Xiangen Hu

Recent Advances in Conversational Intelligent Tutoring Systems

We report recent advances in intelligent tutoring systems with conversational dialogue. We highlight progress in terms of macro and microadaptivity. Macroadaptivity refers to a system’s capability to select appropriate instructional tasks for the learner to work on. Microadaptivity refers to a system’s capability to adapt its scaffolding while the learner is working on a particular task. The advances in macro and microadaptivity that are presented here were made possible by the use of learning progressions, deeper dialogue and natural language processing techniques, and by the use of affect-enabled components. Learning progressions and deeper dialogue and natural language processing techniques are key features of DeepTutor, the first intelligent tutoring system based on learning progressions. These improvements extend the bandwidth of possibilities for tailoring instruction to each individual student which is needed for maximizing engagement and ultimately learning.

GPT.EXE: A powerful tool for the visualization and analysis of general processing tree models

This paper introduces GPT.EXE, a computer program for designing and implementing general processing tree (GPT) models. First, designing and building GPT models using this program is discussed. The second major emphasis is a description of various statistical procedures that can be carried out with GPT.EXE. There is also a brief section on the on-line documentation of this program. Throughout the text, pictures of windows from the program are displayed to help explain the procedures being described by the text.

AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring

AutoTutor is a natural language tutoring system that has produced learning gains across multiple domains (e.g., computer literacy, physics, critical thinking). In this paper, we review the development, key research findings, and systems that have evolved from AutoTutor. First, the rationale for developing AutoTutor is outlined and the advantages of natural language tutoring are presented. Next, we review three central themes in AutoTutor’s development: human-inspired tutoring strategies, pedagogical agents, and technologies that support natural-language tutoring. Research on early versions of AutoTutor documented the impact on deep learning by co-constructed explanations, feedback, conversational scaffolding, and subject matter content. Systems that evolved from AutoTutor added additional components that have been evaluated with respect to learning and motivation. The latter findings include the effectiveness of deep reasoning questions for tutoring multiple domains, of adapting to the affect of low-knowledge learners, of content over surface features such as voices and persona of animated agents, and of alternative tutoring strategies such as collaborative lecturing and vicarious tutoring demonstrations. The paper also considers advances in pedagogical agent roles (such as trialogs) and in tutoring technologies, such semantic processing and tutoring delivery platforms. This paper summarizes and integrates significant findings produced by studies using AutoTutor and related systems.

Vicarious Learning: Effects of Overhearing Dialog and Monologue-Like Discourse in a Virtual Tutoring Session.

In two experiments, students overheard two computer-controlled virtual agents discussing four computer literacy topics in dialog discourse and four in monologue discourse. In Experiment 1, the virtual tutee asked a series of deep questions in the dialog condition, but only one per topic in the monologue condition in both studies. In the dialog conditions of Experiment 2, the virtual tutee asked either deep questions, shallow questions, or made comments. In a fourth “dialog” condition, the comments were spoken by the virtual tutor. The discourse spoken by the virtual tutor was identical in the dialog and monologue conditions, except in the fourth dialog condition. In both studies, learners wrote significantly more content and significantly more relevant content in the deep question condition than in the monologue condition. No other differences were significant. Results were discussed in terms of advanced organizers, schema theory, and discourse comprehension theory.

Multinomial processing tree models: An implementation

Multinomial processing tree (MPT) models have been widely used by researchers in cognitive psychology. This paper introduces MBT.EXE, a computer program that makes MPT easy to use for researchers. MBT.EXE implements the statistical theory developed by Hu and Batchelder (1994). This user-friendly software can be used to construct MPT models and conduct statistical inferences, including point and interval estimation, hypothesis testing, and goodness of fit. Furthermore, this program can be used to examine the robustness of MPT models. Algorithms for parameter estimation, hypothesis testing, and Monte Carlo simulation are presented.

