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This study examines pronunciation assessment in English as a Foreign Language, comparing holistic and atomistic evaluations by human raters—native and non-native speakers—and the artificial intelligence (AI) tool ELSA Speak. Over six months, university students in English as a Foreign Language were assessed across four phases. Two questions guided the research: How do ELSA Speak's holistic scores align with human judgments? How do atomistic evaluations of phonological errors differ between AI and humans? This study used a mixed-methods approach to analyze quantitative scores and qualitative error patterns. Findings revealed asymmetry: ELSA Speak consistently tracked progress, while human ratings were less stable and time sensitive. AI-powered tools focused on segmental features, whereas humans, especially natives, captured suprasegmental aspects. Rather than replacement, the study advocates integration—combining AI's diagnostic precision with human perceptual depth. A hybrid model emerges as the most pedagogically sound approach in the evolving landscape of language assessment.

Background Simulation-Based Learning (SBL) serves as a valuable pedagogical approach in nursing education, encompassing varying levels of fidelity. While previous reviews have highlighted the potential effectiveness of SBL in enhancing nursing students’ competencies, a gap persists in the evidence-base addressing the long-term retention of these competencies. This systematic review aimed to evaluate the impact of SBL on nursing students’ knowledge and skill acquisition and retention. Method A comprehensive search of electronic databases, including CINAHL, PubMed, Embase, Scopus, and Eric, was conducted from 2017 to 2023 to identify relevant studies. The Joanna Briggs critical appraisal tools were used to assess the methodological quality of the included studies. A total of 33 studies (15 RCTs and 18 quasi-experimental) met the inclusion criteria and were included in the review. A descriptive narrative synthesis method was used to extract relevant data. Results The cumulative sample size of participants across the included studies was 3,670. Most of the studies focused on the impact of SBL on life-saving skills like cardiopulmonary resuscitation (CPR) or other life-support skills. The remaining studies examined the impact of SBL on critical care skills or clinical decision-making skills. The analysis highlighted consistent and significant improvements in knowledge and skills. However, the evidence base had several limitations, including the heterogeneity of study designs, risk of bias, and lack of long-term follow-up. Conclusion This systematic review supports the use of SBL as a potent teaching strategy within nursing education and highlights the importance of the ongoing evaluation and refinement of this approach. While current evidence indicates enhancing knowledge and skill acquisition, limited studies evaluated the retention beyond five months, constraining generalisable claims regarding durability. Further research is essential to build on the current evidence and address gaps in knowledge related to the retention, optimal design, implementation, and evaluation of SBL interventions in nursing education.

We systematically compared two coding approaches to generate training datasets for machine learning (ML): (i) a holistic approach based on learning progression levels and (ii) a dichotomous, analytic approach of multiple concepts in student reasoning, deconstructed from holistic rubrics. We evaluated four constructed response assessment items for undergraduate physiology, each targeting five levels of a developing flux learning progression in an ion context. Human-coded datasets were used to train two ML models: (i) an 8-classification algorithm ensemble implemented in the Constructed Response Classifier (CRC), and (ii) a single classification algorithm implemented in LightSide Researcher’s Workbench. Human coding agreement on approximately 700 student responses per item was high for both approaches with Cohen’s kappas ranging from 0.75 to 0.87 on holistic scoring and from 0.78 to 0.89 on analytic composite scoring. ML model performance varied across items and rubric type. For two items, training sets from both coding approaches produced similarly accurate ML models, with differences in Cohen’s kappa between machine and human scores of 0.002 and 0.041. For the other items, ML models trained with analytic coded responses and used for a composite score, achieved better performance as compared to using holistic scores for training, with increases in Cohen’s kappa of 0.043 and 0.117. These items used a more complex scenario involving movement of two ions. It may be that analytic coding is beneficial to unpacking this additional complexity.

