東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Statistical Learning for Process Data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical Learning for Process Data./
作者:	Wang, Zhi.
面頁冊數:	1 online resource (161 pages)
附註:	Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
Contained By:	Dissertations Abstracts International83-02B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28548891click for full text (PQDT)
ISBN:	9798516988684

Statistical Learning for Process Data.
Wang, Zhi.

Statistical Learning for Process Data. - 1 online resource (161 pages)

Source: Dissertations Abstracts International, Volume: 83-02, Section: B.

Thesis (Ph.D.)--Columbia University, 2021.

Includes bibliographical references

Computer-based tests facilitate the collection of problem-solving processes, also known as process data. Response processes recorded in computer log files provide a new venue for investigating and understanding human behaviors. This thesis focuses on the development of statistical learning methods for process data and considers the following three problems.The first problem is feature extraction. Response processes are noisy and of non-standard formats. To exploit information in process data, we propose two generic methods that summarize response processes to vectors so that standard statistical tools such as regression models are applicable. In Chapter 2, features are extracted using multidimensional scaling and a pairwise dissimilarity measure of response processes. Chapter 3 utilizes autoencoder and recurrent neural network to explore the latent structure of process data. For both methods, empirical studies show that the extracted features preserve a substantial amount of information in the observed processes and have greater predictive power for many variables than the traditional item responses.The second problem is assessment based on process data. We present a statistical procedure in Chapter 4 that incorporates process information to improve the latent trait estimation of item response theory models. The procedure is data-driven and can be easily implemented by means of regression models. Theoretical guarantee is established for the mean squared error reduction. Application of this new process-data-based estimator to a real dataset shows that it achieves higher reliability than the traditional item-response-theory-based estimator. The third problem is identification of problem-solving strategies for exploratory analysis. The approach presented in Chapter 5 segments individual process into a sequence of more homogeneous subprocesses using action predictability. Each subprocess is associated with a subtask whereby long and complex response process can be transformed into shorter and more interpretable subtask sequence. Using this approach, problem-solving strategies can be visualized and compared among groups of respondents and process information can be decomposed for further analysis.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798516988684Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

AutoencoderIndex Terms--Genre/Form:

542853
Electronic books.

Statistical Learning for Process Data.
LDR:03582nmm a2200385K 4500 001 2357527
005 20230725053504.5
006 m o d
007 cr mn ---uuuuu
008 241011s2021 xx obm 000 0 eng d
020 $a 9798516988684
035 $a (MiAaPQ)AAI28548891
035 $a AAI28548891
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Wang, Zhi. $3 1900689
245 1 0 $a Statistical Learning for Process Data.
264 0 $c 2021
300 $a 1 online resource (161 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: Dissertations Abstracts International, Volume: 83-02, Section: B.
500 $a Advisor: Liu, Jingchen; Ying, Zhiliang.
502 $a Thesis (Ph.D.)--Columbia University, 2021.
504 $a Includes bibliographical references
520 $a Computer-based tests facilitate the collection of problem-solving processes, also known as process data. Response processes recorded in computer log files provide a new venue for investigating and understanding human behaviors. This thesis focuses on the development of statistical learning methods for process data and considers the following three problems.The first problem is feature extraction. Response processes are noisy and of non-standard formats. To exploit information in process data, we propose two generic methods that summarize response processes to vectors so that standard statistical tools such as regression models are applicable. In Chapter 2, features are extracted using multidimensional scaling and a pairwise dissimilarity measure of response processes. Chapter 3 utilizes autoencoder and recurrent neural network to explore the latent structure of process data. For both methods, empirical studies show that the extracted features preserve a substantial amount of information in the observed processes and have greater predictive power for many variables than the traditional item responses.The second problem is assessment based on process data. We present a statistical procedure in Chapter 4 that incorporates process information to improve the latent trait estimation of item response theory models. The procedure is data-driven and can be easily implemented by means of regression models. Theoretical guarantee is established for the mean squared error reduction. Application of this new process-data-based estimator to a real dataset shows that it achieves higher reliability than the traditional item-response-theory-based estimator. The third problem is identification of problem-solving strategies for exploratory analysis. The approach presented in Chapter 5 segments individual process into a sequence of more homogeneous subprocesses using action predictability. Each subprocess is associated with a subtask whereby long and complex response process can be transformed into shorter and more interpretable subtask sequence. Using this approach, problem-solving strategies can be visualized and compared among groups of respondents and process information can be decomposed for further analysis.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 517247
650 4 $a Problem solving. $3 516855
650 4 $a Simulation. $3 644748
650 4 $a Accuracy. $3 3559958
650 4 $a Datasets. $3 3541416
650 4 $a Response time. $3 3562212
650 4 $a Experiments. $3 525909
650 4 $a Web browsers. $3 3697281
650 4 $a Variables. $3 3548259
650 4 $a Data processing. $3 680224
650 4 $a Age groups. $2 bicssc $3 2081388
650 4 $a Advisors. $3 3560734
650 4 $a Visualization. $3 586179
650 4 $a Case studies. $2 itrt $3 996239
650 4 $a Websites. $2 gtt $3 1062467
653 $a Autoencoder
653 $a Exploratory analysis
653 $a Feature extraction
653 $a Latent trait estimation
653 $a Multidimensional scaling
653 $a Process data
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0463
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a Columbia University. $b Statistics. $3 1682152
773 0 $t Dissertations Abstracts International $g 83-02B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28548891 $z click for full text (PQDT)