1/15/2023 – Version C.23.01
Version C.23.01 is the fourth version of the German Speaking Assessment. This version was created following the deprecation of the previously used version of a speech recognition solution and the adoption of a different solution that showed better accuracy following a human rating study.
After this process to confirm the best speech recognition engine, thousands of previously administered German Version B.21.01 assessments were rescored with the selected model. This score data was used to update the item difficulty parameters for the test form and to create a new scoring algorithm and machine learning model in order to best predict German speaking ability.
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Technical Details
What’s improved
Speech recognition model now uses a new speech recognition solution that over performed the updated version of the previous solution.
Machine learning model shows very high agreement with predicted score of previous version.
1/28/2021 – Version B.21.01
Version B.21.01 consisted of some technical improvements to the assessment including a more direct and stable connection to IBM Watson for scoring. Prior collected data was rescored via this connection and informed a slight update to the scoring algorithm.
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Technical Details
What’s improved
- Direct to IBM Watson Scoring
- Scoring algorithm update
5/05/2020 – Version B.20.05
Version B.20.05 this was a minor update in test content. The items selected for the form was adjusted to optimize those that would most accurately reflect ability as scored by the automated scoring solution.
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Technical Details
What’s improved
- Updated items in form to optimize scoring using IBM Watson.
5/01/2020 – Version A.20.05
Version A.19.03 was the first version for the German Speaking Assessment. We conducted a calibration pilot to determine the item and ability parameters to create an assessment form and accompanying scoring algorithm to best predict German speaking ability.
NOTE: Which user confirms the update to this version will be recorded in the version history.
Technical Details
What’s improved
- Used Rasch infit/outfit statistics to screen calibration data to the most informative items and response patterns.
- Incorporated regression model to predict ability with a high-degree of reliability