Fontal Olaia, Ibañez-Etxeberria Alex, Arias Víctor B, Arias Benito
Didactics of Musical, Plastic and Corporal Expression, University of Valladolid, Campus Miguel Delibes, Valladolid, 47011, Spain.
Didactics of Mathematics, Experimental and Social Sciences, University of the Basque Country UPV-EHU, Plaza Oñati, 3, San Sebastián, 20018, Spain.
Heliyon. 2024 Oct 19;10(21):e39466. doi: 10.1016/j.heliyon.2024.e39466. eCollection 2024 Nov 15.
To date, there have been scarcely any published studies dedicated to developing instruments with sufficient guarantees of accuracy, validity, and reliability for the assessment of heritage learning in digital contexts. Furthermore, very few such studies on this subject have employed the network analysis methodology. The present study seeks to address these research gaps by applying network analysis methodology to the responses of 1323 participants concerning 49 items grouped into seven dimensions constituting the Heritage Learning Sequence (knowing, understanding, respecting, valuing, caring, enjoying, and transmitting). Network estimation was conducted using the Gaussian Graphical Model with regularization, ensuring both the accuracy of the network and the stability of centrality indices. The results indicate that satisfactory values have been achieved in both the network structure and in terms of predictability, replicability, and sensitivity. Finally, the invariance of the network structure among groups (male/female and random subsamples) has been demonstrated. These findings offer promising avenues for further research on heritage learning assessment using the network analysis methodology.
迄今为止，几乎没有已发表的研究致力于开发在数字环境中评估遗产学习时具有足够准确性、有效性和可靠性保证的工具。此外，关于这个主题的此类研究很少采用网络分析方法。本研究旨在通过将网络分析方法应用于1323名参与者对49个项目的回答来填补这些研究空白，这些项目分为构成遗产学习序列的七个维度（知晓、理解、尊重、重视、关心、欣赏和传承）。使用带有正则化的高斯图形模型进行网络估计，确保网络的准确性和中心性指标的稳定性。结果表明，在网络结构以及可预测性、可重复性和敏感性方面都取得了令人满意的值。最后，证明了各组（男性/女性和随机子样本）之间网络结构的不变性。这些发现为使用网络分析方法进一步研究遗产学习评估提供了有希望的途径。
