Book ChapterDOI
Automatic Generation and Assessment of Student Assignments for Parallel Programming Learning
Zhenxiao Luo,Zelong Wang,Di Wu,Xiaojun Hei,Yunfei Du +4 more
- pp 184-194
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TLDR
An automatic assignment generation and assessment system to help students learn parallel programming that can automatically generate an overall assessment of student assignments by using fuzzy string matching, which provides an approximate reference score of objective questions.Abstract:
The course of parallel programming is becoming more and more important for the education of students majoring in computer science. However, it is not easy to learn parallel programming well due to its high theory and practice requirements. In this paper, we design and implement an automatic assignment generation and assessment system to help students learn parallel programming. The assignments can be generated according to user behaviors and thus able to guide students to learn parallel programming step by step. Besides, it can automatically generate an overall assessment of student assignments by using fuzzy string matching, which provides an approximate reference score of objective questions. Subjective questions can be assessed directly by comparing the answer to the reference answer. This system also provides a friendly user interface for students to complete online assignments and let teachers manage their question database. In our teaching practice, students can learn parallel programming more effectively with the help of such an assignment generation and assessment system.read more
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TL;DR: In this paper, a deep reinforcement learning-based adaptive bitrate (ABR) scheme called VBSSR is proposed to stream video chunks with complex scenes at a low bitrate level to reduce bandwidth consumption, and then boost the video quality by leveraging the technique of super-resolution at the client-side.
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Inferring Prerequisite Relationships Among Learning Resources for HPC Education
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Proceedings ArticleDOI
Student Programs Performance Scoring Based on Probabilistic Latent Semantic Analysis and Multi-granularity Feature Fusion for MOOC
TL;DR: In this paper , a multi-granularity feature fusion automatic scoring method based on potential semantic analysis was proposed to solve the problem of low accuracy of automatic scoring for programming questions on MOOC platform, and the experimental results show that the average accuracy of the method proposed in this paper outperforms the dynamic test method and the traditional static method using the test case results only, and automatic machine scoring results are highly consistent with the human score.
Proceedings ArticleDOI
Estimating Student Grades through Peer Assessment as a Crowdsourcing Calibration Problem
TL;DR: This paper proposed an algorithm under the same concept that could provide accurate automated grading, an overview of students' weaknesses from peer feedback, and identify reviewers who lack an understanding of certain concepts.
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