Investigating students' interaction patterns and dynamic learning sentiments in online discussions

Huang, Changqin; Han, Zhong Mei; Li, Ming Xi; Jong, Morris Siu‐Yung; Tsai, Chin Chung

doi:10.1016/j.compedu.2019.05.015

Cited by 68 publications

(47 citation statements)

References 55 publications

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“…Besides the learning support from the teachers, the students also valued that they were arranged to work in groups to peer‐evaluate the individually collected information and co‐develop the arguments for answering the sub‐questions (eg, see the interview excerpts of Amin, Bella and Corbin). This supportive and peer‐rewarding rapport fostered during the In‐class Peer Discussion phase is always indispensable for the success of knowledge co‐construction (Huang, Han, Li, Jong, & Tsai, 2019; Scardamalia & Bereiter, 2006).…”

Section: Discussionmentioning

confidence: 99%

Integrating interactive learner‐immersed video‐based virtual reality into learning and teaching of physical geography

Jong

Tsai

Xie

et al. 2020

Brit J Educational Tech

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Section: Discussionmentioning

confidence: 99%

Integrating interactive learner‐immersed video‐based virtual reality into learning and teaching of physical geography

Jong

Tsai

Xie

et al. 2020

Brit J Educational Tech

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“…Prior studies stated that different types of academic emotions play crucial roles in students' interaction and learning persistence [13]. For example, Huang et al [14] revealed that positive academic emotions (e.g., enjoyment, happiness) might lead to more interaction between instructors and students. In contrast, negative academic emotions (e.g., anxiety, boredom) might hinder learning engagement [15].…”

Section: Introductionmentioning

confidence: 99%

Investigating the Influence of Interaction on Learning Persistence in Online Settings: Moderation or Mediation of Academic Emotions?

Huang

Han

et al. 2020

IJERPH

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Learning persistence is a critical element for successful online learning. The evidence provided by psychologists and educators has shown that students’ interaction (student-student (SS) interaction, student-instructor (SI) interaction, and student-content (SC) interaction) significantly affects their learning persistence, which is also related to their academic emotions. However, few studies explore the relations among students’ interaction, academic emotions and learning persistence in online learning environments. Furthermore, no research has focused on multi-dimensional students’ interaction and specific academic emotions. Based on person-environment interaction model and transactional distance theory, this study investigates the relationship between students’ interaction and learning persistence from the perspective of moderation and mediation of academic emotions including enjoyment, boredom, and anxiety. Data were collected from 339 students who had online learning experience in China. AMOS 22.0 (IBM, Armonk, NY, USA) and SPSS 22.0 (IBM, Armonk, NY, USA) were employed to analyze the mediating and moderating effects of academic emotions, respectively. The results revealed that students’ interaction and academic emotions directly related to learning persistence. Specifically, enjoyment, anxiety and boredom had significant mediating and moderating effects on the relationship between students’ interaction and learning persistence. Based on these findings, we further discussed the theoretical and practical implications on how to facilitate students’ learning persistence in online learning environments.

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“…However, this approach is still essentially categorizing student text into known categories. In student comments, though, some of the emotional expression of the text is the subject of a knowledge point or a specific thing, for example, learner autonomy [55] and students' interaction patterns in online discussions [56]. These require a more detailed aspect extraction method to customize the extraction of all aspect categories in the text for a more granular emotional analysis of student comments.…”

Section: Discussionmentioning

confidence: 99%

Academic Emotion Classification and Recognition Method for Large-scale Online Learning Environment—Based on A-CNN and LSTM-ATT Deep Learning Pipeline Method

Feng

Wei

Pan

et al. 2020

IJERPH

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Subjective well-being is a comprehensive psychological indicator for measuring quality of life. Studies have found that emotional measurement methods and measurement accuracy are important for well-being-related research. Academic emotion is an emotion description in the field of education. The subjective well-being of learners in an online learning environment can be studied by analyzing academic emotions. However, in a large-scale online learning environment, it is extremely challenging to classify learners’ academic emotions quickly and accurately for specific comment aspects. This study used literature analysis and data pre-analysis to build a dimensional classification system of academic emotion aspects for students’ comments in an online learning environment, as well as to develop an aspect-oriented academic emotion automatic recognition method, including an aspect-oriented convolutional neural network (A-CNN) and an academic emotion classification algorithm based on the long short-term memory with attention mechanism (LSTM-ATT) and the attention mechanism. The experiments showed that this model can provide quick and effective identification. The A-CNN model accuracy on the test set was 89%, and the LSTM-ATT model accuracy on the test set was 71%. This research provides a new method for the measurement of large-scale online academic emotions, as well as support for research related to students’ well-being in online learning environments.

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Investigating students' interaction patterns and dynamic learning sentiments in online discussions

Cited by 68 publications

References 55 publications

Integrating interactive learner‐immersed video‐based virtual reality into learning and teaching of physical geography

Integrating interactive learner‐immersed video‐based virtual reality into learning and teaching of physical geography

Investigating the Influence of Interaction on Learning Persistence in Online Settings: Moderation or Mediation of Academic Emotions?

Academic Emotion Classification and Recognition Method for Large-scale Online Learning Environment—Based on A-CNN and LSTM-ATT Deep Learning Pipeline Method

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