Autograding of Programming Skills

N. Narmada, P. Pati
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引用次数: 1

Abstract

To evaluate a learner’s knowledge of programming language skills, assessments are given. Grading of these is usually done manually which not only is tedious but prone to error due to repetition and fatigue. In this work, we employ pre-trained language models to perform automated grading of "C" programming language. Embeddings from different transformers on pre-assessed codes are used as feature vectors to train a wide range of regressors for the scoring task. Root-mean-square error (RMSE) is the metric utilized to compare the scores of these regressors. It’s observed that embeddings from T5-model with CatBoost regressor gives the least error around 15%.
编程技能的自动升级
为了评估学习者对编程语言技能的知识,会进行评估。这些评分通常是手工完成的,这不仅是乏味的,而且容易因重复和疲劳而出错。在这项工作中,我们使用预训练的语言模型来执行“C”编程语言的自动分级。在预评估代码上使用不同变压器的嵌入作为特征向量来训练用于评分任务的广泛回归量。均方根误差(RMSE)是用来比较这些回归量得分的度量。观察到,使用CatBoost回归器的t5模型的嵌入误差最小,约为15%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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