Learning, goals, and learning goals: A perspective on goal-driven learning

Leake, David; Ram, Ashwin

doi:10.1007/bf00849065

Cited by 22 publications

(5 citation statements)

References 40 publications

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“…certain properties on the basis of perceived characteristics (Holland, Holyoak & Thagard, 1989 ;Anderson, 1991 ;Leake & Ram, 1995 ;Wisniewski, 1995 ;Kersten & Billman, 1997 ;Ross & Makin, 1999;Murphy, 2002). That is, cognitive systems do not generalize randomly or completely.…”

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confidence: 99%

The role of prediction in construction-learning

2005

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It is well-established that (non-linguistic) categorization is driven by a functional demand of prediction. We suggest that prediction likewise may well play a role in motivating the learning of semantic generalizations about argument structure constructions. We report corpora statistics that indicate that the argument frame or construction has roughly equivalent cue validity as a predictor of overall sentence meaning as the morphological form of the verb, and has greater category validity. That is, the construction is at least as reliable and more available than the verb. Moreover, given the fact that many verbs have quite low cue validity in isolation, attention to the contribution of the construction is essential.

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confidence: 99%

The role of prediction in construction-learning

2005

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“…Thus, this example demonstrates that goals articulate two dimensions of learning: the focus of attention and resources needed to achieve the result. As Leake and Ram [54] argued, "a goal-driven learner determines what to learn by reasoning about the information it needs, and determines how to learn by reasoning about the relative merits of alternative learning strategies in the current circumstances" (p. 389).…”

Section: Goal Setting and Promptingmentioning

confidence: 99%

Educational Design Principles of Using AI Chatbot That Supports Self-Regulated Learning in Education: Goal Setting, Feedback, and Personalization

Chang,

Lin,

Hajian

et al. 2023

Sustainability

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The invention of ChatGPT and generative AI technologies presents educators with significant challenges, as concerns arise regarding students potentially exploiting these tools unethically, misrepresenting their work, or gaining academic merits without active participation in the learning process. To effectively navigate this shift, it is crucial to embrace AI as a contemporary educational trend and establish pedagogical principles for properly utilizing emerging technologies like ChatGPT to promote self-regulation. Rather than suppressing AI-driven tools, educators should foster collaborations among stakeholders, including educators, instructional designers, AI researchers, and developers. This paper proposes three key pedagogical principles for integrating AI chatbots in classrooms, informed by Zimmerman’s Self-Regulated Learning (SRL) framework and Judgment of Learning (JOL). We argue that the current conceptualization of AI chatbots in education is inadequate, so we advocate for the incorporation of goal setting (prompting), self-assessment and feedback, and personalization as three essential educational principles. First, we propose that teaching prompting is important for developing students’ SRL. Second, configuring reverse prompting in the AI chatbot’s capability will help to guide students’ SRL and monitoring for understanding. Third, developing a data-driven mechanism that enables an AI chatbot to provide learning analytics helps learners to reflect on learning and develop SRL strategies. By bringing in Zimmerman’s SRL framework with JOL, we aim to provide educators with guidelines for implementing AI in teaching and learning contexts, with a focus on promoting students’ self-regulation in higher education through AI-assisted pedagogy and instructional design.

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“…The second principle captures the direction of learning. In a certain cases learning can be consciously directed and monitored through meta-cognitive processes, but the actual appearance of knowledge structure change takes a place below the level of consciousness and is guided by subconscious processes that are trying to separate the meaning from experience (Leake & Ram, 1995;Anderson, 1983;Laird et al, 1985;Schank & Abelson, 1977;Berlyne, 1960). The third principle describes a learning context through the knowledge accessibility prism.…”

Section: Theoretical Basismentioning

confidence: 99%

Theoretical Inquiry-Based Learning Insights on Natural Science Education: from the Source to 5E Model

Dagys

2017

PED

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Abstract. The article presents a theoretical inquiry-based learning (hereinafter can be called IBL) methodology insights on natural science education. Seeking for an answer to the questions such as when did inquiry-based learning first appeared, which educational philosophy it derived from. It examines on what principles inquiry-based learning is rested on. An overview of IBL models, introduced information on exploration levels. Revealing inquiry-based learning advantages.

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Learning, goals, and learning goals: A perspective on goal-driven learning

Cited by 22 publications

References 40 publications

The role of prediction in construction-learning

The role of prediction in construction-learning

Educational Design Principles of Using AI Chatbot That Supports Self-Regulated Learning in Education: Goal Setting, Feedback, and Personalization

Theoretical Inquiry-Based Learning Insights on Natural Science Education: from the Source to 5E Model

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