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bibliography.bib
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@ -1671,6 +1671,26 @@
file = {/home/charlotte/sync/Zotero/storage/3V7WRXCU/Hattie and Timperley - 2007 - The Power of Feedback.pdf}
}
@inproceedings{haynes-magyarCodespecComputerProgramming2022,
title = {Codespec: {{A Computer Programming Practice Environment}}},
shorttitle = {Codespec},
booktitle = {Proceedings of the 2022 {{ACM Conference}} on {{International Computing Education Research}} - {{Volume}} 2},
author = {{Haynes-Magyar}, Carl Christopher and {Haynes-Magyar}, Nathaniel James},
year = {2022},
month = aug,
series = {{{ICER}} '22},
volume = {2},
pages = {32--34},
publisher = {{Association for Computing Machinery}},
address = {{New York, NY, USA}},
doi = {10.1145/3501709.3544278},
url = {https://dl.acm.org/doi/10.1145/3501709.3544278},
urldate = {2024-02-09},
isbn = {978-1-4503-9195-5},
keywords = {Adaptive Learning Systems,Computer Programming Practice,Scaffolding},
file = {/home/charlotte/sync/Zotero/storage/DU5TNBA9/Haynes-Magyar and Haynes-Magyar - 2022 - Codespec A Computer Programming Practice Environm.pdf}
}
@article{hernandez-de-menendezLearningAnalyticsState2022,
title = {Learning Analytics: State of the Art},
shorttitle = {Learning Analytics},

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book.org
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@ -286,15 +286,10 @@ Note that in this platform, it is not the student themself who is writing code.
:END:
At this point in history, the idea of a web-based automated assessment system for programming education is no longer new.
But still, more and more new platforms were being written.[fn:: See also https://xkcd.com/927/.]
All of these platforms support automated assessment of code submitted by students, but try to differentiatie themselves through the features they offer.
The FPGE platform by\nbsp{}[cite/t:@paivaManagingGamifiedProgramming2022] offers gamification features.
iWeb-TD\nbsp{}[cite:@fonsecaWebbasedPlatformMethodology2023] integrates a full-fledged editor.
PLearn\nbsp{}[cite:@vasyliukDesignImplementationUkrainianLanguage2023] recommends extra exercises to its users.
JavAssess\nbsp{}[cite:@insaAutomaticAssessmentJava2018] tries to automate grading.
And finally, GradeIT\nbsp{}[cite:@pariharAutomaticGradingFeedback2017] features automatic hint generation.
But still, more and more new platforms were being written.[fn:: For a possible explanation, see https://xkcd.com/927/.]
All of these platforms support automated assessment of code submitted by students, but try to differentiate themselves through the features they offer.
The FPGE platform by\nbsp{}[cite/t:@paivaManagingGamifiedProgramming2022] offers gamification, iWeb-TD\nbsp{}[cite:@fonsecaWebbasedPlatformMethodology2023] integrates a full-fledged editor, PLearn\nbsp{}[cite:@vasyliukDesignImplementationUkrainianLanguage2023] recommends extra exercises to its users, JavAssess\nbsp{}[cite:@insaAutomaticAssessmentJava2018] tries to automate grading, and GradeIT\nbsp{}[cite:@pariharAutomaticGradingFeedback2017] features automatic hint generation.
** Learning analytics and educational data mining
:PROPERTIES:
@ -322,7 +317,7 @@ It also briefly details a study we collaborated on with researchers from Jyväsk
In Chapter\nbsp{}[[#chap:feedback]], we first give an overview of how Dodona changed manual assessment in our own educational context.
We then finish the chapter with some recent work on a machine learning method we developed to predict what feedback teachers will give when manually assessing student submissions.
Finally, Chapter\nbsp{}[[#chap:discussion]] concludes the dissertation with some discussion on the previous chapters and some possibilities for future work.
Finally, Chapter\nbsp{}[[#chap:discussion]] concludes the dissertation with some discussion on the current status of Dodona, the research related to it, and some possibilities for future work.
* What is Dodona?
:PROPERTIES: