(Over)Estimating Shared Cultural Knowledge: Evidence for early use of a knowledge prediction heuristic in children

<p>Both adults and children accurately and efficiently predict what other people know, despite interacting with a diverse range of individuals who each have different knowledge sets. To reduce the cognitive cost of predicting each individual’s knowledge, there is evidence that we use heuristics to make generalisable predictions about the way specific kinds of knowledge are shared with others. Yet, little research examines the function of a knowledge prediction heuristic, the input needed to produce accurate knowledge predictions, or changes across development. I propose that children use a heuristic to predict others’ knowledge, and that this heuristic functions by considering the type of knowledge being predicted, and characteristics of the individual whose knowledge is being predicted. Chapter 2 demonstrates that 3- to 6-year-old children accurately and selectively predict who shares different pieces of their knowledge. Children also predict knowledge accurately in a third-party task, providing evidence for the use of a generalisable heuristic rather than simple associations or personal experience. Chapter 3 and Chapter 4 demonstrate knowledge overestimation errors, predicted by the heuristic I propose. 4-year-olds, but not 6-year-olds, overattribute knowledge to others if the knowledge item being predicted is an example of a cultural knowledge item (typically shared with strangers from the same social groups). Yet, even 4-year-olds do not make this over-attribution error when predicting an example of a typically episodic knowledge item (not typically shared with any strangers). Chapter 4 provides initial evidence that feelings of closeness or shared episodic knowledge with a partner (but not simply shared group membership) decrease 4- and 6-year-olds consideration of this partner’s perspective. Taken together, these findings provide evidence for an early-emerging knowledge prediction heuristic which considers the type of knowledge being predicted and characteristics of the individual whose knowledge is being predicted to facilitate accurate yet efficient knowledge predictions.</p>

(Over)Estimating Shared Cultural Knowledge: Evidence for early use of a knowledge prediction heuristic in children

<p>Both adults and children accurately and efficiently predict what other people know, despite interacting with a diverse range of individuals who each have different knowledge sets. To reduce the cognitive cost of predicting each individual’s knowledge, there is evidence that we use heuristics to make generalisable predictions about the way specific kinds of knowledge are shared with others. Yet, little research examines the function of a knowledge prediction heuristic, the input needed to produce accurate knowledge predictions, or changes across development. I propose that children use a heuristic to predict others’ knowledge, and that this heuristic functions by considering the type of knowledge being predicted, and characteristics of the individual whose knowledge is being predicted. Chapter 2 demonstrates that 3- to 6-year-old children accurately and selectively predict who shares different pieces of their knowledge. Children also predict knowledge accurately in a third-party task, providing evidence for the use of a generalisable heuristic rather than simple associations or personal experience. Chapter 3 and Chapter 4 demonstrate knowledge overestimation errors, predicted by the heuristic I propose. 4-year-olds, but not 6-year-olds, overattribute knowledge to others if the knowledge item being predicted is an example of a cultural knowledge item (typically shared with strangers from the same social groups). Yet, even 4-year-olds do not make this over-attribution error when predicting an example of a typically episodic knowledge item (not typically shared with any strangers). Chapter 4 provides initial evidence that feelings of closeness or shared episodic knowledge with a partner (but not simply shared group membership) decrease 4- and 6-year-olds consideration of this partner’s perspective. Taken together, these findings provide evidence for an early-emerging knowledge prediction heuristic which considers the type of knowledge being predicted and characteristics of the individual whose knowledge is being predicted to facilitate accurate yet efficient knowledge predictions.</p>

A Cluster-based Personalized Item Recommended Approach on the Educational Assessment System

Personalized item recommendation enables the educational assessment system to make deliberate efforts to perform appropriate assessment strategies that fit the needs, purposes, preferences, and interests of individual teachers. This study presents a dynamically personalized item-recommendation approach that is based on clustering in-serve teachers with assessment compiling interest and preference characteristics to recommend available, best-fit candidate items to support teachers to construct their classroom assessment. A two-round assessment constructing activity was being adopted to collect and extract these teacher’ assessment knowledge (item selected preference behaviors), and through the designed item-recommendation mechanism to facilitate IKMAAS [1] to recommend proper items to meet different individual in-serve teachers. To evaluate the effectiveness and usability for the cluster-based personalized item-recommendation, the assessment system log analysis and the questionnaire collected from participating teachers’ perceptions were being used. The results showed the proposed item-recommendation approach based on clustered teachers’ assessment knowledge can effectively improve their educational assessment construction.

Selection of scenarios in qualitative models: The case of a government tenders model

The task of this methodological paper is to clarify the process of selection of scenarios in qualitative models. Articles on qualitative modeling usually do not cover the topic of scenarios selection exhaustively, only the basic operations are (sometimes) described. This lack of detail might lead to confusion and overly simplified understanding of the process of model development when new users meet with qualitative models. We outline the basic principle of consistency, i.e. that scenarios inconsistent with a given knowledge item entered into the qualitative model are discarded from the model. With help of this principle, the vast set of all “imaginable” scenarios (2712in our case) can be reduced to just 7 scenarios in less than 40 steps. A manageable number of scenarios is important to enable interpretation and practical use, e.g. to evaluate concrete tasks and policies. For our demonstration we use our previously published model of government tenders. The current paper can help those who want to understand qualitative models and their development better, it is not restricted to the problem of qualitative modeling of government tenders.