The Common Education Data Standards (CEDS) are an education data management initiative whose purpose is to streamline the understanding of data within and across P-20W institutions and sectors. CEDS includes a common vocabulary complete with standard elements names, definitions, and option sets. This repository contains all the CEDS elements, definitions, option sets and their definitions, and entities and definitions. Its purpose is to expand that vocabulary to meet the needs of every education stakeholder. The expanded vocabulary is then added to the CEDS Integration Data Store and CEDS Data Warehouse – the other two repositories located here.
This is for capturing needs not currently supported by the CEDS model. Please do not send or share actual data as examples in this issue or in attachments.
Author(s)
PSLC DataShop
Authoring Organization(s)
Carnegie Mellon University
Timezone - timezone
Dataset Level <= 100 chars - This element is variable and can allow for a hierarchical representation of the problems. In our transaction export, multiple columns of this type are allowed. A Dataset Level. An example of the correct use of this column heading is Level (Unit), where "Unit" is the dataset level title and the value in the column is the levelname (e.g., "Understanding Fractions"). The Level column should always be of the format Level (level_title). The level title must be ≤ 100 characters and consist of letters, numbers, dashes, underscores, and spaces. If a dataset level title is not included, it will become "Default". Multiple Level columns are OK. For additional description, see the level element in the Guide. In tutor-message format XML, level "title" is referred to as "type".
Total Num Hints integer - The total number of hints available for a problem.
Student Response Type <= 30 chars - ATTEMPT or HINT_REQUEST
Student Response Subtype <= 30 chars - polynomial (generally discreet values)
Tutor Response Type - RESULT or HINT_MSG
Feedback Classification <= 255 chars - A further classification of the outcome. See action_evaluation / classification in the Guide. Note that if Feedback Classification has a value, Feedback Text must have a value as well.
Feedback Text <= 65,535 chars - The body of a hint, success, or error message shown to the student.
Condition Name <= 80 chars - "A study/experimental condition. Must always be paired with Condition Type, even if a condition does not have a condition type. A describes a study condition, in the case that these data are being collected in the context of a research study."
Condition Type <= 255 chars - A condition classification. Must always be paired with Condition Name, even if a condition does not have a condition type. Multiple Condition Type columns are OK. If Condition Type is specified, Condition Name must have a value as well.
Student-Step Export Data:
Hints integer - Total number of hints requested by the student for the step. (<= Total Num Hints)
Condition <= 65,535 chars - The name and type of the condition the student is assigned to. In the case of a student assigned to multiple conditions (factors in a factorial design), condition names are separated by a comma and space. This differs from the transaction format, which optionally has "Condition Name" and "Condition Type" columns.
KC (model-name) <= 65,535 chars - (Only shown when the "Knowledge Components" option is selected.) Knowledge component(s) associated with the correct performance of this step. In the case of multiple KCs assigned to a single step, KC names are separated by two consecutive tildes ("~\~").
Opportunity (model-name) integer - (Only shown when the "Knowledge Components" option is selected.) An opportunity is the first chance on a step for a student to demonstrate whether he or she has learned the associated knowledge component. Opportunity number is therefore a count that increases by one each time the student encounters a step with the listed knowledge component. In the case of multiple KCs assigned to a single step, opportunity number values are separated by two tildes ("~~") and are given in the same order as the KC names.
Predicted Error Rate (model-name) double - A hypothetical error rate based on the Additive Factor Model (AFM) algorithm. A value of "1" is a prediction that a student's first attempt will be an error (incorrect attempt or hint request); a value of "0" is a prediction that the student's first attempt will be correct. For specifics, see below "Predicted Error Rate" and how it's calculated. In the case of multiple KCs assigned to a single step, Datashop implements a compensatory sum across all of the KCs, thus a single value of predicted error rate is provided (i.e., the same predicted error rate for each KC assigned to a step). For more detail on Datashop's implementation for multi-skilled step, see Model Values page.
