4.6 Article

The Future of Educational Technologies for Engineering Education

Journal

IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES
Volume 14, Issue 5, Pages 613-623

Publisher

IEEE COMPUTER SOC
DOI: 10.1109/TLT.2021.3120771

Keywords

Engineering education; Market research; Internet; Games; Conferences; Tools; Europe; Engineering education trends; Google Trends; technology-enhanced learning; technology meta-trends

Funding

  1. Madrid Regional Government through the e-Madrid-CM project [S2018/TCS-4307]
  2. European Social Fund (FSE)
  3. European Regional Development Fund (FEDER)
  4. Higher Technical School of Industrial Engineering, Universidad Nacional de Educacion a Distancia (UNED) [2021IEQ12S]

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This article analyzes a survey conducted in 2019 among 259 experts in engineering education to forecast the impact of information and communication technologies on engineering education. The study highlights adaptive and personalized learning technologies, learning analytics, and open educational resources as the most promising technologies in the short term. It also reveals a correlation between the experts' predictions and social interest in the education category on Google Trends.
This article analyzes a survey delivered in 2019 to 259 experts in engineering education that asked them to forecast information and communication technologies, which were most likely to impact the practice of engineering education based on the expert's discipline (electrical, electronics, mechanical, telecommunications engineering, computer science, etc.) and region. The analysis was performed from different perspectives. First, a descriptive approach was used to analyze technologies that are considered as the most important for the experts and a nonparametric perspective approach was adopted to evaluate variation in the responses according to discipline, region, and years of experience. Second, a decision tree technique was used to establish technology profiles based on when the experts expected that the technologies will impact mass engineering education and the challenges or requirements needed to get widespread use. Third, the Spearman correlation coefficient was used to determine the value of social interest, measured through Google Trends, as a predictor of expert ratings of different technologies. Results indicate adaptive and personalized learning technologies, learning analytics, and open educational resources are the three most promising technologies that will impact engineering education in the short term. Results also show a relation between the predictions made by the experts in 2019 and the social interest in the Google Trends education category.

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