Understanding Idea Creation in Collaborative Discourse through Networks: The Joint Attention-Interaction-Creation (AIC) Framework

In Computer-Supported Collaborative Learning, ideas generated through collaborative discourse are informative indicators of students' learning and collaboration. Idea creation is a product of emergent and interactive socio-cognitive endeavors. Therefore, analyzing ideas requires capturing contextual information in addition to the ideas themselves. In this paper, we propose the Joint Attention-Interaction-Creation (AIC) framework, which captures important dynamics in collaborative discourse, from attention and interaction to creation. The framework was developed from the networked lens, informed by natural language processing techniques, and inspired by socio-semantic network analysis. A case study was included to exemplify the framework's application in classrooms and to illustrate its potential in broader contexts

Analytics for Knowledge Creation: Towards Epistemic Agency and Design-Mode Thinking

Innovation and knowledge creation call for high-level epistemic agency and design-mode thinking, two competencies beyond the traditional scopes of schooling. In this paper, we discuss the need for learning analytics to support these two competencies, and more broadly, the demand for education for innovation. We ground these arguments on a distinctive Knowledge Building pedagogy that treats education as a knowledge-creation enterprise. By critiquing current learning analytics for their focus on static-state knowledge and skills, we argue for agency-driven, choice-based analytics more attuned to higher order competencies in innovation. We further describe ongoing learning analytics initiatives that attend to these elements of design. Prospects and challenges are discussed, as well as broader issues regarding analytics for higher order competencies

The Synthesis Lab: Empowering Collaborative Learning in Higher Education through Knowledge Synthesis

The ability to synthesize information has emerged as a critical skill for success across various fields. However, within the field of education, there is a lack of systematic understanding and well-defined design infrastructures that address the mechanisms and processes of knowledge synthesis in collaborative learning settings. In this poster, we introduce a design innovation - The Synthesis Lab, which aims to support students in synthesizing ideas from their online discussions in higher education classrooms. The tool offers structured work-spaces for students to decompose the synthesis process into intermediate synthesis products and features two key iterative processes of knowledge synthesis in collaborative settings: categorizing peers' ideas into conceptual building blocks and developing a synthesis of the discussions. Future implementation and evaluation of the design will make significant contributions to both research and practice

MOOCs as an Alternative for Teacher Professional Development. Examining Learner Persistence in One Chinese MOOC

Massive Open Online Courses (MOOCs) have developed into a significant international movement, showing great promise in addressing equity, quality, and efficiency issues in global education. To date, many MOOCs have been developed specifically for teacher professional development (TPD). In this regard, an important empirical question remains to be addressed: How and to what extent can MOOCs support equity, quality, and efficiency in teacher professional development? To help fill this knowledge gap, this study, conducted from 2014 to 2016, focused on persistent teacher-learners in a TPD MOOC that was offered for seven consecutive rounds by the X-Learning Center of Peking University. The study found that more than 15% of the 105,383 teachers who enrolled in this MOOC were persistent teacher-learners, defined as learners who enrolled in multiple rounds. Data analysis showed that these persistent Keywords: MOOC, teacher professional development, persistent teacher-learners, self-regulated learning teacher-learners had diverse motivations for re-enrollment, including refreshing conceptual understanding, achieving higher scores, earning course certification, and discussing practical problems. The study also found that the persistent teacher learners developed self-regulated learning skills in the course of multiple rounds of the MOOC and showed significantly higher learning achievement than one-time enrollees. Qualitative and quantitative analysis of both clicklog data and interview data revealed additional insights into the persistent teacherlearners’ learning within the MOOC and their real-world teaching practice beyond the MOOC. Overall, this study contributes to an improved understanding of the potential of MOOCs as an alternative TPD delivery mode in developing countries and sheds light on the future design of effective TPD through MOOCs.This work was created with financial support from the UK Government’s Department for International Development and the International Development Research Centre, Canada. The views expressed in this work are those of the authors and do not necessarily represent those of the UK Government’s Department for International Development; the International Development Research Centre, Canada or its Board of Governors; or the Foundation for Information Technology Education and Developmen

