Personalised e-learning system using learner profile ontology and sequential pattern mining-based recommendation

Abstract

Abstract:The ever-zooming issue of personalisation in the domain of the e-learning or the adaptive e-learning has emerged as a hot subject of intriguing debate among the inquisitive investigators in the last few years. In the learning contents module, all the critical learning materials in the shape of text, video or audio are collected and saved by means of the data management approach so as to effectively orchestrate the entire learning material. In the profile ontology module, the learner profile is saved as the ontology with a clear-cut framework and data. The Markov model is also utilised in this regard. In the instructor module, the online tutoring for each and every learner in accordance with the protocol devised by means of the captioned techniques is carried out. The execution is performed through the JAVA programming and protégé device and the performance is subjected to assessment.Keywords: e-learning; prefix-span algorithm; graphical user interface; dataset description.Reference to this paper should be made as follows: Muruganandam, S. and Srininvasan, N. (2017) 'Personalised e-learning system using learner profile ontology and sequential pattern mining-based recommendation', Int.

Analytics on talent search examination data

“…Education is a complex process with three main stakeholders that is students, teachers and policymakers having different expectations. The students are looking for a personalised learning environment (Muruganandam and Srininvasan, 2017), teachers are interested in support and resources for improvement in teaching practice and management/policymakers are interested in designing the policy for student retentions and graduation rate, etc. (Chatti et al, 2012;Rubel and Jones, 2016).…”

User Profiling in a SPOC: A method based on User Video Clickstream Analysis

“…In fact, since a long time, User profiles have played a significant role in recommender systems, retrieval information systems, Search Personalization. But nowadays; user profiling is becoming a widely used technique in many applications like Adaptive Websites, ebusiness applications [22], information seeking [21], web browsing, one-to-one marketing [25], e-commerce websites [26,27], web personalization systems [28], Adaptive MOOCs [3,4,6,8,9,29,30], E-learning systems [16,[31][32][33][34][35].…”

“…Let then define , the most common value (0 or 1) for the video interest for all cluster's members. The "mode" designs the value that occurs most often: (15) For the user , the video interest concerning the video Vi is evaluated by the following relation: (16) Therefore, a list of videos for that the interest value is equal to 1 can be proposed to the user .…”

“…In fact, Personalization techniques are a classic solution recommended by many experts for improving learning and adapting courses to the learning preferences and characteristics of the students can enhance the learning process, leading to an increased learner satisfaction [12,13,14,15]. Furthermore, in the last few years, recommendation techniques in the technology-enhanced learning (TEL) field, have invited increasing interest and are employed to enable learners in finding pertinent educational content to meet their profiles [16]. TEL recommendation systems have to be enlarged to various Web-based learning environments such as massive open online courses (MOOC) [17] User profile is one of the issues that it is addressed by personalization systems to recommend individual items such as products, services, documents, etc.…”

User Behavior Prediction Using Enhanced Pattern Tree Data Structure and Web Usage Mining

“…Each node corresponds to the occurrence of a specific page in a transaction. It is annotated with the number of users having reached the node across the same trail prefix[4][15].…”