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Following the COVID-19 outbreak, teaching and learning have been forced to move fully to the Internet rather than the conventional offline medium. As a result, the use of M-learning has risen dramatically, which was neither expected or anticipated. The challenges and benefits of such widespread usage are beginning to emerge in front of us. Thus, in this paper, we systematically review the benefits and challenges of leveraging M-learning for Science and Technology courses during the COVID-19 pandemic by educators and learners. Related articles were obtained from various databases, namely, IEEE, ACM Digital Library, ScienceDirect, and Springer. In total, 4210 related articles were initially found. Upon executing careful selection criteria, 22 articles were selected for review. After that, the advantages and threats were identified and discussed. As per our findings, it was determined that M-learning has excellent potential to be an effective platform for education provided that the identified shortcomings are resolved. This review will be helpful for education stakeholders and institutions to gauge the impact of leveraging M-learning as the only means for education to proceed. Moreover, it reveals the strengths and shortcomings that would aid in adjusting the relevant policies administered by the institutions. Furthermore, application developers will be able to comprehend the expected features that should be included in novel M-learning platforms.

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Text classification which is an integral part of text mining has caught much attention in various industries and fields recently. The ability is in assigning text documents to one or more pre-defined categories based on content similarity. While most of application of text classification focuses on document level, question classification works at much granular level such as sentence and phrase. There have been numerous studies on question classification in accordance to Bloom taxonomy in assessments to measure cognitive level of learners in higher learning institutions. But it has not been effective yet to resolve overlapping issue of Bloom taxonomy verb keywords being assigned to more than one category of Bloom taxonomy. The presence of this poses a problem in respect of classifying a particular question into a right category of Bloom taxonomy. And feature extraction plays an important role in improving the accuracy of classifier such as Support Vector Machine in question classification. Much of the past related research work focus on feature extraction methods such as bag of word (BOW) and syntactic analysis to classify questions and to address the issue, an improvement in feature extraction is needed. In view of this, this study proposes an integrated approach in feature extraction involving semantic aspect in classifying questions in accordance to Bloom taxonomy. Support Vector Machine classifier is used as it is well known for its high accuracy in text classification. With all this in place, an improved accuracy in classifying questions in accordance to Bloom taxonomy can be expected.

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