Chapter 38 Learning Analytics

In this chapter, we present an overview of the field by articulating definitions and existing models of learning analytics. Case examples of learning analytics from Asian researchers are then summarized and reported. This is followed by an exploration of the key tensions in this field. The chapter concludes with a discussion of potential areas for future research in this area

It’s About Time: 4th International Workshop on Temporal Analyses of Learning Data

Interest in analyses that probe the temporal aspects of learning continues to grow. The study of common and consequential sequences of events (such as learners accessing resources, interacting with other learners and engaging in self-regulatory activities) and how these are associated with learning outcomes, as well as the ways in which knowledge and skills grow or evolve over time are both core areas of interest. Learning analytics datasets are replete with fine-grained temporal data: click streams; chat logs; document edit histories (e.g. wikis, etherpads); motion tracking (e.g. eye-tracking, Microsoft Kinect), and so on. However, the emerging area of temporal analysis presents both technical and theoretical challenges in appropriating suitable techniques and interpreting results in the context of learning. The learning analytics community offers a productive focal ground for exploring and furthering efforts to address these challenges as it is already positioned in the “‘middle space’ where learning and analytic concerns meet” (Suthers & Verbert, 2013, p 1). This workshop, the fourth in a series on temporal analysis of learning, provides a focal point for analytics researchers to consider issues around and approaches to temporality in learning analytics

EWT: Efficient Wavelet-Transformer for Single Image Denoising

Transformer-based image denoising methods have achieved encouraging results in the past year. However, it must uses linear operations to model long-range dependencies, which greatly increases model inference time and consumes GPU storage space. Compared with convolutional neural network-based methods, current Transformer-based image denoising methods cannot achieve a balance between performance improvement and resource consumption. In this paper, we propose an Efficient Wavelet Transformer (EWT) for image denoising. Specifically, we use Discrete Wavelet Transform (DWT) and Inverse Wavelet Transform (IWT) for downsampling and upsampling, respectively. This method can fully preserve the image features while reducing the image resolution, thereby greatly reducing the device resource consumption of the Transformer model. Furthermore, we propose a novel Dual-stream Feature Extraction Block (DFEB) to extract image features at different levels, which can further reduce model inference time and GPU memory usage. Experiments show that our method speeds up the original Transformer by more than 80%, reduces GPU memory usage by more than 60%, and achieves excellent denoising results. All code will be public.Comment: 12 pages, 11 figur

What Roles Do Chinese Health Sciences Libraries Play in Their Nation\u27s Cigarette Smoking Public Health Crisis?

Objectives: Cigarette smoking remains a major cause of death in China. Are health sciences libraries in China currently providing awareness, advocacy, or research support for the societal benefits of smoking reduction? Methods: Following institutional review board approval, Library contacts for Chinese schools of medicine, public health, and pharmacy were identified. A bilingual online survey was constructed to obtain respondents’ demographic detail and answers to questions about library resources and services that constitute academic awareness, advocacy, curriculum, or research support about tobacco and smoking. Results: 43% of reporting librarians work on a smoke-free campus. 100% of all reporting libraries work in smoke-free libraries, though 6% of the reporting libraries offer a smoking room for staff. All reporting libraries contain printed material on the dangers of smoking. Student requests for materials or acquisition recommendations are infrequent. More than 60% of the librarians report medical residents occasionally ask for tobacco-related literature. Nearly 60% of librarians reported faculty occasionally ask for materials about smoking. More than 60% of instructors were reported to occasionally ask for database searches about cigarettes or tobacco. 33% of librarians reported creating a collection guide about smoking. 15% of reporting libraries hosted a traveling exhibit on smoking. Conclusion: Some Chinese health sciences libraries are providing public health information and collaborating with faculty and students to support the reduction of smoking and tobacco use. Anecdotal statements collected from survey participants confirms their awareness of the educational and advocacy roles librarians play in their country\u27s smoking crisis

