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Interactive teaching is very popular in the field of education, especially in college preschool education teaching. In this study, the experimental performance of the evaluation system based on scientific emotion construction is analyzed by the machine learning algorithm. The experimental results show that the classification model realized by the machine learning algorithm is feasible and effective for the construction of an interactive design teaching evaluation system of preschool education in colleges and universities. The main research content of this experiment is for the teaching evaluation text, this paper intends to use NB, KNN, LR, and SVM algorithms as the meta-learning algorithm in the framework, the classification effect of the meta-classifier is ranked as SVM > LR > KNN > NB. From the above data comparison, the SVM meta-classifier in the algorithm in this paper has achieved the best classification effect. The SVM meta-classifier based on the algorithm in this paper performs well, and the classification accuracy rate reaches 92.1%. This shows that this model is very suitable for studying the. And the classification accuracy rate reaches 92.1%. This shows that this model is very suitable for studying the construction of interactive teaching evaluation systems under emotional education. • The experimental results show that the classification model realized by machine learning algorithm. • This shows that this model is very suitable for studying the construction of interactive teaching. • The main research content of this experiment is for the teaching evaluation text. • This paper intends to use NB, KNN, LR and SVM algorithms as the meta-learning algorithm. [ABSTRACT FROM AUTHOR]

Background: Blended online courses, which combine synchronous and asynchronous online activities, have expanded rapidly in higher education. How to enhance student engagement in such courses is unclear, although it is recognized that student engagement is malleable through instructional strategies. Objectives: Given the above, this study aims to examine the influence of categories of strategies on student engagement in blended online courses. Methods: A conceptual framework of instructional strategies indicated as fostering student engagement in the relevant literature was first presented, divided in eight categories (structure, pace, relevance, active, choice, relationships, explanations, guide). Then a research framework linking the categories of strategies to student engagement dimensions (emotional‐cognitive, social, behavioral) was built and tested in blended online courses. Data collected in various disciplines and university levels at four universities (n = 482) were examined using partial least squares structural equation modeling. Results and Conclusions: The structural model examination confirmed the combined effects of categories of instructional strategies on student engagement in such courses in all disciplines. Particularly, this study revealed that 1) establishing trusting relationships, 2) demonstrating the relevance of activities, content, and resources, and 3) maintaining a sustained course pace significantly impacted student engagement in blended online courses in all disciplines. Takeaways: This study draws upon the blended learning literature to bring together key instructional strategies that foster student engagement while highlighting empirical quantitative evidence of their effects on student engagement in blended online courses. Detailed measures of categories of instructional strategies and student engagement dimensions also provide reliable instruments for future research. Lay Description: What is currently known about the subject matter?: Student engagement has important repercussions on perseverance, in‐depth learning, satisfaction, and academic success.Blended online courses merge the benefits of synchronous interactions and online flexibility in terms of time, place, or even study pace.Student engagement is influenced by contextual variations like course modalities and instructional strategies.The literature lacks a quantitative large‐scale portrait of how instructional strategies influence student engagement in blended online courses in higher education. What does this paper add to the existing body of research?: Partial least squares structural equation modelling was used to examine the effects of eight categories of instructional strategies on emotional‐cognitive, social, and behavioural dimensions of student engagement.Emotional‐cognitive engagement is largely affected by instructional strategies, while social and behavioural engagement are moderately affected by these.Establishing trusting relationships; demonstrating the relevance of activities, content, and resources; and maintaining a sustained course pace have the most significant effects on student engagement in blended online courses. Implications of study findings for practitioners: Teachers should explicitly support students in the process of developing interpersonal relationships in blended online courses to foster student social engagement, that is, interactions and sense of connectedness between them.Ensuring a sustained pace, dynamism, and a diversity of teaching and learning activities, both synchronously and asynchronously, promotes student engagement in blended online courses.Highlighting or demonstrating the relevance of activities and contents fosters emotional‐cognitive and behavioural student engagement. [ABSTRACT FROM AUTHOR]

The main semantic symbol systems for people to express their emotions include natural language and music. The analysis and establishment of semantic association between language and music is helpful to provide more accurate retrieval and recommendation services for text and music. Existing researches mainly focus on the surface symbolic features and association of natural language and music, which limits the performance and interpretability of applications based on semantic association of natural language and music. Emotion is the main meaning of music expression, and the semantic range of text expression includes emotion. In this paper, the semantic features of music are extracted from audio features, and the semantic matching model of audio emotion analysis is constructed to analyze ethnic music audio emotion through feature extraction ability of deep structure. The model is based on the framework of emotional semantic matching technology and realizes the emotional semantic matching of music fragments and words through semantic emotional recognition algorithm. Multiple experiments show that when W = 0.65 , the recognition rate of multichannel fusion model is 88.42%, and the model can reasonably realize audio emotion analysis. When the spatial dimension of music data changes, the classification accuracy reaches the highest when the spatial dimension is 25. Analysing the semantic association of audio promotes the application of folk music in occupational therapy. [ABSTRACT FROM AUTHOR]

The ability to exploit students' sentiments using different machine learning techniques is considered an important strategy for planning and manoeuvring in a collaborative educational environment. The advancement of machine learning technology is energised by the healthy growth of big data technologies. This helps the applications based on Sentiment Mining (SM) using big data to become a common platform for data mining activities. However, very little has been studied on the sentiment application using a huge amount of available educational data. Therefore, this paper has made an attempt to mine the academic data using different efficient machine learning algorithms. The contribution of this paper is two-fold: (i) studying the sentiment polarity (positive, negative and neutral) from students' data using machine learning techniques, and (ii) modelling and predicting students' emotions (Amused, Anxiety, Bored, Confused, Enthused, Excited, Frustrated, etc.) using the big data frameworks. The developed SM techniques using big data frameworks can be scaled and made adaptable for source variation, velocity and veracity to maximise value mining for the benefit of students, faculties and other stakeholders. [ABSTRACT FROM AUTHOR]

Business houses and marketers have been relying on social media to affect consumer opinions and purchasing behavior. In this paper a framework has been proposed to identify and quantify the emotive value of any movie trailer. The proposed framework made use of Dlib-ml (a machine learning toolkit) and a Genetic Algorithm inspired Support Vector Machine algorithm (GA i SVM) for parameter tuning and classification and emotive analysis of movie trailers. A case study comprising of 141 movies trailers released from Jan 1, 2017 till April 31, 2018 was done to investigate the relationship between emotive score of a movie trailer and financial returns associated with it. Results revealed a direct correlation between emotive score of a movie trailer and financial returns. Further, it was concluded that an emotionally intense movie trailer could result high financial returns in comparison to non-much emotionally intense trailers. • Release of movie trailer has a positive effect on the stock value of the movie. • Higher the emotional appeal of trailer, higher is the impact on stock value. • SVM performs better if its parameter tuning is done. [ABSTRACT FROM AUTHOR]