Studying the Diagnostic Effectiveness of English Education Based on Rule Mining and Question Type Association Analysis

The greatest difficulty in implementing diagnostic assessment lies in the tracking, recording, real-time feedback, and correction of English learners’ learning process, and these difficulties are difficult to be solved when human beings (teachers or students) are the assessment subjects. To this end, this paper uses data mining to build a question type association model, machine learning to build an English education prediction model, and finally adds the existing knowledge point association model to the system to obtain a diagnostic evaluation model. Based on the diagnostic evaluation model, a question association rule-based algorithm is designed and implemented and validated in three aspects: grouping time, test recommendation, and performance improvement. Based on the requirements analysis, the authors designed and implemented a diagnostic assessment subsystem using the diagnostic assessment model and the related paper grouping algorithm and added it to the university English diagnostic practice system. The diagnostic assessment model for English education proposed in this paper can accurately evaluate the learning status of English learners, dynamically diagnose the learning obstacles of English learners, and effectively provide better practice guidance and test recommendations for English learners’ learning status and knowledge-based question type obstacles.