Opinion mining in higher education: a corpus-based approach

Comprehensive analytics of COVID-19 vaccine research: From topic modeling to topic classification

COVID-19 vaccine research has played a vital role in successfully controlling the pandemic, and the research surrounding the coronavirus vaccine is ever-evolving and accruing. These enormous efforts in knowledge production necessitate a structured analysis as secondary research to extract useful insights. In this study, comprehensive analytics was performed to extract these insights, which has moved the boundaries of data analytics in secondary research in the vaccine field by utilizing topic modeling, sentiment analysis, and topic classification based on the abstracts of related publications indexed in Scopus and PubMed. By applying topic modeling to 4803 abstracts filtered by this study criterion, 8 research arenas were identified by merging related topics. The extracted research areas were entitled "Reporting," "Acceptance," "Reaction," "Surveyed Opinions," "Pregnancy," "Titer of Variants," "Categorized Surveys," and "International Approaches." Moreover, the investigation of topics sentiments variations over time led to identifying researchers' attitudes and focus in various years from 2020 to 2022. Finally, a CNN-LSTM classification model was developed to predict the dominant topics and sentiments of new documents based on the 25 pre-determined topics with 75 % accuracy. The findings of this study can be utilized for future research design in this area by quickly grasping the structure of the current research on the COVID-19 vaccine. Through the findings of current research, a classification model was developed to classify the topic of a new article as one of the identified topics. Also, vaccine manufacturing firms will achieve a niche market by having a schema to invest in the gap of fields that have yet to be concentrated in extracted topics.

Towards Figurative Expression Enhancement: Effects of the SVVR-Supported Worked Example Approach on the Descriptive Writing of Highly Engaged Students

During the past decades, many researchers have attempted to explore effective teaching methods for developing students’ descriptive writing performance. In this study, the worked example was implemented as an effective way of guiding students to provide step-by-step solutions to learning tasks. Moreover, a spherical video-based virtual reality (SVVR) environment was provided to place students in real-world situations which enabled them to experience the learning contexts in depth. A pretest-posttest quasi experimental study was conducted to explore the influence of the SVVR-supported worked example approach and engagement level on students’ Chinese descriptive writing performance. A total of 79 fourth-grade elementary school students participated in this study. The experimental group used SVVR with worked examples to complete Chinese writing assignments, whereas the control group used videos and worked examples. The results showed no significant effects of the SVVR-supported worked example approach compared with the conventional worked example approach regarding organization, sensory details, or creativity dimensions. As for the figurative expression dimension, students in the SVVR-supported worked example approach condition scored significantly higher. Moreover, high engagement students significantly outperformed low engagement students in all four writing performance dimensions. Additionally, a significant interaction effect between learning approach and engagement level on figurative expression was found.