The field of Artificial Intelligence in Education (AIED) cares by supporting the needs of learners with technology, and does so carefully by leveraging a broad set of methodologies to understand learners and instructi...
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ISBN:
(数字)9783031363368
ISBN:
(纸本)9783031363351;9783031363368
The field of Artificial Intelligence in Education (AIED) cares by supporting the needs of learners with technology, and does so carefully by leveraging a broad set of methodologies to understand learners and instruction. Recent trends in AIED do not always live up to these values, for instance, projects that simply fit data-driven models without quantifying their real world impact. This work discusses opportunities to deepen careful and caring AIED research by developing theories of instructional design using computational models of learning. A narrow set of advances have furthered this effort with simulations of inductive and abductive learning that explain how knowledge can be acquired from experience, initially produce mistakes, and become refined to mastery. In addition to being theoretically grounded, explainable, and empirically aligned with patterns in human data, these systems show practical interactive task learning capabilities that can be leveraged in tools that interactively learn from natural tutoring interactions. These efforts present a dramatically different perspective on machinelearning in AIED than the current trends of data-driven prediction.
Over the last few years, research on learning and memory has become increasingly interdisciplinary. In the past, theories of learning, as a prerogative of psychologists, were generally formulated in purely verbal term...
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Over the last few years, research on learning and memory has become increasingly interdisciplinary. In the past, theories of learning, as a prerogative of psychologists, were generally formulated in purely verbal terms and evaluated exclusively at the behavioral level. At present, scientists are trying to build theories with a quantitative and biological flavor, seeking to embrace more complex behavioral phenomena. Pavlovian conditioning, one of the simplest and ubiquitous forms of learning, is especially suited for this multiple level analysis (i.e., quantitative, neurobiological, and behavioral), in part because of recent discoveries showing a correspondence between behavioral phenomena and associative properties at the cellular and systems levels, and in part because of its well established quantitative theoretical tradition. The present review, examines the mayor quantitative theories of Pavlovian conditioning and the phenomena to which they have been designed to account. In order to provide researchers from different disciplines with a simple guideline about the rationale of the different theoretical choices, all the models are described through a single formalism based on the neural network connectionist perspective. (C) 2004 Elsevier Inc. All rights reserved.
Simulations of human learning can be used as computationalmodels for evaluating theories of learning. They can also be taught interactively to author intelligent tutoring systems. Prior simulated learner systems have...
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ISBN:
(纸本)9783031363351;9783031363368
Simulations of human learning can be used as computationalmodels for evaluating theories of learning. They can also be taught interactively to author intelligent tutoring systems. Prior simulated learner systems have learned inductively from worked examples and correctness feedback. This work introduces a mechanism where simulated learners can also learn from natural language. Using a neural grammar parser with additional symbolic processing steps, we simulate the production of loose interpretations of verbal instructions. These interpretations can be combined with worked examples to resolve the ambiguities of either form of instruction alone. We find that our system has practical benefits over an alternative method using github Copilot and slightly better accuracy.
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