Designing relational pedagogies with jam2jamXO

This paper examines the affordances of the philosophy and practice of open source and the application of it in developing music education software. In particular I will examine the parallels inherent in the ‘openness’ of pragmatist philosophy in education (Dewey 1916, 1989) such as group or collaborative learning, discovery learning (Bruner 1966) and learning through creative activity with computers (Papert 1980, 1994). Primarily I am interested in ‘relational pedagogies’ (Ruthmann and Dillon In Press) which is in a real sense about the ethics of the transaction between student and teacher in an ecology where technology plays a more significant role. In these contexts relational pedagogies refers to how the music teacher manages their relationships with students and evaluates the affordances of open source technology in that process. It is concerned directly with how the relationship between student and teacher is affected by the technological tools, as is the capacity for music making and learning. In particular technologies that have agency present the opportunity for a partnership between user and technology that enhances the capacity for expressive music making, productive social interaction and learning. In this instance technologies with agency are defined as ones that enhance the capacity to be expressive and perform tasks with virtuosity and complexity where the technology translates simple commands and gestures into complex outcomes. The technology enacts a partnership with the user that becomes both a cognitive and performative amplifier. Specifically we have used this term to describe interactions with generative technologies that use procedural invention as a creative technique to produce music and visual media

Towards adaptive multi-robot systems: self-organization and self-adaptation

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible

PRISMA-DFLLM: An Extension of PRISMA for Systematic Literature Reviews using Domain-specific Finetuned Large Language Models

With the proliferation of open-sourced Large Language Models (LLMs) and efficient finetuning techniques, we are on the cusp of the emergence of numerous domain-specific LLMs that have been finetuned for expertise across specialized fields and applications for which the current general-purpose LLMs are unsuitable. In academia, this technology has the potential to revolutionize the way we conduct systematic literature reviews (SLRs), access knowledge and generate new insights. This paper proposes an AI-enabled methodological framework that combines the power of LLMs with the rigorous reporting guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). By finetuning LLMs on domain-specific academic papers that have been selected as a result of a rigorous SLR process, the proposed PRISMA-DFLLM (for Domain-specific Finetuned LLMs) reporting guidelines offer the potential to achieve greater efficiency, reusability and scalability, while also opening the potential for conducting incremental living systematic reviews with the aid of LLMs. Additionally, the proposed approach for leveraging LLMs for SLRs enables the dissemination of finetuned models, empowering researchers to accelerate advancements and democratize cutting-edge research. This paper presents the case for the feasibility of finetuned LLMs to support rigorous SLRs and the technical requirements for realizing this. This work then proposes the extended PRISMA-DFLLM checklist of reporting guidelines as well as the advantages, challenges, and potential implications of implementing PRISMA-DFLLM. Finally, a future research roadmap to develop this line of AI-enabled SLRs is presented, paving the way for a new era of evidence synthesis and knowledge discovery

Implication of an Objectivist-Constructivist Blended Approach on Students’ Achievement and Satisfaction in University-Level Beginner String Technique Classes

Teaching music generally implicates multidimensional process whereby an assortment of approaches should be incorporated in order to provide the right and proper ambience for teaching and learning process. The need to explore and further understand the complexity in teaching beginner string technique class instigates the study which explore the implication of an objectivist-constructivist blended approach for teaching beginners string technique class. This paper explores the impact of blended-approach teaching on students’ achievement and satisfaction in Malaysian university-level beginner string technique classes. Using a quasi-experimental non-equivalent control group post-test only design, students (N = 40) from two local public universities were assigned to one of two treatment condition: experimental group, where students were presented with blended approach instructional material, and control group, where students were presented with conventional instructional material. Students completed the course of 14 weeks. The implementation results revealed that the proposed blended approach contributes to meaningful and efficient learning

ZYN: Zero-Shot Reward Models with Yes-No Questions

In this work, we address the problem of directing the text generations of a LLM towards a desired behavior, aligning the generated text with the preferences of the human operator. We propose using another language model as a critic, reward model in a zero-shot way thanks to the prompt of a Yes-No question that represents the user preferences, without requiring further labeled data. This zero-shot reward model provides the learning signal to further fine-tune the base LLM using reinforcement learning, as in RLAIF; yet our approach is also compatible in other contexts such as quality-diversity search. Extensive evidence of the capabilities of the proposed ZYN framework is provided through experiments in different domains related to text generation, including detoxification; optimizing sentiment of movie reviews, or any other attribute; steering the opinion about a particular topic the model may have; and personalizing prompt generators for text-to-image tasks. Code to be released at \url{https://github.com/vicgalle/zero-shot-reward-models/}