Cluster-based predictive modeling to improve pedagogic reasoning

Legaspi, Roberto; Sison, Raymund; Fukui, Kenichi; Numao, Masayuki

doi:10.1016/j.chb.2007.01.007

Cited by 9 publications

(2 citation statements)

References 17 publications

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“…It is worth mentioning that these authors have created a validated procedure for the assessment of pedagogical reasoning that seems compatible with the finer-grained model of the present study. Finally, pedagogic reasoning was used in a different context by Legaspi, Sison, Fukui and Numao (2008) to refer to the computations of an intelligent tutoring system that produce a dynamic model of the learner to which the system adapts. In this section, the model of pedagogical reasoning tested in this study is presented and then situated in a more general framework of cognitive functioning.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

From Laboratory to Authentic Contexts: Two Studies of Pedagogical Reasoning Across Four Levels of Expertise

Mercier

2012

WJE

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A cognitive model of how teachers plan instruction was validated in laboratory settings but remained to be tested empirically in authentic situations. The objective of this work is to describe and compare pedagogical reasoning in laboratory and authentic contexts and across expertise levels. The "state-driven hypothesis" and the "knowledge-driven hypothesis" were used in two studies to show how pedagogical reasoning was performed by novices and experts in laboratory (n=18) and in authentic context (n=14). Globally, the results show (1) similarities and differences in how pedagogical reasoning unfolds in laboratory and authentic contexts and (2) how domain knowledge influences only some aspects of this process. The work presented lays the foundations for the fine-grained study of how domain knowledge determines problem-solving in pedagogical-reasoning. Keywords: Pedagogical reasoning, Expertise, Teacher cognition, Teacher knowledge IntroductionTeacher planning has been identified as a crucial activity for innovation in teaching and for teacher development (Hasweh, 2005;McCutcheon & Milner, 2002). It is mainly during planning that teachers reflect on their teaching, make adjustments, and consider implementing innovative methods and tools. In order to further foster planning skills in student teachers, a model that shows how teachers think during planning was needed. Indeed, recent conceptualizations of instructional systems design in professional domains hinge on specifications of global and authentic tasks, including procedures and knowledge associated to them (van Merriënboer & Boot, 2009). Such a view represents a potentially fruitful bridge between expertise research and educational psychology. Among other things, this bridge motivates the use of cognitive task analysis and expert-novice research to inform pedagogical design. Whereas cognitive task analysis is essential in specifying what is to be learned for competent performance in a domain (Schraagen, 2009), expert-novice research typically unveils "a trajectory to expertise" (Lajoie, 2003). As a precursor to the design of a technology-enhanced learning environment for teacher planning, a model of pedagogical reasoning has been developed from a cognitive perspective. It was tested with samples of teachers across a broad range of expertise levels, in individual and collaborative performance settings. These studies were conducted in laboratory settings over a relatively short period of time. Results have shown on the one hand how pedagogical reasoning unfolds and, on the other hand, on which domain knowledge this process hinges when the task is to elaborate learning activities on the basis of the description of a single student with learning difficulties.

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Section: Theoretical Frameworkmentioning

confidence: 99%

From Laboratory to Authentic Contexts: Two Studies of Pedagogical Reasoning Across Four Levels of Expertise

Mercier

2012

WJE

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“…An analysis agent which automatically processes behavioural clusters once collected, in the terms of human-system relationship takes into account states of knowledge and behaviour of human operators together with the system possible responsive actions. A Self-Organizing Map could be used [10] as the clustering algorithm. Micro models of behaviour, repeated continously are the most important here.…”

Section: Behaviour Clusters-collecting and Processing Signs Of Bad Bementioning

confidence: 99%

Development of a risk assessment system based on pattern matching of behavioural fault models

2010

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Accidents are often repeated. Not learning from past accidents leads towards new ones. The larger part of occupational accidents are caused by the mistakes of human operators, by their mis-judgements and negligence. Behavioural fault models could describe, on the basis of accident and incident experience, the occurence of an accident by the mistakes made by the operator, putting into balance the initial causes of the mistaken actions (lack of knowledge, trying to shortcut longer but safer procedures, etc.) and the events that occured because of these causes. Behavioural fault models are perfectly able to be developed using ontologies, an assessment system based on past knowledge, and driven by ontologies could be very usefull to judge the safety at a workplace. The paper describes our research in developing such an ontological driven safety assessment system prototype and also the obtained results of running this prototype in Romanian small and medium enterprises. The system starts with the building of a behavioural normal activity model-specific, for example, for the activity of work at heights. By pattern matching this model with behavioural fault models developed from past experiences and also by direct observation of the workplace a quantified degree of safety could be established.

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Teaching-Learning by Means of a Fuzzy-Causal User Model

Peña-Ayala

2009

MICAI 2009: Advances in Artificial Intelligence

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Cluster-based predictive modeling to improve pedagogic reasoning

Cited by 9 publications

References 17 publications

From Laboratory to Authentic Contexts: Two Studies of Pedagogical Reasoning Across Four Levels of Expertise

From Laboratory to Authentic Contexts: Two Studies of Pedagogical Reasoning Across Four Levels of Expertise

Development of a risk assessment system based on pattern matching of behavioural fault models

Teaching-Learning by Means of a Fuzzy-Causal User Model

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