Work

Addressing the Data Set Dilemma With Personally Relevant Data Generation and Distributed Labeling in the Classroom

Year: 2023

Type: conference-paper

Abstract: Educators and learning scientists are now encountering the "data set dilemma" whereby the ongoing massive generation of new data still does not yet yield data sets that are easily used for learning purposes given the varied needs and goals of different teachers and classrooms.We have developed a technology innovation, dubbed "Datamax", that generates customizable simulated data that can be quickly labeled by students through crowdsourcing.Then, students have a data set to which they are personally connected and were involved in modifying or creating.We report the rationale, precedent in learning sciences and CSCL literature, and interfaces of Datamax along with qualitative excerpts from high school students using it for a college application scenario.

Source: Computer-supported collaborative learning/The Computer-Supported Collaborative Learning Conference

DOI: 10.22318/cscl2023.992082

Authors: Victor R. Lee, Parth Sarin, Isabel Sieh, Anika Fuloria

Institution: Stanford University

Language: English

Topic: Online Learning and Analytics

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: bronze

OpenAlex ID: W4387954397