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

Cross-subject emotion recognition using hierarchical feature optimization and support vector machine with multi-kernel collaboration

Objective. Due to individual differences, it is greatly challenging to realize the multiple types of emotion identification across subjects.Approach. In this research, a hierarchical feature optimization method is proposed in order to represent emotional states effectively based on peripheral physiological signals. Firstly, sparse learning combined with binary search is employed to achieve feature selection of single signals. Then an improved fast correlation-based filter is proposed to implement fusion optimization of multi-channel signal features. Aiming at overcoming the limitations of the support vector machine (SVM), which uses a single kernel function to make decisions, the multi-kernel function collaboration strategy is proposed to improve the classification performance of SVM.Main results. The effectiveness of the proposed method is verified on the DEAP dataset. Experimental results show that the proposed method presents a competitive performance for four cross-subject  types of emotion identification with an accuracy of 84% (group 1) and 85.07% (group 2). Significance. The proposed model with hierarchical feature optimization and SVM with multi-kernel function collaboration demonstrates superior emotion recognition accuracy compared to state-of-the-art techniques. In addition, the analysis based on DEAP dataset composition characteristics presents a novel perspective to explore the emotion recognition issue more objectively and comprehensively.

New Trends in Emotion Recognition Using Image Analysis by Neural Networks, A Systematic Review

Facial emotion recognition (FER) is a computer vision process aimed at detecting and classifying human emotional expressions. FER systems are currently used in a vast range of applications from areas such as education, healthcare, or public safety; therefore, detection and recognition accuracies are very important. Similar to any computer vision task based on image analyses, FER solutions are also suitable for integration with artificial intelligence solutions represented by different neural network varieties, especially deep neural networks that have shown great potential in the last years due to their feature extraction capabilities and computational efficiency over large datasets. In this context, this paper reviews the latest developments in the FER area, with a focus on recent neural network models that implement specific facial image analysis algorithms to detect and recognize facial emotions. This paper's scope is to present from historical and conceptual perspectives the evolution of the neural network architectures that proved significant results in the FER area. This paper endorses convolutional neural network (CNN)-based architectures against other neural network architectures, such as recurrent neural networks or generative adversarial networks, highlighting the key elements and performance of each architecture, and the advantages and limitations of the proposed models in the analyzed papers. Additionally, this paper presents the available datasets that are currently used for emotion recognition from facial expressions and micro-expressions. The usage of FER systems is also highlighted in various domains such as healthcare, education, security, or social IoT. Finally, open issues and future possible developments in the FER area are identified.

Fuzzy clustering algorithm for university students' psychological fitness and performance detection

Students' psychological fitness is unavoidable, hindering personal development, social interactions, peer influence, and adolescence. Academic stress may be the most dominant factor affecting college students' mental well-being. Therefore, improving the monitoring of mental health issues among college students is a vital topic for study. However, identifying the student's stress level is challenging, leading to uncertainty. Hence, this paper suggests Heuristic Fuzzy C-means Clustering Algorithm (HFCA) for analyzing college students' stress levels, psychological well-being and academic performance detection. The data are collected from the Kaggle stress dataset for predicting student mental health. This study investigates the psychological factors affecting students' academic performance using the suggested HFCA. Students' performance may be predicted using the Fuzzy Cognitive Map (FCM) in this study. This study used fuzzy clustering algorithms to discover the most crucial aspects of student success, such as student involvement and satisfaction. A better understanding of the risk factors for and protective factors against poor mental health can serve as the basis for developing policies and targeted interventions to prevent mental health problems and guarantee that at-risk students can access the help they need. The experimental analysis shows the proposed method HFCA to achieve a high student performance ratio of 96.7%, cognitive development ratio of 97.2%, student engagement ratio of 97.5% and prediction ratio of 95.1% compared to other methods.

Influence of OTC on OLE for college students: mediating effect of academic emotion and moderating effect of gender

With the impact of the global epidemic, online learning has become an irreplaceable form of learning for students, and has been widely concerned by the education circles. Based on Noddings' caring theory and social role theory, A survey of 1954 college students was conducted to examine students' on-line teacher care (OTC), online academic emotion (OAE) and online learning engagement (OLE). The results of correlation analysis find that: (1) There are significant positive correlations among the three variables: OTC, OAE and OLE; (2) OAE plays a part of mediating role between OTC and OLE; (3) Gender has a significant moderating effect on the first half of the mediation path in the model of "OTC→ OAE→OLE". OTC has a significant positive predictive effect on OAE, and among them, the positive predictive effect of male college students is stronger. The conclusion of this study contributes to reveal the formation mechanism and individual differences of college students' OLE, which has reference value for the intervention of college students' OLE.

