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dc.contributor.authorBallera, Melvin
dc.contributor.authorAteya, Ismail Lukandu
dc.contributor.authorAbdalla, Radwan
dc.date.accessioned2015-06-08T12:10:04Z
dc.date.available2015-06-08T12:10:04Z
dc.date.issued2014
dc.identifier.urihttp://hdl.handle.net/11071/3966
dc.descriptionJournal article published in The International Journal of E-Learning and Educational Technologies in the Digital Media (IJEETDM)en_US
dc.description.abstractAn e-learning website is not sufficient to fully attain the results of online education. There also is a need to align the educational objectives into the design of the assessment to improve and develop cognition, critical thinking and problem-solving skills. Previous studies have explored the potentials of the assessment models but few ventured into their implementation. Others only proposed and introduced conceptual frameworks. The implementation of these proposals, however, revealed that the question type in the assessment phase neglected to align their questionnaire formats into a cognitive schema. At present, the standard multiple-choice question is the most frequently used of the question type of e-learning assessments. However, if this type is the only format adopted by e-learning developers, then the potentially rich and embedded assessment of the computer platform will be given up. This paper focuses on the design of assessment questions, which is created and guided by the hierarchical Bloom cognitive taxonomy and by utilizing rich media formats. Results conducted for eighteen weeks show a dramatic increase in the academic performance of the students. Likewise, digital transcripts converted from the collected perceptions after training undergoes sentiment analysis have correlated with the student improved academic throughput.en_US
dc.language.isoenen_US
dc.publisherThe Society of Digital Information and Wireless Communications (SDIWC)en_US
dc.subjectBloom cognitive taxonomyen_US
dc.subjecte-learningen_US
dc.subjectinteractive-cognitive assessmentsen_US
dc.subjectlearning throughputen_US
dc.subjectsentiment analysisen_US
dc.titleImproving learning throughput in e-learning using interactive-cognitive based assessmenten_US
dc.typeArticleen_US


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