Learning in immersed collaborative virtual environments: design and implementation

Figures & data

Table 1. Condensed theoretical framework (left) and its operationalization (right) to facilitate design for learning in immersive virtual learning environments.

Condensed overview of theoretical framework Main steps in the design for learning approach (Fowler, 2015): Determine: Intended learning outcomes Learning requirements (objectives: what, activities: how) for the learning stages of Mayes and Fowler (1999): Conceptualization, construction, dialogue Teaching and learning approach Yes/no sufficient match between learning requirements and potential learning benefits of virtual environments in the framework of Dalgarno and Lee (2010): Spatial knowledge representation, experiential learning, engagement, contextual learning, collaborative learning If match is sufficient: Representational fidelity and learner interaction elements aligned with learning requirements, yielding an optimal benefit-cost trade-off Learning specification Cognitive theory of multimedia learning (Mayer, 2009, 2014) Highlights of possibilities for reducing cognitive load: Segmenting principle:   Allow learner to decide when to receive new information Pre-training principle:   Create understanding of subparts before commencing learning Coherence principle:   Remove information extraneous to learning Generative processing:   Employ activities deepening understanding

Recommendations to facilitate design for learning in immersive virtual environments (De Back et al., 2021) Broad design choices: Apply frameworks of Dalgarno and Lee (2010) and Fowler (2015) Apply strategies to optimize cognitive load Coherence: Restrict the environment to elements likely to benefit learning Segmenting (if applicable): Let learner decide the receipt of new content Pre-training: Implement an introduction and practice phase Use an intuitive user interface with natural embodied interactions Generative processing: Foster generative actions using embodiment Guide learning: Clear instructions, direct feedback and game logic Match real and virtual locomotion. Otherwise: Provide locomotion options Consider strategies to foster collaborative learning Design the content and instructions to be equally accessible to all learners Build active participation and interdependence into the instructional design Make learner interactions clearly visible to foster discussion and learning Leverage platform specific affordances VR: Embodiment, voice, avatars. CAVE: Embodied actions with 3D mouse Mitigate platform specific limitations VR: No facial expressions: Foster collaboration with other means (see above) CAVE: Leverage single-user interaction to foster generative collaboration. Limit parallax if extraneous to learning, implement perspective switching Strategies for additional benefits Explore the use of gamification elements while retaining focus on learning Reduce effort, time & cost: Use a modular design when adaptability is desired Use automated performance analysis to benefit learners and educators

Note. VR = virtual reality.

Figure 1. Structure of virtual environment inside the CAVE: side and bird’s-eye view (top left, top right), scoreboard, lever to verify answers, model of whole brain (bottom left), individual brain areas (bottom right). Colored dots indicate areas where connections can be drawn between different elements.

Figure 2. Examples of implemented strategies to foster learning. Intuitive interaction: using 3D mouse to highlight areas of interest (top left), provide answer by drawing connection (top right), pull lever to verify answer (middle left). Feedback on right/wrong answers (middle right), collaborative learning (bottom left), turn taking instruction to foster active participation (bottom right).

Table 2. Examples of the technical implementation of the recommendations into the collaborative virtual learning environment.

Recommendations	Implementation examples
Optimization of cognitive load
Coherence	Stimuli and interactions limited to those consistent with the intended learning outcomes
Segmenting	Educational content divided into stages and phases. Progression under learner control
Pre-training	Introduction and practice phase before start of learning
Intuitive interface with embodied interactions	Tracked 3D mouse and glasses, virtual scissors and lever for interactions, large dots to indicate points of interaction, perspective switching with single button push
Generative processing	Exploration with natural motions, embodied line drawing and cutting in 3D space, task split into exploration, reconstruction and review
Guide learning	Instructions, direct feedback on answers, game logic to keep learners on track
Match real and virtual locomotion	Natural movement using four wall CAVE system
Foster collaborative learning
Content and instructions equally accessible to all	Content prominently displayed on CAVE walls, instructions in both text and audio, interactions relevant to learning kept visible until no longer needed
Design for active participation and interdependence	Drawing and walking required to finish stage, turn-based alternation between direct interaction and group discussion
Clearly visualize learner interactions	Interactions indicated using bright colored lines
Leverage platform specific affordances
Embodied actions with 3D mouse	Tracked 3D mouse used for embodied interaction with the educational content
Mitigate platform specific limitations
Leverage single-user interaction to foster generative collaboration	Tracked glasses for all learners to overcome single-user perspective limitation of CAVE, reduced parallax by placing virtual objects at distance from learners
Strategies for additional benefits
Gamification to retain focus on learning	Clear learning goals, stages, direct feedback, scores
Reduce effort, time & cost	Generic task and modular structure to easily reconfigure the virtual learning environment
Automated performance analysis	Enabled using data of tracked 3D mouse and glasses

Figure 3. Dynamic logging model. The model shows how incoming data streams on learner actions (indicated with circles in the figure) with the virtual environment and body-related information are logged, making the information available for in-depth analysis, and is automatically processed by a replay module and a behavior encoder, respectively outputting: (1) a visual replay of learner behavior; (2) an analysis overview of individual and collaborative behavior types of choice.

Figure 4. Implementation of the model. Top: Example results of automated (behavioral) analysis, showing group statistics as well as individual performance metrics. The metrics are explained in the section preceding this figure. Bottom: Actual scene inside CAVE (left) and corresponding visual replay, with colored lines indicating gaze direction of the learners (right).

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