The Future of Educational Technologies for Engineering Education

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, mech...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on learning technologies 2021-10, Vol.14 (5), p.613-623
Main Authors: Martin, Sergio, Lopez-Martin, Esther, Moreno-Pulido, Alexis, Meier, Russ, Castro, Manuel
Format: Article
Language:English
Subjects:
Computer Software
Conferences
Correlation
Correlation coefficients
Decision Making
Decision trees
Educational Resources
Educational technology
Educational Trends
Engineering Education
Engineering education trends
Europe
Futures (of Society)
Games
Google Trends
Information Technology
Internet
Learning
Mathematical analysis
Open Educational Resources
Predictor Variables
Profiles
Specialists
technology meta-trends
technology-enhanced learning
Telecommunications engineering
Trend Analysis
Trends
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1939-1382
1939-1382
2372-0050
DOI:10.1109/TLT.2021.3120771