Design observations for interagency collaboration

We present 14 design observations for public safety networks (PSNs) and describe how they may apply more broadly to a wider range of inter-organizational systems within the public sector. A PSN is an interagency collaboration focused on developing and using information systems in support of information sharing and functional interoperability among public safety organizations engaged in law enforcement, criminal justice, and emergency response. We base our design observations upon an analysis of an extensive survey of 80 PSNs plus 6 in-depth case studies. The design observations identify commonalities that can guide agencies participating in interagency collaborations in addressing the interlocking issues they face. Our goal in presenting this set of design observations is to: (1) encourage improved PSN systems design and (2) draw attention to the importance of jointly addressing governance and technological considerations when designing PSNs.     

In good company: The influence of peers on industry engagement by academic scientists

Previous research on academic entrepreneurship and engagement with industry has found that the behaviour of academics is influenced by their local social context. However, we know little about the mechanisms that produce this effect. We argue that academic scientists’ industry engagement is influenced significantly by the behaviour of their peers, that is, the behaviour of colleagues of similar seniority. Using insights from social psychology, we hypothesize that these peer effects are produced by the mechanism of social comparison. In an analysis of data from multiple sources for 1370 UK academic scientists and engineers, we find that peer effects are stronger for early career individuals and weaker for star scientists, suggesting the incidence of social comparison. We argue that individuals look to their immediate peers for inspiration, because they view them as an important reference group and use them as a benchmark for their own ambitions and behaviours. Our findings have important implications for how universities may encourage scientists’ behaviours by paying attention to local work contexts.     

Situated mathematics teaching within electrical engineering courses

The initial phase of undergraduate engineering degree programmes often comprises courses requiring mathematical expertise which in some cases clearly exceeds school mathematics, but will be imparted only later in mathematics courses. In this article, an approach addressing this challenge by way of example within a fundamentals of electrical engineering course is presented. The concept focuses on gaining specific mathematical knowledge and competencies in the technical context of this course. For this purpose, a complementary blended learning scenario centring around a web-based learning platform and involving an adaptation of the course was developed. The concept particularly considers the heterogeneity of today's student groups and is discussed with regard to related approaches, didactical considerations, and technical implementation. For the interventions, the results of a questionnaire-based evaluation proving students' acceptance and positive influence on examination performance are presented.     

Academic engagement: A review of the literature 2011-2019

We provide a systematic review of the literature on academic engagement from 2011 onwards, which was the cut-off year of a previous review article published in Research Policy. Academic engagement refers to knowledge-related interactions of academic scientists with external organisations. It includes activities such as collaborative research with industry, contract research, consulting and informal ties. We consolidate what is known about the individual, organisational and institutional antecedents of academic engagement, and its consequences for research, commercialisation, and society at large. Our results suggest that individual characteristics associated with academic engagement include being scientifically productive, senior, male, locally trained, and commercially experienced. Academic engagement is also socially conditioned by peer effects and disciplinary characteristics. In terms of consequences, academic engagement is positively associated with academics’ subsequent scientific productivity. We propose new areas of investigation where evidence remains inconclusive, including individual life cycle effects, the role of organisational contexts and incentives, cross-national comparisons, and the impact of academic engagement on the quality of subsequent research as well as the educational, commercial and society-wide impact.     

How the existence of special schools affects the placement of students with special needs in inclusive primary schools

Although inclusion is the declared goal, the transition from a system based on special schools to an inclusive school system has only been progressing very slowly in individual countries. In an evolving school system, the existing special schools keep struggling to justify their existence. This study investigates the regional distribution effects based on official school data and shows the influence of a pull effect on special schools as a distance effect of special schools affecting the placement of students. For this purpose, official school statistics including all students at special and regular schools in the years 2010, 2015 and 2020 (N = 11 280 040) are evaluated in a spatiotemporal comparison using Educational Data Mining. In a hierarchical regression model on school placement in inclusive schools, the distance between primary and special schools has the highest influence (β = 0.48) on the inclusion rate (i.e., the proportion of students with special needs who are educated in regular schools in relation to all students with disabilities), along with the size (β = −0.14) and the density of special schools in a district (β = −0.12). The effects differ according to the population density of the region and are stronger in large cities. When the proportion of students with and without SEN in regular schools is considered (support rate), the density of special schools has the greatest impact on school placement (ß = 43.44). Self-preservation of schools, traditional funding systems and regional differences between urban and rural areas are discussed as possible reasons.     

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.     

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.