Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Assessment of the Gastrointestinal System I: Subjective Data01:17

Assessment of the Gastrointestinal System I: Subjective Data

647
Assessing the gastrointestinal (GI) system is a complex process that begins with collecting subjective data. This data, collected through patient interviews, provides crucial insights into the patient's health history, perception patterns, and lifestyle habits, all contributing significantly to GI health.
Health History
The initial step in assessing the GI system is obtaining a comprehensive health history. This includes inquiring about the patient's history or presence of problems...
647
Assessment of the Cardiovascular System I: Subjective Data01:23

Assessment of the Cardiovascular System I: Subjective Data

822
A thorough health history and physical assessment are essential for identifying cardiovascular disease (CVD) symptoms and distinguishing them from other health issues.
Initial Enquiry
Ask the patient about their primary concern and thoroughly explore all reported symptoms.
Medical History
Investigate past illnesses affecting the cardiovascular system, such as angina, anemia, rheumatic fever, congenital heart disease, stroke, thrombophlebitis, dysrhythmias, varicosities
Inquire about symptoms...
822
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

37.7K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
37.7K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

44.2K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
44.2K
Data Reporting and Recording01:24

Data Reporting and Recording

5.4K
Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
5.4K
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

269
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
269

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Dependent latent class partial credit models for careless and insufficient effort responding in online survey data.

Behavior research methods·2026
Same author

Planned missingness in intensive longitudinal studies: Extensions and comparisons of multiform designs.

Behavior research methods·2026
Same author

Effects of encapsulated algae oil supplements on the production of docosahexaenoic acid-enriched milk in mid-lactation dairy cows.

JDS communications·2026
Same author

Epilepsy-IEDs: An automated machine learning model for detecting interictal epileptiform discharges from scalp electroencephalograms.

iScience·2026
Same author

Bayesian evaluation for latent variable models: A tutorial on computing information criteria and bayes factors with the r package bleval.

Psychological methods·2026
Same author

Advances in Interleukin-2 Engineering and Delivery Systems for Cancer Immunotherapy.

ACS applied bio materials·2026

Related Experiment Video

Updated: Jan 27, 2026

Data Communication Based on MQTT in a Polymer Extrusion Process
08:15

Data Communication Based on MQTT in a Polymer Extrusion Process

Published on: July 15, 2022

3.8K

Assessment of Collaborative Problem Solving Based on Process Stream Data: A New Paradigm for Extracting Indicators

Jianlin Yuan1, Yue Xiao2, Hongyun Liu2,3

  • 1Educational Science Research Institute, Hunan University, Changsha, Hunan, China.

Frontiers in Psychology
|March 14, 2019
PubMed
Summary

A new method effectively extracts and models data for assessing collaborative problem solving (CPS) skills in human-to-human interactions. This approach provides a valid and feasible way to measure this crucial 21st-century skill.

Keywords:
collaborative problem solvingdyad dataindicator extractingmultidimensional modelprocess stream data

More Related Videos

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

549
Neutron Crystallography Data Collection and Processing for Modelling Hydrogen Atoms in Protein Structures
10:10

Neutron Crystallography Data Collection and Processing for Modelling Hydrogen Atoms in Protein Structures

Published on: December 1, 2020

5.6K

Related Experiment Videos

Last Updated: Jan 27, 2026

Data Communication Based on MQTT in a Polymer Extrusion Process
08:15

Data Communication Based on MQTT in a Polymer Extrusion Process

Published on: July 15, 2022

3.8K
Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

549
Neutron Crystallography Data Collection and Processing for Modelling Hydrogen Atoms in Protein Structures
10:10

Neutron Crystallography Data Collection and Processing for Modelling Hydrogen Atoms in Protein Structures

Published on: December 1, 2020

5.6K

Area of Science:

  • Educational Assessment
  • Cognitive Science
  • Human-Computer Interaction

Background:

  • Collaborative Problem Solving (CPS) is a key 21st-century skill requiring effective assessment methods.
  • Existing human-to-human assessment approaches face challenges in extracting and modeling process data.
  • Human-to-human interaction provides richer data for understanding cognitive processes in CPS.

Purpose of the Study:

  • To propose and validate a novel paradigm for extracting indicators and modeling dyad data in human-to-human CPS assessment.
  • To develop and implement online tasks to capture process stream data during collaborative problem solving.
  • To evaluate the feasibility and validity of the new paradigm using data from Chinese students.

Main Methods:

  • Extraction of individual and group indicators from process stream data.
  • Application of a within-item multidimensional Rasch model to fit dyad data.
  • Development of five online tasks with an asymmetric mechanism for practice and formal testing.

Main Results:

  • The proposed paradigm demonstrated good model fit for dyad data.
  • Indicator parameter estimates and fitting indexes were acceptable.
  • Students were effectively differentiated based on their CPS performance.

Conclusions:

  • The new paradigm is a feasible and valid method for assessing collaborative problem solving skills in human-to-human interactions.
  • The approach successfully extracts meaningful indicators from process stream data.
  • Further research is needed to explore limitations and expand upon the current findings.