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Emerging technologies are leading to the development of several new opportunities to guide and enhance learning that were unimaginable a few years ago. The move towards adopting mobile learning technologies is fast growing in both academic and industrial sectors. Mobile learning uses portable devices linked to a commercial public network, including different types of mobile phones and handheld computers. For mobile users, as well in all mobile applications, SMS messaging is found to be the most useful and convenient way of communication technology. In case of mobile learning, there are only limited forms for conducting tests using true/false method, multiple choice selection method etc. Answering short questions is a better way of testing the students to get more details about a particular entity. The practice of messaging can be thought of highly useful for answering such short-answer questions. The case where answers are to be given as short messages to the assessors, evaluating them may not be that much easier when compared to other simple types of tests. This paper focuses on using SMS for answering 'short words-answers' types of questions and evaluating them using simple matching process, providing enough feedback

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Learners with reading difficulties normally face significant challenges in understanding the text-based learning materials. In this regard, there is a need for an assistive summary to help such learners to approach the learning documents with minimal difficulty. An important issue in extractive summarization is to extract cohesive summary from the text. Existing summarization approaches focus mostly on informative sentences rather than cohesive sentences. We considered several existing features, including sentence location, cardinality, title similarity, and keywords to extract important sentences. Moreover, learner-dependent readability-related features such as average sentence length, percentage of trigger words, percentage of polysyllabic words, and percentage of noun entity occurrences are considered for the summarization purpose. The objective of this work is to extract the optimal combination of sentences that increase readability through sentence cohesion using genetic algorithm. The results show that the summary extraction using our proposed approach performs better in-measure, readability, and cohesion than the baseline approach (lead) and the corpus-based approach. The task-based evaluation shows the effect of summary assistive reading in enhancing readability on reading difficulties.