International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 07 Issue: 09 | Sep 2020 www.irjet.net p-ISSN: 2395-0072 © 2020, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 3160 Enhancing Learner Engagement through his Own Experiences which will be Catalysed by AI Teacher as well as Human Teacher Aishwarya S. Deore 1 , Kunal Shah 2 , Umesh Pawar 3 1 P.G. Student, Department of Computer Science and Engineering, SOCSE, Sandip University, Nashik, India 2 Visiting Faculty at CDAC-ACTS, Pune, India 3 Assistant Professor of Department of Computer Science and Engineering, SOCSE, Sandip University, Nashik, Inida ----------------------------------------------------------------------***--------------------------------------------------------------------- Abstract: In this Paper, E-learning has become an essential factor in the modern educational system. In today’s diverse student population, learning must recognize the difference in student personalities to make the learning process more personalized and to help overcome leaning model. The learner engagement increases the learner satisfaction and improves the learner performance in learning. Today’s recommender system is a relatively new area of research in machine learning. It is a main idea to build relationship between the users (learner characteristics), learning material and makes the decision to select the most appropriate learning material to specific learner. The objective of recommendation system in learning methodology is to present the learner with most required learning material so that he does not wastes his time in traverse from one material to another. This method done with the help of artificial intelligence algorithms like Collaborative Based Filtering, Content Based Filtering, Hybrid Content- Collaborative Based Filtering, k-mean clustering and also used to matrix factorization techniques. Index Terms - E-Learning, Recommender system, Data mining, learning style, Human teacher, learning objective, Machine learning, Matrix Factorization. I. Introduction Learner engagement is a measure that reflects the quality and quantity of a learner's participation in their learning process and every other aspect of their educational program. E-learning is indeed a revolutionary way to provide education in life long term, comparing with the traditional face-to-face style teaching and learning. Learner Engagement in learning is important to guarantee a good academic result. It leads to achievement by increasing the quality of Learner Engagement. That is, content understanding and skill capabilities are enhanced when learner are committed to building knowledge and employing deeper learning strategies. Recommendation Systems are software tools based on machine learning and information retrieval techniques that provide recommendations for potential useful items to someone's interest. Most of the modern e-Learning systems are still producing the same educational resources in the same way to learners with various profiles it is specifically on personalized motivations in the form of feedback, advices and reminders in learning content. A recommender system is a chunk of software that helps users to identify the most interesting and relevant learning items from a large number of items. Recommender systems may be based on collaborative filtering (by user ratings), content-based filtering (by keywords), and hybrid filtering (by both collaborative and content-based filtering). The scope of the learner engagement system is the integration of collection and analysis of data from a variety of sources facilitates retrieval and analysis of data to allow individuals to make informed decisions about allocating resources and enabling interventions that promote successful learning strategies. Although much research has been done on the recommendation system; but as far as the author's knowledge, most researchers focus on the accuracy of recommendation systems in predicting recommendations rather than knowledge acquired by students with the help of artificial intelligence algorithms. II. Related work Hanaa el fazazi, mohammed qbadou, intissar salhi, khalifa mansouri [1] In this paper focus on personalize an e- learning system according to the learner's requirements and knowledge level in a learning process. This system should adapt the learning experience according to the goals of the individual learner. Its present a recommender e- learning approach which utilizes recommendation techniques for educational data mining specifically for identifying e-learners' learning preferences. The proposed approach is based on three modules, a domain module which contains all the knowledge for a particular area, a learner module which uses to identify learners' learning preferences and activities and a recommendation module which pre-processes data to create a suitable recommendation list and predicting performances. Recommended resources are obtained by using level of knowledge of learners in different steps and the range of recommendation techniques based on content-based filtering and collaborative approaches. Several techniques such as classification, clustering and association rules are used to improve personalization with filtering techniques to provide a recommendation and assist learners to improve their performance. Then, the system presents the recommendation list according to the results of learner's evaluation and profile. In the same context and in order to develop the learning process our future work will be