Although online learning platforms are gradually becoming commonplace in modern society, learners’ high dropout rates and serious academic performance require more attention within the virtual learning environment (VLE). This study aims to predict students’ performance in a specific course as it is continuously running, using the statistic personal biographical information and sequential behavior data with VLE. To achieve this goal, a novel recurrent neural network (RNN)-gated recurrent unit (GRU) joint neural network is proposed to fit both static and sequential data, where the data completion mechanism is also adopted to fill the missing stream data. To incorporate the sequential relationship of learning data, three kinds of time-series deep neural network algorithms: simple RNN, GRU, and LSTM are first taken into consideration as baseline models. Their performances are compared in identifying at-risk students. Experimental results on Open University Learning Analytics Dataset (OULAD) show that simple methods like GRU and simple RNN have better results than the relatively complex LSTM model. The results also reveal that different models have different peak performance time, which results in the proposed joint model that achieves over 80% prediction accuracy of at-risk students at the end of the semester.

Personalized learning paths aim to save learning time and improve learning achievements by providing the most appropriate learning sequence for heterogeneous students. Most existing methods that construct personalized learning paths focus on students’ characteristics or knowledge structure, while ignoring the critical roles of learning states. This study describes a dynamic personalized learning path planning algorithm to recommend appropriate knowledge points for online students based on their learning states and the difficulty of each knowledge point. The proposed method first calculates the difficulty of knowledge points automatically and constructs a knowledge difficulty model. Then, a dynamic knowledge mastery model is built based on learning behavior and normalized test scores. Finally, a path that satisfies students’ personalized changing states is generated. To achieve the aforementioned goal, a novel method that calculates the difficulty of knowledge points automatically is proposed. Moreover, the personalized learning path planning method proposed in this research is not limited to a particular course. To evaluate the method, we use a series of approaches to verify the impact of the personalized path on student learning. The experimental results demonstrate that the proposed algorithm can effectively generate personalized learning paths. Results demonstrate that the personalized path proposed by the algorithm can improve effective behavior rates, course completion rates and learning efficiency. Results also show that the personalized learning paths based on student states would help students to master knowledge.

In the field of object detection, deep learning models have achieved great success in recent years. Despite these advances, detecting small objects remains difficult. Most objects in aerial images have features that are a challenge for traditional object detection techniques, including small size, high density, high variability, and varying orientation. Previous approaches have used slicing methods on high-resolution images or feature maps to improve performance. However, existing slicing methods inevitably lead to redundant computation. Therefore, in this article we present a novel adaptive slicing method named ASAHI (Adaptive Slicing Aided Hyper Inference), which can dramatically reduce redundant computation using an adaptive slicing size. Specifically, ASAHI focuses on the number of slices rather than the slicing size, that is, it adaptively adjusts the slicing size to control the number of slices according to the image resolution. Additionally, we replace the standard non-maximum suppression technique with Cluster-DIoU-NMS due to its improved accuracy and inference speed in the post-processing stage. In extensive experiments, ASAHI achieves competitive performance on the VisDrone and xView datasets. The results show that the mAP50 is increased by 0.9% and the computation time is reduced by 20–25% compared with state-of-the-art slicing methods on the TPH-YOLOV5 pretrained model. On the VisDrone2019-DET-val dataset, our mAP50 result is 56.4% higher, demonstrating the superiority of our approach.

In the information age, MOOCs (Massive Open Online Courses), micro-classes, flipping classroom, and other blended teaching scenes have improved students learning outcomes. However, incorporating technologies into experimental courses, especially electronic and electrical experiments, has its own characteristics and difficulties. The focus of this paper is to introduce virtual technology into an electronic circuit experiment course and to explore its teaching strategy, thereby realizing the informatization of experiment teaching. First, this paper explores the design concepts and implementation details of the digital circuit virtual laboratory, which is then developed based on previous literature and a prequestionnaire to users. Second, the informatization process of the experiment learning model based on traditional custom lab benches is shown through a blended learning scheme that integrates the online virtual laboratory. Finally, the experiment information system is verified and analyzed with a control group experiment and questionnaires. The blended program turned out to be an effective teaching model to complement the deficiencies in existing physical laboratories. The research conclusions show that the virtual experiment system provides students with a rich, efficient, and expansive experimental experience, in particular, the flexibility, repeatability, and visual appeal of a virtual platform could promote the development of students’ abilities in active learning, reflective thinking, and creativity.

COVID-19 has affected traditional instructional activities. Home-based isolation and restrictive movement measures have forced most learning activities to move from an offline to an online environment. Multiple studies have also demonstrated that teaching with virtual tools during the COVID-19 pandemic is always ineffective. This study examines the different characteristics and challenges that virtual tools brought to online education in the pre-pandemic and pandemic era, with the aim of providing experience of how virtual tools supported purely online learning during a health crisis. By searching keywords in public databases and review publications, this study tries to summarize the major topics related to the research theme. These topics are the characteristics of learning supported by technologies in pre-pandemic and pandemic era, the challenges that education systems have faced during the COVID-19 pandemic. This study also compares the functions, advantages and limitations of typical virtual tools, which has rarely been done in previous studies. This study tries to present the features of virtual tools that support online learning and the challenges regarding real-life risk scenarios, and tries to provide educational institutions with a distinct perspective for efficient teaching and learning in future potential health crises.