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Improving collaborative problem-solving competency through information systems in physics education
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Fabien Kunis , Maya Gaydarova , Ivelina Kotseva; Improving collaborative problem-solving competency through information systems in physics education. AIP Conf. Proc. 11 December 2023; 2939 (1): 050002. https://doi.org/10.1063/5.0178545
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Today's challenges increasingly impact education, a crucial pillar of modern society. New forms of active learning and challenges to education systems have been increasingly discussed, researched, and explored since the 21st century. Key global organizations such as UNESCO, European Union, and OECD note the need to reflect the skills and competencies of the 21st century according to relevant national educational programs. Collaborative problem solving was selected by the Organization for Economic Co-operation and Development (OECD) as a key competence, which was examined in the 2015 PISA international survey. Extensive international studies indicate that collaborative problem solving is crucial for successfully integrating adolescents into society and the workforce. The aim of the study is researching the possibilities of forming collaborative problem-solving competency in a school physics course. Students worldwide have been isolated for a long time because of the coronavirus. This isolation is a serious prerequisite for the deteriorating social skills of students. Lack of communication can lead to impaired teamwork skills. Therefore, our goal is to explore information systems opportunities for testing and improving collaborative problem-solving competency. A methodology has been proposed for implementing the collaborative problem solving in the school course in physics. The study has been carried out to test the information system we have developed and its ability to improve students collaborative problem-solving skills. Students from seventh to tenth grade are tested according to the established methodology for determining their competence level for collaborative problem solving. The results are summarized and analyzed.
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Title: comm: collaborative multi-agent, multi-reasoning-path prompting for complex problem solving.
Abstract: Large Language Models (LLMs) have shown great ability in solving traditional natural language tasks and elementary reasoning tasks with appropriate prompting techniques. However, their ability is still limited in solving complicated science problems. In this work, we aim to push the upper bound of the reasoning capability of LLMs by proposing a collaborative multi-agent, multi-reasoning-path (CoMM) prompting framework. Specifically, we prompt LLMs to play different roles in a problem-solving team, and encourage different role-play agents to collaboratively solve the target task. In particular, we discover that applying different reasoning paths for different roles is an effective strategy to implement few-shot prompting approaches in the multi-agent scenarios. Empirical results demonstrate the effectiveness of the proposed methods on two college-level science problems over competitive baselines. Our further analysis shows the necessity of prompting LLMs to play different roles or experts independently. We release the code at: this https URL
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The PISA 2015 Collaborative Problem Solving assessment was the first large-scale, international assessment to evaluate students' competency in collaborative problem solving. It required students to interact with simulated (computer) in order to solve problems. These dynamic, simulated agents were designed to represent different profiles of ...
On the 21 st of November 2017, PISA releases its report on the first-ever international collaborative problem solving assessment. The report examines students' ability to work in groups to solve problems and explores the role of education in building young people's skills in solving problems collaboratively. This month's PISA in Focus ...
PISA 2015 defines collaborative problem-solving competency as: the capacity of an individual to effectively engage in a process whereby two or more agents attempt to solve a problem by sharing the understanding and effort required to come to a solution and pooling their knowledge, skills and efforts
PISA 2015 Results (Volume V): Collaborative Problem Solving, is one of five volumes that present the results of the PISA 2015 survey, the sixth round of the triennial assessment. It examines students' ability to work with two or more people to try to solve a problem. ... OECD iLibrary is the online library of the Organisation for Economic ...
This chapter describes the rationale behind measuring 15-year-olds' collaborative problem-solving skills for the first time in the Programme for International Student Assessment (PISA). It explains the content and processes that are reflected in the collaborative problem-solving items used in the computer-based assessment, and describes how ...
With its first ever assessment of collaborative problem solving, PISA 2015 addresses the lack of internationally comparable data in this field, allowing countries and economies to see where their students stand in relation to students in other education systems. Some 52 countries and economies participated in the collaborative problem‑solving ...
collaborative problem solving was a new domain in PISA 2015, the OECD average performance was set at 500 score points and the standard deviation across OECD countries at 100 score points. This established the benchmark against which each country's collaborative problem-solving performance was compared.
