Effect of Technology Acceptance on Blended Learning Satisfaction: The Serial Mediation of Emotional Experience, Social Belonging, and Higher-Order Thinking.

This study explored the relationship between technology acceptance and learning satisfaction in the context of blended learning, with a particular focus on the mediating effects of online behaviors, emotional experience, social belonging, and higher-order thinking. A total of 110 Chinese university students participated in this study and completed a questionnaire at the end of 11 weeks of blended learning. The results demonstrate that technology acceptance directly and indirectly relates to blended learning satisfaction. The mediation analysis further revealed two significant mediating pathways from technology acceptance to blended learning satisfaction: one through higher-order thinking, and the other through serial mediation of emotional experience, social belonging, and higher-order thinking. Moreover, there was no significant mediating effect of online learning behaviors on blended learning satisfaction. Based on these results, we have proposed practical implications for improving blended learning practice to promote learner satisfaction. These results contribute to our understanding of blended learning as an integrated construct under the triadic interplay of technical environment, learning behaviors, and individual perceptions.

How Do K-12 Students' Perceptions of Online Learning Environments Affect Their Online Learning Engagement? Evidence from China's COVID-19 School Closure Period

A learning environment's quality has crucial influence on a student's engagement. In this study, we utilized a structural equation modeling approach to explore the structural relationships between students' perceptions of an online learning environment and their online learning engagement during China's COVID-19 school closure period by focusing on an online learning environment and the specific features that facilitate student engagement. The online learning environment was conceptualized as a multidimensional structure consisting of four elements: pedagogy, social interaction, technology, and the consideration of home learning conditions. Student engagement was conceptualized as a multifaceted construct comprising behavioral, emotional, and cognitive engagement. The results showed that teaching presence significantly predicted deep behavioral engagement (β= 0.246), emotional engagement (β= 0.110), and cognitive engagement (β= 0.180). Social presence significantly positively predicted cognitive engagement (β= 0.298) and emotional engagement (β= 0.480), whereas its effect on behavioral engagement was not significant. The perceived ease of technology use significantly predicted only emotional engagement (β= 0.324), and the family learning presence significantly predicted only behavioral engagement (β= 0.108). The results also indicated that emotional and cognitive engagement had indirect effects on the predictive power of the online learning environment for behavioral engagement. These findings provide valuable guidelines and effective strategies for teachers and parents to design suitable online learning environments to enhance K-12 student engagement.

Promoting STEAM Education in Primary School through Cooperative Teaching: A Design-Based Research Study

The COVID-19 pandemic has highlighted the importance of students' information literacy, computer skills, and research competencies for self-regulated learning and problem solving. STEAM education, with interdisciplinary knowledge building and higher-order thinking development as its main purpose, is considered essential for students' sustainable development in the post-pandemic era. However, STEAM education in China's K-12 schools is facing several problems, such as insufficient qualified teachers, unsustainable development, and difficulty in achieving meaningful discipline integration. To address these problems, this study proposes an innovative STEAM education model supported by cooperative teaching and theories of project-based learning and collaborative learning. After two iterations of design, evaluation, and revision, the proposed STEAM education model and a set of instructional design principles were validated. The resulting model features a multi-teacher cooperative strategy, detailed and diverse scaffolding, familiar themes for students, the integration of STEAM education into formal curricula, and extended instruction hours. The study results suggest that cooperative teaching can facilitate meaningful discipline integration and can alleviate the STEAM faculty shortage. This study produced five proven instructional design principles for conducting STEAM education supported by cooperative teaching in primary schools.

Augmented Reality in K–12 Education: A Systematic Review and Meta-Analysis of the Literature from 2000 to 2020

With its capacity to support student-centered learning through digital transformation and shared experience, augmented reality (AR) has received increasing attention from both researchers and practitioners as an emerging technology to achieve innovative and sustainable education. Therefore, this study systematically reviewed the literature on the application of augmented reality in K–12 education settings between 2000 and 2020. After two stages of screening, 129 articles were selected, and the key research results were analyzed and integrated by adopting a coding scheme including basic information, instruction contexts, technical features, instructional design, and research results. The results revealed interesting findings regarding the augmented reality literature in terms of publication patterns, application fields, technological affordances, instructional designs, and methods. Furthermore, a meta-analysis was conducted to examine the effectiveness of augmented reality-based instruction, and the results showed a large overall effect size (g = 0.919) with three significant moderators. Finally, the practical significance of AR-based instruction and a future research agenda are discussed.

Statistical Analysis and Machine Learning Prediction of Disease Outcomes for COVID-19 and Pneumonia Patients.

The Coronavirus Disease 2019 (COVID-19) has spread all over the world and impacted many people's lives. The characteristics of COVID-19 and other types of pneumonia have both similarities and differences, which confused doctors initially to separate and understand them. Here we presented a retrospective analysis for both COVID-19 and other types of pneumonia by combining the COVID-19 clinical data, eICU and MIMIC-III databases. Machine learning models, including logistic regression, random forest, XGBoost and deep learning neural networks, were developed to predict the severity of COVID-19 infections as well as the mortality of pneumonia patients in intensive care units (ICU). Statistical analysis and feature interpretation, including the analysis of two-level attention mechanisms on both temporal and non-temporal features, were utilized to understand the associations between different clinical variables and disease outcomes. For the COVID-19 data, the XGBoost model obtained the best performance on the test set (AUROC = 1.000 and AUPRC = 0.833). On the MIMIC-III and eICU pneumonia datasets, our deep learning model (Bi-LSTM_Attn) was able to identify clinical variables associated with death of pneumonia patients (AUROC = 0.924 and AUPRC = 0.802 for 24-hour observation window and 12-hour prediction window). The results highlighted clinical indicators, such as the lymphocyte counts, that may help the doctors to predict the disease progression and outcomes for both COVID-19 and other types of pneumonia.

Effects of First-Time Experiences and Self-Regulation on College Students' Online Learning Motivation: Based on a National Survey during COVID-19

The COVID-19 pandemic has forced many college students in developing countries to engage in online learning for the first time, and the sudden transit has raised concerns regarding students' competencies for, perception of, and attitude towards online learning. To address those concerns, this study measured three essential constructs of online learning (self-regulated learning, perceived presences, and learning motivation) based on a national survey in China (N = 12,826) and employed structural equation modeling to investigate their intertwined relationship. The study results reveal that (1) college students' academic achievement cannot effectively predict their self-regulated learning in an online learning context;(2) self-regulation can be further differentiated into general and task-specific strategies with a varying impact on three types of presences;(3) online learning motivation is best predicted by cognitive presence, followed by social presence and teaching presence;and (4) the path of task-specific self-regulated learning →cognitive presence →online learning motivation generates the largest positive compound effect. Implications for online teaching and learning practice are also discussed through the stakeholder perspectives of students, teachers, and platform developers.