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TopIn the current research on ecological aesthetic education methods in colleges and universities, many scholars both domestically and internationally have explored different directions and made many achievements (Adjei et al., 2021). For instance, Ni (2021) has categorized methods of ecological aesthetic education based on student types, conducting multiple combinations according to individual characteristics. Chen (2022) found significant errors in the teaching design of ecological aesthetic education in foreign colleges and universities, hindering its large-scale application due to internal inconsistencies. Thus, they proposed an interactive teaching classification system based on short-term memory characteristics.
Yuan et al. (2021) found substantial differences in aesthetic appreciation among various types of ecological works. They proposed a teaching platform based on color vision error delay, with adaptive improvements reflecting ecological aesthetic characteristics. However, they encountered drawbacks in scalability. Lin and Chang (2022) underscored the importance of developing a data matrix teaching system based on multiple aesthetic degrees, realizing individualized instruction and compensating for differences according to students’ aesthetic talents (Lin & Chang, 2022).
Patureau et al. (2021) found that many professors in modern university classrooms still carry out static teaching methods rooted in traditional two-dimensional aesthetics, ignoring the potential effects of intelligent algorithms and dynamic aesthetic strategies on different student populations. Therefore, they proposed a high-value classification teaching strategy based on genetic algorithms. Furthermore, they analyzed the lack of a comprehensive multi-angle aesthetic system and methodology in both domestic and foreign ecological aesthetic education research, proposing a theory of ecological aesthetic education based on short-time and multi-attitude integration. This is also verified through empirical research (Ahmad et al., 2019).
Zhen et al. (2022) combined interactive teaching strategies with like the Kruskal algorithm improving the framework of ecological aesthetic education. They constructed an intelligent matching analysis system based on multidimensional space-time soul algorithm, tailoring teaching mechanisms to individual student responses. Prat et al. (2021) proposed a classification algorithm with high matching degrees based on multi-angle framework thinking decision-making levels, conducting experimental teaching for students of different ages to meet common requirements and teaching objectives in ecological aesthetic education in colleges and universities.
Li et al. (2022) found knowledge gaps and classification fuzzy points in ecological aesthetic education. Combined with IoT technology, they built a multi-dimensional value matching analysis interactive teaching strategy adaptable to various student aptitudes, albeit suited for Western ecological aesthetic education, thus exhibiting certain limitations.
Based on the aforementioned domestic and international research, it can be found that studies on ecological aesthetic education focus on the innovation of educational methods and the building of interactive teaching platforms, while rarely carrying out breakthrough innovation in online multi-person interaction (Connolly et al., 2021). Moreover, existing innovative teaching models mostly adopt regressive difference algorithms based on reliability classification, which makes the existing teaching models less practical and inconsistent with actual teaching directions and objectives (De Brún et al., 2022). Therefore, it is of great significance to study the interactive teaching methods for ecological aesthetic education in universities based on the Kruskal algorithm and the digital architecture of the IoT (Adkins, 2021).