Cultivating Innovation Readiness in Biology Education: The Mediating Roles of Plant Attitudes and Scientific Argumentation in Deep Learning and Cognitive Flexibility

Ismail Ismail; Asham  Bin Jamaluddin; Abd.  Muis; Andi  Citra Pratiwi; Muhiddin  Palennari; Adnan Adnan

doi:10.48161/qaj.v6n2a2429

Authors

Ismail Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Negeri Makassar, Makassar 90222, Indonesia; https://orcid.org/0009-0009-1529-6987
Asham Bin Jamaluddin Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Negeri Makassar, Makassar 90222, Indonesia; https://orcid.org/0000-0003-0562-072X
Abd. Muis Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Negeri Makassar, Makassar 90222, Indonesia; https://orcid.org/0009-0001-5483-4882
Andi Citra Pratiwi Department of Educational Studies, Macquarie University, Sydney 2109, Australia. https://orcid.org/0000-0001-9317-8964
Muhiddin Palennari Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Negeri Makassar, Makassar 90222, Indonesia; https://orcid.org/0000-0002-1445-9846
Adnan Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Negeri Makassar, Makassar 90222, Indonesia; https://orcid.org/0000-0001-6376-2801

DOI:

https://doi.org/10.48161/qaj.v6n2a2429

Keywords:

Innovation readiness, Scientific argumentation, Plant attitudes, Cognitive flexibility, Deep learning

Abstract

Innovation readiness has become an essential competency in biology education, enabling students to engage in evidence-based reasoning, problem-solving, and responsible scientific innovation. This study examined the relationships among cognitive flexibility, deep learning, plant attitudes, scientific argumentation, and innovation readiness among undergraduate Biology and Biology Education students. A cross-sectional survey was conducted with 538 students at Universitas Negeri Makassar, Indonesia, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Two structural models were compared to evaluate the contribution of plant attitudes to the predictive performance of the framework. The findings demonstrated that the inclusion of plant attitudes substantially improved the explanatory power of the model. Cognitive flexibility significantly predicted both plant attitudes and scientific argumentation, influencing innovation readiness primarily through indirect pathways. Deep learning emerged as the strongest predictor of innovation readiness through both direct and mediated effects. Plant attitudes significantly enhanced scientific argumentation and directly contributed to innovation readiness, while scientific argumentation further strengthened students’ readiness for innovation-oriented activities. Multi-group analysis revealed generally stable structural relationships across academic programs, although the effect of plant attitudes on innovation readiness differed significantly between Biology and Biology Education students. The study highlights the importance of integrating affective engagement with plants, deep learning strategies, and scientific argumentation practices to foster innovation readiness in biology education.

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