Intelligent tutoring system

About: Intelligent tutoring system is a research topic. Over the lifetime, 3472 publications have been published within this topic receiving 58217 citations. The topic is also known as: ITS.

Neural imaging to track mental states while using an intelligent tutoring system

Abstract: Hemodynamic measures of brain activity can be used to interpret a student's mental state when they are interacting with an intelligent tutoring system. Functional magnetic resonance imaging (fMRI) data were collected while students worked with a tutoring system that taught an algebra isomorph. A cognitive model predicted the distribution of solution times from measures of problem complexity. Separately, a linear discriminant analysis used fMRI data to predict whether or not students were engaged in problem solving. A hidden Markov algorithm merged these two sources of information to predict the mental states of students during problem-solving episodes. The algorithm was trained on data from 1 day of interaction and tested with data from a later day. In terms of predicting what state a student was in during a 2-s period, the algorithm achieved 87% accuracy on the training data and 83% accuracy on the test data. The results illustrate the importance of integrating the bottom-up information from imaging data with the top-down information from a cognitive model.

Web-Based Text Structure Strategy Instruction Improves Seventh Graders' Content Area Reading Comprehension.

Abstract: Reading comprehension in the content areas is a challenge for many middle grade students. Text structure-based instruction has yielded positive outcomes in reading comprehension at all grade levels in small and large studies. The text structure strategy delivered via the web, called Intelligent Tutoring System for the Text Structure Strategy (ITSS), has proven successful in large-scale studies at 4th and 5th grades and a smaller study at 7th grade. Text structure-based instruction focuses on selection and encoding of strategic memory. This strategic memory proves to be an effective springboard for many comprehension-based activities such as summarizing, inferring, elaborating, and applying. This was the first large-scale randomized controlled efficacy study on the web-based delivery of the text structure strategy to 7th-grade students. 108 classrooms from rural and suburban schools were randomly assigned to ITSS or control and pretests and posttests were administered at the beginning and end of the school year. Multilevel data analyses were conducted on standardized and researcher designed measures of reading comprehension. Results showed that ITSS classrooms outperformed the control classrooms on all measures with the highest effects reported for number of ideas included in the main idea. Results have practical implications for classroom practices. (PsycINFO Database Record (c) 2017 APA, all rights reserved)

Adaptation of Problem Presentation and Feedback in an Intelligent Mathematics Tutor

Abstract: We have developed an intelligent tutor for teaching remedial mathematics to community college students. This domain is fairly narrow in scope and is an important component of the college curriculum. The target audience often retains fragments of knowledge from previous courses which can aid them in learning; alternately, misconceptions can present conceptual stumbling blocks if students have misremembered what they learned previously. Thus, a system built with a strong student model can greatly benefit the teaching process. The tutor described in this paper tracks student skills along with a general acquisition factor, and uses this information for topic selection, problem generation, problem presentation, and dynamic feedback.

Language and Discourse Are Powerful Signals of Student Emotions during Tutoring

Abstract: We explored the possibility of predicting student emotions (boredom, flow/engagement, confusion, and frustration) by analyzing the text of student and tutor dialogues during interactions with an Intelligent Tutoring System (ITS) with conversational dialogues. After completing a learning session with the tutor, student emotions were judged by the students themselves (self-judgments), untrained peers, and trained judges. Transcripts from the tutorial dialogues were analyzed with four methods that included 1) identifying direct expressions of affect, 2) aligning the semantic content of student responses to affective terms, 3) identifying psychological and linguistic terms that are predictive of affect, and 4) assessing cohesion relationships that might reveal student affect. Models constructed by regressing the proportional occurrence of each emotion on textual features derived from these methods yielded large effects (R2 = 38%) for the psychological, linguistic, and cohesion-based methods, but not the direct expression and semantic alignment methods. We discuss the theoretical, methodological, and applied implications of our findings toward text-based emotion detection during tutoring.

A practical student model in an intelligent tutoring system

Abstract: We consider two questions related to student modeling in an intelligent tutoring system: 1) what kind of student model should we build when we design a new system; and 2) should we divide the student model into different components depending on the information involved. We consider these two questions in the context of a conversational intelligent tutoring system, CIRCSIM-Tutor. We first analyze the range of decisions that the system needs to make and define the information needed to support these decisions. We then describe four distinct models that provide different aspects of this information, taking into consideration the nature of the domain and the constraints provided by the tutoring system. Finally, we briefly discuss our experiments with enhancing the student model in CIRCSIM-Tutor and some general problems regarding building and evaluating different student models.