Accurately tracing the state of learner knowledge contributes to providing high-quality intelligent support for computer-supported programming learning. However, knowledge tracing is difficult when learners have only had a few practice opportunities, which is often common in block-based programming. This article proposed two knowledge tracing models that can exploit the problem-solving process data generated by learners from a single programming task. A novel metric, the approaching index, was developed using the tree edit distance in abstract syntax trees to measure the similarities between the learners’ intermediate solutions and the optimal solution. The proposed method allows for each learner's programming path to be represented as a raw approaching index sequence ( AISeq ) or as a single variable ( AIScore ) by averaging the AISeq . A logistic regression model was first designed to predict the learners’ performances using their AIScore , the number of attempts, and their current performance. A second model, a recurrent neural network model, was also developed to directly use the AISeq and to make predictions. To verify the effectiveness of these models, a series of statistical analyses and experiments were conducted on two existing large-scale block-based programming datasets, the results from which revealed that the proposed models were competitive with four state-of-the-art models on multiple metrics, such as the precision-recall curve, accuracy, specificity, and Cohen's Kappa. Especially, the proposed models were found to be more robust than the compared models in predicting who would fail to complete the tasks.

中文翻译：

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使用解决问题的过程数据在单个编程实践中进行知识跟踪

准确地跟踪学习者知识的状态有助于为计算机支持的编程学习提供高质量的智能支持。但是，当学习者只有很少的实践机会时，很难进行知识跟踪，这在基于块的编程中通常很常见。本文提出了两种知识跟踪模型，它们可以利用学习者从单个编程任务中生成的问题解决过程数据。使用抽象语法树中的树编辑距离来开发一种新的度量标准，即接近索引，以测量学习者的中间解决方案和最佳解决方案之间的相似性。提出的方法允许将每个学习者的编程路径表示为原始的接近索引序列（ AISeq ）或作为单个变量（ AIScore ）的平均 AISeq 。首先设计了逻辑回归模型来使用学生的学习能力来预测他们的表现AIScore ，尝试次数及其当前效果。还开发了第二种模型，即递归神经网络模型，以直接使用AISeq并做出预测。为了验证这些模型的有效性，对两个现有的基于块的大型编程数据集进行了一系列的统计分析和实验，结果表明所提出的模型与四个最新模型具有竞争力基于多个指标，例如精确召回曲线，准确性，特异性和科恩的Kappa。特别是，在预测谁将无法完成任务方面，所提出的模型比比较的模型更健壮。