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- Title
The Go-Lab Platform, an Inquiry-learning Space: Investigation into Students' Technology Acceptance, Knowledge Integration, and Learning Outcomes.
- Authors
Sui, Chi-Jung; Chen, Hsin-Chueh; Cheng, Ping-Han; Chang, Chun-Yen
- Abstract
Utilizing an inquiry-learning space (ILS) via the Go-Lab platform, we investigated students' technology acceptance, knowledge integration [KI] process, and learning outcomes of both the high-achiever KI student and low-achiever KI student. This study aimed to understand how students engage in knowledge integration tasks using an inquiry-learning space to learn Mendelian genetics and realize the relationship between student KI and domain knowledge. Using a quasi-experimental design, we examined 41 seventh-grade students in Taiwan completing Mendelian genetics KI tasks in ILS, pre/post-testing of domain knowledge tests, and a technology acceptance questionnaire. The analysis of students' interaction in the ILS and domain knowledge tests was conducted through descriptive statistics, t-tests, Pearson's correlation, and content analysis to indicate the direction and relationship between students' KI processes and learning outcomes. A technology acceptance questionnaire was analyzed through descriptive statistics and Pearson's correlation to reveal whether students accepted learning on the Go-Lab platform. The study showed that (i) students responded positively to the perceived usefulness of the Go-Lab platform, (ii) the high-achiever KI students could gradually construct links from simple to complex and diverse among genetic ideas, and the low-achiever almost built simple links, but (iii) the high-achiever and low-achiever KI students had similar learning outcomes. These findings have implications that the instruction design of knowledge integration tasks promotes students' Mendelian genetics conceptual understanding and KI progress.
- Subjects
TAIWAN; EDUCATIONAL outcomes; PEARSON correlation (Statistics); MENDEL'S law; MOBILE learning
- Publication
Journal of Science Education & Technology, 2023, Vol 32, Issue 1, p61
- ISSN
1059-0145
- Publication type
Article
- DOI
10.1007/s10956-022-10008-x