An Emergentist Account of Collective Cognition in Collaborative Problem Solving

As a first step toward an emergentist theory of collective cognition in collaborative problem solving, we present a proto-theoretical account of how one might conceive and model the intersubjective processes that organize collective cognition into one or another--convergent, divergent, or tensive--cognitive regime. To explore the sufficiency of our emergentist proposal we instantiate a minimalist model of intersubjective convergence and simulate the tuning of collective cognition using data from an empirical study of small-group, collaborative problem solving. Using the results of this empirical simulation, we test a number of preliminary hypotheses with regard to patterns of interaction, how those patterns affect a cognitive regime, and how that cognitive regime affects the efficacy of a problem-solving group

Education as a Complex System: Conceptual and Methodological Implications

Education is a complex system, which has conceptual and methodological implications for education research and policy. In this article, an overview is first provided of the Complex Systems Conceptual Framework for Learning (CSCFL), which consists of a set of conceptual perspectives that are generally shared by educational complex systems, organized into two focus areas: collective behaviors of a system, and behaviors of individual agents in a system. Complexity and research methodologies for education are then considered, and it is observed that commonly used quantitative and qualitative techniques are generally appropriate for studying linear dynamics of educational systems. However, it is proposed that computational modeling approaches, being extensively used for studying nonlinear characteristics of complex systems in other fields, can provide a methodological complement to quantitative and qualitative education research approaches. Two research case studies of this approach are discussed. We conclude with a consideration of how viewing education as a complex system using complex systems’ conceptual and methodological tools can help advance education research and also inform policy

An Emergentist Account of Collective Cognition in Collaborative Problem Solving

As a first step toward an emergentist theory of collective cognition in collaborative problem solving, we present a proto-theoretical account of how one might conceive and model the intersubjective processes that organize collective cognition into one or another convergent, divergent, or tensive cognitive regime. To explore the sufficiency of our emergentist proposal we instantiate a minimalist model of intersubjective convergence and simulate the tuning of collective cognition using data from an empirical study of small-group, collaborative problem solving. Using the results of this empirical simulation, we test a number of preliminary hypotheses with regard to patterns of interaction, how those patterns affect a cognitive regime, and how that cognitive regime affects the efficacy of a problem-solving group

Problem-solving in virtual environment simulations prior to direct instruction for differential diagnosis in medical education: An experimental study

Background: Despite acquiring vast content knowledge about the functioning of the human body through university teaching, medical students struggle to transfer that knowledge to one of the core disciplinary practices - differential diagnosis. The authors aimed to overcome this problem by implementing computer-based virtual environment simulations in medical education courses. Methods: In an experimental study, the authors compared problem-solving in medical computer-based virtual environment simulations prior to instruction with an instruction-first approach. They compared the effects on isomorphic testing and transfer performance of clinical knowledge and clinical reasoning skills as well as evoked learning mechanisms. The study took place in spring 2021 with undergraduate medical students in the scope of a medical trajectory course. Due to Corona-Virus-19 measures participants completed all study activities remotely from home. Results: The authors did not find any learning activity sequence to be superior to the other. However, when looking at the two learning activities individually, they found that problem-solving in computer-based virtual environment simulations and direct instruction might be equally effective for learning content knowledge. Nevertheless, problem-solving in computer-based virtual environment simulations with formative feedback might be more effective for learning clinical reasoning skills than mere instruction. Conclusions: The findings indicate that problem-solving in computer-based virtual environment simulations might be more effective for learning clinical reasoning skills than mere theoretical instruction. The present study has a high level of ecological validity because it took place in a realistic setting where students had to perform all learning and testing tasks autonomously

Impact of Explicit Failure and Success-driven Preparatory Activities on Learning

Unscaffolded problem-solving before receiving instruction cangive students opportunities to entertain their exploratory hy-potheses at the expense of experiencing initial failures. Priorliterature has argued for the efficacy of such Productive Fail-ure (PF) activities in preparing students to “see” like an expert.Despite growing understanding of the socio-cognitive mecha-nisms that affect learning from PF, the necessity of success orfailure in initial problem-solving attempts is still unclear. Con-sequently, we do not know yet whether some ways of succeed-ing or failing are more efficacious than others. Here, we reportempirical evidence from a recently concluded classroom PF in-tervention (N=221), where we designed scaffolds to explicitlypush student problem-solving towards success via structuring,but also radically, towards failure via problematizing. Our ra-tionale for explicit failure scaffolding was rooted in facilitatingproblem-space exploration. We subsequently compared thedifferential preparatory effects of success-driven and failure-driven problem-solving on learning from subsequent instruc-tion. Results suggested explicit failure scaffolding during ini-tial problem-solving to have a higher impact on conceptual un-derstanding, compared to explicit success scaffolding. Thistrend was more salient for the task topic with greater difficulty

