Deliberate performance: Accelerating expertise in natural settings

Deliberate practice—meaning drill‐like practice under the direction of a coach—is key to developing expertise in sports and music. But working professionals and businesspeople typically have no time for practice. We propose deliberate performance as a type of practice that professionals and businesspeople can pursue while they work as a way to accelerate their progression to becoming experts. Four deliberate performance exercises are described: estimation, experimentation, extrapolation, and explanation.     

Early Maternal Employment and Children's Vocabulary and Inductive Reasoning Ability: A Dynamic Approach

This study investigates the relationship between early maternal employment history and children's vocabulary and inductive reasoning ability at age 5, drawing on longitudinal information on 2,200 children from the Growing Up in Scotland data. Prior research rarely addresses dynamics in maternal employment and the methodological ramifications of time‐variant confounding. The present study proposes various measures to capture duration, timing, and stability of early maternal employment and uses inverse probability of treatment weighting to control for time‐variant confounders that may partially mediate the effect of maternal employment on cognitive scores. The findings suggest only modest differences in the above ability measures between children who have been exposed to very different patterns of eary maternal employment, but with similar observed covariate history.     

Empirical Research on Performance Improvement

Renewed interest in research on performance improvement provides an ideal opportunity to examine how much empirical work is actually published and to identify the focus of research in the field. This paper describes a study conducted to examine the empirical research on performance improvement. The study involved a content analysis of articles published in PIQ from 1997 through 2000. Results indicated that empirical research accounted for about one‐third of all articles published in PIQ. A detailed analysis of the empirical research studies published in the journal is provided.     

Using natural language processing to support peer-feedback in the age of artificial intelligence: A cross-disciplinary framework and a research agenda

Advancements in artificial intelligence are rapidly increasing. The new-generation large language models, such as ChatGPT and GPT-4, bear the potential to transform educational approaches, such as peer-feedback. To investigate peer-feedback at the intersection of natural language processing (NLP) and educational research, this paper suggests a cross-disciplinary framework that aims to facilitate the development of NLP-based adaptive measures for supporting peer-feedback processes in digital learning environments. To conceptualize this process, we introduce a peer-feedback process model, which describes learners' activities and textual products. Further, we introduce a terminological and procedural scheme that facilitates systematically deriving measures to foster the peer-feedback process and how NLP may enhance the adaptivity of such learning support. Building on prior research on education and NLP, we apply this scheme to all learner activities of the peer-feedback process model to exemplify a range of NLP-based adaptive support measures. We also discuss the current challenges and suggest directions for future cross-disciplinary research on the effectiveness and other dimensions of NLP-based adaptive support for peer-feedback. Building on our suggested framework, future research and collaborations at the intersection of education and NLP can innovate peer-feedback in digital learning environments.

Practitioner notes

What is already known about this topic

• There is considerable research in educational science on peer-feedback processes.
• Natural language processing facilitates the analysis of students' textual data.
• There is a lack of systematic orientation regarding which NLP techniques can be applied to which data to effectively support the peer-feedback process.

What this paper adds

• A comprehensive overview model that describes the relevant activities and products in the peer-feedback process.
• A terminological and procedural scheme for designing NLP-based adaptive support measures.
• An application of this scheme to the peer-feedback process results in exemplifying the use cases of how NLP may be employed to support each learner activity during peer-feedback.

Implications for practice and/or policy

• To boost the effectiveness of their peer-feedback scenarios, instructors and instructional designers should identify relevant leverage points, corresponding support measures, adaptation targets and automation goals based on theory and empirical findings.
• Management and IT departments of higher education institutions should strive to provide digital tools based on modern NLP models and integrate them into the respective learning management systems; those tools should help in translating the automation goals requested by their instructors into prediction targets, take relevant data as input and allow for evaluating the predictions.