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Title: Assessment as a pedagogy and measuring tool in promoting deep learning in institutions of higher learning.
Authors: Masuku, Mandla Mfundo.
Sabela, Primrose Thandeka.
School of Development Studies
School of Development Studies
Keywords: Assessments.;Deep learning.;Higher education.;Students.;Surface learning.;Pedagogy.
Issue Date: 2021
Publisher: Sciedu Press
Abstract: Traditionally, the key principle of assessment was based on the depth and intensity of the knowledge taught in class. In our modern state, the notion of assessment is more about learning and less about whether it is deep or surface learning. This could be attributed to challenges facing higher education, such as marketisation, massification, access and success. This article aims to demonstrate the significance of assessment as a pedagogical and measuring tool to promote deep learning in institutions of higher learning. It analyses how different types of assessment could contribute to deep learning while enhancing critical thinking and analytical skills. The article adopted the qualitative research approach to appraise critically and examine the literature on assessment in higher education. The sequence in which assessment tasks are presented, the pedagogical approaches adopted and measurement tools used should aim to present general non-threatening questions. The article recognises Bloom’s taxonomy as it classifies educational learning objectives in the manner that accommodates deep learning. This article suggests that assessment should be made explicit, aligned with learning outcomes that consider deep learning in terms of acquisition of knowledge, comprehension, application, analysis, synthesis and understanding of basic concepts in what is learnt. It concludes that students need to be engaged in their assessment to enable them to develop skills and dispositions that prepare them for the future as socially responsible citizens. Research needs to be conducted on the higher education challenges that compromise the quality of assessment as this could have negative effects on the development of deep learning.
Description: Please note that only UMP researchers are shown in the metadata. To access the co-authors, please view the full text.
DOI: 10.5430/ijhe.v10n2p274
Appears in Collections:Journal articles

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