with the data. The confirmatory factor analysis also demonstrated a good model fit of the correlated five-factor teaching presence framework. Therefore, the teaching presence measurement framework consisting of design and organization, discourse facilitation, direct instruction, assessment, and technological support, can serve as an effective tool to support teaching presence measurement and to provide guidance for instructors’ online teaching.

show abstract

Section: Introductionmentioning

confidence: 99%

Constructing a Teaching Presence Measurement Framework Based on the Community of Inquiry Theory

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, there are cautionary notes on the use of default settings for convergence (Zitzmann & Hecht, 2019) or on the use of default prior distributions (Smid & Winter, 2020). Nevertheless, Bayesian factor analysis (BFA) is meanwhile regularly used (e.g., Bonafede et al, 2021; Wang et al, 2021; Weide et al, 2021). It follows from the regular use of BFA that plausible values as factor score estimates computed from BFA (Asparouhov & Muthén, 2010a, 2010b) may also be used regularly.…”

mentioning

confidence: 99%

Coefficients of Factor Score Determinacy for Mean Plausible Values of Bayesian Factor Analysis

Beauducel

Hilger²

2022

Educational and Psychological Measurement

View full text Add to dashboard Cite

In the context of Bayesian factor analysis, it is possible to compute plausible values, which might be used as covariates or predictors or to provide individual scores for the Bayesian latent variables. Previous simulation studies ascertained the validity of mean plausible values by the mean squared difference of the mean plausible values and the generating factor scores. However, the mean correlation of sets of single plausible values of different factors were shown to be an adequate estimator of the correlation between factors. Using sets of single plausible values to compute a mean prediction in secondary analysis implies that their determinacy should be known. Therefore, a plausible value-based determinacy coefficient allowing for estimation of the determinacy of single plausible values was proposed and evaluated by means of two simulation studies. The first simulation study demonstrated that the plausible value-based determinacy coefficient is an adequate estimate of the correlation of single plausible values with the population factor. It is also shown that the plausible value-based determinacy coefficient of mean plausible values approaches the conventional, model parameter-based determinacy coefficient with increasing number of imputations. The second simulation study revealed that the plausible value-based determinacy coefficient and the model parameter-based determinacy coefficient yield similar results even for misspecified models in small samples. It also revealed that for small sample sizes and a small salient loading size, the coefficients of determinacy overestimate the validity, so that it is recommended to report the determinacy coefficients together with a bias-correction to estimate the validity of plausible values in empirical settings.

show abstract

“…The knowledge acquired should be sufficient and relevant to every borderless society, IT society, and competitive society and should improve performance competency and the caliber of information collection and processing. (Anupan & Chimmalee, 2022;Bikanga Ada, 2018;Li et al, 2013;Qiao et al, 2021;Seta et al, 2022;Wang et al, 2021). Pandemic COVID-19 (New Normal) New lifestyles during the COVID-19 pandemic.…”

Section: Digital Transformation In Education Frameworkmentioning

confidence: 99%

Empowering Pre-Service Teachers for the Digital Transformation of Education from New Normal to Next Normal

Sangboonraung,

Daungtod,

Nilsook

et al. 2024

JEL

View full text Add to dashboard Cite

The purposes of research were to comparison of digital technology used for the instructional management during the Covid-19 pandemic during the New Normal period and the endemic Covid-19 during Next Normal period adopted by pre-service teachers; and to develop the landscape of digital technology used for instructional management during the New Normal and the Next Normal periods adopted by pre-service teachers. The participants included 400 pre-service teachers. The research tool was an online questionnaire. The data were analyzed by mean and standard deviation. The results showed the comparison of digital technology for the instructional management in the New Normal and the Next Normal as follows. Regarding the New Normal, the finding were: (a) some websites and applications such as Google Meet, Line, YouTube, and Facebook Messenger were used to communication with students; (b) some websites and applications, namely Google classroom, MS Teams, Moodle, and Thaimooc were used to send and receive student’s works; (c) some learning management systems, namely Google classroom, MS Teams, Moodle, and Thaimooc were used; (d) some digital media libraries such as YouTube, Trueplookpanya, DLTV, Satellite Distance Education Foundation, SciMath Knowledge Library, and IPST were used; (e) some online measurement and evaluation tools such as Google Forms, Kahoot, Quizizz, and Microsoft Forms were used and (f) some teaching guidelines and tools, like Video assignment, General Activities, and Attendance be used later. Regarding the Next Normal, the finding were: (a) some websites and applications were used to communicate with students, including Line, Facebook, Messenger, Youtube and Google classroom; (b) some websites and applications like Line, Facebook Messenger, YouTube, and Google classroom were used to send and receive student’s assignments; (c) some learning management systems like Google classroom, MS Teams, Moodle, Edmodo, and Thaimooc were also used; (d) some digital media libraries like Google classroom, YouTube, Trueplookpanya, OBEC Content Center - NEDA, and DLTV (Satellite Distance Education Foundation) were also helpful; (e) some online measurement and evaluation tools like Google Forms, Kahoot, Quizizz, and Microsoft Forms were also used; and (f) some teaching guidelines and tools like Assignment, Announcements and Update, Student Support, and Grade Book will also be used later. The digital technology landscape consists of 6 parts: (1) the websites and applications used for communication with students; (2) the websites and applications provides for students to submit their assignments; (3) the learning management systems; (4) the digital media libraries; (5) the online measurement and evaluation tools; and (6) the teaching guidelines and tools to be used later.

show abstract

The structure of Chinese beginning online instructors’ competencies: evidence from Bayesian factor analysis

Cited by 7 publications

References 28 publications

Constructing a Teaching Presence Measurement Framework Based on the Community of Inquiry Theory

Constructing a Teaching Presence Measurement Framework Based on the Community of Inquiry Theory

Coefficients of Factor Score Determinacy for Mean Plausible Values of Bayesian Factor Analysis

Empowering Pre-Service Teachers for the Digital Transformation of Education from New Normal to Next Normal

Contact Info

Product

Resources

About