Role taking and knowledge building in a blended university course

Role taking is an established approach for promoting social cognition. Playing a specific role within a group could lead students to exercise collective cognitive responsibility for collaborative knowledge building. Two studies explored the relationship of role taking to participation in a blended university course. Students participated in the same knowledge-building activity over three consecutive, five-week modules and enacted four roles designed in alignment with knowledge building pedagogy (Scardamalia and Bereiter 2010). In Study 1, 59 students were distributed into groups with two conditions: students who took a role in Module 2 and students who did not take a role, using Module 1 and 3 as pre and post tests. Results showed no differences in participation in Module 1, higher levels of writing and reading for role takers in Module 2, and this pattern was sustained in Module 3. Students with the Synthesizer role were the most active in terms of writing and the second most active for reading; students with the Social Tutor role were the most active for reading. In Study 2, 143 students were divided into groups with two conditions: students who took a role in Module 1 and students who did not take a role. Content analysis revealed that role takers tended to vary their contributions more than non-role takers by proposing more problems, synthesizing the discourse, reflecting on the process and organization of activity. They also assumed appropriate responsibilities for their role: the Skeptic prioritizes questioning of content, the Synthesizer emphasizes synthesizing of content, and the Social Tutor privileges maintaining of relationships. Implications of designing role taking to foster knowledge building in university blended courses are discussed

Influence of participation, facilitator styles, and metacognitive reflection on knowledge building in online university courses

Understanding how to foster knowledge building in online and blended learning environments is a key for computer-supported collaborative learning research. Knowledge building is a deeply constructivist pedagogy and kind of inquiry learning focused on theory building. A strong indicator of engagement in knowledge building activity is the socio-cognitive dynamic of epistemic agency, in which students exercise a higher level of agency for setting forth their ideas and negotiating fit with those of others rather than relying on their teacher. The purpose of this paper is to investigate the influence of (a) levels of participation, (b) facilitator styles and (c) metacognitive reflection on knowledge building in two blended, post-secondary education contexts. A study of a total of 67 undergraduate students suggest that high levels of participation, a supportive facilitator style, and ample opportunities for metacognitive reflection on the students’ own participation strategies are most conducive for fostering epistemic agency for knowledge building. Implications of these results for research and instructional design of online courses are discussed

Real Estate Asset Management Companies’ Economies of Scale: Is It a Dream or Reality? The Italian Case

The research focuses on a sample of 26 Italian real estate asset management companies (Società di Gestione del Risparmio “SGR”)—whose asset management is totally linked to real estate funds—that considers a period of six years (2013–2018). Using some variables extrapolated from the internal accountability of each SGR, the analysis investigates possible relationships between them to verify the presence or absence of economies of scale of Italian real estate management companies by multivariate regressions. The results show that there is no single model for profit maximization and cost minimization, but all depends on the business model that each SGR decides to adopt

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Development of the ChatGPT, Generative Artificial Intelligence and Natural Large Language Models for Accountable Reporting and Use (CANGARU) Guidelines

The swift progress and ubiquitous adoption of Generative AI (GAI), Generative Pre-trained Transformers (GPTs), and large language models (LLMs) like ChatGPT, have spurred queries about their ethical application, use, and disclosure in scholarly research and scientific productions. A few publishers and journals have recently created their own sets of rules; however, the absence of a unified approach may lead to a 'Babel Tower Effect,' potentially resulting in confusion rather than desired standardization. In response to this, we present the ChatGPT, Generative Artificial Intelligence, and Natural Large Language Models for Accountable Reporting and Use Guidelines (CANGARU) initiative, with the aim of fostering a cross-disciplinary global inclusive consensus on the ethical use, disclosure, and proper reporting of GAI/GPT/LLM technologies in academia. The present protocol consists of four distinct parts: a) an ongoing systematic review of GAI/GPT/LLM applications to understand the linked ideas, findings, and reporting standards in scholarly research, and to formulate guidelines for its use and disclosure, b) a bibliometric analysis of existing author guidelines in journals that mention GAI/GPT/LLM, with the goal of evaluating existing guidelines, analyzing the disparity in their recommendations, and identifying common rules that can be brought into the Delphi consensus process, c) a Delphi survey to establish agreement on the items for the guidelines, ensuring principled GAI/GPT/LLM use, disclosure, and reporting in academia, and d) the subsequent development and dissemination of the finalized guidelines and their supplementary explanation and elaboration documents.Comment: 20 pages, 1 figure, protoco

Bibliometric Analysis of Publisher and Journal Instructions to Authors on Generative-AI in Academic and Scientific Publishing

We aim to determine the extent and content of guidance for authors regarding the use of generative-AI (GAI), Generative Pretrained models (GPTs) and Large Language Models (LLMs) powered tools among the top 100 academic publishers and journals in science. The websites of these publishers and journals were screened from between 19th and 20th May 2023. Among the largest 100 publishers, 17% provided guidance on the use of GAI, of which 12 (70.6%) were among the top 25 publishers. Among the top 100 journals, 70% have provided guidance on GAI. Of those with guidance, 94.1% of publishers and 95.7% of journals prohibited the inclusion of GAI as an author. Four journals (5.7%) explicitly prohibit the use of GAI in the generation of a manuscript, while 3 (17.6%) publishers and 15 (21.4%) journals indicated their guidance exclusively applies to the writing process. When disclosing the use of GAI, 42.8% of publishers and 44.3% of journals included specific disclosure criteria. There was variability in guidance of where to disclose the use of GAI, including in the methods, acknowledgments, cover letter, or a new section. There was also variability in how to access GAI guidance and the linking of journal and publisher instructions to authors. There is a lack of guidance by some top publishers and journals on the use of GAI by authors. Among those publishers and journals that provide guidance, there is substantial heterogeneity in the allowable uses of GAI and in how it should be disclosed, with this heterogeneity persisting among affiliated publishers and journals in some instances. The lack of standardization burdens authors and threatens to limit the effectiveness of these regulations. There is a need for standardized guidelines in order to protect the integrity of scientific output as GAI continues to grow in popularity.Comment: Pages 16, 1 figure, 2 table

Computing Optimal Equilibria and Mechanisms via Learning in Zero-Sum Extensive-Form Games

We introduce a new approach for computing optimal equilibria and mechanisms via learning in games. It applies to extensive-form settings with any number of players, including mechanism design, information design, and solution concepts such as correlated, communication, and certification equilibria. We observe that optimal equilibria are minimax equilibrium strategies of a player in an extensive-form zero-sum game. This reformulation allows us to apply techniques for learning in zero-sum games, yielding the first learning dynamics that converge to optimal equilibria, not only in empirical averages, but also in iterates. We demonstrate the practical scalability and flexibility of our approach by attaining state-of-the-art performance in benchmark tabular games, and by computing an optimal mechanism for a sequential auction design problem using deep reinforcement learning