Learning analytics for the global south

Learning Analytics for the Global South is a compilation of papers commissioned for the Digital Learning for Development (DL4D) project. DL4D is part of the Information Networks in Asia and Sub-Saharan Africa (INASSA) program funded jointly by the International Development Research Centre (IDRC) of Canada and the Department for International Development (DFID) of the United Kingdom, and administered by the Foundation for Information Technology Education and Development (FIT-ED) of the Philippines. DL4D aims to examine how digital learning could be used to address issues of equity, quality, and efficiency at all educational levels in developing countries. Over the past two years, DL4D has brought together leading international and regional scholars and practitioners to critically assess the potentials, prospects, challenges, and future directions for the Global South in key areas of interest around digital learning. It commissioned discussion papers for each of these areas from leading experts in the field: Diana Laurillard of the University College London Knowledge Lab, for learning at scale; Chris Dede of Harvard University, for digital game-based learning; Charalambos Vrasidas of the Centre for the Advancement of Research and Development in Educational Technology, for cost-effective digital learning innovations; and for learning analytics, the subject of this compilation, Dragan Gašević of the University of Edinburgh Moray House School of Education and School of Informatics. Each discussion paper is complemented by responses from a developing country-perspective by regional experts in Asia, Latin America, Africa, and the Middle East. Learning Analytics for the Global South considers how the collection, analysis, and use of data about learners and their contexts have the potential to broaden access to quality education and improve the efficiency of educational processes and systems in developing countries around the world. In his discussion paper, Prof. Gašević articulates these potentials and suggests how learning analytics could support critical digital learning and education imperatives such as quality learning at scale and the acquisition of 21st century skills. Experts from Africa (Paul Prinsloo of the University of South Africa), Mainland China (Bodong Chen of the University of Minnesota, USA and Yizhou Fan of Peking University, People’s Republic of China), Southeast Asia (Ma. Mercedes T. Rodrigo of the Ateneo de Manila University, Philippines), and Latin America (Cristóbal Cobo and Cecilia Aguerrebere, both of the Ceibal Foundation, Uruguay) situate Prof. Gašević’s proposals in their respective regional contexts, framing their responses around six key questions: 1. What are the main trends and challenges in education in your region? 2. How can learning analytics address these challenges? 3. What models of learning analytics adoption would be most effective in your region? 4. What are the barriers in adoption of learning analytics in your region and how could these be mitigated? 5. How do you envision ethical use and privacy protection in connection with learning analytics being addressed in your region? 6. How can the operationalization of learning analytics be futureproofed in your region? We hope that this compilation will serve as a springboard for deeper conversations about the adoption and sustained use of learning analytics in developing countries – its potential benefits and risks for learners, educators, and educations systems, as well as the ways to move forward that are rigorous, context-appropriate, ethical, and accountable.This work was created with financial support from the UK Government’s Department for International Development and the International Development Research Centre, Canada. The views expressed in this work are those of the authors and do not necessarily represent those of the UK Government’s Department for International Development; the International Development Research Centre, Canada or its Board of Governors; the Foundation for Information Technology Education and Development; or the editors

Infrastructuring for Knowledge Building: Advancing a framework for sustained innovation

Despite the wide implementations and extensive research base that has developed on knowledge building communities, continued efforts are required to address the challenges of implementing innovations in diverse contexts as well as sustaining them over time. In this paper, we draw on the idea of infrastructuring as an emergent, multilevel approach that can shed new light on ways to do this. After defining the notion of infrastructuring and showing its unique potential to sustain knowledge building, we examine three cases of infrastructuring within the context of efforts to grow knowledge building innovations in existing educational ecologies. This paper offers some new insights into how infrastructuring can be conceptualized to expand and sustain knowledge building innovations. © 2023 Progedit. All rights reserved