On the 21st of November 2017, PISA releases its report on the first-ever international collaborative problem solving assessment. The report examines students' ability to work in groups to solve problems and explores the role of education in building young people's skills in solving problems collaboratively. This month's PISA in Focus provides an overview of the assessment's results and ...
The OECD Programme for International Student Assessment (PISA) examines not just what students know in science, reading and mathematics, but what they can do with what they know. ... Collaborative Problem Solving, is one of five volumes that present the results of the PISA 2015 survey, the sixth round of the triennial assessment. It examines ...
It describes the circumstances under which collaborative problem solving might best be used, with consequences for the design of tasks to assess the component skills. It highlights the characteristics of problemsolving tasks, interdependence between problem solvers and the asymmetry of stimulus and response, through a focus on task design.
PISA in Focus #78. What the data tell us. Student performance in collaborative problem solving. • Students in Singapore score higher in collaborative problem solving than students in all other participating countries and economies, followed by students in Japan. • On average across OECD countries, 28% of students are able to solve only ...
The OECD Programme for International Student Assessment (PISA) seeks to answer that question through the most comprehensive and rigorous international assessment of student knowledge and skills. ... This revised edition includes the framework for collaborative problem solving, which was evaluated for the first time, in an optional assessment ...
This month's PISA in Focus describes what it means, according to PISA, to be competent in collaborative problem solving. Along with the skills needed to solve problems individually, a good team member also has to develop and maintain a shared understanding of the problem with his or her teammates, take the actions needed to solve the problem, and establish and maintain team organisation.
Collaborative problem solving was selected by the Organization for Economic Co-operation and Development (OECD) as a key competence, which was examined in the 2015 PISA international survey. Extensive international studies indicate that collaborative problem solving is crucial for successfully integrating adolescents into society and the workforce.
The Human-Agent vs. Human-Human discussion. After the individual interactive problem-solving assessment in 2012, the OECD decided that problem-solving skills would be assessed again in 2015 (Csapó & Funke, Citation 2017).However, this time the focus of the assessment was the individual's capacity for solving problems collaboratively instead of on his or her own.
This chapter describes the rationale behind measuring 15-year-olds' collaborative problem-solving skills for the first time in the Programme for International Student Assessment (PISA). It explains the content and processes that are reflected in the collaborative problem-solving items used in the computer-based assessment, and describes how student proficiency in this domain is measured and ...
Data and research on education including skills, literacy, research, elementary schools, childhood learning, vocational training and PISA, PIACC and TALIS surveys., This report presents the conceptual foundations of the OECD Programme for International Student Assessment (PISA), now in its seventh cycle of comprehensive and rigorous international surveys of student knowledge, skills and well ...
PISA 2015 Results (Volume V) (Summary in English) more info: https://doi.org/10.1787/93ba004b-en Share Buy Buy
The observed gender gap bars replicate findings reported in the original OECD descriptive reports for collaborative problem solving (OECD, 2017a). The results support the hypothesis that boys and girls do not perform at similar levels in collaborative problem solving (Hypothesis 1), and in fact, indicate that girls tend to outperform boys.
Introduction. In recent years, there has been a growing interest in the potential of interactive technologies, notably multi-touch devices (Beauchamp et al. Citation 2019; Joyce-Gibbons Citation 2023), to support problem-solving activities in the primary school.The benefits of collaborative learning have a strong evidence base in research over the past four decades (e.g. Roschelle Citation ...
Large Language Models (LLMs) have shown great ability in solving traditional natural language tasks and elementary reasoning tasks with appropriate prompting techniques. However, their ability is still limited in solving complicated science problems. In this work, we aim to push the upper bound of the reasoning capability of LLMs by proposing a collaborative multi-agent, multi-reasoning-path ...