Note on Translation Introduction by Michael Holquist Response to a Question from the Novy Mir Editorial Staff The Bildungsroman and Its Significance in the History of Realism (Toward a Historical Typology of the Novel) The Problem of Speech Genres The Problem of the Text in Linguistics, Philology, and the Human Sciences: An Experiment in Philosophical Analysis From Notes Made in 1970-71 Toward a Methodology for the Human Sciences Index

Speech Genres and Other Late Essays

An Intelligent Tutoring System (ITS) is a computer software that help students in learning educational or academics concepts in customized environment. ITSs are instructional systems that have capability to facilitate user by providing instantaneous feedback and instructions without any human intervention. The advancement of new technologies has integrated computer based learning with artificial intelligence methods with aim to develop better custom-made education systems that referred as ITS. One of the important factors that affect students learning process is self-learning; all students cannot have similar experience of learning scholastic concepts from same educational material. Because students have individual differences that make some topics difficult or easy to understand regarding taken subjects. These systems have capability to improve teaching and learning process in different educational domains while respecting individual learning needs. In this study an attempt is made to review the research in field of ITSs and highlight the educational areas or domains in which ITSs have been introduced. Techniques, delivering modes and evaluation methodologies that have been used in developed ITSs have also been discussed in this work. This work will be helpful for both academia and new comers in the field of ITSs to further strengthen basis of tutoring systems in educational domains.

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A Systematic Review of Domains, Techniques, Delivery Modes and Validation Methods for Intelligent Tutoring Systems

Recently Intelligent Tutoring Systems (ITS) and Computer-Supported Collaborative Learning (CSCL) have got much attention in the field of computer science, artificial intelligence, cognitive psychology, and educational technologies. An ITS is a technologically intelligent system that provides an adaptive learning paradigm for an individual learner only, while CSCL is also a technology-driven learning paradigm that supports groups of learners in pertaining knowledge by collaboration. In a multidisciplinary research field—the Learning Sciences, both individual and collaborative learning have their own significance. This research aims to extend ITS for collaborative constructivist view of learning using CSCL. Integrating both design architecture of CSCL and ITS, this research model propose a new conceptual framework underpinning “Intelligent Tutoring Supported Collaborative Learning (ITSCL)”. ITSCL extend ITS by supporting multiple learners interacting system. ITSCL support three different types of interaction levels. The first level of interaction supports individual learning by learner-tutor interaction. The second and third level of interaction support collaborative learning, by learner-learner interaction and tutor-group of collaborative learners’ interactions, respectively. To evaluate ITSCL, a prototype model was implemented to conduct few experiments. The statistical results extrapolate the learning gains, measured from Paired T-Test and frequency analysis, contend a significant learning gain and improvement in the learning process with enhanced learning performance.

/pdf/intelligent-tutoring-supported-collaborative-learning-itscl-53uahdv7r8.pdf

Intelligent Tutoring Supported Collaborative Learning (ITSCL): A Hybrid Framework

A methodological approach to enable natural language interaction in an Intelligent Tutoring System

Automatic speech recognition (ASR) has considerable potential to model aspects of classroom discourse with the goals of automated assessment, feedback, and instructional support. However, modeling student talk is besieged by numerous challenges including a lack of data for child speech, low signal to noise ratio, speech disfluencies, and multiparty chatter. This raises the question as to whether contemporary ASR systems, which are benchmarked on adult speech in idealized conditions, can be used to transcribe child speech in classroom settings. To address this question, we collected a dataset of 32 audio recordings of 30 middle-school students engaged in small group work (dyads, triads and tetrads) in authentic classroom settings. The audio was sampled, segmented, and transcribed by humans as well as three ASR engines (Google, Rev.ai, IBM Watson). Whereas all three ASRs had high word error rates, these mainly consisted of deletion errors. Further, Google successfully transcribed a greater proportion of utterances than the other two, but with more word substitutions; insertions were low across the board. ASR accuracy was robust to different speakers and recording idiosyncrasies evidenced by <5% of variance in error rates attributable to the student and recording session. We found that ASR errors had a larger negative effect on downstream natural language processing tasks at the word, phrase, and semantic levels rather than at the discourse level. Our findings indicate that ASR can be used to extract meaningful information from noisy classroom speech and might be more suitable for applications that require higher precision but are tolerant of lower recall.

Challenges and Feasibility of Automatic Speech Recognition for Modeling Student Collaborative Discourse in Classrooms

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