A calibrated scale to measure heritage learning in digital environments. A network analysis approach

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.