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Michelle Pauley Murphy; Woei Hung – TechTrends: Linking Research and Practice to Improve Learning, 2024
Constructing a consensus problem space from extensive qualitative data for an ill-structured real-life problem and expressing the result to a broader audience is challenging. To effectively communicate a complex problem space, visualization of that problem space must elucidate inter-causal relationships among the problem variables. In this…
Descriptors: Information Retrieval, Data Analysis, Pattern Recognition, Artificial Intelligence
Ehren Helmut Pflugfelder; Joshua Reeves – Journal of Technical Writing and Communication, 2024
The use of generative artificial intelligence (GAI) large language models has increased in both professional and classroom technical writing settings. One common response to student use of GAI is to increase surveillance, incorporating plagiarism detection services or banning certain composing activities from the classroom. This paper argues such…
Descriptors: Technical Writing, Artificial Intelligence, Supervision, Teaching Methods
Jamie Magrill; Barry Magrill – Teaching & Learning Inquiry, 2024
The rapid advancement of artificial intelligence technologies, exemplified by systems including Open AI's ChatGPT, Microsoft's Bing AI, and Google's Bard (now Gemini 1.5Pro), present both challenges and opportunities for the academic world. Higher education institutions are at the forefront of preparing students for this evolving landscape. This…
Descriptors: Higher Education, Artificial Intelligence, Technological Advancement, Technology Integration
Margaret A.L. Blackie – Teaching in Higher Education, 2024
Large language models such as ChatGPT can be seen as a major threat to reliable assessment in higher education. In this point of departure, I argue that these tools are a major game changer for society at large. Many of the jobs we now consider highly skilled are based on pattern recognition that can much more reliably be carried by fine-tuned…
Descriptors: Artificial Intelligence, Synchronous Communication, Science and Society, Evaluation
Silvia García-Méndez; Francisco de Arriba-Pérez; Francisco J. González-Castaño – International Association for Development of the Information Society, 2023
Mobile learning or mLearning has become an essential tool in many fields in this digital era, among the ones educational training deserves special attention, that is, applied to both basic and higher education towards active, flexible, effective high-quality and continuous learning. However, despite the advances in Natural Language Processing…
Descriptors: Higher Education, Artificial Intelligence, Computer Software, Usability
Sanchez-Ferreres, Josep; Delicado, Luis; Andaloussi, Amine Abbab; Burattin, Andrea; Calderon-Ruiz, Guillermo; Weber, Barbara; Carmona, Josep; Padro, Lluis – IEEE Transactions on Learning Technologies, 2020
The creation of a process model is primarily a formalization task that faces the challenge of constructing a syntactically correct entity, which accurately reflects the semantics of reality, and is understandable to the model reader. This article proposes a framework called "Model Judge," focused toward the two main actors in the process…
Descriptors: Models, Automation, Validity, Natural Language Processing
Michal Bobula – Journal of Learning Development in Higher Education, 2024
This paper explores recent advancements and implications of artificial intelligence (AI) technology, with a specific focus on Large Language Models (LLMs) like ChatGPT 3.5, within the realm of higher education. Through a comprehensive review of the academic literature, this paper highlights the unprecedented growth of these models and their…
Descriptors: Artificial Intelligence, Information Technology, Natural Language Processing, Literature Reviews
Mahowald, Kyle; Kachergis, George; Frank, Michael C. – First Language, 2020
Ambridge calls for exemplar-based accounts of language acquisition. Do modern neural networks such as transformers or word2vec -- which have been extremely successful in modern natural language processing (NLP) applications -- count? Although these models often have ample parametric complexity to store exemplars from their training data, they also…
Descriptors: Models, Language Processing, Computational Linguistics, Language Acquisition
Schuler, Kathryn D.; Kodner, Jordan; Caplan, Spencer – First Language, 2020
In 'Against Stored Abstractions,' Ambridge uses neural and computational evidence to make his case against abstract representations. He argues that storing only exemplars is more parsimonious -- why bother with abstraction when exemplar models with on-the-fly calculation can do everything abstracting models can and more -- and implies that his…
Descriptors: Language Processing, Language Acquisition, Computational Linguistics, Linguistic Theory
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
Gibson, Andrew; Kitto, Kirsty; Bruza, Peter – Journal of Learning Analytics, 2016
Modern society demands renewed attention on the competencies required to best equip students for a dynamic and uncertain future. We present exploratory work based on the premise that metacognitive and reflective competencies are essential for this task. Bringing the concepts of metacognition and reflection together into a conceptual model within…
Descriptors: Metacognition, Reflection, Writing Assignments, Undergraduate Students
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
Friesen, Norm – Mind, Culture, and Activity, 2009
As an alternative to dominant cognitive-constructivist approaches to educational technology, this article makes the case for what has been termed a discursive, or postcognitive, psychological research paradigm. It does so by adapting discursive psychological analyses of conversational activity to the study of educational technology use. It applies…
Descriptors: Constructivism (Learning), Psychological Studies, Educational Technology, Psychology
Bateman, John; Tenbrink, Thora; Farrar, Scott – Discourse Processes: A Multidisciplinary Journal, 2007
This article argues that a clear division between two sources of information--one oriented to world knowledge, the other to linguistic semantics--offers a framework within which mechanisms for modelling the highly flexible relation between language and interpretation necessary for natural discourse can be specified and empirically validated.…
Descriptors: Semantics, Linguistics, Teaching Methods, Models
A Prediction Model of Foreign Language Reading Proficiency Based on Reading Time and Text Complexity
Kotani, Katsunori; Yoshimi, Takehiko; Isahara, Hitoshi – Online Submission, 2010
In textbooks, foreign (second) language reading proficiency is often evaluated through comprehension questions. In case, authentic texts are used as reading material, such questions should be prepared by teachers. However, preparing appropriate questions may be a very demanding task for teachers. This paper introduces a method for automatically…
Descriptors: Foreign Countries, Reading Comprehension, Reading Materials, Predictive Measurement