Diversity in the physician workforce lags behind the rapidly changing US population. Since the gateway to becoming a physician is medical school, diversity must be addressed in the admissions process. The Association of American Medical Colleges has implemented a Holistic Review Initiative aimed at assisting medical schools with broadening admission criteria to include relevant, mission-driven attributes and experiences in addition to academic preparation to identify applicants poised to meet the needs of a diverse patient population. More evidence is needed to determine whether holistic review results in a more diverse selection process. One of the keys to holistic review is to apply holistic principles in all stages of the selection process to ensure qualified applicants are not overlooked. This study examines whether the use of holistic review during application screening at a new medical school increased the diversity of applicants selected for interview. Using retrospective data from the first five application cycles at the Oakland University William Beaumont School of Medicine (OUWB), the author compared demographic and experiential differences between the applicants selected using holistic review, including experiences, attributes and academic metrics, to a test sample selected solely using academic metrics. The dataset consisted of the total group of applicants selected for interview in 2011 through 2015 using holistic review (n = 2773) and the same number of applicants who would have been selected for an interview using an academic-only selection model (n = 2773), which included 1204 applicants who were selected using both methods (final n = 4342). The author used a combination of cross-tabulation and analysis of variance to identify differences between applicants selected using holistic review and applicants in the test sample selected using only academics. The holistic review process yielded a significantly higher than expected percent of female ( adj. resid . = 13.2 , p  < .01), traditionally underrepresented in medicine ( adj. resid . = 15.8, p  < .01), first generation ( adj. resid . = 5.8, p  < .01), and self-identified disadvantaged ( adj resid . = 11.5, p  < .01) applicants in the interview pool than selected using academic metrics alone. In addition, holistically selected applicants averaged significantly more hours than academically selected students in the areas of pre-medical school paid employment (F = 10.99, mean difference = 657.99, p  < .01) and community service (F = 15.36, mean difference = 475.58, p  < .01). Using mission-driven, holistic admissions criteria comprised of applicant attributes and experiences in addition to academic metrics resulted in a more diverse interview pool than using academic metrics alone. These findings add support for the use of holistic review in the application screening process as a means for increasing diversity in medical school interview pools.

Retrospective voting is a crucial component of democratic accountability. A large literature on retrospective voting in the United States finds that the president’s party is rewarded in presidential elections for strong economic performance and punished for weak performance. By contrast, there is no clear consensus about whether politicians are held accountable for the local economy at other levels of government, nor how voters react to the economy in a complex system of multilevel responsibility. In this study, we use administrative data on county-level economic conditions from 1969 to 2018 and election results across multiple levels of government to examine the effect of the local economy on elections for local, state, and federal offices in the United States. We find that the president’s party is held accountable for economic performance across nearly all levels of government. We also find that incumbents are held accountable for the economy in U.S. House and gubernatorial elections. Our findings have broad implications for literatures on representation, accountability, and elections.

Whether inverted faces are processed locally or involve holistic processing has been debated for several years. This study conducted two experiments to explore the extent of holistic processing of inverted faces. Experiment 1 adopted a face-congruency paradigm that orthogonally manipulated stimulus congruency and orientation. Experiment 2 employed the complete congruency paradigm to test whether misalignment effects of inverted faces are related to holistic processing. The results of both experiments consistently demonstrated that inverted faces are processed not only locally but also holistically, and that misalignment disrupts the holistic processing of inverted faces. Subsequently computational modeling showed that in the congruent condition, the contributions of holistic and local information in inverted face processing performance were 24% and 76%, respectively, whereas in the incongruent condition, they were 10% and 90%, respectively. Together, the present study reveals that also inverted faces are processed holistically, albeit to a lower degree than upright faces.

