A Framework for PO Attainment in HEI’s using Machine Learning in India

K. Gupta, Meenu Khurana, D. Prasad, A. Garg, Deepali Gupta
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Abstract

Outcome-based learning comprehends in the overall technical and professional growth of a student. This generates a need for clear liberation of the desired educational outcomes in the structuring of the curriculum. The unequivocal expounded outcome well represents the type of course offered and coverage of topics. It is imperative to construct courses that can align with the objectives of the Programme. This constructive alignment can facilitate the efficacious implementation of teaching and learning processes and assessment tasks. The outcome-based approach provides a mechanism to ensure the accountability and quality assurance to an educational Programme. Therefore, it establishes a need to map learning outcomes of each course with the Programme Objectives to analyze the attainment of objectives specific to a Programme. The result strongly indicates whether the students can achieve the course learning objectives. Therefore, this study liberates a detailed discussion on Outcome- Based Education, PO Attainment and proposes a framework for PO Attainment. Subsequently, the research work presents a case study of a HEI in India that has successfully implemented the concept of PO Attainment. The dataset of the HEI has been applied using Naïve Bayes and K* classifiers to verify the results.
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印度高等教育机构使用机器学习实现PO的框架
以结果为基础的学习包括学生的整体技术和专业成长。这就需要在课程结构中明确地解放期望的教育成果。明确阐述的结果很好地代表了所提供的课程类型和主题范围。必须开设符合《纲领》目标的课程。这种建设性的一致性可以促进教学过程和评估任务的有效实施。基于结果的方法提供了一种机制,以确保教育方案的问责制和质量保证。因此,需要将每门课程的学习成果与方案目标相结合,以分析方案具体目标的实现情况。该结果强烈地反映了学生是否能够达到课程的学习目标。因此,本研究对结果为本的教育与个人成就进行了详细的讨论,并提出了个人成就的框架。随后,研究工作提出了一个印度高等教育的案例研究,该案例成功地实施了PO成就的概念。HEI的数据集已经使用Naïve贝叶斯和K*分类器来验证结果。
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