Friday, July 13, 2018
This release features more improvements to Tigris, an online workflow authoring tool which is part of LearnSphere. These improvements make it possible for users to better contribute data, analytics and explanations of their workflows.
The Tigris components, still in the left-hand pane, are now displayed in a tree structure. The categories and the organization of the components is the same, and in addition, each category has a "Recently Used" folder. This makes it easier to find the components you most frequently use. We've also added the ability to search for components. The list will update to only show components relevant to the search term. Users can search components by name or any relevant information, e.g., the component author or the input file type.
We consolidated Import functionality into a single component. Now, choosing a data file is simpler because the file type hierarchy is built into the import process. In the new Import options panel, you'll be prompted to specify your file type, which will then filter the list of available, relevant DataShop files. Alternatively, you can upload your own data file in the other tab, as shown below.
Component input nodes are no longer restricted to a single file. This means that components which analyze data across files are no longer limited by their number of input nodes. For example, the OutputComparator component, which allows for visual side-by-side comparison of variables across tab-delimited, XML or Property files, now supports an unlimited number of input files.
The Learning Curve component now allows users to plot multiple predicted error rate curves on a single graph. In the first example we show the predicted error rates for four different KC models, generated using the Analysis AFM component. Because this component allows for multiple input files, we can also use this component to show the predicted error rate curves across different analyses, in this case, AFM and BKT (shown in the second figure).
The DataStage Aggregator component aggregates student transaction data from DataStage, the Stanford dataset repository from online courses.
The Anonymize component allows users to securely anonymize a column in an input CSV file. The generated output will be the original input data with the specified column populated with anonymized values. The anonymous values are generated using a salt (or "key" value). This component is useful when anonymizing students across multiple files, consistently.
* These components are available at LearnSphere@Memphis.