This article investigates the feasibility of using automated scoring methods to evaluate the quality of student-written essays. In 2012, Kaggle hosted an Automated Student Assessment Prize contest to find effective solutions to automated testing and grading. This article: a) analyzes the datasets from the contest – which contained hand-graded essays – to measure their suitability for developing competent automated grading tools; b) evaluates the potential for deep learning in automated essay scoring (AES) to produce sophisticated testing and grading algorithms; c) advocates for thorough and transparent performance reports on AES research, which will facilitate fairer comparisons among various AES systems and permit study replication; d) uses both deep neural networks and state-of-the-art NLP tools to predict finer-grained rubric scores, to illustrate how rubric scores are determined from a linguistic perspective, and to uncover important features of an effective rubric scoring model. This study’s findings first highlight the level of agreement that exists between two human raters for each rubric as captured in the investigated essay dataset, that is, 0.60 on average as measured by the quadratic weighted kappa (QWK). Only one related study has been found in the literature which also performed rubric score predictions through models trained on the same dataset. At best, the predictive models had an average agreement level (QWK) of 0.53 with the human raters, below the level of agreement among human raters. In contrast, this research’s findings report an average agreement level per rubric with the two human raters’ resolved scores of 0.72 (QWK), well beyond the agreement level between the two human raters. Further, the AES system proposed in this article predicts holistic essay scores through its predicted rubric scores and produces a QWK of 0.78, a competitive performance according to recent literature where cutting-edge AES tools generate agreement levels between 0.77 and 0.81, results computed as per the same procedure as in this article. This study’s AES system goes one step further toward interpretability and the provision of high-level explanations to justify the predicted holistic and rubric scores. It contends that predicting rubric scores is essential to automated essay scoring, because it reveals the reasoning behind AIED-based AES systems. Will building AIED accountability improve the trustworthiness of the formative feedback generated by AES? Will AIED-empowered AES systems thoroughly mimic, or even outperform, a competent human rater? Will such machine-grading systems be subjected to verification by human raters, thus paving the way for a human-in-the-loop assessment mechanism? Will trust in new generations of AES systems be improved with the addition of models that explain the inner workings of a deep learning black box? This study seeks to expand these horizons of AES to make the technique practical, explainable, and trustable.

Various studies empirically proved the value of highly informative feedback for enhancing learner success. However, digital educational technology has yet to catch up as automated feedback is often provided shallowly. This paper presents a case study on implementing a pipeline that provides German-speaking university students enrolled in an introductory-level educational psychology lecture with content-specific feedback for a lecture assignment. In the assignment, students have to discuss the usefulness and educational grounding (i.e., connection to working memory, metacognition or motivation) of ten learning tips presented in a video within essays. Through our system, students received feedback on the correctness of their solutions and content areas they needed to improve. For this purpose, we implemented a natural language processing pipeline with two steps: (1) segmenting the essays and (2) predicting codes from the resulting segments used to generate feedback texts. As training data for the model in each processing step, we used 689 manually labelled essays submitted by the previous student cohort. We then evaluated approaches based on GBERT, T5, and bag-of-words baselines for scoring them. Both pipeline steps, especially the transformer-based models, demonstrated high performance. In the final step, we evaluated the feedback using a randomised controlled trial. The control group received feedback as usual (essential feedback), while the treatment group received highly informative feedback based on the natural language processing pipeline. We then used a six items long survey to test the perception of feedback. We conducted an ordinary least squares analysis to model these items as dependent variables, which showed that highly informative feedback had positive effects on helpfulness and reflection. (DIPF/Orig.).

To guide school practitioners in the identification and intervention of youth with anxious school refusal, this systematic review used an ecological lens to examine the factors that differentiated children and adolescents with school refusal from those without. Based on the rigorous protocol from the Center for Reviews and Dissemination’s (CRD) internationally recognized guidelines, 15 studies examining 67 different factors were identified. Results reveal 44 individual, social and contextual factors that differentiate youth with school refusal from peers without school refusal. Findings highlight the centrality of anxiety, or anxiety-related symptoms, and diverse learning needs as main points of contrast between youth with school refusal and those without. Implications of an ecological understanding of the factors associated with school refusal for selective and indicative prevention by school and mental health practitioners are discussed.