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Edited and reviewed by: Huali Wang, Peking University Sixth Hospital, China

This article was submitted to Quantitative Psychology and Measurement, a section of the journal Frontiers in Psychology

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

The increasing use of computer-based testing and learning environments is leading to a significant reform on the traditional form of measurement, with tremendous extra available data collected during the process of learning and assessment (Bennett et al.,

The recent advances in computer technology enhance the convenient collection of process data in computer-based assessment. One such example is time-stamped action data in an innovative item which allow for the interaction between a respondent and the item. When a respondent attempts an interactive item, his/her actions are recorded, in the form of an ordered sequence of multi-type, time-stamped events. These sorts of data stored in log files, referred to as

With the availability of process data in addition to response data, the measurement field is becoming increasingly interested in borrowing additional auxiliary information from the responding process to serve different assessment purposes. For instance, recently researchers proposed different models for response time and the joint modeling of responses and response time (e.g.,

This Research Topic (formed in this edited e-book) intends to explore the forefront of responding to the needs in modeling new data sources and incorporating process data in the statistical modeling of multiple possible assessment data. This edited book presents the cutting-edge research related to utilizing process data in addition to product data such as item responses in educational and psychological measurement for enhancing accuracy in ability parameter estimation (e.g.,

Throughout the book, the methods for analyzing process data in technology-enhanced innovative items in large-scale assessment for high-stakes decisions are addressed (e.g.,

The book chapters demonstrate the use of process data and the integration of process and product data (item responses) in educational and psychological measurement. The chapters address issues in adaptive testing, problem-solving strategy, validity of test score interpretation, item pre-knowledge detection, cognitive diagnosis, complex dependence in joint modeling of responses and response time, and multidimensional modeling of these data types. The originality of this book lies in the statistical modeling of innovative assessment data such as log data, response time data, collaborative problem-solving tasks, dyad data, change process data, testlet data, and multidimensional data. Further, new statistical models are presented for analyzing process data in addition to response data such as transition profile analysis, the event history analysis approach, hidden Markov modeling, conditional scaling, multilevel modeling, text mining, Bayesian covariance structure modeling, mixture modeling, and multidimensional modeling. The integration of multiple data sources and the use of process data provides the measurement field with new perspectives to solve assessment issues and challenges such as problem-solving strategy, cheating detection, and cognitive diagnosis.

An overview of all the papers included in this Research Topic is summarized in

leveraging process data to explore test-takers' behaviors and problem-solving strategies,

proposing joint modeling for response accuracy and response times,

proposing new statistical models on analyzing response processes (e.g., time-stamped sequential events),

advancing cognitive diagnostic models with new data sources, and

using data streams in estimating collaborative problem-solving skills.

An overview of papers collected in this Research Topic.

Exploring multiple goals in interactive problem-solving items | Extracted response process variables, correctness of responses | Cluster analysis, logistics, and least-squares regression | Interactive problem-solving in PISA 2012 | |

Proposing a validity research that uses processing times to provide both convergent and discriminant validity evidence for the construct interpretation of reasoning and reading ability scores | Response data, response times | MLR estimator (maximum likelihood estimation with robust standard error) | PIAAC 2012 literacy assessments | |

Exploring successful and unsuccessful strategies with process data in complex problem-solving items | Response process data, correctness of responses | N-grams model | Interactive problem-solving items | |

Exploring response times in complex simulation-based tasks to understand test-takers' interactions | Response data, response times | Cluster analysis and hierarchical framework for joint modeling item responses and response times | Interactive problem-solving items | |

Detecting examinees with pre-knowledge in experimental data with conditional scaling of response times | Item scores, response times | Cluster analysis, factor analysis | Simulation study and empirical study in GRE quantitative testing | |

Understanding test-takers' choices using hidden Markov modeling of process data | Response data, answer change, item difficulty | Hidden Markov model | Self-adapted tests | |

Using data mining techniques in analyzing process data and making comparisons among machine-learning algorithms in exploring problem-solving items | Extracted response process variables, correctness of responses | Multiple machine learning algorithms: supervised techniques (CART, gradient boosting, random forest, and SVM), unsupervised techniques (SOM, k-means) | Interactive problem-solving in PISA 2012 | |

Exploring test-takers' problem-solving strategies with a modified multilevel mixture IRT model | Extracted response process variables, correctness of responses | Modified multilevel mixture IRT model, latent class analysis | Interactive problem-solving in PISA 2012 | |

Exploring sequential patterns in problem-solving items and relationship with individual differences in background variables | Extracted response process variables, response data, background variables | N-grams model, feature selection model, regression analysis | PIAAC 2012 problem-solving in technology-rich environment | |

Proposing a joint model for multidimensional abilities and multifactor speed | Response data, response times | Joint modeling of response and response time, exploratory factor analysis | Simulation study and empirical study in computer-based math assessment (PISA 2012) | |

Proposing a joint model for item response and time-on-task to increase the precision of ability estimates | Response data, response times | Multidimensional latent model for response and response time | Interactive problem-solving in PISA 2012 | |

