Literature DB >> 34382131

ProcData: An R Package for Process Data Analysis.

Xueying Tang1, Susu Zhang2, Zhi Wang3, Jingchen Liu4, Zhiliang Ying3.   

Abstract

Process data refer to data recorded in log files of computer-based items. These data, represented as timestamped action sequences, keep track of respondents' response problem-solving behaviors. Process data analysis aims at enhancing educational assessment accuracy and serving other assessment purposes by utilizing the rich information contained in response processes. The R package ProcData presented in this article is designed to provide tools for inspecting, processing, and analyzing process data. We define an S3 class 'proc' for organizing process data and extend generic methods summary and print for 'proc'. Feature extraction methods for process data are implemented in the package for compressing information in the irregular response processes into regular numeric vectors. ProcData also provides functions for making predictions from neural-network-based sequence models. In addition, a real dataset of response processes from the climate control item in the 2012 Programme for International Student Assessment is included in the package.
© 2021. The Psychometric Society.

Entities:  

Keywords:  autoencoder; multidimensional scaling; process data analysis; sequence model

Mesh:

Year:  2021        PMID: 34382131     DOI: 10.1007/s11336-021-09798-7

Source DB:  PubMed          Journal:  Psychometrika        ISSN: 0033-3123            Impact factor:   2.290


  6 in total

1.  Long short-term memory.

Authors:  S Hochreiter; J Schmidhuber
Journal:  Neural Comput       Date:  1997-11-15       Impact factor: 2.026

2.  Latent Feature Extraction for Process Data via Multidimensional Scaling.

Authors:  Xueying Tang; Zhi Wang; Qiwei He; Jingchen Liu; Zhiliang Ying
Journal:  Psychometrika       Date:  2020-06-22       Impact factor: 2.500

3.  Data Mining Techniques in Analyzing Process Data: A Didactic.

Authors:  Xin Qiao; Hong Jiao
Journal:  Front Psychol       Date:  2018-11-23

4.  Statistical Analysis of Complex Problem-Solving Process Data: An Event History Analysis Approach.

Authors:  Yunxiao Chen; Xiaoou Li; Jingchen Liu; Zhiliang Ying
Journal:  Front Psychol       Date:  2019-03-18

5.  Taking a Closer Look: An Exploratory Analysis of Successful and Unsuccessful Strategy Use in Complex Problems.

Authors:  Matthias Stadler; Frank Fischer; Samuel Greiff
Journal:  Front Psychol       Date:  2019-05-07

6.  Exploring Multiple Goals Balancing in Complex Problem Solving Based on Log Data.

Authors:  Yan Ren; Fang Luo; Ping Ren; Dingyuan Bai; Xin Li; Hongyun Liu
Journal:  Front Psychol       Date:  2019-09-27
  6 in total
  1 in total

1.  Identidication of novel biomarkers in non-small cell lung cancer using machine learning.

Authors:  Fangwei Wang; Qisheng Su; Chaoqian Li
Journal:  Sci Rep       Date:  2022-10-06       Impact factor: 4.996

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.