Embracing the Data-Driven Paradigm: A Comprehensive Framework for Effective Teaching and Learning in Higher Education

P. Sepeng, M. M. Moleko
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Abstract

The present study investigated the pragmatic implementation and efficacy of data-driven instructional approaches. The complete framework is underpinned by stated objectives, a robust data infrastructure, adaptive practises, and ongoing collaboration. The present study adopted a mixed-methods research design, incorporating qualitative interviews with both academics and students, alongside quantitative evaluations of outcomes from Learning Management Systems (LMS). The objective of this study was to examine the complex correlation between instructional practises and data analytics. This study provided evidence of the potential of data-driven methodologies to significantly enhance customised learning and inform timely pedagogical decisions. Nevertheless, the efficacy of these interventions is contingent upon comprehensive training, endorsement from relevant parties, and ongoing enhancement. This study contributes to the current scholarly discourse on the correlation between data analytics and teaching. The argument posits the importance of a strategic integration that considers both technological and human-centric considerations. Keywords: Data-driven Pedagogy, Data Literacy, Learning Management Systems
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拥抱数据驱动范式:高等教育有效教学的综合框架
本研究调查了数据驱动教学方法的实际实施情况和效果。完整的框架以明确的目标、强大的数据基础设施、适应性实践和持续合作为基础。本研究采用了混合方法研究设计,包括对学者和学生的定性访谈,以及对学习管理系统(LMS)成果的定量评估。本研究的目的是探讨教学实践与数据分析之间的复杂关联。这项研究提供了证据,证明数据驱动方法有可能显著提高定制化学习的效果,并为及时的教学决策提供信息。然而,这些干预措施的有效性取决于全面的培训、相关方的认可和持续的改进。本研究为当前关于数据分析与教学之间相关性的学术讨论做出了贡献。该论点认为,考虑到技术和以人为本的因素,进行战略整合非常重要:数据驱动教学法、数据素养、学习管理系统
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