June 24, 2017
June 24, 2017
June 28, 2017
The current education system is not producing enough engineers to meet market demand for several reasons, including misalignment of curriculum with companies’ demands, high students’ dropout levels in the Engineering programs, students’ motivational problems, and lack of adequate technological tools that improve the teaching and learning processes. This poses a great challenge for engineering education. Some of the current questions are: how to improve the quality of teaching engineering? What are the differences between engineering education and the education in other fields? What tools can be developed to improve the teaching and learning process?.
Given these concerns, there is a growing number of Master’s thesis and PhD dissertations focusing on learning technologies, learning analytics, models and theories related to Engineering Education. These theses come from programs offered by colleges of education, or by departments of engineering education within colleges of engineering, or by programs in traditional engineering departments in colleges of engineering. This results in a wide spectrum of topics and perspectives that vary from education of engineering to engineering education.
This paper focus on the evolution of engineering education research around the world by processing and analyzing dissertation documents and theses related to Engineering Education, utilizing a variety of data analysis techniques. The first approach includes the classification and ranking of the dissertation and theses by analyzing metadata fields such as: title, abstract, institution, topics, keywords, and date of publication. The second approach consisted of an unstructured search, using unsupervised learning clustering algorithms (Machine Learning) and information retrieval techniques for text analysis. With this approach the computer based on the abstract content, can group, classify and rank the documents and topics automatically without the need of additional metadata.
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