Promoting deep writing with immersive technologies: An SVVR‐supported Chinese composition writing approach for primary schools

Abstract

Writing is a recording process involving complex dynamic behaviours, which is closely connected with authentic contexts. A free authentic context can form a link with students' life experience and their prior knowledge, so that students' deep writing skills can be stimulated. However, in traditional writing activities, the lack of authentic experience is an important reason for students' poor writing achievements and low behavioural engagement. To address this problem, the current study used a spherical video‐based virtual reality (SVVR)‐supported Chinese composition writing approach, which can provide authentic contexts to support students' deep writing. An SVVR experiential learning model was developed and a quasi‐experiment was conducted in a primary school to verify the effectiveness of the proposed approach. A total of 59 fourth‐grade students from China were recruited and were randomly divided into the experimental group, which adopted the SVVR‐supported learning approach, and the control group, which adopted the non‐SVVR‐supported learning approach. The results show that the proposed approach can promote students' transition of learning behavioural engagement from medium to high, as well as the development of deep writing skills. It was found that students learning with the SVVR approach who acquired better deep writing skills tended to demonstrate better writing performance in relation to linguistic expressiveness and creative thinking than the students learning with the non‐SVVR approach. What is already known about this topic There is an urgent need to use immersive technologies to promote deep learning and stimulate students' learning behavioural engagement in writing. Spherical video‐based virtual reality (SVVR) is one of the immersive VRs with a low technical barrier and low cost, which meets the needs of school settings. SVVR has been applied in areas of language, engineering, science, etc., while its impacts on students' composition writing achievements have generally been ignored, especially in primary schools. The characteristics of immersion, interaction and imagination in SVVR that allow students to have opportunities of experiential learning can potentially promote students' writing performance. What this paper adds A novel experiential learning model supported by the SVVR learning system was developed. An SVVR‐supported composition writing approach based on the proposed experiential learning model was used to promote primary school students' deep writing. It was found that students' composition writing performance, learning behavioural engagement and deep writing skills can be improved by applying the SVVR‐supported learning approach in writing courses. Implications for practice and/or policy It is worth promoting the application of SVVR in school settings because it is a low‐tech and low‐cost method of creating immersive learning environments. The SVVR‐supported composition writing approach is useful for promoting primary school students' deep writing. Further investigations on ...

Online educational video engagement prediction based on dynamic graph neural networks

“…GC-LSTM (Chen et al , 2022a, 2022b). GC-LSTM was initially proposed for dynamic graph link prediction, but its ability to model spatiotemporal features also makes it applicable to dynamic graph node regression.…”

“…They use temporal convolutions or recurrent connections to model the evolution of the graph structure over time, and graph convolutional layers to compute the hidden representations of nodes and edges. Some of the most popular and classic DGNNs include: diffusion convolutional recurrent neural network (DCRNN) (Li et al, 2017), the graph convolutional recurrent neural network based on long short-term memory network (GConvLSTM) (Seo et al, 2018) and graph convolutional long short-term memory (GC-LSTM) (Chen et al, 2022a(Chen et al, , 2022b.…”

“…Finally, update the hidden state H t using the previous time step's hidden state H tÀ1 , the input node features matrix X t , update gate u t and reset gate r t : (Chen et al, 2022a(Chen et al, , 2022b. GC-LSTM was initially proposed for dynamic graph link prediction, but its ability to model spatiotemporal features also makes it applicable to dynamic graph node regression.…”

“…Prior to predicting engagement with video lectures, it is imperative to establish a clear definition of engagement. Research has indicated that everyday connotations of engagement refer to involvement, commitment, passion, enthusiasm, absorption, focused effort, zeal, dedication and energy (Schaufeli, 2013; Chen et al , 2022a, 2022b). However, the focus of our study is on student participants and the process of watching videos is equivalent to the process of learning.…”

The Effects of Spherical Video-Based Virtual Reality and Conventional Video on Students’ Descriptive Writing Achievement and Motivation: A Comparative Study

“…However, there is not enough empirical research to prove that the teaching effect of SVVR is better than that of CV in students' descriptive article writing learning process, especially in primary school (Y. Chen et al, 2022). This attracts researchers to conduct a study to compare the effects of SVVR and CV learning environments on primary school students' descriptive article writing learning outcomes.…”