Data Collection within the LMSA Kit
Making Data Available for the LMSA Kit
A new project is created via File-> New in the LMSA Kit. It contains no data and still needs to be filled with them. There are several ways to make learning group's data available to the LMSA Kit:
- Manual Input:
All the data needed by the LMSA Kit can be entered either directly via the user interface or via acquisition plug-ins by hand. For large amounts of data (entire learning groups), this approach is not recommended, as it is very time consuming. At the same time there is the possibility that errors will creep into the data record. However, it may become necessary if an import plug-in (at the moment) is not available for an LMS or any other data source.
However, manual input is great for making minor corrections in the dataset by hand. Data organized in tables can easily be changed via the built-in editor. If a data field is editable, double-click in the relevant field and it will open for editing. Once the change has been entered, all you have to do is click somewhere else to apply the changes. More complex data fields have corresponding buttons that open a editor to perform the changes. The only exception is the test data, it must be changed via a menu command.
New data can easily be created by hand. You can use the empty, bottom line and fill it with data to enter new data. It will become a finished record after completing all inputs. Below the new record, an empty line is displayed again, it can be used for a new entry again. The procedure is described in more detail in the next (sub) chapters on the various databases. - Data Import:
Many learning management systems (LMS) allow the export of stored data. The export format uses common data formats that teachers can use for their records or lesson planning. Many of these export files can be imported into the LMSA Kit via the included import plug-ins. If the LMS used offers export functionality and a suitable import plug-in is available, the required effort to enter the data can be reduced to a minimum.
The chapter on the data import plug-ins provides an overview of which data sources can already be imported. There is also a description how to proceed in detail. Additionally, you will find corresponding cross-references to the sub-chapters on the individual data stocks that the LMSA Kit uses.
Types of Databases
The LMSA Kit uses two databases: The test data with the results / answers of the students from the individual learning controls within the LMS, the task descriptions of the individual tasks as well as the personal data to offer further services.
- Test Data
The data of the individual learning controls are needed for predicting students' performance in the criteria. It is the only data that the LMSA Kit needs to work properly. All other data is only needed for added ease of use, more accurate forecasts, or other functions.
The data of the learning checks must be broken down into individual scores - achieved score in each learning task. The import functions of the LMSA Kit clearly support the user during their use. Hand typing takes a lot of time, but may be advisable if, for example, the data from a classwork or other paper-based learning exam should also be used. - Tasks
The Tasks section contains an enumeration of all tasks contained in the LMSA Kit's dataset. For each task, on the one hand, characteristic data are kept, which describe the task more closely and help with the analysis, such as: task title, achievable score, task type, task text, etc. On the other hand, statistical data is generated, which can also be seen as first findings such as: number of solution attempts, average score achieved, etc. Statistic data is updated by the LMSA Kit at the user's request at any time.
The characteristic information is not mandatory. If needed for an analysis, this data can be estimated directly by the LMSA Kit. The estimated data may differ slightly from the true values, but it will generally meet the requirements for a forecast. - User Data
The data of the participants are not mandatory necessary. Within the test data only the system ID of the participants is used. The participants' data allocate personal data such as name, matriculation number, etc. to these system IDs, thus helping to clearly illustrate the results of the analyzes. Other personal information, such as the e-mail address, can be used to automatically send feedback messages.
If there is no corresponding record in the user data when importing a test, an empty entry is created in the user data. The personal data can be added in this record after the test import by hand. On the other hand, in this way, it is possible to deal with anonymous data. This can be useful, for instance, to increase the forecasting quality by drawing on test data of old cohorts.