Proposing a joint model for a speed-accuracy tradeoff hierarchical model based on cognitive experiment | Response data, response times | Bayesian MCMC algorithm, speed-accuracy hierarchical model | Simulation study and empirical study in Raven's Standard Progressive Matrices | |

Proposing a Bayesian modeling framework for response accuracy, response times, and other process data variables | Response data, response times, extracted response process variables | Bayesian covariance structure models | Simulation study and empirical study in PIAAC 2012 cognitive assessments | |

Proposing a parameterized joint model of response data and response time to detect invariance by gender and mode between computer-based and paper-based tests | Response data, response times | Bivariate generalized linear IRT model framework (B-GLIRT) | PISA 2012 and PISA 2009 reading assessments | |

An overview of models for joint modeling of response times and response accuracy in cognitive tests | Response data, response times | Multiple response models and joint models of response data and response times | Literature review | |

Modeling response time and responses in multidimensional health measurement | Response data, response times | Multidimensional-graded response model, hierarchical joint model of responses and response times | Health measurement | |

Proposing a mixture learning model that utilizes the response times and response accuracy in learning progression | Response data, response times | Diagnostic classification model framework, Bayesian estimation | Simulation study and empirical study in a computer-based learning environment | |

Proposing a joint model for response accuracy and response times with consideration on non-linear conditional dependence | Response data, response times | Joint model for quadratic conditional dependence, joint model for multiple-category conditional dependence, indicator-level non-parametric moderation method | Simulation study and empirical study in high-stakes arithmetic assessment | |

Therapeutic change process research through multilevel and text mining | Life narratives textual data and response data | Multilevel models, text mining | Epidemiologic Studies Depression Scale and life narratives (CES-D) | |

Investigating how the major outcome of a confirmatory factor investigation is preserved when scaling the variance of a latent variable by the various scaling methods | Scaling data | Multiple confirmatory factor analysis | Simulation study and empirical study in Multitrait-Multimethod (MTMM) design | |

Proposing a model with a leakage parameter to better characterize the item leaking process and develop a more generalized detection method by monitoring responses of test-takers | Response data | Generalized linear model for detection, leakage simulation model | Simulation study and empirical study in operational computerized adaptative testing | |

Proposing a multidimensional IRT approach for dynamically monitoring ability growth in adaptive learning systems | Response data, response times | Multidimensional IRT | Simulation study and web-based learning platform | |

Proposing an event history analysis approach to predict duration and outcome of solving a complex problem by making use of process data | Time-stamped sequential events data, correctness of responses | Regression model | Interactive problem-solving in PISA 2012 | |

Comparing termination rules for variable-length CD-CAT from the information theory perspective | Response data, test construction variables | Multiple cognitive diagnostic models | Simulation study | |

Proposing a model to integrate differential evolution optimization into the EM framework in the log-linear cognitive diagnostic model estimation | Response data | Log-linear cognitive diagnostic model with EM algorithm, differential evolution | Simulation study and empirical study in assessment of a health profession | |

Proposing a joint testlet cognitive diagnostic model for paired local item dependence using response time and response accuracy | Response data, response times | Joint testlet cognitive diagnosis modeling | PISA 2015 computer-based math assessment | |

Characterizing interactive communications in collaborative problem-solving using a conditional transition profile approach | Conversations collected in a computer-based collaborative problem-solving platform | Conditional transition profile, cluster analysis | Collaborative problem-solving platform | |

Assessing collaborative problem-solving competence by extracting indictors from process stream data and modeling dyad data | Process stream data in collaborative problem solving, response data | Multidimensional Random Coefficients Multinomial Logit Model (MRCMLM) | Collaborative problem-solving platform adapted from a problem-solving task in PISA 2012 |

The above categorization focused on each paper's core contribution though some papers can be cross-classified. The papers' key findings and advancements impressively represent the current state-of-the-art methods in the field of process data analysis in educational and psychological assessments. As topic editors, we were happy to receive such a great collection of papers with various foci and submit these publications right as digital assessments are booming. The papers collected in this Research Topic are also diverse in data types, statistical approaches, and assessment with an extensive scope in both high-stake and low-stake assessments, covering research fields in education, psychology, health, and other applied disciplines.

As one of the first comprehensive books addressing the modeling and application of process data, this e-book has drawn great attention since its debut was cross-loaded on three journals in

As more and more data are being collected in computer-based testing, process data will become a very important source of information to validate and facilitate measuring response accuracy and provide supplementary information in understanding test-takers' behaviors, the reasons of missing data, and links with motivation studies. There is no doubt that there is high demand of such research in the large-scale assessment, both high-stake and low-stake, as well as in the personalized learning and assessment to tailor the best source and methods to help people learn and grow. This book is a timely addition to the current literature on psychological and educational measurement. It is expected to be applied more extensively in educational and psychological measurement, such as in computerized adaptive testing and dynamic learning.

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

QH was partially supported by the National Science Foundation grants IIS-1633353.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

We thank all authors who have contributed to this Research Topic and the reviewers for their valuable feedback on the manuscript.