Background: High-fidelity simulation (HFS) has become a widely used and established pedagogy for teaching clinical nursing skills. Nevertheless, there are few evidence-based instruments that validate the effectiveness of simulation learning in mainland China. Methods: The Simulation Learning Effectiveness Inventory (SLEI) was adapted and validated for use in this study. Psychometric evaluation, incorporating classical test theory and item response theory (IRT) methods, was performed with 533 third-year undergraduate nursing students who were recruited from May 2017 to July 2018. Results: The findings of exploratory and confirmatory factor analyses revealed that the simplified Chinese version of the SLEI (SLEI-SC) was composed of six factors, namely, course arrangement, equipment resource, debriefing, clinical ability, problem solving, and confidence, which explained 60.84% of the total variance. The Cronbach’s α, MIIC, marginal reliability, and test–retest reliability values obtained for the total scale were 0.88, 0.38, 0.96, and 0.88, respectively. Furthermore, the difference between the total scores for learning effectiveness pre- and post-course was statistically significant (t = 2.59, p < 0.05, Cohen’s d = 0.60). IRT analysis showed that the SLEI-SC discriminates well between students with high and low levels of learning effectiveness and offers information about a broad range of learning effectiveness measures. The relationship between final course grade and total score on the SLEI-SC was statistically significant (r = 0.63, p < 0.05). Conclusion: We demonstrated initial psychometric evidence and support for the 31-item SLEI-SC as a developmentally appropriate instrument for assessing the learning effectiveness of all phases of HFS use with nursing students.

Question matching is the fundamental task in retrieval-based dialogue systems which assesses the similarity between Query and Question. Unfortunately, existing methods focus on improving the accuracy of text similarity in the general domain, without adaptation to the financial domain. Financial question matching has two critical issues: (1) How to accurately model the contextual representation of a financial sentence? (2) How to accurately represent financial key phrases in an utterance? To address these issues, this paper proposes a novel Financial Knowledge Enhanced Network (FinKENet) that significantly injects financial knowledge into contextual text. Specifically, we propose a multi-level encoder to extract both sentence-level features and financial phrase-level features, which can more accurately represent sentences and financial phrases. Furthermore, we propose a financial co-attention adapter to combine sentence features and financial keyword features. Finally, we design a multi-level similarity decoder to calculate the similarity between queries and questions. In addition, a cross-entropy-based loss function is presented for model optimization. Experimental results demonstrate the effectiveness of the proposed method on the Ant Financial question matching dataset. In particular, the Recall score improves from 73.21% to 74.90% (1.69% absolute).