In education, learning concentration is closely related to the quality of learning, and teachers can adjust their teaching methods accordingly to improve the learning outcomes of students. Particularly in head-mounted virtual reality interactions, current methods for assessing learning concentration cannot be fully applied to new interactive environments because immersion shaping and cognitive formation differ from the conventional education. Therefore, in this study, a learning concentration assessment method is proposed to measure the learning concentration of students in head-mounted virtual interaction, using the expression score, visual focus rate, and task mastery as evaluation indicators. In addition, the weights of the evaluation indicators can be configured to be included in the calculation of learning concentration depending on the characteristics of different types of courses. The results of a usability evaluation indicate that the learning concentration of students can be effectively evaluated using the proposed method. By developing and implementing strategies for optimizing learning effects, the learning concentration and assessment scores of students increased by 18% and 15.39%, respectively.

No abstract

Learning style is the endogenous cause of students' unique behaviors when they are performing learning tasks. The adaptive learning system that considers learning style can provide a personalized experience to stimulate students' enthusiasm, which had been widely studied in recent years. However, most of such existing systems are constructed based on a desktop environment, which leads to the less‐than‐ideal effect of personalized learning due to the limitation of interaction means and environmental dimension. Therefore, an adaptive virtual reality learning method based on the learning style model was proposed in this study. This method continuously iterated the identification of learning style based on students' subjective and objective data. Then, the content of virtual learning environment was sustainedly adjusted according to the identification results, thus enabling the environment to dynamically adapt to students' learning styles. To evaluate the feasibility of this proposed method, a controlled experiment on 152 participants was conducted. Results show that this method obtained relatively stable and accurate results of learning style identification, with an accuracy range of 74.38%–80.30%. Moreover, learning in the virtual environment constructed based on this method had a positive impact on students' learning motivation and outcomes.