Design and Effect Evaluation of College English Software Teaching Platform Based on IoT Integration

The teaching and learning process has changed significantly as a result of the incorporation of Internet of Things (IoT) technology in education, particularly in the area of language learning. Technology innovations improve learning experiences in college English classes, which are essential for career and academic achievement. Traditional teaching approaches, however, frequently find it difficult to successfully engage learners in large classes. The goal of this research is to design and assess IoT-integrated college English software teaching platforms. By utilizing IoT-enabled devices, the platform gathers real-time data on students’ interactions with the software and learning patterns. These data are analyzed using min–max normalization for pre-processing and Fast Fourier Transform (FFT) to extract frequency-based characteristics from time-domain data. Electric Fish Optimized Deep Belief Network (EFO-DBN) was used to forecast student performance based on the interaction. The DBN algorithms enhance teaching methods and customize the learning process. In particular, EFO techniques are used to optimize the platform’s functionality, guaranteeing a more efficient and flexible learning environment. It measures learning outcomes before and after utilizing the platform in a range of English proficiency levels using a comparative analysis. Performance indicators like accuracy, student engagement, and language competence growth are employed to evaluate the platform’s effectiveness. The performance metrics are accuracy ratio (96.5%), reliability ratio (95.6%), efficiency ratio (97.3%), probability ratio (96.2%), accuracy (98%), specificity (98.43%), sensitivity (96.3%), precision (97.1%), average login time (0.2[Formula: see text]s), evaluation time (0.3[Formula: see text]s), and communication delay (0.44[Formula: see text]s). The findings show that the IoT-based platform greatly improves learning outcomes, including student satisfaction and language proficiency. Ultimately, the incorporation of IoT and Deep Learning (DL) into English learning platforms presents encouraging opportunities for individualized, data-driven learning languages.