Sample Selector

Sample Selector is a tool for creating and editing samples, or groups of data you compare across—they're not "samples" in the statistical sense, but more like filters.

By default, a single sample exists: "All Data". With the Sample Selector, you can create new samples to organize your data.

You can use samples to:

A sample is composed of one or more filters, specific conditions that narrow down your sample.

Creating a sample

The general process for creating a sample is to:

The effect of multiple filters

DataShop interprets each filter after the first as an additional restriction on the data that is included in the sample. This is also known as a logical "AND". You can see the results of multiple filters in the sample preview as soon as all filters are "saved".

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Dataset Info

The Dataset Info report provides both an overview and context for the currently selected dataset. It may answer questions such as how and when were these data collected? What's the scope of the dataset? If the owner of the dataset chose to provide other information about the dataset, that is displayed here as well.

The main components of the Dataset Info report are:

Dataset Overview and Statistics

The dataset overview table is presented at the top of the Dataset Info report. This report loads by default when you select a dataset to browse. If you don't see the overview, click the main tab titled Dataset Info followed by the Overview link below.

In the Overview table, a number of fields describe the dataset's characteristics. If you are the PI or a co-PI for this study, most of these fields should be editable. Move your mouse over a field: if the background color changes, you can click to edit the field.

The overview fields are:

Category Description
Project The group of datasets to which this dataset belongs (e.g., French Culture)
Dataset Describes a collection of data (e.g., Geometry-AllStudents)
Curriculum Describes the curriculum given to students (e.g., Algebra I)
Dates The date range(s) for when these data were collected
Domain/LearnLab The Domain/LearnLab group to which this data belongs (e.g., Language/Chinese or Math/Algebra).
Principal Investigator The owner of the dataset, including his or her email
Tutor The title of the tutor software used to collect data (e.g., Algebra 1 2005 or CTAT 1.6)
Description A description of the dataset.
Has Study Data Whether or not the dataset contains data that are the result of a study.
Hypothesis The hypothesis that was tested. Only displayed if "Has Study Data" is "yes".
Status The status of the dataset (e.g., Closed or Ongoing)
School(s) The school(s) where these data were collected.
Additional Notes Any additional information about the dataset

The statistics table, described below, is generated from the data and is therefore not editable.

Category Description
Number of Students The total number of students for which there is data.
Number of Unique Steps The number of unique steps in the dataset, where uniqueness is defined as a step within a specific problem hierarchy (the curriculum location where the problem appears). The same step attempted by two students equals only one unique step.
Total Number of Steps The number of steps in the dataset, where each student-step counts as one step. The same step attempted by two students equals two steps in the total number of steps. For example, if problem A has steps S1, S2, and S3, and student A does S1 and S2 while student B does S2 and S3, and there is just that problem in the dataset, then there are 3 unique steps and 4 total steps.
Total Number of Transactions The total number of transactions in the dataset.
Total Student Hours The number of hours of student activity in the dataset, represented by the sum of the duration of all student transactions in the dataset.
Knowledge Component Model(s) The knowledge component models for this dataset (e.g., Default, Manual-Model). The number of unique knowledge components in the model is displayed following each model listed.
Version 4.2.3 July 30, 2010 LearnLab logo