The Linux laboratory course is a fundamental core course for computer science and information related majors, characterized by strong practicality and complex operational chains. Traditional evaluation systems mainly rely on students' submitted lab reports, which are result-oriented, lacking process analysis and contextual awareness, making it difficult to comprehensively and dynamically reflect students’ actual abilities and learning status. To this end, this paper proposes an evaluation system for Linux laboratory courses that integrates knowledge graphs with multimodal perception. It integrates key technologies such as code behavior analysis, speech and facial emotion recognition, system operation trajectory extraction, and knowledge graph reasoning. The system takes multimodal inputs—including student behavior logs, oral defense speech, facial expressions, operation flows, and error types—and applies focal contrastive learning and a Neo4j-based knowledge graph model to achieve comprehensive scoring and personalized feedback. Experimental results demonstrate that the proposed system significantly improves evaluation accuracy, consistency, and the stimulation of students’ learning motivation, providing a new paradigm and technological support for teaching reform in Linux and other system-oriented laboratory courses.
Key words
Linux laboratory course /
knowledge graph /
multimodal perception /
focal contrastive learning
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