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Assessment of skills and adaptive learning for parametric exercises combining knowledge spaces and item response theory

机译：参数练习的技能和自适应学习评估知识空间和项目响应理论

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Many computer systems implement different methods for the estimation of students' skills and adapt the generated exercises depending on such skills. Knowledge Spaces (KS) is a method for curriculum sequencing but fine-grained decisions for selecting next exercises among the candidates are not taken into account, which can be obtained with the application of techniques such as Item Response Theory (IRT). The combination of KS and IRT can bring advantages since the semantics of both models are included but some issues such as the required local independence of IRT should be considered. In addition, an open issue is how to handle with parametric exercises for skill modelling, i.e. exercises which are not static content but that can change from instance to instance depending on some parameters and a student can try to solve them again with different parameters after correct resolution. The correct inclusion of several instances of the parametric exercises on the adaptive decisions is important since the adaptation process can improve. This work describes two new algorithms for skill modelling and for adaptation of exercises that integrate IRT and KS to have a more powerful approach with more knowledge in the models and at the same time provides a solution for taking into account parametric exercises where a student should solve an exercise correctly several times to get proficiency. We have evaluated the different skill modelling algorithms using real data of students from their interactions in an Intelligent Tutoring System, and the correspondent adaptation algorithms using a simulator. Results show that the accuracy of the prediction is good with values of RMSE under 0.35. Both proposed algorithms got similar results on the accuracy of the prediction but one of them is better regarding performance. Changes of the buffer size for the MLE in IRT did not have a significant effect on the accuracy and on the performance. There is a tradeoff for selecting one of the two proposed algorithms: while the first algorithm has better performance time for the calculation of the ability (because there is no need of calculation of local abilities), the second algorithm has better performance time for the selection of the next exercise and better accuracy and depending on the scenario one or another should be selected. (C) 2018 Elsevier B.V. All rights reserved.
机译：许多计算机系统为学生的技能估算，并根据此类技能调整生成的练习来实现不同的方法。知识空间（KS）是一种用于课程测序的方法，但不考虑用于选择候选人中的下一个练习的细粒度决策，这可以通过应用诸如项目响应理论（IRT）的技术来获得。 KS和IRT的组合可以带来优势，因为这两种模型的语义都包括，但应考虑一些问题，例如所需的IRT本地独立性。此外，开放问题是如何处理参数化技能建模的参数练习，即不静态内容的练习，但这可以根据某些参数和学生可以尝试在正确的不同参数中再次解决它们的实例来改变实例。解析度。由于适应过程可以改善，因此对自适应决策的参数练习的若干实例正确包含了几种情况。这项工作描述了两个新的技能建模算法，并为整合IRT和KS进行了适应的练习，以具有更强大的方法，在模型中具有更多的知识，同时为学生解决的参数练习提供了一种解决方案正确的练习几次才能熟练。我们已经评估了使用智能辅导系统中的互动的实际数据评估了不同的技能建模算法，以及使用模拟器的记者自适应算法。结果表明，预测的准确性好，RMSE值为0.35。两个提议的算法都与预测的准确性相似，但其中一个更好地对性能更好。 IRT中MLE的缓冲区大小的变化对准确性和性能没有显着影响。选择两个提议的算法之一的权衡：虽然第一算法具有更好的性能时间来计算能力的计算（因为不需要计算局部能力），但第二算法具有更好的选择性能时间应选择下一个练习和更好的准确性，并且应选择一个或另一个的方案。 （c）2018 Elsevier B.v.保留所有权利。

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