有效可靠的遗产教育学习过程解释模型

IF 2.6 1区 艺术学 Q2 CHEMISTRY, ANALYTICAL
Olaia Fontal, Víctor B. Arias, Benito Arias
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引用次数: 0

摘要

背景遗产教育面临的主要挑战是,如何根据经验证据确定能够解释遗产学习的动词及其层次关系。遗产学习序列(HLS)根据(a)理论研究、(b)国际标准分析和(c)遗产教育计划评估,选择了七个动词(了解-理解-尊重-评价-分享-享受-传递)。本研究有以下目标:(a)澄清遗产学习过程;(b)检验一个理论模型,该模型将构成遗产学习序列(HLS)的动词以及它们之间的关系分组;(c)确定可能的子模型,以解释不同的遗产学习行程。方法对(N = 1454)个人施测 Q-Herilearn 量表(之前使用 SEM 和 IRT 模型进行了校准),重点关注衡量遗产学习的七个因子(与每个 HLS 动词相对应)。研究结果所获得的结果为验证 HLS 提供了充分的保证,并表明所提出的模型(遗产学习模型)具有足够的解释力、预测力和总体拟合度;所有 12 个定义遗产学习的主要动词之间的假设直接影响关系都得到了证实。学习模型为(a)设计有效、高效、全面的遗产学习测量工具和(b)遗产教育设计中的可操作性提供了一个重要的结构框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A valid and reliable explanatory model of learning processes in heritage education

A valid and reliable explanatory model of learning processes in heritage education

Background

The main challenge in heritage education is to identify the verbs—and their hierarchical relations—that explain heritage learning as based on empirical evidence. The Heritage Learning Sequence (HLS) selects seven verbs (Knowing-Understanding-Respecting-Valuing-Caring-Enjoying-Transmitting) on the basis of (a) theoretical studies, (b) analyses of international standards, and (c) evaluation of heritage education programs. The study has the following objectives: (a) to clarify the heritage learning process; (b) to test a theoretical model that groups the verbs that make up the Heritage Learning Sequence (HLS), as well as the relationships between them; (c) to identify possible sub-models that explain the different heritage learning itineraries.

Methods

The Q-Herilearn scale (previously calibrated using SEM and IRT models) was administered to \(N = 1454\) individuals, focusing on seven factors (corresponding to each HLS verb) that measure heritage learning. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used as a general analytical strategy.

Findings

The results obtained provided sufficient guarantees to validate the HLS and showed the adequate explanatory and predictive power and general fit of the proposed model (Heritage Learning Model); all twelve hypothesized direct influence relations between the main verbs that define heritage learning were confirmed. The statistical significance values suggested the existence of other internal subsequences that could be explored in further studies.

Contribution

Learning modeling provides a key structural framework for (a) the design of effective, efficient, and comprehensive tools to measure heritage learning and (b) their operationalization in heritage education designs.

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来源期刊
Heritage Science
Heritage Science Arts and Humanities-Conservation
CiteScore
4.00
自引率
20.00%
发文量
183
审稿时长
19 weeks
期刊介绍: Heritage Science is an open access journal publishing original peer-reviewed research covering: Understanding of the manufacturing processes, provenances, and environmental contexts of material types, objects, and buildings, of cultural significance including their historical significance. Understanding and prediction of physico-chemical and biological degradation processes of cultural artefacts, including climate change, and predictive heritage studies. Development and application of analytical and imaging methods or equipments for non-invasive, non-destructive or portable analysis of artwork and objects of cultural significance to identify component materials, degradation products and deterioration markers. Development and application of invasive and destructive methods for understanding the provenance of objects of cultural significance. Development and critical assessment of treatment materials and methods for artwork and objects of cultural significance. Development and application of statistical methods and algorithms for data analysis to further understanding of culturally significant objects. Publication of reference and corpus datasets as supplementary information to the statistical and analytical studies above. Description of novel technologies that can assist in the understanding of cultural heritage.
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