The effects of biofeedback‐based stimulated recall on self‐regulated online learning: A gender and cognitive taxonomy perspective

Zhai, Xuesong; Fang, Qiansheng; Dong, Yan; Zhi-hui, Wei; Yuan, Jing; Cacciolatti, Luca; Yang, Yalong

doi:10.1111/jcal.12284

Cited by 21 publications

(18 citation statements)

References 46 publications

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“…The interaction between an individual and an environment is considered as an extremely critical factor in understanding individuals and determining their behaviors [21]. The model of person-environment interaction proposed by Moos [22] focused on the bi-directional relationship between people and environments that is correlated to a range of various states in the psychological and physical domains, such as cognitive [23] and motivation [24]. In an online learning environment, based on person-environment interaction theory, Neufeld et al [21] proposed that academic constructs (e.g., learning persistence and engagement) reflected the interaction between a student and his or her educational environment.…”

Section: Person-environment Interaction Modelmentioning

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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Section: Person-environment Interaction Modelmentioning

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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show abstract

“…Moreover, eye-tracking technology has been used for the study of reading comprehension and memory-related tasks among students. Zhai et al [16] found that SRL techniques improved reading comprehension. They likewise found differences relating to scan-paths between men and women.…”

Section: Areas Of Application Of Eye-tracking Technologymentioning

confidence: 99%

Lifelong Learning from Sustainable Education: An Analysis with Eye Tracking and Data Mining Techniques

et al. 2020

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The use of learning environments that apply Advanced Learning Technologies (ALTs) and Self-Regulated Learning (SRL) is increasingly frequent. In this study, eye-tracking technology was used to analyze scan-path differences in a History of Art learning task. The study involved 36 participants (students versus university teachers with and without previous knowledge). The scan-paths were registered during the viewing of video based on SRL. Subsequently, the participants were asked to solve a crossword puzzle, and relevant vs. non-relevant Areas of Interest (AOI) were defined. Conventional statistical techniques (ANCOVA) and data mining techniques (string-edit methods and k-means clustering) were applied. The former only detected differences for the crossword puzzle. However, the latter, with the Uniform Distance model, detected the participants with the most effective scan-path. The use of this technique successfully predicted 64.9% of the variance in learning results. The contribution of this study is to analyze the teaching–learning process with resources that allow a personalized response to each learner, understanding education as a right throughout life from a sustainable perspective.

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“…For example, the eye-tracking technique could capture learners' eye fixation to track the attended area, but the reasons for the foci may be attributed to different affections such as interest, anxiety, or even distraction. An additional source of data such as EEG could help to make a more accurate assessment [52].…”

Section: E Categories Of the Application Dimensionmentioning

confidence: 99%

A Review of Artificial Intelligence (AI) in Education from 2010 to 2020

et al. 2021

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This study provided a content analysis of studies aiming to disclose how artificial intelligence (AI) has been applied to the education sector and explore the potential research trends and challenges of AI in education. A total of 100 papers including 63 empirical papers (74 studies) and 37 analytic papers were selected from the education and educational research category of Social Sciences Citation Index database from 2010 to 2020. The content analysis showed that the research questions could be classified into development layer (classification, matching, recommendation, and deep learning), application layer (feedback, reasoning, and adaptive learning), and integration layer (affection computing, role-playing, immersive learning, and gamification). Moreover, four research trends, including Internet of Things, swarm intelligence, deep learning, and neuroscience, as well as an assessment of AI in education, were suggested for further investigation. However, we also proposed the challenges in education may be caused by AI with regard to inappropriate use of AI techniques, changing roles of teachers and students, as well as social and ethical issues. The results provide insights into an overview of the AI used for education domain, which helps to strengthen the theoretical foundation of AI in education and provides a promising channel for educators and AI engineers to carry out further collaborative research.

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The effects of biofeedback‐based stimulated recall on self‐regulated online learning: A gender and cognitive taxonomy perspective

Cited by 21 publications

References 46 publications

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

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

Lifelong Learning from Sustainable Education: An Analysis with Eye Tracking and Data Mining Techniques

A Review of Artificial Intelligence (AI) in Education from 2010 to 2020

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