Abstract
Many universities undertake mixed learning to meet the required needs. Mixed learning is a blend of F2F classroom education and online learning education. The strength of mixed learning is that it supports student cognitive styles more than non-mixed learning. The right mix of mixed learning provides more constructive and conducive learning. Meanwhile, the programming language is the primary skill that students must master to create computer application programs. The question is: Is there an effect on student cognitive style and learning methods on mixed material 30% F2F and 70% asynchronous online and on the contrary mixture on student programming skills? Therefore, this study aims to determine the effect of reciprocal interaction between cognitive styles and mixed learning methods on programming skill achievement. This research method is experimental research. The study found that: Although there is no difference in the achievement of student learning skills based on tests on mixed learning methods, further test on student cognitive styles found that there are differences in the achievement of student learning skills in mixed learning methods; students with auditory and visual cognitive style who learn with mixed learning-2 have better programming skill achievement than students with auditory cognitive style who learn with mixed learning-2; students with kinesthetic and visual cognitive styles who learn with mixed learning-2 have superior programming skill achievement compared to students with kinesthetic cognitive styles who learn with mixed learning-1. The research novelty is: There has been no previous research on the reciprocal effect of cognitive styles and mixed learning methods with a mixture of 30% F2F and 70% online and vice versa.
Highlights
The rapid advancement of Information and Communication Technology (ICT) makes it easier to realize multimedia in learning to support student cognitive styles
The two-way Anova test concluded that: (a) There was no difference in the programming skills achieved between students who learn with mixed learning with a mixture of around 30% F2F learning and about 70% asynchronous online learning and students who learn with mixed learning with a blend of approximately 70% F2F learning and about 30% asynchronous online learning (b) There are differences in programming skills acquired between students who have different cognitive styles both in mixed learning with a mixture of about 30% F2F learning and about 70% asynchronous online learning and in mixed learning with a combination of about 70% F2F learning and about 30% asynchronous online learning
The results with Tukey post-hoc test concluded that: (a) Students with auditory and visual cognitive styles who learn with mixed learning with a mixture of around 30% F2F learning and about 70% asynchronous online learning have better programming skills achievement than students with auditory cognitive styles who study with mixed learning with a blend of approximately 70% F2F learning and about 30% asynchronous online learning (b) Students with kinesthetic and visual cognitive styles who learn with mixed learning with a blend of approximately 70% F2F learning and about 30% asynchronous online learning have programming skills that are superior to students with kinesthetic cognitive styles who learn with mixed learning with a mixture of around 30% F2F learning and about 70% asynchronous online learning
Summary
The rapid advancement of Information and Communication Technology (ICT) makes it easier to realize multimedia in learning to support student cognitive styles. The rapid development of ICT has increased pressure for universities to include greater use of technology and innovation in the curriculum (Tyler-Wood et al, 2018). It is not surprising, if many tertiary institutions adopted mixed learning approaches as a solution (Nazarenko, 2015). One of the strengths of mixed education is that it provides a conducive learning environment for students and supports a variety of student cognitive styles (Pierce, 2017). Mixed learning can maximize student learning outcomes by applying appropriate technology learning to fit student
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