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Fix figure range

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Charlotte Van Petegem 2024-01-22 17:51:59 +01:00
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@ -2019,7 +2019,7 @@ Our study has several strengths and promising implications for future practice a
First, we were able to predict success based on historical metadata from earlier cohorts, and we are already able to do that early on in the semester.
In addition to that, the accuracy of our predictions is similar to those of earlier efforts\nbsp{}[cite:@asifAnalyzingUndergraduateStudents2017; @vihavainenPredictingStudentsPerformance2013; @kovacicPredictingStudentSuccess2012] while we are not using prior academic history or interfering with the students’ usual learning workflows.
However, there are also some limitations and work for the future.
While our visualizations of the features (Figures\nbsp{}[[fig:passfailfeaturesAcorrect]]\nbsp{}through\nbsp{}[[fig:passfailfeaturesBwrong]]) are helpful to indicate which features are important at which stage of the course in view of increasing versus decreasing the odds of passing the course, they may not be oversimplified and need to be carefully interpreted and placed into context.
While our visualizations of the features (Figures\nbsp{}[[fig:passfailfeaturesAevaluation]]\nbsp{}through\nbsp{}[[fig:passfailfeaturesBwrong]]) are helpful to indicate which features are important at which stage of the course in view of increasing versus decreasing the odds of passing the course, they may not be oversimplified and need to be carefully interpreted and placed into context.
This is where the expertise and experience of teachers comes in.
These visualizations can be interpreted by teachers and further contextualized towards the specific course objectives.
For example, teachers know the content and goals of every series of exercises, and they can use the information presented in our visualizations in order to investigate why certain series of exercises are more or less important in view of passing the course.