忧伤
发表于 2025-3-23 11:22:19
https://doi.org/10.1007/978-3-531-94266-7As follows the research problem, objective and anticipated contribution are stated.
erythema
发表于 2025-3-23 15:23:51
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斜谷
发表于 2025-3-23 22:06:25
https://doi.org/10.1007/978-3-531-94266-7Firstly, this chapter will introduce the technological background needed to understand how a state-of-the-art auto-grader may look and secondly elaborate on related work in the field of automatic grading.
LATER
发表于 2025-3-23 23:03:44
https://doi.org/10.1007/978-3-531-94266-7This 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.
失眠症
发表于 2025-3-24 06:05:42
https://doi.org/10.1007/978-3-531-94266-7There 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.
是贪求
发表于 2025-3-24 10:01:44
https://doi.org/10.1007/978-3-531-94266-7This chapter discusses the thesis, elaborates on the limitations and further research opportunities. Hereby, it is divided into preprocessing, data augmentation, pre-training, fine-tuning, and bias.
油膏
发表于 2025-3-24 13:57:31
https://doi.org/10.1007/978-3-531-94266-7The 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.
Tracheotomy
发表于 2025-3-24 18:55:48
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无政府主义者
发表于 2025-3-24 21:40:07
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generic
发表于 2025-3-25 00:15:54
Research Background,Firstly, this chapter will introduce the technological background needed to understand how a state-of-the-art auto-grader may look and secondly elaborate on related work in the field of automatic grading.