The Learning Sciences in China:Historical Development and Future Trends

Purpose:This study charts the developmental history of the learning sciences(LS)in China and analyzes the direction of its future development.Design/Approach/Methods:An extensive literature review is presented to compile notable events in the development of LS in China over different periods.The findings are then systematically sorted and interpreted.Findings:This study maps the LS developmental process across three main periods:prescientific,psychological paradigm,and initial scientific paradigm.China has achieved certain initial developments in LS and has formed collaborative bodies to conduct sustained and in-depth learning research.However,the field remains at a preliminary developmental stage in China.Overall,there is still no sound mechanism for the production of new knowledge,and contributions bearing distinctive Chinese characteristics remain insufficient.Originality/Value:This article presents a comprehensive analysis of the development of LS in China.It proposes four main areas for future growth:forming a stable academic community,developing appropriate curriculum and teaching materials,creating an effective mechanism within the discipline for knowledge production,and establishing a local system of discourse for the discipline.

Bolstering integrity in environmental data science and machine learning requires understanding socioecological inequity

Socioecological inequity in environmental data science—such as inequities deriving from data-driven approaches and machine learning(ML)—are current issues subject to debate and evolution.There is growing consensus around embedding equity throughout all research and design domains—from inception to administration,while also addressing procedural,distributive,and recognitional factors.Yet,practically doing so may seem onerous or daunting to some.The current perspective helps to alleviate these types of concerns by providing substantiation for the connection between environmental data science and socioecological inequity,using the Systemic Equity Framework,and provides the foundation for a paradigmatic shift toward normalizing the use of equity-centered approaches in environmental data science and ML settings.Bolstering the integrity of environmental data science and ML is just beginning from an equity-centered tool development and rigorous application standpoint.To this end,this perspective also provides relevant future directions and challenges by overviewing some meaningful tools and strategies—such as applying the Wells-Du Bois Protocol,employing fairness metrics,and systematically addressing irreproducibility;emerging needs and proposals—such as addressing data-proxy bias and supporting convergence research;and establishes a ten-step path forward.Afterall,the work that environmental scientists and engineers do ultimately affect the well-being of us all.

VISHIEN-MAAT:Scrollytelling visualization design for explaining Siamese Neural Network concept to non-technical users

The past decade has witnessed rapid progress in AI research since the breakthrough in deep learning.AI technology has been applied in almost every field;therefore,technical and non-technical endusers must understand these technologies to exploit them.However existing materials are designed for experts,but non-technical users need appealing materials that deliver complex ideas in easy-tofollow steps.One notable tool that fits such a profile is scrollytelling,an approach to storytelling that provides readers with a natural and rich experience at the reader’s pace,along with in-depth interactive explanations of complex concepts.Hence,this work proposes a novel visualization design for creating a scrollytelling that can effectively explain an AI concept to non-technical users.As a demonstration of our design,we created a scrollytelling to explain the Siamese Neural Network for the visual similarity matching problem.Our approach helps create a visualization valuable for a shorttimeline situation like a sales pitch.The results show that the visualization based on our novel design helps improve non-technical users’perception and machine learning concept knowledge acquisition compared to traditional materials like online articles.

Strategies for Designing Online Course Assignments: Based on “XuetangX” Course Samples

Assignments are an important tool to evaluate learners’learning effectiveness in online courses.Clarifying assignment design strategies is of great significance for promoting the quality construction of online education courses.This paper uses Bloom’s taxonomy framework revised by Anderson and Krathwohl(2001)as a reference to label the knowledge types and cognitive dimensions in the assignment context of eight courses.Combined with a literature review,a discipline–objective–schedule(DOS)three-dimensional analysis framework based on the achievement of curriculum objectives,the design of chapter schedule,and the heterogeneity of disciplines is constructed to conduct an in-depth analysis of online course assignment design strategies.The research findings show that the online course assignment design strategy has an obvious curriculum objective orientation,follows the gradual learning rule,and presents typical disciplinary differences.The study finds that the current assignment design of online courses has three issues:first,a mismatch between assignment design and curriculum objectives;second,a lack of diversity in assignment formats;and third,insufficient comprehensiveness of some subject assignments.Based on the above discussions,corresponding suggestions are provided.