Collection of Foreign Language Learners’ Behavioral Data on Online Platforms and Construction of Teaching Feedback Mechanisms

The integration of Internet technology and foreign language teaching has led to the emergence of online foreign language learning, and a large amount of foreign language learner behavioral data generated in this regard urgently needs to be collected, processed and analyzed. This paper proposes a data collection scheme for foreign language learners’ behavioral data on the network platform, completes the preprocessing of foreign language learners’ behavioral data by means of data cleansing and data conversion, and proposes the lag sequence analysis method to study the sequence of learners’ learning behaviors. Data analysis of foreign language learners’ behavioral data on the “China University MOOC” online platform. The four categories of course learning, task completion, interpersonal interaction and academic performance are all significantly correlated (Sig<0.01), and clusters of learners with high immersion, low immersion and high immersion are obtained. Behavioral sequences of browsing the page content and viewing the test questions (R1Q1), browsing the questions and then doing the questions (Q1Q2), doing the questions and saving this attempt and viewing the progress of answering the questions (Q2Q3), doing the questions and saving this attempt and viewing the progress of answering the questions (Q2Q3), reviewing the test questions after submitting them and viewing the answers (Q4Q5), and viewing the answers and then also tending to browse the other questions or the questions of the test they had already done (Q5Q1) Z-scores were greater than +1.96, and the frequency of occurrence reached the significance level (p<0.05). Combined with the results of behavioral data analysis of foreign language learners, the construction strategy of the corresponding teaching feedback mechanism is proposed.