Deep Learning Ability of Students from Superior and Non-Superior Classes at Microscopic Level of Protein

Author:

Erman E,Martini ,Rosdiana L,Wakhidah N

Abstract

Abstract Deep learning is urgently required to achieve scientific literacy and to develop high order thinking skills. This study aimed to describe the deep learning ability of science education major students in a university in East Java at the microscopic level of protein. Twenty students from superior class and thirty students from non-superior class were involved voluntarily in the survey study. By using content analysis that is read carefully students’ tasks, examined students’ ability in identify, define, and explain biochemical aspects of protein case, and apply the knowledge to explain the case, and determined students’ learning status using rubrics, we found that students from both classes successful identified, defined, and explained aspects of protein cases macroscopically. However, microscopically, no students from the superior class explained protein aspects in the cases, while 11% of students from non-superior class explained them successfully. Seven percent of student from non-superior class used their protein knowledge to explain the protein cases they explored successfully. We also found that 7% of students from the non-superior class performed deep learning at microscopic level of protein, while no students from the superior class performed the same. However, majority students from both classes performed surface learning at microscopic level of protein. The results imply that both superior and non-superior students are difficult to learn protein microscopically. Learning strategy to help students attain meaningful learning of biochemistry are needed by the students.

Publisher

IOP Publishing

Subject

General Physics and Astronomy

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3