Related Experiment Video
Updated: Jan 25, 2026
08:33
High Density Event-related Potential Data Acquisition in Cognitive Neuroscience
Published on: April 16, 2010
13.0K
Getting Messy with Authentic Data: Exploring the Potential of Using Data from Scientific Research to Support Student
Melissa K Kjelvik1,2, Elizabeth H Schultheis1,2
1BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI 48824.
CBE Life Sciences Education
|May 11, 2019
Summary
Working with authentic scientific data enhances students' data literacy skills. This approach integrates quantitative reasoning and data science, preparing students for data-intensive careers.
Area of Science:
- Data Science Education
- STEM Education
- Quantitative Reasoning
Background:
- Data literacy is crucial for students in STEM and beyond.
- Authentic data experiences can foster stronger data literacy development.
- Understanding the interplay between quantitative reasoning, data science, and data literacy is key.
Purpose of the Study:
- To explore the benefits of using authentic scientific data for teaching data literacy.
- To identify features of authentic data that impact learning complexity.
- To suggest future research directions for data literacy instruction.
Main Methods:
- Conceptual exploration of data literacy, quantitative reasoning, and data science.
- Analysis of authentic data set characteristics (selection, curation, scope, size, messiness).
- Discussion of pedagogical implications and future research needs.
Main Results:
- Authentic data provides a practical context for developing data literacy.
- Data set complexity influences instructional strategies.
- Further research is needed to define learning outcomes and best practices.
Conclusions:
- Integrating authentic data into curricula is vital for effective data literacy education.
- Instructional design must consider the inherent complexities of real-world data.
- Future work should focus on empirical validation of teaching methods and learning outcomes.
More Related Videos
Related Concept Videos
00:59How Data are Classified: Numerical Data
37.1K
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...
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
37.1K
01:11How Data are Classified: Categorical Data
43.1K
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...
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...
43.1K
01:24Data 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
01:30Data Collection I
7.9K
Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of...
7.9K
01:03Data Validation
6.4K
Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
Nursing assessment guides are generally based on holistic models rather than medical...
6.4K
01:15Data Validation
713
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
Key parameters for method validation include:
713