A Smart Learning Assistant to Promote Learning Outcomes in a Programming Course

Xiaotong Jiao, X. Yu, Haowei Peng, Xue Zhang
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引用次数: 1

Abstract

Blended learning has gained wide popularity, but its superiority is limited by insufficient connection between online and offline learning due to technological anxiety and complexity, which hampers the achievement of prospective learning effect. To shatter these limits, a smart learning assistant based on Wechat Mini Program is proposed that incorporates a score ranking mechanism based on explainable machine learning to improve learning interests in programming, a learning material recommendation with deep neural networks to solve the student's confusion in personalized learning source selection, and a learning review mechanism based on deep learning achievements to enhance teacher-student communication and student-student cooperation in learning. In addition, approximately 3200 learners are involved to investigate learning requirements and test system performance. The experimental and practical results demonstrate the superiority of the smart learning assistant and the effectiveness gained by promoting learning outcomes in blended learning.
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提高编程课程学习效果的智能学习助手
混合学习得到了广泛的普及,但由于技术焦虑和复杂性,其优势受到限制,线上和线下学习之间的联系不够紧密,阻碍了预期学习效果的实现。为了打破这些限制,本文提出了一种基于微信小程序的智能学习助手,它结合了基于可解释机器学习的分数排名机制,以提高编程的学习兴趣,结合深度神经网络的学习材料推荐,以解决学生在个性化学习资源选择方面的困惑,建立基于深度学习成果的学习回顾机制,加强师生交流和学生合作学习。此外,约有3200名学习者参与调查学习需求和测试系统性能。实验和实践结果证明了智能学习助手的优越性,以及在混合学习中提高学习效果的有效性。
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