How can we use technology to support students' engagement with ideas? Watch this video to find out more about Knowledge Forum (KF), an online collaboration platform that scaffolds students' thinking about ideas.

Knowledge Forum: Technology to Support Creative Work with Ideas

Knowledge Forum (KF) is an online environment that supports Knowledge Building Discourse and creative work with ideas. In Knowledge Forum, students enter questions, ideas, information, and so on, as multimedia notes into a shared community space. KF notes can include images, videos, and documents. Students can also build-on, annotate, and co-author notes. Knowledge Forum’s visual interface gives an overview of a discussion as it is unfolding, making student thinking visible and the process of idea improvement tangible (see Figure 10).

        Students’ contributions can be organized thematically within views, which are the spaces in which discussion occurs. Views allow for multiple dialogues to take place within the KF community at the same time, and help to give organization to community knowledge (see Figures 11 and 12). Images and drawings can be uploaded onto the background of views, so that students can take charge of their own discussion spaces by re-arranging notes and customizing their views to help organize and express their ideas.

        While it is certainly possible to do Knowledge Building without using Knowledge Forum, the technology offers invaluable support and enhancement to the idea improvement process by giving community ideas an infinite space to live and grow, and by offering powerful new ways to assess students’ discourse through the use of automated tools and assessments. As an open and ever-evolving space, it supports assessment-for-learning by giving teachers an opportunity to trace students’ ideas over time and a chance to see where student research is heading.

Wikis’ potential for collaborative knowledge building

Knowledge Space Visualizer (KSV)

Knowledge Building Discourse Explorer (KBDeX): an application of social network analysis (SNA) for knowledge building discourse

In CSCL environments, understanding technological affordances is essential, as collaborative learning involves intricate dynamics and technology plays a unique role in facilitating these interactions (Suthers, 2006). Grasping these affordances is crucial for leveraging technology to create effective collaborative learning experiences. While technology offers many affordances, few are easily noticeable and practical (Jeong & Hmelo-Silver, 2016). Building on this understanding, Jeong and Hmelo-Silver (2016) proposed seven core affordances of CSCL, delineating opportunities for learners to (1) engage in a joint task, (2) communicate, (3) share resources, (4) engage in productive collaborative learning processes, (5) engage in co-construction, (6) monitor and regulate collaborative learning, and (7) find and build groups and communities. This is crucial for our study as it provides a foundational framework for understanding CSCL technologies, addressing the specific functional needs and challenges faced by learners and instructors in collaborative settings. Each affordance is unpacked into various dimensions with different technological solutions, emphasizing the complexity and adaptability required for effective CSCL environments. It underscores the necessity of integrating both technological and pedagogical strategies to meet the demands of collaborative learning environments effectively.

Immersive technologies

Immersive technology, encompassing VR, AR, MR, and XR (Handa et al., 2012), blurs the boundaries among physical, virtual, and simulated worlds. This paper narrows its focus to AR and VR due to their pronounced impact on sensory and dynamic multimodal interactive experiences. AR overlays digital information onto the real world, enhancing users' senses (Azuma, 1997), while VR generates interactive virtual environments simulating real-life experiences (Lee et al., 2013). Immersive VR, such as head-mounted displays (HMDs), provides a high level of immersion by blocking out physical cues, unlike non-immersive VR displayed on traditional interfaces (Suh & Prophet, 2018). Immersion is a critical factor considered in this study, acknowledged in various dimensions, including an objective measure of a system’s vividness (Cummings & Bailenson, 2016) and the subjective feeling of participating in a realistic virtual experience (Dede, 2009). Additionally, Xu et al. (2021) introduced the concept of embodied immersion, integrating a sense of immersion, presence, and agency into three major dimensions: physical, sensory, and cognitive immersion. Consequently, this paper delves into the literature on immersive VR and AR technologies, emphasizing their significance in fostering immersive experiences.

        Immersive technologies have gained attention in educational research for their potential to promote learning and enrich learning environments by allowing users to interact with the content (Bricken, 1991). They offer interactive visualizations supporting group projects, field trips, and many more. Immersive technologies have been gradually introduced as potential tools for learning and teaching in collaborative learning. Particularly, these technologies offer co-located or remote users a range of advantages, including authentic problem-solving scenarios (e.g., Planey et al., 2023a, 2023b), lifelike 3D representations of figures and objects (e.g., Drey et al., 2022), haptic feedback (e.g., Webb et al., 2022), embodied interactions (e.g., Kang et al., 2021), social interactions and communication (e.g., Yeh et al., 2018), and more. They have been applied successfully in diverse fields such as planetary science (e.g., Brenner et al., 2021), holiday plans (e.g., Geszten et al., 2018), and language learning (e.g., Lin et al., 2023; Perry, 2021).

Collaborative learning analytics (CLA) tools

Learning analytics is commonly defined as “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs’’ (Philip et al., 2011). The primary purpose of learning analytics is to support students’ agency and development of higher-order skills and therefore enhance students’ learning (Chen et al., 2016; Tsai et al., 2020). Aligning with this purpose, a suite of analytical tools that are grounded in KB pedagogical theory and practices have been developed in the KB field. These analytics tools that are integrated in KF include the previously developed Analytic Toolkit (ATK) using log data (Burtis, 1998) and the applets on vocabulary and social network analysis (SNA) developed later. These tools generate sociograms that visualize reading and build-on activities and calculate the corresponding network densities (Teplovs, 2010). Zhang et al. (2009) used network density for reading to measure awareness of contribution of peers and used network density for build-on or reference networks to measure connectedness to peers.

