(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 12, No. 4, 2021 690 | Page www.ijacsa.thesai.org Learners Classification for Personalized Learning Experience in e-Learning Systems A. JOHN MARTIN 1 Research Scholar Department of Computer Science Sacred Heart College Tirupattur, India M. MARIA DOMINIC 2 Assistant Professor Department of Computer Science Sacred Heart College Tirupattur, India F. Sagayaraj Francis 3 Professor Department of Computer Science and Engineering, Puducherry Technological, Pondicherry, India Abstract—The investigators are inspired by the increasing need and the demand for educational applications and the Learning Management Systems which provide learning objects centered on the learning style of the learners. The technique in which the learners acquire, process, gain the information is unique; these unique characteristics affect their learning process. Hence it is essential to consider and understand the uniqueness among the learners to deliver learner-centric learning objects. The investigators present a system to classify the learners based on the time spent by the learner on learning content of different types. The types of learning content are identified with the percentage of visual, auditory, read/write and kinesthetic in learning object. The prominent learning style called VARK (Visual, Auditory, Read/Write and Kinesthetic) is used to classify the learners. This system classifies the learner and recommends the learning objects based on their learning preference, it also facilitates the faculty members or the content creators to prepare and provide personalized learning objects based on the learning style of the learners. Keywords—Learning style; learning profile; learning objects; e- Learning; personalization I. INTRODUCTION Today, the need for education is the need of the hour in all the sectors. Learning can be defined as a change in the behaviour as a result of experience. The process of learning involves reception and transformation of received information. During the reception process diverse senses are engaged in gathering information from external sources, whereas transformation activity results in internal activities like memorization, inception, inference, pondering and reflection [14]. The acquisition of knowledge and processing the gained knowledge is uneven among learners. There are relative parameters that identify the learning style of a learner. Hence there is a need to adapt strategies to meet the learner preferences to in delivering the learning object whether they are physical or virtual. The process of personalization happens through the investigation of the student’s preferences. It is possible to create a model that fulfils the need of the learner based on the information obtained through the investigation [1]-[3]. According to Bruner [3,15], the learner understands the knowledge through four sensory modes, they are Visual (screening pictures, symbols, chars and diagrams), Aural or Auditory (listening, discussing with peer), Read / Write (reading and writing), and Kinesthetic (use of Hands on exercise, case studies, Demonstrations.). The learning style of a learner is determined by the way in which information is received and processed. Predefined mathematical equations and a set of questionnaires are the traditional ways used to determine learning style of the learners. This may not be appropriate because students prefer more than one mode of learning style because the percentage of time spent on each types of learning object will also vary. To satisfy a given learning style, the teacher or the content creator must use the approach that could meet the needs of diverse learning perspective. Hence the proposed system focuses on the following key contributions. To recommend a novel but practical approaches to classify the learner based the time spent in each type of learning content to have personalized learning object or learner centred learning objects. a) To experiment the work through an exemplary case with the data available in Arts and Science College. The investigation is systematized as follows. Section II describes the State-of-Art of the existing system. Section III provides the proposed works that includes i. architectural design ii. The methodology to classify the learners based on the learner’s preference and the experimental result and evaluation of the system have been explained in Section III. Finally the effectiveness of the system and future action plan is discussed. II. STATE OF THE ART A. Learning Objects Any digital form or non-digital form of resource that are used to support learning activity is called Learning Objects. It is a collection of content items used by the learner in the technology assisted learning process. Instances of Learning Objects encompass multimedia content, reference to a web page, visuals, textual content, demonstration and software tools. The Learning objects will have the following characteristics size, duration, interoperability, reusability and multiple context of the content [13]. The significance of the learning object to the learner can be identified by the time spent on a particular learning content type.