Scaffolding is widely used in online learning; however, it is unclear whether scaffolding can effectively enhance students’ online learning performance. To evaluate its effectiveness, we conducted a meta-analysis that included 83 effects sizes from 32 articles published between 2011 and 2021. The findings demonstrated that scaffolding had a significant role in improving students’ online learning performance with an overall effect size of 0.53. In addition, we analyzed the effects of five moderator variables to gain insights into how scaffolding affects students’ online learning performance under different conditions. The discipline, learning outcome type, and scaffolding type were identified as significant moderators. It is expected that the research results will guide teachers to design online scaffolding strategies and improve students’ online learning performance.

As online learning platforms become prevalent, online learning has been an important way for college students. Online learning engagement, as an evaluation of online learning quality, is crucial for enhancing learning quality and promoting higher education by investigating college students’ engagement and its influencing factors in the online learning environment. This paper aims to identify key factors affecting college students’ online learning behavioral engagement. Based on a literature review, the Delphi expert consultation method was used to build an assessment framework covering five dimensions (participation, concentration, interaction, challenge, and self-monitoring) with sixteen specific indicators. The Analytic Hierarchy Process (AHP) determined the weights of these factors. Then, data from 63 students using the “Ketangpai” online learning platform were collected and analyzed to explore the correlation and predictive relationships between online learning behavior indicators and academic achievements. The results showed a strong correlation between the frequency of accessing online learning resources and long-term online learning and academic performance, and a prediction model was established. The framework offers theoretical and methodological insights for designing online learning activities and evaluating learning quality. It supports intervening in and assessing college students’ online learning processes and improving learning quality. Also, exploring the relationship helps educators formulate personalized online teaching strategies, improving online education effectiveness and students’ learning experiences.

Cognitive engagement is a crucial factor that shapes successful learning outcomes, but our understanding of the factors that influence such engagement in the smart classroom context remains limited. This study aims to narrow this research gap by exploring the relationships among college students’ perceptions of the smart learning environment, perceived usefulness of mobile technology, achievement emotions, and cognitive engagement. A total of 1293 college students completed an online questionnaire survey, and 1076 valid responses were received. Structural equation modeling was used to analyze the interrelationships among these factors. The results revealed that students’ perceptions of the smart classroom environment and perceived usefulness of mobile technology as well as two achievement emotions (pride and anxiety) significantly impact cognitive engagement. Both pride and anxiety act as mediators in the relationships among perceptions of smart classroom environments, the perceived usefulness of mobile technology, and cognitive engagement, in which context the mediating effect of pride is stronger than that of anxiety. These findings have practical implications for instructors, who should focus on implementing strategies that promote positive achievement emotions when students use mobile technology in smart classrooms. Additionally, these findings can inform the design and construction of smart classroom environments. Moreover, our study has limitations due to reliance on online data collection and self-reported data, which may introduce biases and measurement errors. Future research should incorporate multimodal data and advanced technologies for a comprehensive assessment to better understand students’ engagement in smart learning environments, while also considering individual factors and the educational context to enhance the effectiveness of mobile technology in supporting students’ emotions and achievement.