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Development of assessment indicators for measuring the student learning effects of artificial intelligence‐based robot design
Author(s) -
Shyr WenJye,
Yang FuChun,
Liu PoWen,
Hsieh YingMing,
You CiSyong,
Chen DyiCheng
Publication year - 2019
Publication title -
computer applications in engineering education
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.478
H-Index - 29
eISSN - 1099-0542
pISSN - 1061-3773
DOI - 10.1002/cae.22118
Subject(s) - delphi method , robot , field (mathematics) , artificial intelligence , test (biology) , delphi , computer science , engineering management , engineering , mathematics , paleontology , pure mathematics , biology , operating system
This study identified a number of indicators that are essential for measuring the learning effects on students involved in artificial intelligence (AI)‐based robot design. Ten experts were recruited as Delphi group members, including three mechanical engineers in the field of robot design and seven scholars from a university. The data collected from the questionnaires were analyzed using Z values from the Kolmogorov–Smirnov test. The questionnaire was used to identify assessment indicators in six dimensions: (a) Remember, (b) understand, (c) apply, (d) analyze, (e) evaluate, and (f) create. Our findings provide a valuable reference for educators in the field of engineering and technology education involved in the development of programs in AI‐based robot design.