--Detection of carelessness in digital learning platforms has relied on the contextual slip model, which leverages conditional probability and Bayesian Knowledge Tracing (BKT) to identify careless errors, where students make mistakes despite having the knowledge. However, this model cannot effectively assess carelessness in questions tagged with multiple skills due to the use of conditional probability. This limitation narrows the scope within which the model can be applied. Thus, we propose a novel model, the Beyond-Knowledge Feature Carelessness (BKFC) model. The model detects careless errors using performance factor analysis (PF A) and behavioral features distilled from log data, controlling for knowledge when detecting carelessness. We applied the BKFC to detect carelessness in data from middle school students playing a learning game on decimal numbers and operations. We conducted analyses comparing the careless errors detected using contextual slip to the BKFC model. Unexpectedly, careless errors identified by these two approaches did not align. We found students' post-test performance was (corresponding to past results) positively associated with the carelessness detected using the contextual slip model, while negatively associated with the carelessness detected using the BKFC model. These results highlight the complexity of carelessness and underline a broader challenge in operationalizing carelessness and careless errors. Academic discussions of carelessness in classrooms date back to the 1950s [1]. Often viewed as the result of ineffective self-regulation, carelessness is thought to occur when students commit hurried or impulsive behaviors that result in mistakes on problems that could have been answered correctly. By distinguishing mistakes made due to carelessness from those caused by other factors, such as lack of knowledge, adaptive instruction can be provided to engage or reengage students in the effective use of self-regulation during the process of problem-solving. In the last several decades, two streams of work have run in parallel to investigate carelessness and detect careless behaviors.

We consider regret minimization in repeated games with a very large number of actions. Such games are inherent in the setting of AI safety via debate, and more generally games whose actions are language-based. Existing algorithms for online game playing require computation polynomial in the number of actions, which can be prohibitive for large games. We thus consider oracle-based algorithms, as oracles naturally model access to AI agents. With oracle access, we characterize when internal and external regret can be minimized efficiently. We give a novel efficient algorithm for internal regret minimization whose regret and computation complexity depend logarithmically on the number of actions. This implies efficient oracle-based computation of a correlated equilibrium in large games. We conclude with experiments in the setting of AI Safety via Debate that shows the benefit of insights from our algorithmic analysis.

Questionnaires in the behavioral and organizational sciences tend to be lengthy: survey measures comprising hundreds of items are the norm rather than the exception. However, recent literature suggests that the longer a questionnaire takes, the higher the probability that participants lose interest and start responding carelessly. Consequently, in long surveys a large number of participants may engage in careless responding, posing a major threat to internal validity. We propose a novel method to identify the onset of careless responding (or an absence thereof) for each participant. Specifically, our method is based on combined measurements of up to three dimensions in which carelessness may manifest (inconsistency, invariability, fast responding). Since a structural break in either dimension is potentially indicative of carelessness, our method searches for evidence for changepoints along the three dimensions. Our method is highly flexible, based on machine learning, and provides statistical guarantees on its performance. In simulation experiments, we find that it achieves high reliability in correctly identifying carelessness onset, discriminates well between careless and attentive respondents, and can capture a wide variety of careless response styles, even in datasets with an overwhelming presence of carelessness. In addition, we empirically validate our method on a Big 5 measurement. Furthermore, we provide freely available software in R to enhance accessibility and adoption by empirical researchers.