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The purpose of this study is to increase the understanding about undergraduate life science students’ conceptions concerning the role of photosynthesizing plants in the ecosystem, utilizing a network analysis method. Science learning requires the integration and linking of abstract and often counterintuitive concepts successfully into multifaceted networks. The quality of these networks, together with their abilities to communicate via the language of science, influences students’ success in academic, verbal problem-solving tasks. This study contributes to investigating students’ understanding, utilizing a modern network analysis method in exploring first-year university life science students’ written answers. In this study, a total of 150 first-year life science students answered two open-ended tasks related to the role of photosynthesizing plants in the ecosystem. A network analysis tool was used in exploring the occurrence of different-level science concepts and the interrelatedness between these concepts in students’ verbal outputs. The results showed that the richness of concept networks and students’ use of macro-concepts were remarkably varied between the tasks. Higher communicability measures were connected to the more abundant existence of macro-concepts in the task concerning the role of plants from the food-chain perspective. In the answers for the task concerning the role of plants regarding the atmosphere, the students operated mainly with single facts, and there were only minor interconnections made between the central concepts. On the basis of these results, the need for more all-encompassing biology teaching concerning complex environmental and socio-economic problems became evident. Thus, methodological and pedagogical contributions are discussed.
Ilona Södervik; Maija Nousiainen; Ismo. Koponen. First-Year Life Science Students’ Understanding of the Role of Plants in the Ecosystem—A Concept Network Analysis. Education Sciences 2021, 11, 369 .
AMA StyleIlona Södervik, Maija Nousiainen, Ismo. Koponen. First-Year Life Science Students’ Understanding of the Role of Plants in the Ecosystem—A Concept Network Analysis. Education Sciences. 2021; 11 (8):369.
Chicago/Turabian StyleIlona Södervik; Maija Nousiainen; Ismo. Koponen. 2021. "First-Year Life Science Students’ Understanding of the Role of Plants in the Ecosystem—A Concept Network Analysis." Education Sciences 11, no. 8: 369.
Associative knowledge networks are often explored by using the so-called spreading activation model to find their key items and their rankings. The spreading activation model is based on the idea of diffusion- or random walk -like spreading of activation in the network. Here, we propose a generalisation, which relaxes an assumption of simple Brownian-like random walk (or equally, ordinary diffusion process) and takes into account nonlocal jump processes, typical for superdiffusive processes, by using fractional graph Laplacian. In addition, the model allows a nonlinearity of the diffusion process. These generalizations provide a dynamic equation that is analogous to fractional porous medium diffusion equation in a continuum case. A solution of the generalized equation is obtained in the form of a recently proposed q-generalized matrix transformation, the so-called q-adjacency kernel, which can be adopted as a systemic state describing spreading activation. Based on the systemic state, a new centrality measure called activity centrality is introduced for ranking the importance of items (nodes) in spreading activation. To demonstrate the viability of analysis based on systemic states, we use empirical data from a recently reported case of a university students’ associative knowledge network about the history of science. It is shown that, while a choice of model does not alter rankings of the items with the highest rank, rankings of nodes with lower ranks depend essentially on the diffusion model.
Ismo Koponen. Systemic States of Spreading Activation in Describing Associative Knowledge Networks II: Generalisations with Fractional Graph Laplacians and q-Adjacency Kernels. Systems 2021, 9, 22 .
AMA StyleIsmo Koponen. Systemic States of Spreading Activation in Describing Associative Knowledge Networks II: Generalisations with Fractional Graph Laplacians and q-Adjacency Kernels. Systems. 2021; 9 (2):22.
Chicago/Turabian StyleIsmo Koponen. 2021. "Systemic States of Spreading Activation in Describing Associative Knowledge Networks II: Generalisations with Fractional Graph Laplacians and q-Adjacency Kernels." Systems 9, no. 2: 22.
