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Selected Papers from AISL 2021 Conference on Improving Scientific Literacy...
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Selected Papers from AISL 2021 Conference on Improving Scientific Literacy through Interdisciplinary Research on Technology-Enhanced Practice

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 7975

Special Issue Editors

Prof. Dr. Knut Neumann

E-Mail Website
Guest Editor
Leibniz Institute for Science and Mathematics Education (IPN), Kiel, Germany
Interests: diagnosing abilities in physics; development of abilities in physics while attending school; physics teachers' professional skills and their development; identifying and supporting talented physics students
Prof. Dr. Troy D. Sadler

E-Mail Website
Guest Editor
Faculty of Experiential Learning, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Interests: science education; experiential learning; socio-scientific issues and issues-based teaching; science teacher education; epistemic practices
Prof. Dr. Chun-Yen Chang

E-Mail Website
Guest Editor
The Graduate Institute of Science Education and the Department of Earth Sciences, National Taiwan Normal University (NTNU), Taipei, Taiwan
Interests: science education; E-learning; interdisciplinary science learning; science communication
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jing Lin

E-Mail Website
Guest Editor
The director of Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing, China
Interests: scientific literacy assessment and improvement; teachers’ professional competency assessment and improvement; STEM education

Special Issue Information

Dear Colleagues,

The 2021 iteration of the AISL conference on Improving Scientific Literacy through Interdisciplinary Research on Technology-Enhanced Practice will be held at Beijing Normal University. Supporting all students in developing the scientific literacy needed to meet personal, national and global challenges is the major goal of 21st century science education. To meet this important goal, it is important to rethink and reconceptualize the vision of scientific literacy and to draw on the possibilities that this century offers in terms of technology-based approaches to improve the teaching and learning of science and its assessment. These technologies not only include new and innovative technologies that may be used in the teaching and learning of science and its assessment, but also technologies that can help better understand the teaching and learning of science and its assessment from a research perspective.

This Special Issue on "Selected papers from AISL 2021 Conference on Improving Scientific Literacy through Interdisciplinary Research on Technology-Enhanced Practice” is expected to select excellent papers presented in AISL 2021 and other high-quality papers on the topic of sustainability. The Special Issue links several disciplines, including science education, neuroscience, educational technology and the social sustainability of human beings. Our aim is to encourage researchers to publish their experimental and theoretical research relating to scientific literacy assessment and learning of science. Potential topics include:

  • Sustainable STEM education.
  • Sustainable technology for STEM education.
  • Sustainable technology-enriched assessment.
  • Socio-scientific issues and social sustainable development.

Prof. Dr. Knut Neumann
Prof. Dr. Troy D. Sadler
Prof. Dr. Chun-Yen Chang
Prof. Dr. Jing Lin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • development of scientific literacy
  • technology-enhanced teaching and learning
  • digital technologies and machine learning
  • positive affect towards science and in science education
  • socio-scientific issues (SSI) teaching and learning

Published Papers (3 papers)

