The flipped learning approach with the use of social media as an emerging technology has changed the quality of learning in English as a Foreign Language (EFL) educational contexts. This review probed the effect of the web-based flipped learning approach on learners’ engagement and critical thinking. The earlier studies revealed the significance of social media in developing learner engagement and critical thinking. Studies indicated that the provision of opportunities for more cooperative and collaborative learning activities, and high-quality interaction through the use of social media can be influential in developing learners’ engagement. Moreover, social media platforms can provide a context for feedback, and various types of challenging tasks that can improve EFL learners’ critical thinking. However, this review implicated that social media in flipped learning approach may be beneficial for instructors, learners, teacher educators, curriculum designers, educational policy-makers, and advisors to be aware of this valuable learner-centered approach.

In order to improve the recognition effect of student weariness emotion in English classroom, this paper combines intelligent Internet of Things technology and big data technology to construct an improvement model of student weariness emotion in English classroom. In the process of student facial expression recognition, according to the given grayscale threshold, this paper extracts the surface contour information from the three-dimensional volume data, extracts the student’s surface contour information, and uses triangular facets to fit to form a triangular mesh. Moreover, this paper renders a triangular mesh model and shows how to speed up the calculation of PFH. In addition, this paper proposes a Fast Point Feature Histogram, which uses an iterative closest point fine registration algorithm for image registration. Finally, this paper constructs an emotion recognition model of students’ weariness in English classroom. From the test results, it can be seen that the student weariness emotion recognition system in English classroom proposed in this paper can effectively identify students’ weariness emotion.

Due to the wide scale of learners, large individual differences and scattered distribution, dialect teaching is difficult to carry out effectively by traditional school education. In order to improve the teaching level of dialect, taking Cantonese as an example, this paper constructs a teaching evaluation system based on multidimensional information. Through the questionnaire and investigation of large Cantonese training institutions in Guangdong Province, the data set is formed, the CMA-ES algorithm with efficient optimization ability is selected to optimize the SVM, and the model is compared with ACO and SVM without optimization algorithm. Experimental results show that the average accuracy of CMA-ES algorithm is 95.85% and the average running time is 21.0 ms on 8 data sets, which has obvious advantages relatively. Based on the evaluation model, the basis for teaching optimization is found through sensitivity analysis, and student’s language expression is the most important index. And with the help of the intelligent voice system, the improvement measures for Cantonese teaching are proposed from the aspects of scene, oral, and scoring.

Compared with the traditional knowledge graph-enhanced recommendation method, this paper introduces a multi-task learning module to alternately train knowledge graphs and recommendations to alleviate the data sparsity and cold start problems in traditional recommendation methods. Specifically, in the multi-task learning module, the item features and contextual content features are taken, and the features after feature interaction are obtained using the interactive attention network, as a way to learn finer-grained features, and then the gating mechanism processes the item features and entity features that fuse the contextual content, which can filter the unimportant features and obtain the important potential features, and can capture the implicit higher-order feature interaction more effectively. Optimized for multi-task learning tasks. The validity of our model was verified on three publicly available datasets.