東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Understanding Emotions in Online Learning: Using Emotional Design and Emotional Measurement to Unpack Complex Emotions During Collaborative Learning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Understanding Emotions in Online Learning: Using Emotional Design and Emotional Measurement to Unpack Complex Emotions During Collaborative Learning./
Author:	Hillaire, Garron Edward.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	297 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-03, Section: A.
Contained By:	Dissertations Abstracts International83-03A.
Subject:	Universal design. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28723259
ISBN:	9798544212195

Understanding Emotions in Online Learning: Using Emotional Design and Emotional Measurement to Unpack Complex Emotions During Collaborative Learning.
Hillaire, Garron Edward.

Understanding Emotions in Online Learning: Using Emotional Design and Emotional Measurement to Unpack Complex Emotions During Collaborative Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 297 p.

Source: Dissertations Abstracts International, Volume: 83-03, Section: A.

Thesis (Ph.D.)--Open University (United Kingdom), 2021.

This item must not be sold to any third party vendors.

Many educational researchers explore the role of emotion in learning and there are many new affordances for emotional measurement. Just as there are many options for emotional measurement there are many theories of emotion. When it comes to the measure of sentiment analysis recent findings suggest it is beneficial to online and blended learning research. The sentiment analysis technologies used for educational research are general purpose technologies suggesting that creating a measure designed for the context of learning would improve the alignment between the measure and context. In addition to aligning measure with the context, there is a need to consider how sentiment analysis relates to emotion theory to determine an appropriate method to evaluate the accuracy of sentiment analysis.In this PhD thesis I adopt the Constructed Theory of Emotion, which considers emotion as a collective intentionality indicating that consensus on emotion is the best approach toward examining accuracy. From this perspective I create a sentiment analysis measure in the context of learning to contribute to emotional learning analytics the emerging sub-field of learning analytics. The field of learning analytics acknowledges that design and measurement are intertwined. I adopt a design-based research approach by designing supports for emotional communication and examining how such a design impacts the accuracy of sentiment analysis. I then examine correlation analysis with other established measures of emotion. The results contribute to the field of emotional learning analytics by:• demonstrating promise for generating a classifier based on student perception• demonstrating benefits of supporting emotion expression in text for students• demonstrating that students' emotion expression in text does not appear to align with their internal emotional experiencesThese findings provide opportunities for further research and suggest caution should be used when interpreting sentiment analysis results in the context of learning.

ISBN: 9798544212195Subjects--Topical Terms:

1535191
Universal design.

Understanding Emotions in Online Learning: Using Emotional Design and Emotional Measurement to Unpack Complex Emotions During Collaborative Learning.
LDR:03219nmm a2200349 4500 001 2349616
005 20230509091128.5
006 m o d
007 cr#unu||||||||
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798544212195
035 $a (MiAaPQ)AAI28723259
035 $a (MiAaPQ)Open_Univ_UK77293
035 $a AAI28723259
040 $a MiAaPQ $c MiAaPQ
100 1 $a Hillaire, Garron Edward. $3 3689026
245 1 0 $a Understanding Emotions in Online Learning: Using Emotional Design and Emotional Measurement to Unpack Complex Emotions During Collaborative Learning.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 297 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-03, Section: A.
500 $a Advisor: Rienties, Bart.
502 $a Thesis (Ph.D.)--Open University (United Kingdom), 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Many educational researchers explore the role of emotion in learning and there are many new affordances for emotional measurement. Just as there are many options for emotional measurement there are many theories of emotion. When it comes to the measure of sentiment analysis recent findings suggest it is beneficial to online and blended learning research. The sentiment analysis technologies used for educational research are general purpose technologies suggesting that creating a measure designed for the context of learning would improve the alignment between the measure and context. In addition to aligning measure with the context, there is a need to consider how sentiment analysis relates to emotion theory to determine an appropriate method to evaluate the accuracy of sentiment analysis.In this PhD thesis I adopt the Constructed Theory of Emotion, which considers emotion as a collective intentionality indicating that consensus on emotion is the best approach toward examining accuracy. From this perspective I create a sentiment analysis measure in the context of learning to contribute to emotional learning analytics the emerging sub-field of learning analytics. The field of learning analytics acknowledges that design and measurement are intertwined. I adopt a design-based research approach by designing supports for emotional communication and examining how such a design impacts the accuracy of sentiment analysis. I then examine correlation analysis with other established measures of emotion. The results contribute to the field of emotional learning analytics by:• demonstrating promise for generating a classifier based on student perception• demonstrating benefits of supporting emotion expression in text for students• demonstrating that students' emotion expression in text does not appear to align with their internal emotional experiencesThese findings provide opportunities for further research and suggest caution should be used when interpreting sentiment analysis results in the context of learning.
590 $a School code: 0949.
650 4 $a Universal design. $3 1535191
650 4 $a Research data management. $3 3689027
650 4 $a Verbal communication. $3 3560678
650 4 $a Instructional design. $3 3172279
650 4 $a Ethics. $3 517264
650 4 $a Confidence intervals. $3 566017
650 4 $a Emotions. $3 524569
650 4 $a Feedback. $3 677181
650 4 $a Distance learning. $3 3557921
650 4 $a Perceptions. $3 3435328
650 4 $a Sentiment analysis. $2 lcstt $3 3266790
650 4 $a Student retention. $3 3562829
650 4 $a Authorship. $3 529425
650 4 $a Algorithms. $3 536374
650 4 $a Collaborative learning. $3 3543645
650 4 $a Comparative analysis. $3 3548341
650 4 $a Accuracy. $3 3559958
650 4 $a Questionnaires. $3 529568
650 4 $a Researchers. $3 3560729
650 4 $a Influence. $3 1973172
650 4 $a Experiments. $3 525909
650 4 $a Data collection. $3 3561708
650 4 $a Methods. $3 3560391
650 4 $a Literature reviews. $3 3559998
690 $a 0394
690 $a 0447
690 $a 0454
710 2 $a Open University (United Kingdom). $3 3427325
773 0 $t Dissertations Abstracts International $g 83-03A.
790 $a 0949
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28723259