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Using Learning Theory for Assessing Effectiveness of Laboratory Education Delivered via a Web-Based Platform

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Smart Industry & Smart Education (REV 2018)

Abstract

Learning styles are defined as a characteristic feature that determines cognitive and psychosocial behavior of learners, perceiving of knowledge, interaction and processing of information in different learning environments. Applying learning style theories in pedagogic concept has brought multiple dimensions in categorizing learning strategies, although such studies on laboratory skill education are limited. Kolb’s Experiential Learning Model, has been widely accepted as an efficient pedagogical model of learning. In this paper, we explore the pedagogical basis for designing life sciences laboratory education and applying Kolb’s learning style inventory for classifying learners into different categories. In the context of bioscience laboratory education, most students learners were reflective observers or assimilators (60%) or divergers (20%) and hence seemed most apt for virtual laboratory based education due to acclivity to standard demonstrations and lectures. Only 20% were convergers or accommodators. Unlike in some engineering students, this classification suggests bioscience laboratory education may be complemented using web-based tools and will need better assessments and virtualization methods.

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Acknowledgement

This work derives direction and ideas from the Chancellor of Amrita Vishwa Vidyapeetham (University), Sri Mata Amritanandamayi Devi. The work is supported by the Sakshat project of National Mission on Education through ICT, Department of Higher Education, Ministry of Human Resource Department and Government of India, and by Embracing the World.

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Correspondence to Shyam Diwakar .

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Diwakar, S., Radhamani, R., Nizar, N., Kumar, D., Nair, B., Achuthan, K. (2019). Using Learning Theory for Assessing Effectiveness of Laboratory Education Delivered via a Web-Based Platform. In: Auer, M., Langmann, R. (eds) Smart Industry & Smart Education. REV 2018. Lecture Notes in Networks and Systems, vol 47. Springer, Cham. https://doi.org/10.1007/978-3-319-95678-7_70

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