Categorizing claims on a website required students to read and interpret content beyond what the visualization tool provides, including evaluating trustworthiness¶

Claim · All claims
Evidence · 1 study · qualitative r3 · q2
Currency · newest study 2018 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? The software provides no visual means to judge trustworthiness of website claims, so students read and interpreted claims to establish categories, with one student focusing on stance and another on trustworthiness. → Solli 2018

Evidence¶

Solli 2018¶

Solli, A., Mäkitalo, Å., & Hillman, T. (2018). Rendering controversial socioscientific issues legible through digital mapping tools. International Journal of Computer-Supported Collaborative Learning. https://doi.org/10.1007/s11412-018-9286-x

q2 · i? · qualitative · r3

Episode 4 interaction analysis: Ana and Ted read claims on vaccin.me to decide if it was "against" the vaccine while Vicky evaluated trustworthiness, providing counterclaims; the node was recolored "a little pink," acknowledging her challenge while moving on.

"The software is not developed to provide users with visual means to determine whether the claims on the websites are trustworthy or substantiated with evidence ."

Discussion¶

Related Claims¶

• Trustworthiness could not be reduced to a fully automated objective; human judgment remains necessary for resolving complex or context-dependent cases — related
• Making trustworthiness metrics and visualizations explicit increased inter-rater reliability among learning engineers evaluating LLM responses — related
• Digital network mapping tools support students in critically selecting and reviewing online data on socio-scientific controversies — a broader claim this one bears on
• Digital mapping tools slow down students' judging and categorizing of online material, prompting collaborative negotiation of how to make sense of a controversy — a broader claim this one bears on
• Students orient to visually prominent nodes (large, central) when beginning to categorize actors on a controversy map — related
• The software's content-blind structure makes important controversy actors difficult to find, prompting students to repurpose tool features to the task — related