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.     

Using virtual microscopy to scaffold learning of pathology: a naturalistic experiment on the role of visual and conceptual cues

New representational technologies, such as virtual microscopy, create new affordances for medical education. In the article, a study on the following two issues is reported: (a) How does collaborative use of virtual microscopy shape students’ engagement with and learning from virtual slides of tissue specimen? (b) How do visual and conceptual cues scaffold students’ reasoning? Fifteen pairs of medical students participated in two sessions in which the students used a virtual microscope as a diagnostic tool in the context of learning pathology. The slides provided the students with varying levels of visual and conceptual cueing. The sessions were videotaped, and the students’ reasoning while using the microscope was analysed. The students’ written answers were analysed in terms of the findings they made and the diagnoses suggested. At a general level, the results show that students engage actively in this kind of virtually-mediated environment. The visual and/or conceptual cues improve students’ performance, and guide the students’ perception and reasoning in a manner that is productive from the point of view of learning to make clinically relevant observations. Scaffolding students’ reasoning process through cues furthermore assists the students in avoiding the most obvious pitfalls such as overlooking critical areas of a specimen. Overall, visual and conceptual cues improve students’ reasoning in perceptual and cognitive terms, while still allowing space for the making of “relevant mistakes” that may further the students’ diagnostic skills.     

Friends’ influence on the development of academic values in mathematics: are there differences between female and male dyads?

European Journal of Psychology of Education - Based on the social cognitive theory and the emotional contagion theory, this study investigated if friends influence (reinforce or change) the...     

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.     

#nowplaying Madonna: a large-scale evaluation on estimating similarities between music artists and between movies from microblogs

Different term weighting techniques such as $TF\cdot IDF$ or BM25 have been used intensely for manifold text-based information retrieval tasks. Their use for modeling term profiles for named entities and subsequent calculation of similarities between these named entities have been studied to a much smaller extent. The recent trend of microblogging made available massive amounts of information about almost every topic around the world. Therefore, microblogs represent a valuable source for text-based named entity modeling. In this paper, we present a systematic and comprehensive evaluation of different term weighting measures, normalization techniques, query schemes, index term sets, and similarity functions for the task of inferring similarities between named entities, based on data extracted from microblog posts. We analyze several thousand combinations of choices for the above mentioned dimensions, which influence the similarity calculation process, and we investigate in which way they impact the quality of the similarity estimates. Evaluation is performed using three real-world data sets: two collections of microblogs related to music artists and one related to movies. For the music collections, we present results of genre classification experiments using as benchmark genre information from allmusic.com . For the movie collection, we present results of multi-class classification experiments using as benchmark categories from IMDb . We show that microblogs can indeed be exploited to model named entity similarity with remarkable accuracy, provided the correct settings for the analyzed aspects are used. We further compare the results to those obtained when using Web pages as data source.     

Associations of temperament traits and mathematics grades in adolescents are dependent on the rater but independent of motivation and cognitive ability

The current study examines associations between self- and teacher-rated temperament traits (activity, inhibition, negative emotionality, persistence, distractibility, and mood) and mathematics grades. The sample includes 310 ninth grade students (mean age 15.0) from several schools in Finland. Analyses were conducted with multilevel modeling. Except for mood, all temperament traits were associated with math grades independent of motivation and standardized cognitive ability test performance (intelligence). However, some relations were found only for teacher-reports and some only for student-reports. The rater dependent differences are of interest and emphasize a need for data from multiple raters before drawing conclusions on how temperament relates to school grades. The results suggest that temperament should be taken into account in schooling and in teacher education.     

Relational integration as a predictor of academic achievement

The current study aimed at applying a broad model of cognitive functions to predict performance in science and language courses at school as well as performance in a science course at university. We hypothesized that performance in science courses was predominantly related to the cognitive function known as relational integration, whereas performance in language courses should be best explained by individuals' short-term memory capacity. The sample consisted of 161 German undergraduate students who were asked to complete 33 cognitive tasks. School grades were also obtained. The analyses revealed that relational integration incrementally explained variance in science grades. Short-term memory acted as a predictor of language grades. However, mental speed was also substantially related to language grades. Predicting university exam scores revealed that short-term memory yielded an incremental predictive power. We conclude that academic performance requires different cognitive functions depending on a domain of study.     

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.