Emotion Recognition Using Different Sensors, Emotion Models, Methods and Datasets: A Comprehensive Review

In recent years, the rapid development of sensors and information technology has made it possible for machines to recognize and analyze human emotions. Emotion recognition is an important research direction in various fields. Human emotions have many manifestations. Therefore, emotion recognition can be realized by analyzing facial expressions, speech, behavior, or physiological signals. These signals are collected by different sensors. Correct recognition of human emotions can promote the development of affective computing. Most existing emotion recognition surveys only focus on a single sensor. Therefore, it is more important to compare different sensors or unimodality and multimodality. In this survey, we collect and review more than 200 papers on emotion recognition by literature research methods. We categorize these papers according to different innovations. These articles mainly focus on the methods and datasets used for emotion recognition with different sensors. This survey also provides application examples and developments in emotion recognition. Furthermore, this survey compares the advantages and disadvantages of different sensors for emotion recognition. The proposed survey can help researchers gain a better understanding of existing emotion recognition systems, thus facilitating the selection of suitable sensors, algorithms, and datasets.

Detection and analysis of graduate students' academic emotions in the online academic forum based on text mining with a deep learning approach

Purpose

The possibility of mental illness caused by the academic emotions and academic pressure of graduate students has received widespread attention. Discovering hidden academic emotions by mining graduate students' speeches in social networks has strong practical significance for the mental state discovery of graduate students.

Design/methodology/approach

Through data collected from online academic forum, a text based BiGRU-Attention model was conducted to achieve academic emotion recognition and classification, and a keyword statistics and topic analysis was performed for topic discussion among graduate posts.

Findings

Female graduate students post more than male students, and graduates majoring in chemistry post the most. Using the BiGRU-Attention model to identify and classify academic emotions has a performance with precision, recall and F1 score of more than 95%, the category of PA (Positive Activating) has the best classification performance. Through the analysis of post topics and keywords, the academic emotions of graduates mainly come from academic pressure, interpersonal relationships and career related.

Originality

A BiGRU-Attention model based on deep learning method is proposed to combine classical academic emotion classification and categories to achieve a text academic emotion recognition method based on user generated content.

School adaptation and adolescent immigrant mental health: Mediation of positive academic emotions and conduct problems

Introduction

Immigrant adolescents must adapt their physical and mental attitudes to attain healthy development due to dramatic changes in their living and learning environments after relocation. From the perspective of positive psychology, this study explored the specific influence of school adaptation on mental health among immigrant adolescents, mainly focusing on the mediating effects of positive academic emotions and conduct problems.

Methods

We selected primary and secondary school students from five relocated resettlement schools in Qianxinan Buyi and Miao Autonomous Prefecture, which has the largest population of relocated people in Guizhou Province, China. Using cluster sampling, 550 relocated students in Grades 5-12 from the five schools were recruited to complete a battery of questionnaires, including the Immigrant Adolescents' School Adaptation Scale, the General Health Scale, and the Positive Academic Emotions Questionnaire, and the Adolescents' Behavioral Tendency Questionnaire. In addition, this study used the bias-corrected bootstrap method to explore the chain-mediating effect of positive academic emotions and conduct problems between school adaptation and mental health.

Results

The results showed that immigrant adolescents had significant gender differences only in conduct problems. However, significant learning stage differences existed in school adaptation, mental health, positive academic emotions, and conduct problems. School adaptation, positive academic emotions, and mental health were significantly positively correlated. In contrast, conduct problems were significantly negatively correlated with mental health. School adaptation influenced mental health through the mediation effects of positive academic emotions and conduct problems. These effects contained three paths: the separate mediation effects of positive academic emotions and conduct problems and the chain mediation effect of positive academic emotions and conduct problems.

