Comparing Bayesian Knowledge Tracing Model Against Naïve Mastery Model
Comparing Bayesian Knowledge Tracing Model Against Naïve Mastery Model
复制标题
贝叶斯知识追踪模型与朴素掌握模型的比较
DOI：
10.1007/978-3-030-80421-3_9
复制
发表时间：
2021
期刊：
影响因子：
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通讯作者：
Kumar, Amruth N.
中科院分区：
文献类型：
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作者：
Getseva, Vanesa;Kumar, Amruth N.
We conducted a study to see if using Bayesian Knowledge Tracing (BKT) models would save time and problems in programming tutors. We used legacy data collected by two programming tutors to compute BKT models for every concept covered by each tutor. The novelty of our model was that slip and guess parameters were computed for every problem presented by each tutor. Next, we used cross-validation to evaluate whether the resulting BKT model would have reduced the number of practice problems solved and time spent by the students represented in the legacy data. We found that in 64.23% of the concepts, students would have saved time with the BKT model. The savings varied among concepts. Overall, students would have saved a mean of 1.28 min and 1.23 problems per concept. We also found that BKT models were more effective at saving time and problems on harder concepts.
