Extraction content recommender model for a personalised e-learning environment based on learner’s course assessment feeds

Abstract

E-learning, also known as online learning or electronic learning, is the acquisition of knowledge through digital technologies and media. In most cases, it refers to a course, program or degree delivered completely online. There are many terms used to describe learning that is delivered online, via the internet, ranging from Distance Education, to computerized electronic learning, online learning, internet learning and many others. Most authors point that consideration of the learner profile (personality, preferences, knowledge, etc.), is an essential and an important element in achieving an efficient and successful teaching. Therefore, it is extremely delicate and difficult to achieve a personalized learning scenario for each learner in the traditional closed classroom. Searching and retrieving information on E-learning environment is inconvenient, inefficient and sometimes time consuming as it relays irrelevant information that requires much time for student to scrutinize it and make meaning out of it, and in this case there is no previous work that has covered how learner’s assessment feeds like uploaded file’s content while answering E-learning environment set course works and quizzes can be incorporated in the extraction content recommender model to create a personalised E-learning environment. The main purpose of this work was to develop a an extraction content recommender model for e-learning personalisation based on learner’s course assessment feeds, which will allow students to obtain results according to their profiles and interests without taking too much time on the E-learning environment while searching relevant information. And the simulation was conducted on the extraction content recommender model with three datasets user profile ,learner's course assessment feeds and E-learning domain resources, we found out that using assessments feeds with different topic discussions in it recommends the value with highest rates and more recommendations are observed on it and when it comes to performance the learner's course assessment feeds with more words and one topic discussion takes too much time to recommend compared to learner's course assessment feeds with few words and more topics discussion. In the future work will be able to enable of our model to extract more assessment feeds from more than one section of the learner uploaded pdf assessment feeds and filter extracted information to get more key words of different topic’s discussions.