Examining the impact of flipped learning on second language development: A quasi-experiment in higher education.

This article reports on a study that examined the impact of a flipped English as a Foreign Language (EFL) course on college students' second language (L2) development. Specifically, an 18-week quasi-experiment was administered in a general English course at a Chinese university, with a total of 612 first-year students randomly assigned to the treatment (n = 137) and control (n = 475) groups. Using the propensity score matching and difference-in-differences (PSM-DID) model, we analyzed norm scores on high-stakes assessments administered on the entry and completion of the intervention. The results revealed a causal link between flipped learning (FL) and improved L2 language performance though the impact of FL had substantial heterogeneity as greater gains were found in reading than in writing and listening. Quantile regression analysis suggested the effectiveness of FL varied greatly by proficiency level in that students in the lowest quantiles achieved high improvement in reading and listening but moderate improvement in writing. We discuss the pedagogical implications of these findings to college L2 flipped instruction and recommend that future research be conducted in a more rigorous experimental design to obtain robust and accurate estimates of the effectiveness of FL.

Online Intelligent Course Education Based on Grid Model Simplification.

In order to explore the teaching efficiency of online intelligent courses and improve the quality of online teaching, this paper builds a classroom intelligent auxiliary management model based on the grid simplification method. Moreover, this paper formulates corresponding teaching strategies through the recognition of student state features, uses a target detector to detect all detection targets from the scene, and then counts the number of detection targets, identifies specific individuals, and judges the individual state. Simultaneously, this paper intercepts candidate subregions from the scene image and then inputs the subregion image to the detector to determine whether the candidate region image is a detection target and formulate corresponding countermeasures. In addition, on the basis of the existing 3D mesh model stitching and editing method, this paper proposes a grid splicing and fusion method based on the idea of partial reuse of the model to calculate the result of the target. Finally, this paper designs experiments to verify the performance of the model. The research results show that the model constructed in this paper is effective.