Broader terms curriculum mapping: Using natural language processing and visual-supported communication to create representative program planning experiences
원문 초록
Accreditation bodies call for curriculum development processes open to all stakeholders, reflecting viewpoints of students, industry, university faculty and society. However, communication difficulties between faculty and non-faculty groups leave unexplored an immense collaboration potential. Using classification of learning objectives, natural language processing, and data visualization, this paper presents a method to deliver program plan representations that are universal, self-explanatory, and empowering. A simple example shows how the method contributes to representative program planning experiences and a case study is used to confirm the method's accuracy and utility.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Capturing Visualization Design Rationale
VIS-Shepherd: Constructing Critic for LLM-based Data Visualization Generation
Mapping correlations and coherence: adjacency-based approach to data visualization and regularity discovery
OLAF: An Open Life Science Analysis Framework for Conversational Bioinformatics Powered by Large Language Models
Challenging the Boundaries of Reasoning: An Olympiad-Level Math Benchmark for Large Language Models
nvAgent: Automated Data Visualization from Natural Language via Collaborative Agent Workflow
토론
이 논문과 연결된 포럼 글이 아직 없습니다.