Abstract:

In the rapidly evolving knowledge and industry landscape of the 21st century, the boundaries between different professional fields are increasingly blurred. This trend underscores the importance of integrating cross-disciplinary expertise and nurturing highly innovative talents. Consequently, cross-disciplinary capabilities have become essential skills for future global talent.

Cross-disciplinary learning aims to cultivate learners' abilities to integrate diverse knowledge and thinking patterns. Through multidimensional thinking and cross-border understanding, learners can describe phenomena and define problems with enhanced self-awareness and cognitive abilities. This approach complements reflective inquiry into the nature of knowledge, fostering the accumulation of relevant insights and the generation of new ideas.

Computational thinking is a vital problem-solving mindset, and programming serves as a concrete way to practice this form of thinking. Due to the sequential nature of programming learning, understanding each unit is crucial for grasping subsequent content, preventing learning gaps. Therefore, addressing learning disparities among students is a significant research focus in cross-disciplinary innovative teaching.

This study explores how to gradually construct cross-disciplinary thematic knowledge through inquiry-based learning, starting from "reflection on cross-disciplinary learning" and "reducing disparities among students in programming learning." Throughout the process, students continuously reflect on and explore the nature of knowledge, building relevant insights and generating new understandings. Peer learning is employed to ensure that knowledge gaps among group members are minimized, enabling all students to participate and complete learning tasks, thereby deepening their learning.

The teaching experiment uses a single-group pre-test and post-test design with the following tools:
(1) Computational Thinking Scale - evaluates students' abilities in problem decomposition, abstraction, algorithms, evaluation, and generalization;
(2) Learning Effectiveness - measures improvements in students' self-directed learning, communication, collaborative cooperation, problem-solving, critical thinking, and creativity and innovation abilities after the intervention;
(3) Cognitive Load Questionnaire - assesses students' intrinsic and extrinsic cognitive loads to determine if course materials, design content, and activities exceed their cognitive capacity.

This will provide valuable insights for future planning of cross-disciplinary capability teaching and programming courses.

Keywords:

Cross-disciplinary learning, Computational thinking, Programming, Peer learning.