Reading comprehension is often assessed by having students read passages and administering a test that assesses their understanding of the text. Shorter assessments may fail to give a full picture of comprehension ability while more thorough ones can be time consuming and costly. This study used data from a conversational intelligent tutoring system (AutoTutor) to assess reading comprehension ability in 52 low-literacy adults who interacted with the system. We analyzed participants' accuracy and time spent answering questions in conversations in lessons that targeted four theoretical components of comprehension: Word, Textbase, Situation Model, and Rhetorical Structure. Accuracy and answer response time were analyzed to track adults' proficiency for comprehension components, and we analyzed whether the four components predicted reading grade level. We discuss the results with respect to the advantages that a conversational intelligent tutoring system assessment may provide over traditional assessment tools and the linking of theory to practice in adult literacy.

This paper describes a new automated disengagement tracking system (DTS) that detects learners’ maladaptive behaviors, e.g. mind-wandering and impetuous responding, in an intelligent tutoring system (ITS), called AutoTutor. AutoTutor is a conversation-based intelligent tutoring system designed to help adult literacy learners improve their reading comprehension skills. Learners interact with two computer agents in natural language in 30 lessons focusing on word knowledge, sentence processing, text comprehension, and digital literacy. Each lesson has one to three dozen questions to assess and enhance learning. DTS automatically retrieves and aggregates a learner's response accuracies and time on the first three to five questions in a lesson, as a baseline performance for the lesson when they are presumably engaged, and then detects disengagement by observing if the learner's following performance significantly deviates from the baseline. DTS is computed with an unsupervised learning method and thus does not rely on any self-reports of disengagement. We analyzed the response time and accuracy of 252 adult literacy learners who completed lessons in AutoTutor. Our results show that items that the detector identified as the learner being disengaged had a performance accuracy of 18.5%, in contrast to 71.8% for engaged items. Moreover, the three post-test reading comprehension scores from Woodcock Johnson III, RISE, and RAPID had a significant association with the accuracy of engaged items, but not disengaged items.

Abstract-The difficulty of switching off unwanted pre-sleep cognitive activities always causes most people with insomnia unable to fall asleep. Previous studies suggested that manipulating pre-sleep thoughts with cognitive task could change sleep onset latency. The interaction effect of cognitive task and unwanted thoughts is a mixed effect in which each mechanism alone is not completely understood. The present study tries to explore the mixed effect and extract the single effect from it. Seven people with insomnia were investigated and the general cognitive task controlled internally by insomniacs themselves was replaced with an audio task in which the participants have to react distinctively to two different auditory stimuli. The reaction time for which reflected the interaction effect was recorded and then analyzed by the autocorrelation and partial autocorrelation method. A general decline tendency which is called decline effect was observed in the single effect and for most insomniacs the length of the decline effect was around 4 or 5 seconds. In addition, it indicated that such decline effect could be a potential candidate for investing the correlation between cognitive load and interaction effect.

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