Understanding about nature of science is important topic in science education as well as in pre-service science teacher education. In science education, Nature of Science (NOS), in its different forms of educational scaffoldings, seeks to provide with students an understanding of features of scientific knowledge and science in general, how scientific knowledge changes and becomes accepted, and what factors guide scientific activities. For a science teacher, deep and broad enough picture of sciences is therefore of importance. This study attempts to show that the research field called Science of Science (SoS) can significantly support building such a panoramic picture of sciences, and through that, significantly support NOS. The SoS approaches the structure and dynamics of science quantitatively, using scientific documents (e.g., publications, reports, books and monographs and patent applications) as trails to map the landscape of sciences. It is argued here that SoS may provide material and interesting cases for NOS, and in so doing enrich NOS in a similarly significant way as history, philosophy and sociology of science (HPSS) scholarship has done thus far. This study introduces several themes based on SoS that are of relevance for NOS as they were introduced and discussed in a pre-service science teachers’ course. The feedback from pre-service teachers shows that introducing SoS, with minimal additional philosophical interpretations and discussions, but simply as evidential facts and findings, sparks ideas and views that come very close to NOS themes and topics. Discussions related to nature of science, and specific educational NOS scaffoldings for it, can find a good companion in SoS; the latter providing facts and evidence of thee structure and dynamics of sciences, the former providing perspectives for interpretations.
Ismo Koponen. Nature of Science (NOS) Being Acquainted with Science of Science (SoS): Providing a Panoramic Picture of Sciences to Embody NOS for Pre-Service Teachers. Education Sciences 2021, 11, 107 .
AMA StyleIsmo Koponen. Nature of Science (NOS) Being Acquainted with Science of Science (SoS): Providing a Panoramic Picture of Sciences to Embody NOS for Pre-Service Teachers. Education Sciences. 2021; 11 (3):107.
Chicago/Turabian StyleIsmo Koponen. 2021. "Nature of Science (NOS) Being Acquainted with Science of Science (SoS): Providing a Panoramic Picture of Sciences to Embody NOS for Pre-Service Teachers." Education Sciences 11, no. 3: 107.
Associative knowledge networks are central in many areas of learning and teaching. One key problem in evaluating and exploring such networks is to find out its key items (nodes), sub-structures (connected set of nodes), and how the roles of sub-structures can be compared. In this study, we suggest an approach for analyzing associative networks, so that analysis is based on spreading activation and systemic states that correpond to the state of spreading. The method is based on the construction of diffusion-propagators as generalized systemic states of the network, for an exploration of the connectivity of a network and, subsequently, on generalized Jensen–Shannon–Tsallis relative entropy (based on Tsallis-entropy) in order to compare the states. It is shown that the constructed systemic states provide a robust way to compare roles of sub-networks in spreading activation. The viability of the method is demonstrated by applying it to recently published network representations of students’ associative knowledge regarding the history of science.
Ismo T. Koponen. Systemic States of Spreading Activation in Describing Associative Knowledge Networks: From Key Items to Relative Entropy Based Comparisons. Systems 2020, 9, 1 .
AMA StyleIsmo T. Koponen. Systemic States of Spreading Activation in Describing Associative Knowledge Networks: From Key Items to Relative Entropy Based Comparisons. Systems. 2020; 9 (1):1.
Chicago/Turabian StyleIsmo T. Koponen. 2020. "Systemic States of Spreading Activation in Describing Associative Knowledge Networks: From Key Items to Relative Entropy Based Comparisons." Systems 9, no. 1: 1.
Heavy-tailed networks, which have degree distributions characterised by slower than exponentially bounded tails, are common in many different situations. Some interesting cases, where heavy tails are characterised by inverse powers λ in the range 1<λ<2, arise for associative knowledge networks, and semantic and linguistic networks. In these cases, the differences between the networks are often delicate, calling for robust methods to characterise the differences. Here, we introduce a method for comparing networks using a density matrix based on q-generalised adjacency matrix kernels. It is shown that comparison of networks can then be performed using the q-generalised Kullback–Leibler divergence. In addition, the q-generalised divergence can be interpreted as a q-generalised free energy, which enables the thermodynamic-like macroscopic description of the heavy-tailed networks. The viability of the q-generalised adjacency kernels and the thermodynamic-like description in characterisation of complex networks is demonstrated using a simulated set of networks, which are modular and heavy-tailed with a degree distribution of inverse power law in the range 1<λ<2.
Ismo T. Koponen; Elina Palmgren; Esko Keski-Vakkuri. Characterising heavy-tailed networks using q-generalised entropy and q-adjacency kernels. Physica A: Statistical Mechanics and its Applications 2020, 566, 125666 .
AMA StyleIsmo T. Koponen, Elina Palmgren, Esko Keski-Vakkuri. Characterising heavy-tailed networks using q-generalised entropy and q-adjacency kernels. Physica A: Statistical Mechanics and its Applications. 2020; 566 ():125666.
Chicago/Turabian StyleIsmo T. Koponen; Elina Palmgren; Esko Keski-Vakkuri. 2020. "Characterising heavy-tailed networks using q-generalised entropy and q-adjacency kernels." Physica A: Statistical Mechanics and its Applications 566, no. : 125666.
