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Dragica Ljubisavljevic; Marko Koprivica; Aleksandar Kostic; Vladan Devedžic – International Association for Development of the Information Society, 2023
This paper delves into statistical disparities between human-written and ChatGPT-generated texts, utilizing an analysis of Shannon's equitability values, and token frequency. Our findings indicate that Shannon's equitability can potentially be a differentiating factor between texts produced by humans and those generated by ChatGPT. Additionally,…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Writing (Composition)
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Hsu, Hao-Hsuan; Huang, Nen-Fu – IEEE Transactions on Learning Technologies, 2022
This article introduces Xiao-Shih, the first intelligent question answering bot on Chinese-based massive open online courses (MOOCs). Question answering is critical for solving individual problems. However, instructors on MOOCs must respond to many questions, and learners must wait a long time for answers. To address this issue, Xiao-Shih…
Descriptors: Foreign Countries, Artificial Intelligence, Online Courses, Natural Language Processing
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Odden, Tor Ole B.; Marin, Alessandro; Caballero, Marcos D. – Physical Review Physics Education Research, 2020
We have used an unsupervised machine learning method called latent Dirichlet allocation (LDA) to thematically analyze all papers published in the Physics Education Research Conference Proceedings between 2001 and 2018. By looking at co-occurrences of words across the data corpus, this technique has allowed us to identify ten distinct themes or…
Descriptors: Physics, Science Education, Educational Research, Conferences (Gatherings)
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Cai, Zhiqiang; Li, Hiyiang; Hu, Xiangen; Graesser, Art – Grantee Submission, 2016
This paper provides an alternative way of document representation by treating topic probabilities as a vector representation for words and representing a document as a combination of the word vectors. A comparison on summary data shows that this representation is more effective in document classification. [This paper was published in:…
Descriptors: Probability, Natural Language Processing, Models, Automation
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Lau, Jey Han; Clark, Alexander; Lappin, Shalom – Cognitive Science, 2017
The question of whether humans represent grammatical knowledge as a binary condition on membership in a set of well-formed sentences, or as a probabilistic property has been the subject of debate among linguists, psychologists, and cognitive scientists for many decades. Acceptability judgments present a serious problem for both classical binary…
Descriptors: Grammar, Probability, Sentences, Language Research
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Khodeir, Nabila Ahmed; Elazhary, Hanan; Wanas, Nayer – International Journal of Information and Learning Technology, 2018
Purpose: The purpose of this paper is to present an algorithm to generate story problems via controlled parameters in the domain of mathematics. The generation process is performed in the problem generation module in the context of an intelligent tutoring system suggested in this paper. Controlling the question parameters allows for adapting the…
Descriptors: Problem Solving, Teaching Methods, Difficulty Level, Natural Language Processing
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Zhang, Lishan; VanLehn, Kurt – Interactive Learning Environments, 2017
The paper describes a biology tutoring system with adaptive question selection. Questions were selected for presentation to the student based on their utilities, which were estimated from the chance that the student's competence would increase if the questions were asked. Competence was represented by the probability of mastery of a set of biology…
Descriptors: Biology, Science Instruction, Intelligent Tutoring Systems, Probability
El-Arini, Khalid – ProQuest LLC, 2013
We live in an era of information overload. From online news to online shopping to scholarly research, we are inundated with a torrent of information on a daily basis. With our limited time, money and attention, we often struggle to extract actionable knowledge from this deluge of data. A common approach for addressing this challenge is…
Descriptors: Information Management, Electronic Learning, Data Collection, Privacy
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Hsu, Anne S.; Chater, Nick; Vitanyi, Paul M. B. – Cognition, 2011
There is much debate over the degree to which language learning is governed by innate language-specific biases, or acquired through cognition-general principles. Here we examine the probabilistic language acquisition hypothesis on three levels: We outline a novel theoretical result showing that it is possible to learn the exact "generative model"…
Descriptors: Linguistics, Prediction, Natural Language Processing, Language Acquisition
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Valdés Aguirre, Benjamín; Ramírez Uresti, Jorge A.; du Boulay, Benedict – International Journal of Artificial Intelligence in Education, 2016
Sharing user information between systems is an area of interest for every field involving personalization. Recommender Systems are more advanced in this aspect than Intelligent Tutoring Systems (ITSs) and Intelligent Learning Environments (ILEs). A reason for this is that the user models of Intelligent Tutoring Systems and Intelligent Learning…
Descriptors: Intelligent Tutoring Systems, Models, Open Source Technology, Computers
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Hay, Jessica F.; Pelucchi, Bruna; Estes, Katharine Graf; Saffran, Jenny R. – Cognitive Psychology, 2011
The processes of infant word segmentation and infant word learning have largely been studied separately. However, the ease with which potential word forms are segmented from fluent speech seems likely to influence subsequent mappings between words and their referents. To explore this process, we tested the link between the statistical coherence of…
Descriptors: Novelty (Stimulus Dimension), Infants, Word Recognition, Probability
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Johnson, Elizabeth K.; Tyler, Michael D. – Developmental Science, 2010
Past research has demonstrated that infants can rapidly extract syllable distribution information from an artificial language and use this knowledge to infer likely word boundaries in speech. However, artificial languages are extremely simplified with respect to natural language. In this study, we ask whether infants' ability to track transitional…
Descriptors: Cues, Artificial Languages, Testing, Infants
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Pelucchi, Bruna; Hay, Jessica F.; Saffran, Jenny R. – Cognition, 2009
Numerous recent studies suggest that human learners, including both infants and adults, readily track sequential statistics computed between adjacent elements. One such statistic, transitional probability, is typically calculated as the likelihood that one element predicts another. However, little is known about whether listeners are sensitive to…
Descriptors: Infants, Test Items, Prediction, Probability
Heintz, Ilana – ProQuest LLC, 2010
The goal of this dissertation is to introduce a method for deriving morphemes from Arabic words using stem patterns, a feature of Arabic morphology. The motivations are three-fold: modeling with morphemes rather than words should help address the out-of-vocabulary problem; working with stem patterns should prove to be a cross-dialectally valid…
Descriptors: Semitic Languages, Dialects, Vowels, Morphemes
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Hertwig, Ralph; Benz, Bjorn; Krauss, Stefan – Cognition, 2008
According to the conjunction rule, the probability of A "and" B cannot exceed the probability of either single event. This rule reads "and" in terms of the logical operator [inverted v], interpreting A and B as an intersection of two events. As linguists have long argued, in natural language "and" can convey a wide range of relationships between…
Descriptors: Semantics, Form Classes (Languages), Probability, Inferences
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