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.

Across two years, we examined the effects of teachers' attempts to implement computer supported collaborative learning (CSCL) communities in classrooms in two high schools on students' knowledge building, strategic learning, and perceptions of the classroom environment. In year one, 429 (fall) and 317 (spring) students in the classes of 8 teachers and in year two, 946 students in the classes of 18 teachers participated. Students in classes where CSCL communities were more fully established reported more knowledge building goals and activities, more question asking, and higher perception of collaboration with fellow students. Students' reports of knowledge building, strategic learning, and perceptions of the classroom were also associated with their classroom achievement. Results suggest that implementing practices and technology supportive of CSCL communities can foster increased student knowledge building and enhance students' perceptions of collaboration in regular classroom environments.

The use of social media to share information, enhance learning, and connect with an online community has grown rapidly over the past 10 years. As social media becomes a more common tool in both formal and informal education, it is imperative to understand how it is used by individuals with disabilities. Through a systematic study of the literature, 215 articles on social media used by individuals with disabilities were selected and 29 selected for in-depth thematic analysis. Six major themes were identified: community, cyberbullying, self-esteem, self-determination, access to technology, and accessibility. To confirm these six categories, we expanded our search, yielding an additional 30 articles, for a total 59 articles reviewed in-depth. Interactions between individuals with disabilities within online communities often had the goal of acquiring knowledge or learning new information. A communities of practice theoretical framework is used to discuss interactions among the elements of social media design, learning, and the building of community by individuals with disabilities.

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.