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

Feng, Xiang; Wei, Yaojia; Pan, Xianglin; Qiu, Longhui; Ma, Yi

doi:10.3390/ijerph17061941

Cited by 43 publications

(24 citation statements)

References 45 publications

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“…Regarding the relationship between BL and emotion, there have been sporadic explorations in the past. Students go through a wide variety of emotional experiences in online and offline learning environments that impact their academic performance and mental health ( Feng X. et al, 2020 ). Halverson and Graham (2019) believe that emotional engagement in BL context can be divided into two dimensions: positive and negative.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

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Unpacking the Emotional Experiences of Learners in a Blended Learning Context

Zhao

Song

2022

Front. Psychol.

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Understanding the relationship between emotion and learning behavior is conducive to learners’ well-being and effective learning. However, previous studies only regarded emotion as an additional variable, and there lacked specific research on academic emotion in the blended learning (BL) context. BL is characterized by systematic integration of online and face-to-face (F2F) learning, hence leading to special emotional experiences. What is the emotional experience of learners in online learning? What is it like face-to-face? Does the connection between the two have an impact on learners’ emotional experience? In order to address these questions and explore the emotional profiles of learners in BL context, this study constructs a typical BL context in a Chinese university, and conducts questionnaire and focus group interviews with 89 participants at the end of the semester. Data analysis showed that learners’ emotions of face-to-face classes are more intense than those of online learning, both positive and negative. As to positive emotions, paired-sample t-test shows that mean values of feeling of challenge, comfort, sense of community, satisfaction, enthusiasm and interest in F2F are significantly higher than those of online learning. About negative emotions, stress, embarrassment, tension and frustration of F2F are significantly stronger than those of online learning, while boredom and disappointment of online learning are more intense than those for F2F section. Theme analysis identified 11 influencing factors of academic emotions, among which degree of difficulty, readiness before class, workload, and interaction are unique to BL and deserve special attention. These findings help form a picture of learners’ academic emotions in BL context. It also provides practical reference for BL course design, so as to inspire emotions which are conducive to effective and in-depth learning.

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Section: Theoretical Backgroundmentioning

confidence: 99%

“…The integration of online and offline learning will inevitably evoke students’ various emotional experiences ( Feng X. et al, 2020 ). Studies have shown that emotions correlate closely with students’ academic performance in BL context ( Ramirez-Arellano et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Unpacking the Emotional Experiences of Learners in a Blended Learning Context

Zhao

Song

2022

Front. Psychol.

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“…The enrolled patients were randomly divided into a training set and a validation set at a ratio of 8:2 using SPSS software (version 25.0; IBM Corp., Armonk, NY, USA). This ratio is the one that is commonly used to divide training and validation datasets (20)(21)(22). Univariate analysis was conducted to identify meaningful candidate variables for the model.…”

Section: Construction and Validation Of The Modelmentioning

confidence: 99%

MRI-Based Radiomic Signature Identifying Secondary Loss of Response to Infliximab in Crohn's Disease

Feng

Chen

et al. 2022

Front. Nutr.

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Up to 50% of patients with Crohn's disease (CD) experience secondary loss of response (SLR) to infliximab. Patients with SLR may show clinical signs of iron deficiency as a result of inflammation despite being iron-replete. The magnetic resonance imaging (MRI)-based radiomic index, R2*, can detect changes in iron metabolism. Therefore, the R2* parameter has considerable potential for detection of SLR to infliximab. The aims of this study were to explore the correlation between R2* and inflammation and to develop a non-invasive nomogram based on R2* to identify SLR to infliximab in patients with CD. Three hundred and twenty-two infliximab-treated patients with CD who underwent magnetic resonance enterography within 2 weeks before or after 54 weeks of infliximab therapy were divided into training and validation datasets at a ratio of 8:2. Point-biserial analysis was conducted to confirm the relationship between R2* and inflammation. A multivariate logistic regression model was created using R2*, CRP and hemoglobin (OR, 1.10, 1.04 and 0.98; P < 0.05). Receiver-operating characteristic curves and the Hosmer-Lemeshow test were used to assess the performance of the model. A correlation between R2* and inflammation was identified. Different trends in R2* and iron status indices were observed between patients with responsive and non-responsive CD, which is worthy of further study. The model was converted to a visualized nomogram that had a good ability to discriminate the outcomes of infliximab therapy with an area under the curve of 0.723 (95% CI, 0.661–0.785) in the training dataset and 0.715 (95% CI, 0.587–0.843) in the validation dataset. We confirmed a correlation between R2* and inflammation in patients with CD. Based on the MRI-based radiomic signature, a novel nomogram was established and validated to facilitate individualized identification of SLR to infliximab in patients with CD.

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“…In the figure, the arrow direction indicates increase of arousal values, when pleasure increases, positive emotion increases; when pleasure decreases, negative emotion appears. According to this theory, surprised and excited correspond to positive emotions; while anger, sad, hate, and fear correspond to negative emotions [18,19]. Positive emotions promote the occurrence of cognition activities, while negative emotions hinder the cognition process [20,21].…”

Section: Facial Expression Recognitionmentioning

confidence: 99%

Machine Learning-Based Student Emotion Recognition for Business English Class

Cui

Wang

Zhao

2021

Int. J. Emerg. Technol. Learn.

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Traditional English teaching model neglects student emotions, making many tired of learning. Machine learning supports end-to-end recognition of learning emotions, such that the recognition system can adaptively adjust the learning difficulty in English classroom. With the help of machine learning, this paper presents a method to extract the facial expression features of students in business English class, and establishes a student emotion recognition model, which consists of such modules as emotion mechanism, signal acquisition, analysis and recognition, emotion understanding, emotion expression, and wearable equipment. The results show that the proposed emotion recognition model monitors the real-time emotional states of each student during English learning; upon detecting frustration or boredom, machine learning will timely switch to the contents that interest the student or easier to learn, keeping the student active in learning. The research provides an end-to-end student emotion recognition system to assist with classroom teaching, and enhance the positive emotions of students in English learning.

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

Cited by 43 publications

References 45 publications

Unpacking the Emotional Experiences of Learners in a Blended Learning Context

Unpacking the Emotional Experiences of Learners in a Blended Learning Context

MRI-Based Radiomic Signature Identifying Secondary Loss of Response to Infliximab in Crohn's Disease

Machine Learning-Based Student Emotion Recognition for Business English Class

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