MUMBAI, India, Sept. 28 -- Intellectual Property India has published a patent application (202621095958 A) filed by Mr. Zameer Salim Sayyed; Srihari Babu Gole; Dr. Santosh Kumar Nayak; Dr. Abhibunnisha Begum; Poornima A; R. Sathishkumar; Yashwanth Sai Kumar V; Mohamed Rabik A; Dr. Shameema T; Dr. S. Kalpana; N. Ageela; and Asya Shahanaz K on August 07, 2026, for Learning Analytics And Machine Learning For Enhancing English Language Pedagogy In Higher Education.
Inventors include Mr. Zameer Salim Sayyed; Srihari Babu Gole; Dr. Santosh Kumar Nayak; Dr. Abhibunnisha Begum; Poornima A; R. Sathishkumar; Yashwanth Sai Kumar V; Mohamed Rabik A; Dr. Shameema T; Dr. S. Kalpana; N. Ageela; and Asya Shahanaz K.
The application for the patent was published on September 25, 2026, under issue no. 39/2026.
Abstract: In order to improve English language instruction in higher education institutions, the current invention relates to an intelligent educational technology system that combines Learning Analytics (LA), Machine Learning (ML), and Natural Language Processing (NLP). Learning Management Systems (LMS), online tests, classroom interactions, attendance records, assignments, quizzes, discussion boards, reading activities, writing tasks, listening exercises, and speaking assessments are just a few of the sources from which the system gathers educational data. To determine student behaviour, language competency, engagement patterns, and academic success, the gathered data is preprocessed and examined using machine learning algorithms and learning analytics approaches. Predictive models are used by the invention to detect at-risk kids, classify learners, predict academic performance, and suggest timely educational interventions. Grammar, vocabulary, writing quality, pronunciation, reading comprehension, listening abilities, and speaking fluency are all automatically assessed by an integrated NLP module, which also provides individualised and impartial feedback. The system creates adaptive learning paths by suggesting personalised learning materials, activities, and tests based on student profiles and predicted insights. Instructors can access real-time analytics, student progress reports, early warning alarms, and evidence-based decision assistance through interactive dashboards. While a continuous learning module periodically retrains the machine learning models using newly generated educational data to improve accuracy and adaptability, an Explainable Artificial Intelligence (XAI) module increases transparency by offering comprehensible explanations for predictions and recommendations. Through intelligent automation and data-driven educational support, the invention dramatically improves student engagement, language competence, teaching efficacy, individualised instruction, and academic success while lowering teacher effort.
Disclaimer: Curated by HT Syndication.
