Design and Effectiveness Evaluation of a Smart Greenhouse Virtual Reality Curriculum Based on STEAM Education

Abstract

This study developed a smart greenhouse virtual reality (VR) curriculum based on STEAM learning and explored its effects on students’ satisfaction and learning outcomes. The objectives included evaluating STEAM capability indicators, the practicability of VR-assisted teaching, constructing the VR curriculum, discussing students’ satisfaction, and assessing the impact on learning effectiveness. The fuzzy Delphi method was used to evaluate the importance of STEAM capabilities and the practicability of VR-assisted teaching. Experimental teaching was carried out on 26 engineering students, and the case study method was adopted for hybrid analysis and discussion based on quality and quantity. The study found that “hands-on skills” and “problem-solving” were the most important capabilities, with the highest practicability in VR-assisted teaching. Based on this, an analysis was conducted on the integrated teaching design, and the smart greenhouse VR teaching materials based on STEAM learning were developed. After 18 weeks of experimental teaching, most students expressed significant positive affirmation of their satisfaction with the “STEAM smart greenhouse VR” curriculum. The study highlights the importance of hands-on skills and problem-solving in VR-assisted teaching. The study suggests that the practicability analysis of VR-assisted teaching should be reviewed according to the curriculum characteristics, and three phases of VR-assisted teaching modes, such as teacher operation, student exercises, and student testing, should be planned to guide students to learn step by step. The curriculum design and planning based on STEAM learning in this study could provide a reference for teachers and researchers to plan students’ STEAM capability training and interdisciplinary capability learning and development. The study highlights the importance of hands-on skills, problem-solving in VR-assisted teaching, and the positive impact of multi-sensory experiences on student learning outcomes. These findings can inform the development of future VR-assisted teaching materials and curricula.

Intelligent Evaluation and Optimization of Postgraduate Education Comprehensive Ability Training under the Mode of “One Case, Three Systems”

“…The results of Table III indicate that the performance of the proposed weighted clustering algorithm for mixed attributes is the best, with a clustering accuracy of 90%, an average number of iterations of 276, and a contour coefficient of 0.2. In contrast, although the STEAM Education+Smart Greenhouse VR [20] algorithm performs relatively stable in clustering accuracy, its overall performance is poor, with a clustering accuracy of 80%, an average number of iterations of 300, and a contour coefficient of 0.15. The Analytic Hierarchy Process+Delphi method [21] achieves good clustering accuracy, reaching 85%, but slightly inferior to the proposed algorithm in other indicators.…”

Development of an Educational Kit Using CAD Software for Simple Machine Learning

“…They can experiment with various component arrangements, investigate how modifications impact mechanical operations, and track cause-and-effect connections. Students can gain a stronger knowledge of the fundamental ideas behind basic machines thanks to this hands-on approach, which encourages experiential learning [17,18]. This adaptability facilitates diversified learning experiences and takes into account the various learning styles and capacities of students, as proposed by Lopez [19,20].…”