Subject: DataShop 10.7 is now available!
From: Cindy Tipper <[email-masked]>
To: DataShop Help <[email-masked]>, [email-masked], [email-masked]
Date: Sat, 27 Feb 2021 16:23:51 -0500
List-Id: <learnlab-announce.lists.andrew.cmu.edu>

DataShop 10.7 released!

   1. We are excited to share that we have added a *Dataset Search* feature
   to DataShop.

   Users can now search for data based on a variety of dataset properties,
   associations, and metrics. The site-wide search is incorporated into the
   new search page for searching help pages, files, and papers. Below the
   site-wide search, the new Dataset Search
   <https://pslcdatashop.web.cmu.edu/DatasetSearch> utilizes over 20
   attributes to filter datasets, such as the Shareable status, Area and
   Subject of study, Tutor types, as well as other qualitative data. Dataset
   Search also supports filtering by quantitative metrics such as the number
   of Transactions, Students, Steps, Knowledge Components, and others.

   For example, in the figure above we see that there are 4 datasets that
   are Shareable, have a subject area of Algebra and use the CTAT tutor.
   Removing the Tutor filter would reveal there are 169 datasets that match
   the subject and shareability filters. The results include a link to each
   dataset as well as it's meta-data information.

   Access to the feature is available in the Explore section in the
   left-hand navigation or the *search* link in the upper right-hand
   corner. Clicking on a link in the filtered search results will include a
   "Back to Search" link on the page you navigate to, remembering your filter
   criteria.

   2. The terms "Domain" and "LearnLab" have been changed to reflect a
   wider scope of data sources for DataShop. They have been changed to "Area"
   and "Subject", respectively.
   3. We have added support for a *Computer Science* Area with the
   following list of Subjects:
      - Introductory Programming
      - Introductory Programming: Java
      - Introductory Programming: Python
      - Machine Learning and Data Science
      - Data Structures and Algorithms
      - Databases
   Please contact us <https://pslcdatashop.web.cmu.edu/about/contact.html> if
   you have Computer Science data that you believe belongs in a different
   Subject; we can add others.
   4. Members of the LearnSphere <http://learnsphere.org/> community have
   added new workflow components.

   Source code for all Tigris components can be found in our GitHub
   repository <https://github.com/LearnSphere/WorkflowComponents>. The
   first three components were developed as part of research efforts for the
   PL2 project <http://personalizedlearning2.org/>. Two of them perform
   analyses on student MAP (Measures of Academic Progress) data, specifically
   RIT scores.

      - Student Growth

      This analysis component computes student MAP RIT score trends, over
      two years, for minoritized and non-minoritized students. RIT
scores are an
      estimation of a student's instructional level. MAP tests are used by
      schools to measure student progress or growth year-to-year.
      - Learning Rate

      This component uses AFM (Additive Factors Modeling) to model student
      growth (or Learning Rate) using two years of MAP test data, considering
      those students who received interventions vs those that did not.
      - Student Demographics

      This visualization component generates four student demographic
      plots, displaying the distribution of students by race, gender,
school and
      grade.
      - Multiskill Converter

      This transform component can be used to convert a multi-skill
      Knowledge Component (KC) model in a DataShop student-step export. The
      component supports either creating a new skill by concatenating
two or more
      skills or creating multiple distinct rows (and opportunity counts) from a
      single multi-skill row. This allows researchers to consider alternative
      ways in which skills present in a dataset can be used to better model
      student behavior.

We hope you find these new features useful. Please reach out if you have
any questions.

-- The DataShop Team
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