Titlebook: Auto-Grader - Auto-Grading Free Text Answers; Robin Richner Book 2022 The Editor(s) (if applicable) and The Author(s), under exclusive lic

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Data,This chapter commences with a description of the provided data, continues with an analysis and preprocessing of the data and ends with a preprocessed data set in the form of a pandas dataframe as a base for the model development. Note that there will still be adjustments to the data depending on the model approach as outlined in chapter 5.

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Model Development,There are five models described as follows which can be divided into three categories. The models in the categories differ in their data augmentation strategy and in their architecture. Data augmentation refers to how the data is fed into the model. The architecture refers to the specific neural networks used.

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Conclusion,The thesis introduced the time constraint that teachers face when it comes to grading free text answer questions. The objective was to create a system that would assist teachers to save time on that task. It could be seen that related work consists of various AI and non AI-based approaches and dates back to 1964.

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Auto-Grader - Auto-Grading Free Text Answers978-3-658-39203-1Series ISSN 2625-3577 Series E-ISSN 2625-3615

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Annette Boeger,Mike Lüdmannwiss nicht damit abgetan, wenn in Wort und Schrift verkündet wird, dass die vornehmste Pflicht jeder Mutter wäre, ihr Kind zu stillen, eine Pflicht, der sich keine Frau entziehen dürfe. Diese und ähnliche Schlagworte enthalten schliesslich nur eine Voraussetzung für die Einleitung des Stillens: sie

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