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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 [right arrow] cognitive presence [right arrow] 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.

Background: Panoramic video as a novel instructional media with increasing educational application. However, empirical research on panoramic video instruction is mostly episodic with conditional and inconclusive results. Objectives: This study reviewed 10 years (2011-2021) of research on panoramic video in education to synthesize, meta-analyse, and critically evaluate the state-of-art research findings, which can inform future educational practice and research directions. Methods: Following the protocol of systematic review, this study selected 83 articles from four major research databases and analysed them from five interrelated aspects: instructional context, technological features, instructional design, research design, and instructional effect. A meta-analysis was also conducted to determine the overall effectiveness of panoramic video on education and the moderating factors. Results and Conclusions: The results revealed a rapid upward trend in publication since 2018 with unequal distribution among regions and learning domains. Despite the technical advancement in immersion and interaction, panoramic videos were found to mainly support the acquisition of content and factual knowledge through direct instruction, and lack in-video scaffolding and assessment. Moreover, many interventions were one-time, short-term studies that are susceptible of novelty effect. Lastly, an overall medium effect (g = 0.711) of panoramic video was reported and six significant moderating factors were identified based on the meta-analysis results. Major takeaways and implications: Panoramic videos are ubiquitously effective across grade levels, learning domains, and pedagogies, but are more suitable for teaching conceptual knowledge. Design features such as interaction, scaffolding, assessment, iteration, and video length deserve our special attention in panoramic video instruction.

Aiming at the problems of high stochasticity and volatility of power loads as well as the difficulty of accurate load forecasting, this paper proposes a power load forecasting method based on CEEMDAN (Completely Integrated Empirical Modal Decomposition) and TCN-LSTM (Temporal Convolutional Networks and Long-Short-Term Memory Networks). The method combines the decomposition of raw load data by CEEMDAN and the spatio-temporal modeling capability of TCN-LSTM model, aiming to improve the accuracy and stability of forecasting. First, the raw load data are decomposed into multiple linearly stable subsequences by CEEMDAN, and then the sample entropy is introduced to reorganize each subsequence. Then the reorganized sequences are used as inputs to the TCN-LSTM model to extract sequence features and perform training and prediction. The modeling prediction is carried out by selecting the electricity compliance data of New South Wales, Australia, and compared with the traditional prediction methods. The experimental results show that the algorithm proposed in this paper has higher accuracy and better prediction effect on load forecasting, which can provide a partial reference for electricity load forecasting methods.

Discussion has been widely used in courses, both online and otherwise, as it provides opportunities for students to construct knowledge through interaction with peers and instructors. Grouping students is a prominent strategy in the use of discussion. However, simply dividing students cannot guarantee active participation and high learning performance. There is therefore a need to pay attention to the structure and/or features of grouping, especially group size and group composition. The study described in this article focuses on the combined effects of group size and group composition in online discussion. It investigates whether students in small groups have different participation behaviors and learning performance compared to students in whole-class discussion. In addition, the influence of group composition is examined by comparing students' participation and learning performance from high, medium, and low social-connected groups. Furthermore, this study also investigates how students' perceived learning experience differs among these three differently-connected group compositions. The results indicate significantly different participation behaviors and learning performance between small-group and whole-class discussion. The effects of group composition are also shown in students' learning behaviors, performance, and perceived experience. The results also reveal both advantages and disadvantages of different group types. The findings are expected to inform the design and implementation of grouping methods and extend our understanding of online discussion.