ArticleFrontiers in psychology2019
Predictive Feature Generation and Selection Using Process Data From PISA Interactive Problem-Solving Items: An Application of Random Forests.
Article in Frontiers in psychology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Understanding Adults' Spatial Cognitive Processes: A Time-Embedded N-Grams Model with Machine Learning.Journal of Intelligence · 2026Article
- Analysis of Log Data From an International Online Educational Assessment System: A Multi-State Survival Modeling Approach to Reaction Time Between and Across Action Sequence.Psychometrika · 2025Article
- Discovering action insights from large-scale assessment log data using machine learning.Scientific reports · 2025Article
- Investigating the Predictive Performance of Process Data and Result Data in Complex Problem Solving Using the Conditional Gradient Boosting Algorithm.Journal of Intelligence · 2025Article
- Exploring Critical Eye-Tracking Metrics for Identifying Cognitive Strategies in Raven's Advanced Progressive Matrices: A Data-Driven Perspective.Journal of Intelligence · 2025Article
- Article
- A machine learning-based procedure for leveraging clickstream data to investigate early predictability of failure on interactive tasks.Behavior research methods · 2023Article
- Using process features to investigate scientific problem-solving in large-scale assessments.Frontiers in psychology · 2023Article
- Examining Humans' Problem-Solving Styles in Technology-Rich Environments Using Log File Data.Journal of Intelligence · 2022Article
- Understanding students' problem-solving patterns: Evidence from an allotted response time in a PISA 2012 item.Frontiers in psychology · 2022Article
- Article
- Editorial: Process Data in Educational and Psychological Measurement.Frontiers in psychology · 2021Article
- Latent Theme Dictionary Model for Finding Co-occurrent Patterns in Process Data.Psychometrika · 2020Article
Corrections and comments
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The Programme for International Student Assessment (PISA) introduced the measurement of problem-solving skills in the 2012 cycle. The items in this new domain employ scenario-based environments in terms of students interacting with computers. Process data collected from log files are a record of students' interactions with the testing platform. This study suggests a two-stage approach for generating features from process data and selecting the features that predict students' responses using a released problem-solving item-the Climate Control Task. The primary objectives of the study are (1) introducing an approach for generating features from the process data and using them to predict the response to this item, and (2) finding out which features have the most predictive value. To achieve these goals, a tree-based ensemble method, the random forest algorithm, is used to explore the association between response data and predictive features. Also, features can be ranked by importance in terms of predictive performance. This study can be considered as providing an alternative way to analyze process data having a pedagogical purpose.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.