Opportunities and Challenges in Neural Dialog Tutoring

Designing dialog tutors has been challenging as it involves modeling the diverse and complex pedagogical strategies employed by human tutors. Although there have been significant recent advances in neural conversational systems using large language models (LLMs) and growth in available dialog corpora, dialog tutoring has largely remained unaffected by these advances. In this paper, we rigorously analyze various generative language models on two dialog tutoring datasets for language learning using automatic and human evaluations to understand the new opportunities brought by these advances as well as the challenges we must overcome to build models that would be usable in real educational settings. We find that although current approaches can model tutoring in constrained learning scenarios when the number of concepts to be taught and possible teacher strategies are small, they perform poorly in less constrained scenarios. Our human quality evaluation shows that both models and ground-truth annotations exhibit low performance in terms of equitable tutoring, which measures learning opportunities for students and how engaging the dialog is. To understand the behavior of our models in a real tutoring setting, we conduct a user study using expert annotators and find a significantly large number of model reasoning errors in 45% of conversations. Finally, we connect our findings to outline future work.Comment: EACL 2023 (main conference, camera-ready

Education as a Complex System: Conceptual and Methodological Implications

Education is a complex system, which has conceptual and methodological implications for education research and policy. In this article, an overview is first provided of the Complex Systems Conceptual Framework for Learning (CSCFL), which consists of a set of conceptual perspectives that are generally shared by educational complex systems, organized into two focus areas: collective behaviors of a system, and behaviors of individual agents in a system. Complexity and research methodologies for education are then considered, and it is observed that commonly used quantitative and qualitative techniques are generally appropriate for studying linear dynamics of educational systems. However, it is proposed that computational modeling approaches, being extensively used for studying nonlinear characteristics of complex systems in other fields, can provide a methodological complement to quantitative and qualitative education research approaches. Two research case studies of this approach are discussed. We conclude with a consideration of how viewing education as a complex system using complex systems’ conceptual and methodological tools can help advance education research and also inform policy

MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning Problems

While automatic dialogue tutors hold great potential in making education personalized and more accessible, research on such systems has been hampered by a lack of sufficiently large and high-quality datasets. Collecting such datasets remains challenging, as recording tutoring sessions raises privacy concerns and crowdsourcing leads to insufficient data quality. To address this, we propose a framework to generate such dialogues by pairing human teachers with a Large Language Model (LLM) prompted to represent common student errors. We describe how we use this framework to collect MathDial, a dataset of 3k one-to-one teacher-student tutoring dialogues grounded in multi-step math reasoning problems. While models like GPT-3 are good problem solvers, they fail at tutoring because they generate factually incorrect feedback or are prone to revealing solutions to students too early. To overcome this, we let teachers provide learning opportunities to students by guiding them using various scaffolding questions according to a taxonomy of teacher moves. We demonstrate MathDial and its extensive annotations can be used to finetune models to be more effective tutors (and not just solvers). We confirm this by automatic and human evaluation, notably in an interactive setting that measures the trade-off between student solving success and telling solutions. The dataset is released publicly.Comment: Jakub Macina, Nico Daheim, and Sankalan Pal Chowdhury contributed equally to this work. Accepted at EMNLP2023 Findings. Code and dataset available: https://github.com/eth-nlped/mathdia

Genomic Diversity and Evolution of Mycobacterium ulcerans Revealed by Next-Generation Sequencing

Mycobacterium ulcerans is the causative agent of Buruli ulcer, the third most common mycobacterial disease after tuberculosis and leprosy. It is an emerging infectious disease that afflicts mainly children and youths in West Africa. Little is known about the evolution and transmission mode of M. ulcerans, partially due to the lack of known genetic polymorphisms among isolates, limiting the application of genetic epidemiology. To systematically profile single nucleotide polymorphisms (SNPs), we sequenced the genomes of three M. ulcerans strains using 454 and Solexa technologies. Comparison with the reference genome of the Ghanaian classical lineage isolate Agy99 revealed 26,564 SNPs in a Japanese strain representing the ancestral lineage. Only 173 SNPs were found when comparing Agy99 with two other Ghanaian isolates, which belong to the two other types previously distinguished in Ghana by variable number tandem repeat typing. We further analyzed a collection of Ghanaian strains using the SNPs discovered. With 68 SNP loci, we were able to differentiate 54 strains into 13 distinct SNP haplotypes. The average SNP nucleotide diversity was low (average 0.06–0.09 across 68 SNP loci), and 96% of the SNP locus pairs were in complete linkage disequilibrium. We estimated that the divergence of the M. ulcerans Ghanaian clade from the Japanese strain occurred 394 to 529 thousand years ago. The Ghanaian subtypes diverged about 1000 to 3000 years ago, or even much more recently, because we found evidence that they evolved significantly faster than average. Our results offer significant insight into the evolution of M. ulcerans and provide a comprehensive report on genetic diversity within a highly clonal M. ulcerans population from a Buruli ulcer endemic region, which can facilitate further epidemiological studies of this pathogen through the development of high-resolution tools