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Research

15 pages, 2332 KiB  
Article
Study of STEM for Sustainability in Design Education: Framework for Student Learning and Outcomes with Design for a Disaster Project
by Ming-Ni Chan and Daisuke Nagatomo
Sustainability 2022, 14(1), 312; https://0-doi-org.brum.beds.ac.uk/10.3390/su14010312 - 28 Dec 2021
Cited by 6 | Viewed by 3115
Abstract
STEM has successfully introduced an interdisciplinary education model that can be used for training students to develop skillsets for the 21st century. STEM Education for Sustainability (STEM4S) expands the scope of education to meet rapidly changing global challenges, such as climate change and [...] Read more.
STEM has successfully introduced an interdisciplinary education model that can be used for training students to develop skillsets for the 21st century. STEM Education for Sustainability (STEM4S) expands the scope of education to meet rapidly changing global challenges, such as climate change and SDGs by the United Nations, which require the multidisciplinary curriculum to be integrated into STEM. Design-based tasks play a significant role in STEM education by promoting students’ critical thinking and problem-solving abilities. While STEM successfully employs design, design education currently conducts subjective procedures and lacks the framework for adopting the critical-thinking process. Therefore, design education can develop students’ cognitive skills by reflecting on STEM learning experiences. This study articulates the framework for design education by investigating problem-based and project-based learning and the double-diamond diagram for innovation. The goal of this study was to apply these observations and formulate the framework for STEM4S. This study examined the framework adopted at the National Taiwan Normal University in the Department of Design, with qualitative analysis of participants and quantitative analysis of questionnaire results. Finally, the researchers discuss the research questions and future applications of this framework. Full article
(This article belongs to the Special Issue Selected Papers from AISL 2021 Conference on Improving Scientific Literacy through Interdisciplinary Research on Technology-Enhanced Practice)
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17 pages, 2519 KiB  
Article
Early Warning System for Online STEM Learning—A Slimmer Approach Using Recurrent Neural Networks
by Chih-Chang Yu and Yufeng (Leon) Wu
Sustainability 2021, 13(22), 12461; https://0-doi-org.brum.beds.ac.uk/10.3390/su132212461 - 11 Nov 2021
Cited by 2 | Viewed by 1969
Abstract
While the use of deep neural networks is popular for predicting students’ learning outcomes, convolutional neural network (CNN)-based methods are used more often. Such methods require numerous features, training data, or multiple models to achieve week-by-week predictions. However, many current learning management systems [...] Read more.
While the use of deep neural networks is popular for predicting students’ learning outcomes, convolutional neural network (CNN)-based methods are used more often. Such methods require numerous features, training data, or multiple models to achieve week-by-week predictions. However, many current learning management systems (LMSs) operated by colleges cannot provide adequate information. To make the system more feasible, this article proposes a recurrent neural network (RNN)-based framework to identify at-risk students who might fail the course using only a few common learning features. RNN-based methods can be more effective than CNN-based methods in identifying at-risk students due to their ability to memorize time-series features. The data used in this study were collected from an online course that teaches artificial intelligence (AI) at a university in northern Taiwan. Common features, such as the number of logins, number of posts and number of homework assignments submitted, are considered to train the model. This study compares the prediction results of the RNN model with the following conventional machine learning models: logistic regression, support vector machines, decision trees and random forests. This work also compares the performance of the RNN model with two neural network-based models: the multi-layer perceptron (MLP) and a CNN-based model. The experimental results demonstrate that the RNN model used in this study is better than conventional machine learning models and the MLP in terms of F-score, while achieving similar performance to the CNN-based model with fewer parameters. Our study shows that the designed RNN model can identify at-risk students once one-third of the semester has passed. Some future directions are also discussed. Full article
(This article belongs to the Special Issue Selected Papers from AISL 2021 Conference on Improving Scientific Literacy through Interdisciplinary Research on Technology-Enhanced Practice)
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12 pages, 1394 KiB  
Article
Remodeling the STEM Curriculum for Future Engineers
by Chun-Hung Lin, Huei Chu Weng, Kuan-Yu Chen and Leon Yufeng Wu
Sustainability 2021, 13(22), 12450; https://0-doi-org.brum.beds.ac.uk/10.3390/su132212450 - 11 Nov 2021
Viewed by 2218
Abstract
Higher education is facing low enrollment, and fewer students are motivated to select STEM majors. This paper reports the results from one university that recently experimentally reformed its undergraduate curriculum to a “theme-based curricula”, the New Engineering Curriculum Program (NECP). The subjects in [...] Read more.
Higher education is facing low enrollment, and fewer students are motivated to select STEM majors. This paper reports the results from one university that recently experimentally reformed its undergraduate curriculum to a “theme-based curricula”, the New Engineering Curriculum Program (NECP). The subjects in this study were 127 engineering students who applied for the NECP at a university in northern Taiwan. An experimental design using the pre- and post-test measurements of the experimental and control groups was applied in this study. The results revealed a significant effect among those who participated as second- and third-year undergraduates in terms of their subject-specific performances and attitudes of learning in various courses. Furthermore, the results showed that students in the NECP showed better learning performance and higher learning motivation than students in the traditional course module. The outcomes and analyses are discussed. Full article
(This article belongs to the Special Issue Selected Papers from AISL 2021 Conference on Improving Scientific Literacy through Interdisciplinary Research on Technology-Enhanced Practice)
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