形成性评价
学习分析
个性化学习
分析
计算机科学
数据科学
知识管理
人工智能
教学方法
心理学
开放式学习
数学教育
合作学习
作者
Tarun Kumar Vashishth,Vikas Sharma,Kewal Krishan Sharma,Bhupendra Kumar,Rajneesh Panwar,Sachin Chaudhary
出处
期刊:Advances in media, entertainment and the arts (AMEA) book series
日期:2024-01-10
卷期号:: 206-230
被引量:51
标识
DOI:10.4018/979-8-3693-0639-0.ch009
摘要
Advancements in artificial intelligence (AI) and learning analytics have opened up new possibilities for personalized education in higher education institutions. This chapter explores the potential of AI-driven learning analytics in higher education, focusing on its application in personalized feedback and assessment. By leveraging AI algorithms and data analytics, personalized feedback can be provided to students, targeting their specific strengths and areas for improvement. Adaptive and formative assessments can also be facilitated through AI-driven learning analytics, enabling personalized and accurate evaluation of students' knowledge and skills. However, ethical considerations, implementation challenges, and faculty training are crucial aspects that must be addressed for successful adoption. As technology continues to evolve, embracing AI-driven learning analytics can enhance student engagement, support individualized learning, and optimize educational outcomes.
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