Add screenshot of evaluation overview to evaluations section
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@ -377,7 +377,6 @@ The coverage that these tests achieved was part of the testing done by the platf
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Tests are written using standard unit testing frameworks\nbsp{}[cite:@edwardsExperiencesUsingTestdriven2007].
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An example of Web-CAT's submission screen can be seen in Figure\nbsp{}[[fig:introductionwebcatsubmission]].
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#+CAPTION: Example of Web-CAT's submission screen for students.
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#+CAPTION: Image taken from\nbsp{}[cite/t:@edwardsWebCATWebbasedCenter2006].
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#+NAME: fig:introductionwebcatsubmission
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@ -688,11 +687,17 @@ Such *code reviews* will be used as a building block for manual assessment.
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:END:
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Teachers can create an *evaluation* for a series to manually assess student submissions for its programming assignments after a specific period, typically following the deadline of some homework, an intermediate test or a final exam.
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An example of an evaluation overview can be seen on Figure\nbsp{}[[fig:whatevaluationoverview]]
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The evaluation embodies all programming assignments in the series and a group of students that submitted solutions for these assignments.
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Because a student may have submitted multiple solutions for the same assignment, the last submission before a given deadline is automatically selected for each student and each assignment in the evaluation.
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This automatic selection can be manually overruled afterwards.
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The evaluation deadline defaults to the deadline set for the associated series, if any, but an alternative deadline can be selected as well.
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#+CAPTION: Pseudonymized overview of an evaluation in Dodona.
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#+CAPTION: For each student, both the correctness of their submission and whether it has been graded yet is shown.
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#+NAME: fig:whatevaluationoverview
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[[./images/whatevaluationoverview.png]]
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Evaluations support *two-way navigation* through all selected submissions: per assignment and per student.
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For evaluations with multiple assignments, it is generally recommended to assess per assignment and not per student, as students can build a reputation throughout an assessment\nbsp{}[cite:@malouffBiasGradingMetaanalysis2016].
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As a result, they might be rated more favourably with a moderate solution if they had excellent solutions for assignments that were assessed previously, and vice versa\nbsp{}[cite:@malouffRiskHaloBias2013].
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images/whatevaluationoverview.png
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