Effects of voice assistant creation using different learning approaches on performance of computational thinking

Designing artificial intelligence (AI) artefact learning has gone beyond command-line-based instruction, to include a low-barrier threshold with block-based programming. Such instructional design must not solely emphasise AI workings. Rather, it must offer students computational thinking (CT) practi...

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Bibliographic Details
Published in:Computers and education Vol. 192; p. 104657
Main Authors: Hsu, Ting-Chia, Chang, Ching, Lin, Yi-Wei
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.01.2023
Subjects:
Artificial intelligence
Computational thinking
Experiential learning
Student learning behaviour
Voice assistant
Computational thinking
Experiential learning
Voice assistant
Artificial intelligence
Student learning behaviour
ISSN:0360-1315, 1873-782X
Online Access:Get full text
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Summary:Designing artificial intelligence (AI) artefact learning has gone beyond command-line-based instruction, to include a low-barrier threshold with block-based programming. Such instructional design must not solely emphasise AI workings. Rather, it must offer students computational thinking (CT) practice to support their AI-related artefact creation while reducing their AI anxiety about future job replacement or sociotechnical blindness. In this study, this research explored an experiential learning approach to improve CT along with AI application capabilities when engaging undergraduate students in creating a voice assistant application (VA app). A total of 56 students participated in the study. The control group (CG) of 26 students used a conventional subject-based learning method, while the experimental group (EG) of 30 students adopted an experiential learning method. This study aimed to examine the differences in the learning achievement of CT and AI concept, as well as the perspectives of AI anxiety, and CT; in the meanwhile, this study analysed the students' learning behaviours using sequential behavioural analysis to discuss the learning process. Results showed that the CT ability of the EG was better than that of the CG, although no significant difference was found between the two groups’ AI concepts and anxiety. The behaviour analysis also revealed that the EG students were willing to ask more questions, and conducted their VA evaluation, whereas the CG students were inclined to focus on the input and output of knowledge, and replicated what the teacher presented. Suggestions and implications are given for future research. •Helping students' voice assistant app creation using the experiential learning approach.•Contributing to developing students' CT and AI learning.•Analyzing students' learning behaviour patterns.
ISSN:0360-1315
1873-782X
DOI:10.1016/j.compedu.2022.104657