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In intelligent tutoring systems (ITS) abundant supportive messages are provided to learners. One implicit assumption behind this design is that learners would actively process and benefit from feedback messages when interacting with ITS individually. However, this is not true for all learners; some gain little after numerous practice opportunities. In the current research, we assume that if the learner invests enough cognitive effort to review feedback messages provided by the system, the learner’s performance should be improved as practice opportunities accumulate. We expect that the learner’s cognitive effort investment could be reflected to some extent by the response latency, then the learner’s improvement should also be correlated with the response latency. Therefore, based on this core hypothesis, we conduct a cluster analysis by exploring features relevant to learners’ response latency. We expect to find several features that could be used as indicators of the feedback usage of learners; consequently, these features may be used to predict learners’ learning gain in future research. Our results suggest that learners’ prior knowledge level plays a role when interacting with ITS and different patterns of response latency. Learners with higher prior knowledge levels tend to interact flexibly with the system and use feedback messages more effectively. The quality of their previous attempts influences their response latency. However, learners with lower prior knowledge perform two opposite patterns, some tend to respond more quickly, and some tend to respond more slowly. One common characteristic of these learners is their incorrect response latency is not influenced by the quality of their previous performance. One interesting result is that those quick responders forget faster. Thus, we concluded that for learners with lower prior knowledge, it is better for them not to react hastily to obtain a more durable memory. 

A longstanding goal of learner modeling and educational data min-ing is to improve the domain model of knowledge that is used to make inferences about learning and performance. In this report we present a tool for finding domain models that is built into an exist-ing modeling framework, logistic knowledge tracing (LKT). LKT allows the flexible specification of learner models in logistic re-gression by allowing the modeler to select whatever features of the data are relevant to prediction. Each of these features (such as the count of prior opportunities) is a function computed for a compo-nent of data (such as a student or knowledge component). In this context, we have developed the “autoKC” component, which clus-ters knowledge components and allows the modeler to compute features for the clustered components. For an autoKC, the input component (initial KC or item assignment) is clustered prior to computing the feature and the feature is a function of that cluster. Another recent new function for LKT, which allows us to specify interactions between the logistic regression predictor terms, is com-bined with autoKC for this report. Interactions allow us to move beyond just assuming the cluster information has additive effects to allow us to model situations where a second factor of the data mod-erates a first factor.