Topic & Tag	Challenges and Questions
PEOPLE
PPL1	**Impact of educators’ diverse backgrounds on visualization teaching **
	Q1: How do we establish who teaches data visualization, understand how they do it and how they succeed?
	Q2: How can we understand the effects of an educator’s background on teaching philosophy, goals and methods?
	Q3: How can we develop educational offerings that support a wide range of backgrounds, methods, and goals?
PPL2	Leveraging and catering to learners’ diverse backgrounds, goals, and needs
	Q4: How do we describe learners in visualization, their goals, motivations and individual needs?
	Q5: How can we understand the diversity in learners and what this brings to their learning and experience?
PPL3	Acknowledging and embracing diversity to allow for mutual learning
	Q6: How do we develop entry points to visualization education that embrace learners’ diversity?
	Q7: How do we work with learners to co-design goals, methods, and learning pathways?
	Q8: How do we develop ways in which educators and learners learn from each other?
	Q9: How can we develop ways in which learners take an active part in the education of their peers?
GOALS & ASSESSMENT
GA1	Identifying learning goals and designing objectives tailored to specific groups of learners
	Q10: How can we develop learning goals specific to data visualization, including for diverse groups of learners?
	Q11: How to evaluate assessment methods for these learning goals?
GA2	Assessing creative, project-based, and problem-oriented work in a fair and efficient manner
	Q12: How do we assess skills related to creativity, problem-solving, collaboration, and group work in data visualization?
	Q13: How do we assess learning in diverse, varied, informal, and workbased education scenarios?
	Q14: How do we assess learners working with different (self-selected, personal) datasets, technologies, collaborators or organizations?
GA3	Assessing learners’ work at scale and distance
	Q15: How do we provide feedback that is transparent, fair, high-quality, and timely when teaching at scale or distance?
MOTIVATION
MTV1	Communicating the need for visualization education
	Q16: How to better communicate goals, levels, expectations, and prerequisites to diverse groups of learners?
	Q17: How do we understand and respond to the factors that motivate people to engage with visualization learning?
	Q18: How do we capture and share visualization success stories and understand what made them a success?
MTV2	Retaining motivation during learning visualization
	Q19: How do we embrace learners’ disciplinary and background knowledge in ways that motivate?
	Q20: How can we balance flexibility in learning, through free-form, learner-centered and individualized activity, with educator workload?
METHODS
MTH1	Fostering core skills around visual representation and interaction
	Q21: How do we identify, prioritize, and develop core skills in visualization?
	Q22: How do we leverage and understand play for data visualization education?
	Q23: How do we support the development of skills and knowledge in interactive exploration and the use of sophisticated visual analytics tools and techniques?
MTH2	Developing ‘specific’ and ‘general’ skills and competencies
	Q24: How do we combine core skills in visualization with broader competencies?
MTH3	Adapting methods to learners and contexts
	Q25: How can we describe, collect, and share the range of activities and methods available for data visualization education?
	Q26: How do we substantiate the activities and methods that work well for individual learners, groups of learners, and diverse contexts?
ENVIRONMENT
ENV1	Providing environments for hands-on, creative, and collaborative work
	Q27: How do specific learning environments affect data visualization education?
	Q28: How do we create approaches that leverage the affordances of specific places, platforms, and other contexts?
ENV2	Using online, hybrid, informal & workplace environments
	Q29: How do we use technology and distributed settings to deliver educational experiences?
	Q30: How can we mitigate the limitations of distant learning for social interaction, hands-on activities, and feedback?
MATERIALS
MAT1	Finding, evaluating, and adapting materials
	Q31: How can we collect, share, and expand the range of materials and methods for data visualization education?
	Q32: How can we substantiate the materials that work well for individual learners, groups of learners, and diverse contexts?
MAT2	Reusing and adapting materials
	Q33: How can we share materials for data visualization education in ways that are as accessible and inclusive as possible?
	Q34: How can we support, promote and encourage the re-use of materials and move towards environmental sustainability?
	Q35: How can we develop frameworks for sharing and re-using materials for data visualization education?
MAT3	Creating and updating materials
	Q36: How can we create, modify and maintain bespoke materials?
	Q37: How can we identify and address gaps in data visualization education provision and develop materials that support this activity?
MAT4	Creating materials for informal, self-paced learning
	Q38: How do we develop intelligent tutoring systems for visualization that provide bespoke feedback and guide learners in their education?
CHANGE
CHG1	Understanding the role and effects of AI
	Q39: How do we conduct reliable assessment of learners’ competencies when AI can outperform students in the kinds of tasks traditionally valued in data visualization education?
	Q40: How do we determine appropriate use of and needs for data visualization as technology changes the discipline and its practice?
	Q41: How do we revise the competencies associated with data visualization education as technology changes the discipline and its practice?
CHG2	Overcoming inertia and adapting to change
	Q42: How can we develop and engage in futuring activities for data visualization education to identify and react to change?
	Q43: How do we develop and support approaches to education and educational systems that are relevant and robust in light of change?