Dr. Hongwei Yang

Digital literacy encompasses the skills needed to effectively navigate and use the digital tools and resources that are essential in today's educational landscape. Students with higher levels of digital literacy often demonstrate self-directed learning skills, enabling them to manage their study schedules and submit assignments in a timely and effective manner. Integrating digital literacy with preparation for self-directed learning is critical to fostering successful online learning experiences. Research into the impact of students' digital literacy and readiness for online learning on their self-directed learning is crucial to understanding the competencies and skills required for online education. Such competencies in learners may have unique effects, especially in specific online learning processes such as emergency remote teaching. Therefore, this study aimed to explore the potential impact of students' digital literacy on their self-directed online learning, with a particular focus on their online learning readiness. In line with the purpose of the study, a cross-sectional survey design was employed, using a structural equation modeling approach. The results showed that digital literacy has a direct and positive effect on online learning readiness. In addition, online learning readiness has a direct and positive influence on self-directed online learning. The results also highlighted that digital literacy indirectly and positively influences individuals' levels of self-directed online learning through their online learning readiness.

This study developed and validated the Turkish version of the Self-Directed Online Learning Scale (SDOLS-T) for assessing students’ perceptions of their self-directed learning (SDL) ability in an online environment. Specifically, this study conducted in two stages multiple categorical confirmatory factor analyses factoring in the ordered categorical structure of the SDOLS-T data. The data in this study came from a parent study which utilized the SDOLS-T and other instruments for data collection. From among the three competing models the literature recommends examining to explain the shared variance of items in a survey, the results at stage 1 showed that the correlated, two-factor structure, originally proposed for the SDOLS, was also the best-fit model for the SDOLS-T. At stage 2, using the best-fit model from stage 1, measurement invariance analyses were conducted to examine the extent to which SDL under the SDOLS-T was understood and measured equivalently across the groups specified by four dichotomous demographic variables: gender, network connection, online learning experience, and grade. The stage 2 results indicate the SDOLS-T reached scalar invariance at least for gender and network connection, thus allowing the comparison of latent or manifest means, or any other scores (e.g., total scores, Rasch scores), across the groups by these two demographic variables. In the end, the findings support the SDOLS-T for use in facilitating educational practice (e.g., improving instructional design), advancing scholarly literature (e.g., investigating SDL measurement and content area issues), and informing policy/decision-making (e.g., increasing retention rates and reducing dropout) in online education in Turkey.

The study took a Rasch measurement theory approach to validating the 10-item Digital Literacy Scale (DLS) using the unidimensional rating scale model (RSM). To that end, the study used the data from a sample of online Turkish university students. The study began the Rasch analysis with all 10 items in the scale and, to improve in the local independence assumption, identified and eliminated two items which did not adequately fit the RSM. Under the eight-item DLS, the assumptions of undimensionality and local independence were both satisfied and the fit of all individual items to the RSM was adequate. Next, the psychometric properties of the eight-item DLS were examined including rating scale effectiveness, relative endorsability of the items, differential item functioning (DIF) by each of three demographic variables: (a) gender, (b) connection device, and (c) grade level. Through the analysis, evidence of reliability and validity was identified which generally supports the use of the DLS instrument among the population of online Turkish university students from which the sample was obtained. The study also identified items which demonstrated either misfit to the model or DIF by the demographic variables, and recommends they be further reviewed and revised for future use.

The objective of this study is to assess students' perceptions of self-directed learning (SDL) skills in online learning using the Turkish version of the Self-Directed Online Learning Scale (SDOLS-T). To achieve this objective, the original SDOLS in English was translated into Turkish using the back-translation method. Subsequently, two separate data collection studies were carried out with a sample of undergraduate students at a university in T & uuml;rkiye at two different time points (T1 and T2). At T1, data was collected using only the SDOLS-T, while at T2, both the SDOLS-T and the Turkish version of the Online Learning Self-Efficacy Scale (OLSES-T) were administered to the students. The data collected at T1 was analyzed to examine the validity of the original factor structure in the Turkish culture. Moreover, measurement invariance across gender and class level categories was tested. Reliability evidence was also assessed through internal consistency and composite reliability tests. At T2, data was collected for both the SDOLS-T and the OLSES-T, and the convergent validity was evaluated using this data. Furthermore, test-retest reliability was measured by calculating the correlation between the means of T1 and T2 to ascertain the stability of the SDOLS-T. The results obtained provide support for the psychometric adequacy of the SDOLS-T and its potential utility in online teaching and learning practices, policy-making, and research in T & uuml;rkiye. Additionally, the implications of this study suggest that the SDOLS is a scale that can effectively measure self-directed learning skills, which are crucial for students' learning, across diverse cultures and facilitate their assessment.

