Counterfactual Monotonic Knowledge Tracing for Assessing Students' Dynamic Mastery of Knowledge Concepts

As the core of the Knowledge Tracking (KT) task, assessing students' dynamic mastery of knowledge concepts is crucial for both offline teaching and online educational applications. Since students' mastery of knowledge concepts is often unlabeled, existing KT methods rely on the implicit paradigm of historical practice to mastery of knowledge concepts to students' responses to practices to address the challenge of unlabeled concept mastery. However, purely predicting student responses without imposing specific constraints on hidden concept mastery values does not guarantee the accuracy of these intermediate values as concept mastery values. To address this issue, we propose a principled approach called Counterfactual Monotonic Knowledge Tracing (CMKT), which builds on the implicit paradigm described above by using a counterfactual assumption to constrain the evolution of students' mastery of knowledge concepts.Comment: Accepted by CIKM 2023, 10 pages, 5 figures, 4 table

Information Extraction on the Web with Credibility Guarantee

The Web became the central medium for valuable sources of information extraction applications. However, such user-generated resources are often plagued by inaccuracies and misinformation due to the inherent openness and uncertainty of the Web. In this work we study the problem of extracting structured information out of Web data with a credibility guarantee. The ultimate goal is that not only the structured information should be extracted as much as possible but also its credibility is high. To achieve this goal, we propose a learning process to optimize the parameters of a probabilistic model that captures the relationships between users, their unstructured contents, and the underlying structured information. Our evaluations on real-world datasets show that our approach outperforms the baseline up to 6 times

Tests of Sample-recovery Models of Cued Recall

Sample-recovery models are a predominant class of episodic memory models that seek to explain why sometimes the representation of an experienced event is not retrieved or retrieved incorrectly. In these models, a correct retrieval occurs if the correct target item was sampled among the alternative studied item, then recovered correctly. In cued recall, participants output the representation of a single experienced event, a target, given a presented test stimulus and some defined relationship between the stimulus and the target. This relationship depends on the kind of cued recall and can rely on either studied or pre-experimental relationships. Sample-recovery models of this task share common testable properties related to both sampling and recovery, which we do across two experiments. Experiment 1 tests the property that sampling in sample-recovery models of cued recall is one process: they combine information about test stimulus and its relationship to the target into a single value and sample in a way consistent with the Luce choice rule. We test this assumption by testing whether manipulating the strengths of these relationships generates differential influence on performance in kinds of cued recall where different relationships between test stimulus and response are probed. The pattern of data is inconsistent with one sample process but is consistent with a sampling procedure that separately samples for a cue given the stimulus and a target given a cue. Experiment 2 tests the assumption that recovery performance is independent of other studied items. We allow some cue and target words to be related to some other untested studied words. Targets with a related word on the study list were associated with more correct responses than targets without one. This suggests that recovery in some way uses the memory for the other studied items to help retrieve. We consider how various models of sample-recovery may be adapted to account for these findings, with a particular focus on the Retrieving Effectively from Memory model