The positive energy of netizens: development and application of fine-grained sentiment lexicon and emotional intensity model

The outbreak of COVID-19 has led to a global health crisis and caused huge emotional swings. However, the positive emotional expressions, like self-confidence, optimism, and praise, that appear in Chinese social networks are rarely explored by researchers. This study aims to analyze the characteristics of netizens' positive energy expressions and the impact of node events on public emotional expression during the COVID-19 pandemic. First, a total of 6,525,249 Chinese texts posted by Sina Weibo users were randomly selected through textual data cleaning and word segmentation for corpus construction. A fine-grained sentiment lexicon that contained POSITIVE ENERGY was built using Word2Vec technology; this lexicon was later used to conduct sentiment category analysis on original posts. Next, through manual labeling and multi-classification machine learning model construction, four mainstream machine learning algorithms were selected to train the emotional intensity model. Finally, the lexicon and optimized emotional intensity model were used to analyze the emotional expressions of Chinese netizens. The results show that POSITIVE ENERGY expression accounted for 40.97% during the COVID-19 pandemic. Over the course of time, POSITIVE ENERGY emotions were displayed at the highest levels and SURPRISES the lowest. The analysis results of the node events showed after the outbreak was confirmed officially, the expressions of POSITIVE ENERGY and FEAR increased simultaneously. After the initial victory in pandemic prevention and control, the expression of POSITIVE ENERGY and SAD reached a peak, while the increase of SAD was the most prominent. The fine-grained sentiment lexicon, which includes a POSITIVE ENERGY category, demonstrated reliable algorithm performance and can be used for sentiment classification of Chinese Internet context. We also found many POSITIVE ENERGY expressions in Chinese online social platforms which are proven to be significantly affected by nod events of different nature.

Changing Positive Academic Emotions of Art Students Utilizing Computer Information Technology Based on the Perspective of Teaching

This study is aimed at exploring the influencing factors on the altering academic mood of art students. With the assistance of computer information technology, the survey utilizing a questionnaire is conducted to explore the influence of different coping styles on the academic moods of art students when the influence of demographic variables is under consideration. It is concluded that the condition of being the only child of art students has a positive and high arousal emotional score of 80.93, which is significantly higher than that of not being an only-child art student of 78.61. Art students are more inclined to take a positive coping style. The scores of negative and high arousal academic emotions are found to be 79.3, 80, and 96.83, respectively, when the grade changes from 1 through 3. The general trend is that the scores of negative and high arousal academic emotions increase when grades go up. Art students experience more negative academic emotions than positive academic emotions when the general characteristics of art students' academic emotions are under consideration. Because females are more sensitive and delicate, they experience more negative academic emotions. Besides, while a positive coping style can positively predict art students' positive academic mood, a negative coping style can positively estimate the negative academic mood. It is concluded that the outcomes could provide a reference for the prediction of academic mood changes in art students.

English Education Tutoring Teaching System Based on MOOC

In the process of continuous reform and development of education and teaching, some traditional teaching modes are gradually eliminated, whereas new teaching modes are gradually recognized by teachers and students. These modes are widely used in education and teaching due to their overwhelming characteristics. Among these emerging teaching modes, massive open online course (MOOC) is a relatively advanced teaching mode with a better application effect. Quickly and accurately detecting the cheating behavior of MOOC learners is of great significance for maintaining the development of the MOOC platform and English education counseling. This paper studies a deep-learning-based hybrid model for MOOC cheating detection. The model greatly improves the detection performance of a single model by integrating CNN, a bidirectional gated recurrent unit, and an attention mechanism. The proposed model selects the English learning behavior data of a MOOC platform to verify the performance of the algorithm. Simulation results show that the proposed scheme can greatly help MOOC-based English education tutoring.

Unpacking the Emotional Experiences of Learners in a Blended Learning Context

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.

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

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.

Differences in Driving Intention Transitions Caused by Driver's Emotion Evolutions

Joining worldwide efforts to understand the relationship between driving emotion and behavior, the current study aimed at examining the influence of emotions on driving intention transition. In Study 1, taking a car-following scene as an example, we designed the driving experiments to obtain the driving data in drivers’ natural states, and a driving intention prediction model was constructed based on the HMM. Then, we analyzed the probability distribution and transition probability of driving intentions. In Study 2, we designed a series of emotion-induction experiments for eight typical driving emotions, and the drivers with induced emotion participated in the driving experiments similar to Study 1. Then, we obtained the driving data of the drivers in eight typical emotional states, and the driving intention prediction models adapted to the driver’s different emotional states were constructed based on the HMM severally. Finally, we analyzed the probabilistic differences of driving intention in divers’ natural states and different emotional states, and the findings showed the changing law of driving intention probability distribution and transfer probability caused by emotion evolution. The findings of this study can promote the development of driving behavior prediction technology and an active safety early warning system.