Friday, March 23, 2018

This release features 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.

  1. Workflow Component Creator
  2. We invite users to contribute their analysis tools and have written a script that can be used to create workflow components. The script can be found in our GitHub repository, along with the source code for all existing components. The component documentation includes a section about running the script. In addition, other changes make it easier to author new components: there is built-in support for processing zip files, component name and type restrictions have been relaxed, and arguments can easily be passed to custom scripts.

  3. Returning Tigris users will find many usability and performance enhancements have been made.
  4. New Analytic component functions
  5. There are four new components available:
  6. More information about the detectors available for use in the Apply Detector component, and papers that have been published about them, can be found here. The detectors can be used to compute particular student model variables. They are computational processes – oftentimes machine-learned – that track psychological and behavioral states of learners based on the transaction stream with the ITS.

    The Output Comparator provides a visual comparison of up to four input files. Supported input formats are: XML, tab-delimited and (key, value)-pair properties files. The output is a tabular display allowing for a side-by-side comparison of specified variables.

    The Text Converter is used to convert XML files to a tab-delimited format, a common format for many of the analysis components in Tigris. The Row Remover allows researchers to transform a tab-delimited data source to meet certain criteria for a dataset. For example, the user can configure the component to remove rows for which values in a particular column are NULL or fall outside the acceptable range.