{"title":"有效可靠的遗产教育学习过程解释模型","authors":"Olaia Fontal, Víctor B. Arias, Benito Arias","doi":"10.1186/s40494-024-01372-5","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Background</h3><p>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.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>The Q-Herilearn scale (previously calibrated using SEM and IRT models) was administered to <span>\\(N = 1454\\)</span> 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.</p><h3 data-test=\"abstract-sub-heading\">Findings</h3><p>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.</p><h3 data-test=\"abstract-sub-heading\">Contribution</h3><p>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.</p>","PeriodicalId":13109,"journal":{"name":"Heritage Science","volume":"44 1","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A valid and reliable explanatory model of learning processes in heritage education\",\"authors\":\"Olaia Fontal, Víctor B. Arias, Benito Arias\",\"doi\":\"10.1186/s40494-024-01372-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3 data-test=\\\"abstract-sub-heading\\\">Background</h3><p>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.</p><h3 data-test=\\\"abstract-sub-heading\\\">Methods</h3><p>The Q-Herilearn scale (previously calibrated using SEM and IRT models) was administered to <span>\\\\(N = 1454\\\\)</span> 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.</p><h3 data-test=\\\"abstract-sub-heading\\\">Findings</h3><p>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.</p><h3 data-test=\\\"abstract-sub-heading\\\">Contribution</h3><p>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.</p>\",\"PeriodicalId\":13109,\"journal\":{\"name\":\"Heritage Science\",\"volume\":\"44 1\",\"pages\":\"\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-08-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Heritage Science\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://doi.org/10.1186/s40494-024-01372-5\",\"RegionNum\":1,\"RegionCategory\":\"艺术学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, ANALYTICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Heritage Science","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1186/s40494-024-01372-5","RegionNum":1,"RegionCategory":"艺术学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
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.
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.