國立虎尾科技大學 |

SenseMate : = An AI-Based Platform to Support Qualitative Coding.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	SenseMate :/
Reminder of title:	An AI-Based Platform to Support Qualitative Coding.
Author:	Overney, Cassandra.
Description:	1 online resource (169 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-10.
Contained By:	Masters Abstracts International85-10.
Subject:	Fine arts. -
Online resource:	click for full text (PQDT)
ISBN:	9798381958287

SenseMate : = An AI-Based Platform to Support Qualitative Coding.
Overney, Cassandra.

SenseMate :An AI-Based Platform to Support Qualitative Coding. - 1 online resource (169 pages)

Source: Masters Abstracts International, Volume: 85-10.

Thesis (M.S.)--Massachusetts Institute of Technology, 2023.

Includes bibliographical references

Unstructured data can be analyzed numerically or qualitatively through methods like sensemaking. One of the key stages of sensemaking is qualitative coding, where the data is divided into units, and each unit is assigned a category or code. Unfortunately, coding is tedious and time-consuming when carried out manually. Finding a balance between manual and fully-automated coding can help increase efficiency while allowing human judgment and preventing systematic machine errors. In this thesis, I propose an accessible semi-automated approach to qualitative coding. First, I apply a novel machine learning method, rationale extraction models, to qualitative coding. These models recommend themes for each unit of analysis in qualitative data and tend to perform better with less ambiguous themes. Through an online experiment, I find that assistance from rationale extraction models increases coding performance and reliability. Next, I execute an iterative, human-centered design process to create SenseMate, an AI-based platform for qualitative coding. After 13 user testing sessions and 3 design iterations, I observe that model overreliance can be minimized through cognitive forcing functions and easy-to-understand model explanations. I also design several ways for users to efficiently provide feedback on machine-generated rationales. To connect my model and design evaluations, I implement a prototype of SenseMate and conduct a summative user evaluation through an online experiment. The evaluation reveals that participants with access to AI assistance have higher coding performances but spend more time on the platform. The effectiveness of various design decisions within SenseMate is also explored. Finally, I discuss a myriad of future work possibilities. Overall, this thesis offers a practical and accessible solution to analyzing unstructured data, which has broad applications for researchers and organizations across various fields.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381958287Subjects--Topical Terms:

1112523
Fine arts.
Subjects--Index Terms:

SensemakingIndex Terms--Genre/Form:

554714
Electronic books.

SenseMate : = An AI-Based Platform to Support Qualitative Coding.
LDR:03217ntm a22003617 4500 001 1146278
005 20240812064359.5
006 m o d
007 cr bn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798381958287
035 $a (MiAaPQ)AAI31091429
035 $a (MiAaPQ)MIT1721_1_151980
035 $a AAI31091429
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Overney, Cassandra. $3 1471639
245 1 0 $a SenseMate : $b An AI-Based Platform to Support Qualitative Coding.
264 0 $c 2023
300 $a 1 online resource (169 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 85-10.
500 $a Advisor: Roy, Deb.
502 $a Thesis (M.S.)--Massachusetts Institute of Technology, 2023.
504 $a Includes bibliographical references
520 $a Unstructured data can be analyzed numerically or qualitatively through methods like sensemaking. One of the key stages of sensemaking is qualitative coding, where the data is divided into units, and each unit is assigned a category or code. Unfortunately, coding is tedious and time-consuming when carried out manually. Finding a balance between manual and fully-automated coding can help increase efficiency while allowing human judgment and preventing systematic machine errors. In this thesis, I propose an accessible semi-automated approach to qualitative coding. First, I apply a novel machine learning method, rationale extraction models, to qualitative coding. These models recommend themes for each unit of analysis in qualitative data and tend to perform better with less ambiguous themes. Through an online experiment, I find that assistance from rationale extraction models increases coding performance and reliability. Next, I execute an iterative, human-centered design process to create SenseMate, an AI-based platform for qualitative coding. After 13 user testing sessions and 3 design iterations, I observe that model overreliance can be minimized through cognitive forcing functions and easy-to-understand model explanations. I also design several ways for users to efficiently provide feedback on machine-generated rationales. To connect my model and design evaluations, I implement a prototype of SenseMate and conduct a summative user evaluation through an online experiment. The evaluation reveals that participants with access to AI assistance have higher coding performances but spend more time on the platform. The effectiveness of various design decisions within SenseMate is also explored. Finally, I discuss a myriad of future work possibilities. Overall, this thesis offers a practical and accessible solution to analyzing unstructured data, which has broad applications for researchers and organizations across various fields.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Fine arts. $3 1112523
653 $a Sensemaking
653 $a AI-based platform
655 7 $a Electronic books. $2 local $3 554714
690 $a 0357
690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Massachusetts Institute of Technology. $3 574355
773 0 $t Masters Abstracts International $g 85-10.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31091429 $z click for full text (PQDT)