A framework of synthesizing tutoring conversation capability with web-based distance education courseware

Whereas existing learning environments on the Web lack high level interactivity, we have developed a human tutor-like tutorial conversation system for the Web that enhances educational courseware through mixed-initiative dialog with natural language processing. The conversational tutoring agent is composed of an animated tutor, a Latent Semantic Analysis (LSA) module, a database with curriculum scripts, and a dialog manager. As in the case of human tutors, the meaning of learners contributions in natural language are compared with the content of expected answers to questions or problems specified in curriculum scripts. LSA is used to evaluate the conceptual matches between learner input and tutor expectations, whereas the dialog manager determines how the tutor adaptively responds to the learner by selecting content from the curriculum script. The integration of available courseware with the tutorial dialog system guarantees the reusability of existing Web tutorials with minimal effort in the modification of the curriculum script and LSA module. This development thereby simplifies the change into more valuable Web based training courseware.

The impact of a technology-based mathematics after-school program using ALEKS on student's knowledge and behaviors

The effectiveness of using the Assessment and LEarning in Knowledge Spaces (ALEKS) system, an Intelligent Tutoring System for mathematics, as a method of strategic intervention in after-school settings to improve the mathematical skills of struggling students was examined using a randomized experimental design with two groups. As part of a 25-week program, student volunteers were randomly assigned to either a teacher-led classroom or a classroom in which students interacted with ALEKS while teachers were present. Students math performance, conduct, involvement, and assistance was needed to complete tasks were investigated to determine overall impact of the two programs. Students assigned to the ALEKS classrooms performed at the same level as students taught by expert teachers on the Tennessee Comprehensive Assessment Program (TCAP), which is given annually to all Tennessee students. Furthermore, students conduct and involvement remained at the same levels in both conditions. However, students in the ALEKS after-school classrooms required significantly less assistance in mathematics from teachers to complete their daily work.

Quantitative Discourse Psychology.

Discourse psychologists investigate the cognitive representations, procedures, and processes that transpire in the human mind when discourse is comprehend and produced. Quantitative discourse psychologists build sophisticated computational, statistical, and mathematical models that simulate these mechanisms. We believe that quantitative discourse psychologists will be important players in the field of discourse processing in future years. The practice of quantitative discourse psychology is illustrated in five research projects that we have conducted, all of which examined naturalistic discourse. These projects investigated reading time, inference generation, the construction of multiple agents (i.e., narrators, characters) in literary short stories, tutorial dialogue, and dialogue patterns in two‐party conversations. There was a major conceptual advance in each of these projects when we adopted an appropriate quantitative approach. The fact that discourse psychologists frequently investigate the processi...

COGNITIVELY INSPIRED NLP-BASED KNOWLEDGE REPRESENTATIONS: FURTHER EXPLORATIONS OF LATENT SEMANTIC ANALYSIS

Natural-language based knowledge representations borrow their expressiveness from the semantics of language. One such knowledge representation technique is Latent semantic analysis (LSA), a statistical, corpus-based method for representing knowledge. It has been successfully used in a variety of applications including intelligent tutoring systems, essay grading and coherence metrics. The advantage of LSA is that it is efficient in representing world knowledge without the need for manual coding of relations and that it has in fact been considered to simulate aspects of human knowledge representation. An overview of LSA applications will be given, followed by some further explorations of the use of LSA. These explorations focus on the idea that the power of LSA can be amplified by considering semantic fields of text units instead of pairs of text units. Examples are given for semantic networks, category membership, typicality, spatiality and temporality, showing new evidence for LSA as a mechanism for knowledge representation. The results of such tests show that while the mechanism behind LSA is unique, it is flexible enough to replicate results in different corpora and languages.

Teaching with the help of talking heads

Talking heads were integrated with two learning systems. In AutoTutor, students learn about computer literacy by holding a conversation with a student. AutoTutor is an animated pedagogical agent that asks deep reasoning questions and engages in a mixed initiative dialog as answers emerge. Students type in information via keyboard whereas AutoTutor delivers discourse sensitive contributions with facial expressions, synthesized speech, and gestures. In the Human Use Regulator Affairs (HURA) Advisor, high ranking officers in the military learn about the ethical use of human subjects on a Web site with a conversational navigational agent.