Abstract:A huge collection of textual, graphical, audio, and video contents are readily available on the Internet to be used for the purpose of learning. Sentimental feedbacks of learners posted at the end of many of these contents may be considered as genuine reactions of the learners who have gone through the contents. Such learners' sentiments are important inputs for judging the acceptability of a learning material. Analyzing such feedbacks using sentiment analysis techniques can identify the best reusable learning contents that may be used for developing new courseware. This can significantly reduce the time and effort of authoring, which is otherwise a difficult, time-consuming, and costly affair.This methodology can also be used for continuous assessment on the learning materials released for use. This paper presents a machine learning-based approach for emotion analysis in e-learning materials. It describes the design and experimental use of a sentiment analysis classifier that uses classifier combination rules to combine polarity scoring and a support vector machine (SVM). The present approach also gives an opportunity for users to train a lexicon on a very specialized set of data, pertaining to the domain of usage. This helps either to enhance polarity scores of certain words that appear more frequently or to add words that are completely missing from the lexicon, and contributes a great deal in determining the polarity scores.

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Abstract-Around the world, many researchers and benchmarking institutions are working on the quality aspects of e-learning system. In a university consortium environment, there should be some sort of checkpoints for assessment of e-learning system because more than one member institution contributes to this e-learning environment.There is a need to assure university consortium members that learners are using an e-learning system which is highly rated. All consortium members contribute to the management, administration and learning content. For example, before accepting from a provider, the content is thoroughly checked by a panel of experts who specialize in content, instructional designing of sequencing and profiles.Similarly, for other areas, segregated forms of checkpoints and modules have been incorporated into the University Consortium Information System to incorporate the quality aspects. In this way, quality of e-learning system is improved. The present work discusses this process and, through investigations, suggests a framework to improve the quality aspects of the University Consortium Education System.

In an effort to find out the best suitable application of mobile learning, several research works are undertaken till date. A review of related papers unveiled that mobile devices act better as a supporting media in teaching and learning scenario. The present worker has developed context specific learning modules i.e. personalized learning contents using an ontology based web service architecture and an experimantal exploration has been done with the target audience to justify the usability of such content in a real time environment. To ensure that the developed contents are acceptable and usable, usability aspects are carefully embedded during the analysis, design and development of the contents. In this paper, the steps to fulfill the usability aspects of the prepared contents are described, an architecture of the dissemination system has been designed and results of the study are presented