The study investigated geoscience students' experiences with online education during the COVID-19 pandemic at the University of Tennessee, Knoxville in the US. We used an existing survey from the online education literature, Online Learning Environment Survey (OLES) composed of three instruments: (a) Community of Inquiry (CoI) consisting of the subscales of Teaching Presence (TP), Social Presence (SP), and Cognitive Presence (CP), (b) Institutional Support (IS), and (c) Self-Directed Online Learning Scale (SDOLS) consisting of the subscales of autonomous learning (AUL) and asynchronous online learning (AOL). The OLES was administered to students taking an online dynamic earth course over multiple semesters: summers 2019, 2020, and fall 2020. The survey data were used to run two sets of analyses: (a) descriptive statistics analyses and (b) Rasch modeling analyses. Per the descriptive statistics analyses, the survey rating subscales from highest to lowest order were AUL, AOL, IS, TP, SP, and CP, respectively, indicating that approximately 85% to 65% of the responses were in favor of the online learning environment. Among all the subscales, the asynchronous online mode was the most favored category by the students. Based on Rasch modeling analyses, asynchronous online teaching could be a favorable course delivery technique for geoscience education. Overall, the survey data shows a general interest in the online delivery of geoscience courses and the effectiveness of this modality, indicating potential for online geoscience programs. Finally, there are possible extensions of the study for future research, such as extending the research to other introductory online geoscience courses.

Although researchers assume that software packages should produce the same results (e.g., model parameter estimates) for the same model, it is not always the case for ordinal regression models. The MASS, ordinal, rms, and VGAM packages in R may use different forms to express the proportional odds model (PO) for ordinal response variables and parameterize it differently, so researchers can easily become confused when they interpret the results. However, there are no studies specifically to address this issue. The purpose of the study was to investigate how to implement the PO model in educational research with multiple packages in R and compare the differences among these packages. Furthermore, it compares the results from the R packages and those from other general purpose software programs, SAS, SPSS Statistics, and Stata with the aim to help researchers to understand the performance of each package.

This study assessed the economic status of the country of origin of US doctoral graduates from the sub‐Saharan African region and their post‐graduation residency plans. The National Science Foundation's 2012–2017 Survey of Earned Doctorates provided data for the study. The data were analysed using descriptive statistics and contingency table analysis. The results revealed an association between the economic status of the country of origin and the post‐graduation residency plan. Sixty percent of students from the low‐income and lower‐middle‐income categories expressed an intention to stay in the United States after graduation. The study concluded that the economic status has a relationship with the decision of sub‐Saharan African doctoral students' residency plans. The recommendation is that sub‐Saharan African countries introduce and implement policies that incentivize high‐calibre personnel trained abroad to return to their home country.

A person-centered approach was adopted to identify distinct classes of learning anxiety (LA) using 158,578 Grade 8 students in 1097 schools in China. Through a nonparametric multilevel latent class analysis, it identified a best-fitting model consisting of three student-level latent classes (low, moderate, and high LA) and three school-level latent classes (rare, moderate, and intense LA schools). To further examine the effects of potential covariates on the student and school latent classes of LA, a multinomial logistic regression was conducted. The main results were, first, girls exhibited a higher odds ratio in moderate and high student LA classes. What's more, students' total academic scores were positively related to their odds of being in the moderate LA class while negatively related to their odds of being in the high LA class. Lastly, school leadership, school belongingness, and teacher-student relationship were all negatively correlated with the odds that schools belong to the intense and moderate LA schools.
•A nonparametric MLCA was conducted to identify student and school classes of learning anxiety (LA);•Three student-level latent classes (low, moderate, and high LA) and three school-level latent classes (rare, moderate, and intense LA schools) were identified;•Examine student background and school factors with the latent classes;•Girls exhibited higher odds in moderate and high student LA classes;•School leadership, school belongingness, and teacher-student relationship negatively relate to the intense LA schools

We investigated how students' lunar phases learning outcomes were affected by student and teacher demographic characteristics (gender, race/ethnicity, spatial thinking ability, and content knowledge). The study identified moderately strong correlations both between students' spatial thinking ability and understanding of lunar phases, as well as between the pre-to-post-intervention scores of the two measures. Multilevel modeling showed significant predictors of learning outcomes from both student and teacher variables. This study furthers works on establishing a connection between student learning outcomes and the content knowledge and spatial ability for themselves as well as their teachers, and shows promise for a project-based instruction approach in aiding in lunar phases understanding.

This study has a didactic purpose to help applied investigators and practitioners to understand the roles of observed categorical data (OCD) in structural equation modelling (SEM) and the appropriate ways of analysing such data under SPSS AMOS. To that end, the study reviews types of OCD (nominal, ordinal, dichotomous and polytomous) and their incorporation into SEM under AMOS to play different roles. The study presents two applications from the health and retirement study where Bayesian statistical inference is used to analyse one set of OCD variables serving as endogenous variables with/without groups created by another OCD variable. Besides, the study demonstrates the typical ways of summarising, reporting and interpreting the results from Bayesian statistics, and compares AMOS with several other SEM programmes (Mplus, R lavaan, Stata and SAS PROC CALIS) on handling OCD. The study concludes with summaries of the findings for its intended audience.