Abstract
The civil engineering educators focused on implementing interdisciplinary learning in artificial intelligence (AI) based on a more innovative application of construction requirements. However, only a few pieces of literature discussed the educational learning efficiency and feedback for this trend. Hence, this study surveyed the 237 data from eight universities that issued the interdisciplinary courses. The factors were modified from the scales in science, technology, engineering, and mathematics education. Further, the descriptive analysis was used to explain this situation in Taiwan. A novel approach based on data envelopment analysis and Mahalanobis distance approaches was proposed to solve this problem. The advantages of the proposed approach were discussed and compared with traditional method. Based on the student gains in the interdisciplinary courses, three groups were clustered and compared. The feedback of a high-input and low-efficiency student group was suggested for improving learning strategies. The sensitivity analysis of this special group showed that effective teaching practice is the key factor in the artificial intelligence courses for civil engineering students. These students may increase technical efficiency by 37% by paying 21% inputs. Therefore, this paper provided a useful and easy approach to make learning strategies for non-informatics students in AI learning.
Highlights
Education: A Case Study in Taiwan.One of the recent educational questions asked is whether construction industry sectors can successfully operate in the digital environment and face future artificial intelligence (AI) challenges
The results indicated that graduate and undergraduate education might focus on AI implementation in the construction industry
How does the proposed Data Envelopment Analysis (DEA)-Mahalanobis distance approach estimate the performance of inputs and outputs in the interdisciplinary learning system? What is the difference between the traditional DEA and the proposed approach?
Summary
Akinosho et al (2020) carefully analyzed previous researches that have implemented each of these deep learning algorithms as deep neural network, convolutional neural network, recurrent neural network, auto-encoders, restricted Boltzmann machines, deep belief networks, and generative adversarial network They believe that there are currently insufficient applications of deep learning in this domain compared to other digital technologies, such as building information modeling (BIM) and other machine learning algorithms [7]. The authors believed that the TOPSIS-Mahalanobis approach framework merits more studies and is applicable to any multi-criteria decision-making issue in any branch of science [15]. These articles show that the Mahalanobis distance is suitable and useful in the utility theory. How does the proposed DEA-Mahalanobis distance approach estimate the performance of inputs and outputs in the interdisciplinary learning system? What is the difference between the traditional DEA and the proposed approach?
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