        To support the collaborative knowledge construction (CKC) process in higher education, CLA tools are designed to demonstrate students' interaction and participation, knowledge construction, and regulation at the group level, with a particular focus on its social and collaborative characteristics (Wise et al., 2021). Widely-used thinking maps have been utilized as working spaces for students' collaborative knowledge construction in higher education, namely mind maps (e.g., visual, non-linear illustrations of ideas and their relationships), concept maps (e.g., connections between relevant concepts, like “lead to” and “result from”), and argument maps (e.g., argumentative structures with nodes symbolizing claims and evidence and links representing relationships like “for” and “against”) (Bodemer & Dehler, 2011; Davies, 2011; Hoffmann, 2015). In addition, CLA tools, such as the knowledge awareness tool, have been applied to track and analyze students' knowledge acquisition and development, with the goal of providing insights on students' knowledge states, fostering student achievement, and supporting instructional practices (Jyothi et al., 2012; Marzano & Miranda, 2021). Text mining and natural language processing techniques have been implemented in CLA tools to demonstrate the connection between keywords, the distribution of discourses, and the relatedness between peers' perspectives (Allen et al., 2022; McNamara et al., 2017; Suthers et al., 2010). However, these CLA tools lack the presentation of comparative perspectives between students and the sharing and negotiation process of perspectives proposed by students during peer interaction, which is essential to reflect the process-based development of collective knowledge during CKC processes in higher education. Considering the CKC nature, it is beneficial to present comprehensive feedback on the CKC process that can diagnose characteristic, change, and development of students' perspectives, including the quantitative frequencies, sequences, and connections of views (Wise et al., 2021).

AI & Agent

The concept of Hybrid Intelligence entails an ideal combination of human with artificial intelligence. This enables a scenario of computer-supported collaborative learning (CSCL), in which multiple learners, the “human agents” construct knowledge together through negotiation, argumentation, and mutual regulation among learners. Decades of research in CSCL have demonstrated that collaboration, when properly supported, leads to higher-order learning outcomes such as critical thinking, metacognition, and knowledge integration.

        To enable these processes, CSCL traditionally relies on instructional scaffolds, such as collaboration scripts and group awareness tools, which provide structure and guidance for effective interaction. Collaboration scripts define roles, phases, or prompts that help learners coordinate tasks, reason together, and engage in transactive discourse (Fischer et al., 2013; Kollar et al., 2018). Group awareness tools, on the other hand, visualize aspects of the group process, such as participation or knowledge contributions, to support regulation and coordination (Bodemer & Dehler, 2011; Janssen & Bodemer, 2013).

        While these supports have proven highly effective, the key challenge for contemporary CSCL lies in adapting support dynamically, matching the level of guidance to learners’ needs and stages of collaboration.

        In this regard, AI-driven conversational agents introduce a new paradigm by dynamically responding to learners’ discourse through natural language processing and machine learning. They can deepen the dialogue by prompting elaboration or broaden it by introducing new perspectives. Our recent empirical findings show that while deepening agents promote cognitive elaboration and broadening agents expand conceptual coverage, too much agent intervention can reduce engagement, pointing towards the need for balance between guidance and autonomy.

在线协作文档工具（常见的8款）【点击即可查看】

思路引导：结合第四讲 小组深度讨论引导单，基于前期使用“知识论坛”或“通用协作工具”的协作学习经验，先通过用户移情图回顾使用体验，再通过反馈捕捉矩阵系统梳理对“知识论坛”（如Knowledge Forum）与“通用协作工具”（如腾讯文档、飞书、Notion等）在知识建构方面优劣的思考，最后，提炼小组观点，形成结论。

Tips：在使用反馈捕捉矩阵系统梳理二者在协作知识建构方面的优劣时，可通过智能体（https://www.coze.cn/s/E7-IUPuAgiA/）查阅相关文献加以分析。【例如，请帮忙查找有关“知识建构”的相关文章，分析······（XX工具的使用体验）在知识建构方面的优劣。】

案例背景：复旦大学元宇宙平台以最初建设的《电离辐射探测与测量虚拟仿真实验》为技术基础，结合复旦大学众多院系的应用场景，共同打造集各院系不同专业内容于一体的元宇宙平台，包括哲学学院、心理系等多个国际知名专业方向。

案例正文：复旦大学教学元宇宙 - Fudanverse 简介

可采用数字人视频或PPT的方式呈现作品（但不限于此）。

元宇宙体验区：Inphi元宇宙体验 https://www.inphi.online/   或  Virbela元宇宙平台 https://www.virbela.com/download

相关视频：元宇宙平台Virbela操作指南

相关指南：VirBELA Open Campus Onboarding         Quick User Guide 1         Quick User Guide 2        Virbela Go User Guide

PS：可基于自身与小组对未来学习环境的思考与想法，通过智能体（https://www.coze.cn/s/E7-IUPuAgiA/）对话，查找相关文献或案例，以启发思考。

例如：“请检索有关‘XX（元宇宙）’的国内外文献，谈谈如何利用元宇宙创设师范生教学实训教室？”

或：“请提供利用元宇宙或VR、AR等相关技术设计智慧教室或智慧学习环境/空间的案例。”

知识建构的12条原则      知识构建：理论、教学法与技术       知识构建与知识创造：理论、教学法与技术