Use Case Background
A review of DataShop and CEDS schemas was conducted between Mike Komisin and Jim Goodell. Contact: datashop-help@lists.andrew.cmu.edu
jgoodell2
commented
3 years ago
This is for capturing needs not currently supported by the CEDS model. Please do not send or share actual data as examples in this issue or in attachments.
Author(s) PSLC DataShop
Authoring Organization(s) Carnegie Mellon University
Use Case Title The following elements support the mapping of DataShop data to CEDS. CEDS_first_pass_090919-18OCT_response.xlsx
Target Date Needed n/a
Use Case Description
Transaction Export Data:
Timezone - timezone Dataset Level <= 100 chars - This element is variable and can allow for a hierarchical representation of the problems. In our transaction export, multiple columns of this type are allowed. A Dataset Level. An example of the correct use of this column heading is Level (Unit), where "Unit" is the dataset level title and the value in the column is the levelname (e.g., "Understanding Fractions"). The Level column should always be of the format Level (level_title). The level title must be ≤ 100 characters and consist of letters, numbers, dashes, underscores, and spaces. If a dataset level title is not included, it will become "Default". Multiple Level columns are OK. For additional description, see the level element in the Guide. In tutor-message format XML, level "title" is referred to as "type". Total Num Hints integer - The total number of hints available for a problem. Student Response Type <= 30 chars - ATTEMPT or HINT_REQUEST Student Response Subtype <= 30 chars - polynomial (generally discreet values) Tutor Response Type - RESULT or HINT_MSG Feedback Classification <= 255 chars - A further classification of the outcome. See action_evaluation / classification in the Guide. Note that if Feedback Classification has a value, Feedback Text must have a value as well. Feedback Text <= 65,535 chars - The body of a hint, success, or error message shown to the student. Condition Name <= 80 chars - "A study/experimental condition. Must always be paired with Condition Type, even if a condition does not have a condition type. A describes a study condition, in the case that these data are being collected in the context of a research study."
Condition Type <= 255 chars - A condition classification. Must always be paired with Condition Name, even if a condition does not have a condition type. Multiple Condition Type columns are OK. If Condition Type is specified, Condition Name must have a value as well.
Student-Step Export Data:
Hints integer - Total number of hints requested by the student for the step. (<= Total Num Hints) Condition <= 65,535 chars - The name and type of the condition the student is assigned to. In the case of a student assigned to multiple conditions (factors in a factorial design), condition names are separated by a comma and space. This differs from the transaction format, which optionally has "Condition Name" and "Condition Type" columns. KC (model-name) <= 65,535 chars - (Only shown when the "Knowledge Components" option is selected.) Knowledge component(s) associated with the correct performance of this step. In the case of multiple KCs assigned to a single step, KC names are separated by two consecutive tildes ("~\~"). Opportunity (model-name) integer - (Only shown when the "Knowledge Components" option is selected.) An opportunity is the first chance on a step for a student to demonstrate whether he or she has learned the associated knowledge component. Opportunity number is therefore a count that increases by one each time the student encounters a step with the listed knowledge component. In the case of multiple KCs assigned to a single step, opportunity number values are separated by two tildes ("~~") and are given in the same order as the KC names. Predicted Error Rate (model-name) double - A hypothetical error rate based on the Additive Factor Model (AFM) algorithm. A value of "1" is a prediction that a student's first attempt will be an error (incorrect attempt or hint request); a value of "0" is a prediction that the student's first attempt will be correct. For specifics, see below "Predicted Error Rate" and how it's calculated. In the case of multiple KCs assigned to a single step, Datashop implements a compensatory sum across all of the KCs, thus a single value of predicted error rate is provided (i.e., the same predicted error rate for each KC assigned to a step). For more detail on Datashop's implementation for multi-skilled step, see Model Values page.
Use Case Background A review of DataShop and CEDS schemas was conducted between Mike Komisin and Jim Goodell. Contact: datashop-help@lists.andrew.cmu.edu
Location of Element in the Domain Entity Schema