Angelo Di Iorio

This paper delves into the integration of gamification techniques within the field of linguistics to enhance data collection for academic research purposes. Through an exploration of the Word Ladders mobile application, designed to elicit hierarchical word associations and therefore linguistic data, the study investigates the potential benefits of gamification in terms of data quality, user experience, and motivation in taking part to the research and to the data collection task. The experimental design examines the advantages of a gamified approach compared to traditional research methods (online surveys), through an experimental session followed by a survey (n=189). Results showed that competition between users is a powerful motivator that can be easily integrated in gamified approaches and less so in classic online surveys, driving engagement and potentially enhancing the scalability of data collection while retaining the quality of data collected in classic lab settings. While challenges persist, our research contributes to the understanding of gamification’s impact on data collection, user experience, and motivation, laying the foundation for transformative advancements in the field of language and communication sciences.

The importance of open bibliographic repositories is widely accepted by the scientific community. For evaluation processes, however, there is still some skepticism: Even if large repositories of open access articles and free publication indexes exist and are continuously growing, assessment procedures still rely on proprietary databases, mainly due to the richness of the data available in these proprietary databases and the services provided by the companies they are offered by. This paper investigates the status of open bibliographic data of three of the most used open resources, namely Microsoft Academic Graph, Crossref, and OpenAIRE, evaluating their potentialities as substitutes of proprietary databases for academic evaluation processes. We focused on the Italian National Scientific Qualification (NSQ), the Italian process for university professor qualification, which uses data from commercial indexes, and investigated similarities and differences between research areas, disciplines, and application roles. The main conclusion is that open data sets are ready to be used for some disciplines, among them mathematics, natural sciences, economics, and statistics, even if there is still room for improvement; but there is still a large gap to fill in others—such as history, philosophy, pedagogy, and psychology—and greater effort is required from researchers and institutions.