The purpose of this study was to examine the underlying mechanism between goal orientations and academic expectation for online learners. We simultaneously studied the structural relationships among 2×2 achievement goal orientations, self-regulated learning (SRL) strategies, supportive online learning behaviors, and expected academic outcome in various online courses with 93 respondents (70 undergraduate and 23 graduate students). Specifically, we tested the mediation effects of both SRL strategies and supportive online learning behaviors on the relationship between achievement goal orientations and students' academic expectations. The results showed that two of the achievement goal orientations-mastery-approach (MAP) goals and mastery-avoidance (MAV) goals-predicted the adoption of SRL strategies and supportive online learning behaviors, which, in turn, predicted students' expected academic outcome for their online course. Specifically, students with higher MAP goals were more likely to adopt different types of SRL strategies and supportive online learning behaviors to facilitate their learning experience, which further enhanced their expectation for their academic outcome. By contrast, students with higher MAV goals were less likely to adopt SRL strategies and supportive online learning behaviors, which, in turn, led to lower grade expectations. . (2019).How college students' achievement goal orientations predict their expected online learning outcome: The mediation roles of self-regulated learning strategies and supportive online learning behaviors. Online Learning, 23(4), 23-41.

The purpose of this study was to investigate a predictive model of online learners’ learning outcomes through machine learning. To create a model, we observed students’ motivation, learning tendencies, online learning-motivated attention, and supportive learning behaviors along with final test scores. A total of 225 college students who were taking online courses participated. Longitudinal data were collected over three semesters (T1, T2, and T3). T3 was used as training data given that it contained the largest sample size across all three data waves. To analyze the data, two approaches were applied: (a) stepwise logistic regression and (b) random forest (RF). Results showed that RF used fewer items and predicted final grades more accurately in a small sample. Furthermore, it selected four items that might potentially be used to identify at-risk learners even before they enroll in an online course.

Objective. After months of lockdown due to the COVID-19 outbreak, the U.S. postsecondary institutions implemented different instruction approaches to bring their students back for the Fall 2020 semester. Given public health concerns with reopening campuses, the study evaluated the impact of Fall 2020 college reopenings on COVID-19 transmission within the 632 U.S. university counties. Study design. This was a retrospective and observational study. Methods. Bayesian Structural Time-Series (BSTS) models were conducted to investigate the county-level COVID-19 case increases during the first 21 days of Fall 2020. The case increase for each county was estimated by comparing the observed time series (actual daily cases after school reopening) to the BSTS counterfactual time series (predictive daily cases if not reopening during the same time frame). We then employed multilevel models to examine the associations between opening approaches (in-person, online, and hybrid) and county-level COVID-19 case increases within 21 and 42 days after classes began. The multigroup comparison between mask and non-mask required states for these associations were also performed given that the statewide guidelines might moderate the effects of college opening approaches. Results. More than 80% of our university county sample did not experience a significant case increase in Fall 2020. There were no significant relationships between opening approaches and community transmission in both mask and non-mask required states. Only small metropolitan counties and counties with a non-community college or a higher percentage of student population showed significantly positive associations with the case number increase within the first 21-day period of Fall 2020. For the longer 42-day period, the counties with a higher percentage of the student population showed a significant case increase. Conclusion. The overall findings underscored the outcomes of U.S. higher education reopening efforts when the vaccines were still under development in Fall 2020. For individual county results, we invite the college and county level decision-makers to interpret their results by using our web application.

During the pandemic, online courses became the major delivery format for most institutions of higher learning across the United States and around the world. However, many students experienced emotional distress as a result and have struggled to adapt to remote learning. To explore how emotional distress relates to other aspects of online learning, including online learning readiness and academic outcome, we asked a sample of 80 college students to participate in an online survey in the fall semester of 2020. Two distinct online learning readiness patterns were found using k-means cluster analysis. Online learning-ready learners showed statistically significant differences from the not-ready online learners on anxiety, boredom, and satisfaction. Moreover, a three-path mediation model based on a theoretical relationship between online learning readiness, emotional state, and expectation of learning outcome was tested using structural equation modeling (SEM). Results showed that readiness positively predicted satisfaction; furthermore, only satisfaction predicted learning expectation and expected grade. The implications of these findings and limitations of the study are discussed.