A Literature Review on Automatic Generation of Examinations

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dc.contributor.authorNdirangu, Peter Ndegwa
dc.contributor.authorMuuro, Elizaphan Maina
dc.date.accessioned2023-05-22T12:46:25Z
dc.date.available2023-05-22T12:46:25Z
dc.date.issued2021
dc.descriptionArticleen_US
dc.description.abstractThe examination is a key activity in determining what the learner has gained from the study. Institutions of higher learning (IHL) perform this activity through various assessment methods (test/examination, practical, etc.). The world today is focused on automation of exam generation which is ongoing with dire need during this period of the COVID-19 pandemic when education is greatly affected, leading to embracing online learning and examination. A text/exam comprises questions and answers that focus on evaluation to determine the student’s conversant level in the area of study. Each question has a cognitive level as described by (Armstrong, 2016) in the revised Bloom’s taxonomy. Questions chosen have cognitive levels based on the level of study and standardization of the exam. There is, therefore, a need to consider the question’s cognitive level along with other factors when generating an examination by incorporating deep learning algorithms.en_US
dc.identifier.citationNdirangu, P. N., Muuro, E. M., & Kihoro, J. M. A Literature Review on Automatic Generation of Examinations. Open Journal for, 77.en_US
dc.identifier.urihttps://doi.org/10.32591/coas.ojit.0402.04077n
dc.identifier.urihttp://ir-library.ku.ac.ke/handle/123456789/25383
dc.language.isoenen_US
dc.publisherCOASen_US
dc.subjectnatural language processingen_US
dc.subjectMLA – machine learning algorithmen_US
dc.subjectAI – artificial intelligenceen_US
dc.titleA Literature Review on Automatic Generation of Examinationsen_US
dc.typeArticleen_US
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