From children's play to intentions : a play analytics framework for constructionist learning apps
Name
1026501579-MIT.pdf
Description
Full printable version
Size
8.02 MB
Format
Adobe PDF
Checksum (MD5)
f76c4770d501cbcc47c85a6646b962f9
Author(s)
Soltangheis, Mina
Advisor(s)
Deb Roy.
Alternative Title
Play analytics framework for constructionist learning apps
Date Issued
2017
Publisher
Massachusetts Institute of Technology
Abstract
Educational games and digital learning environments provide opportunities to collect fine-grained data on how learners engage with these technologies. The number of technologies targeted at literacy learning for children is increasing. However, the majority of them are structured and reward-based. Therefore, the users' behavior and data collected from them have the same limits. In this thesis, however, we assess children's engagement with a constructionist literacy learning app. The open ended nature of play in such an environment gives us the opportunity to analyze children's play not only through what they made while playing but also how they did it. This thesis provides an analytics pipeline from data acquisition to modeling behavioral patterns. This systematic way of capturing significant events in children's play can be used to inform stakeholders such as parents, peers and teachers and engage them with the learning process. It also gives the learning environment more intelligence on when and what to provide scaffolding on. To collect data, we ran two pilot studies and gathered audio and video recordings of play sessions. In addition, all of the children's interactions within the app were automatically logged. The fine-grained longitudinal data collected during the pilot studies provides a rich yet raw corpus. To reveal the patterns hidden in the data, the analytics pipeline parses logs of low-level interactions into abstract representations for sequences of actions in a word construction process. Next, it visualizes the process for each play session and the entire play history. Using the visualizations, I identified and annotated repeated motifs for more intentional sequences of actions during play and used supervised learning models to capture those patterns. The results of this analytical pipeline are currently being used by literacy experts to provide feedback to parents and suggest activities based on the child's process.
Description
Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 73-76).
Subjects
Program in Media Arts and Sciences ()
MIT Department
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
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