Nature of science (NOS) has been a central theme in science education and research on it for nearly three decades, but there is still debate on its proper focus and underpinnings. The focal points of these debates revolve around different ways of understanding the terms “science” and “scientific knowledge”. It is suggested here that the lack of agreement is at least partially related to and reflected as a lack of common vocabulary and terminology that would provide a shared basis for finding consensus. Consequently, the present study seeks motivation from the notions of centrality of lexicons in recognizing the identity of disciplinary communities and different schools of thought within NOS. Here, by using a network approach, we investigate how lexicons used by different authors to discuss NOS are confluent or divergent. The lexicons used in these texts are investigated on the basis of a network analysis. The results of the analysis reveal clear differences in the lexicons that are partially related to differences in views, as evident from the debates surrounding the consensus NOS. The most divergent views are related to epistemology, while regarding the practices and social embeddedness of science the lexicons overlap significantly. This suggests that, in consensus NOS, one can find much basis for converging views, with common understanding, where constructive communication may be possible. The basic vocabulary, in the form of a lexicon, can reveal much about the different stances and the differences and similarities between various disciplinary schools. The advantage of such an approach is its neutrality and how it keeps a distance from preferred epistemological positions and views of nature of knowledge.
Ismo T. Koponen. Usage of Terms “Science” and “Scientific Knowledge” in Nature of Science (NOS): Do Their Lexicons in Different Accounts Indicate Shared Conceptions? Education Sciences 2020, 10, 252 .
AMA StyleIsmo T. Koponen. Usage of Terms “Science” and “Scientific Knowledge” in Nature of Science (NOS): Do Their Lexicons in Different Accounts Indicate Shared Conceptions? Education Sciences. 2020; 10 (9):252.
Chicago/Turabian StyleIsmo T. Koponen. 2020. "Usage of Terms “Science” and “Scientific Knowledge” in Nature of Science (NOS): Do Their Lexicons in Different Accounts Indicate Shared Conceptions?" Education Sciences 10, no. 9: 252.
Science education research is, in many ways, involved with exploring relational aspects of diverse elements that affect students’ learning outcomes; at one end, the elements may be concepts to be learned, and at the other end, the relations between students in different types of learning settings and environments and, ultimately, how such elements may interact
Ismo T. Koponen; Terhi Mäntylä. Editorial: Networks Applied in Science Education Research. Education Sciences 2020, 10, 142 .
AMA StyleIsmo T. Koponen, Terhi Mäntylä. Editorial: Networks Applied in Science Education Research. Education Sciences. 2020; 10 (5):142.
Chicago/Turabian StyleIsmo T. Koponen; Terhi Mäntylä. 2020. "Editorial: Networks Applied in Science Education Research." Education Sciences 10, no. 5: 142.
Learning the wave-particle dualism of electrons and photons plays a central role in understanding quantum physics. Teaching it requires that the teacher is fluent in using abstract and uncommon terms. We inspect the lexical structures of pre-service teachers’ declarative knowledge about the wave-particle dualism of electrons and photons in the context of double-slit interference. The declarative knowledge is analyzed in the form of a lexical network of terms. We focus on lexical structures because, in teaching and learning, knowledge is communicated mostly through lexical structures, i.e., by speaking and writing. Using the lexical networks, we construct the lexicons used by pre-service teachers to express their knowledge of electrons and photons in the context of double-slit interference. The lexicons consist of eight different key terms, each representing a set of closely-related or synonymous terms. The lexicons by 14 pre-service teachers reveal remarkable variation and differences, and are strongly context-dependent. We also analyzed lexicons corresponding to two didactically-oriented research articles on the same topic and found that they also differ. Lexicons paralleling both texts are found among the pre-service teachers’ lexicons. However, only some of the pre-service teachers use such rich vocabulary as would indicate multi-faceted understanding of quantum entities.
Maija Nousiainen; Ismo T. Koponen. Pre-Service Teachers’ Declarative Knowledge of Wave-Particle Dualism of Electrons and Photons: Finding Lexicons by Using Network Analysis. Education Sciences 2020, 10, 76 .
AMA StyleMaija Nousiainen, Ismo T. Koponen. Pre-Service Teachers’ Declarative Knowledge of Wave-Particle Dualism of Electrons and Photons: Finding Lexicons by Using Network Analysis. Education Sciences. 2020; 10 (3):76.
Chicago/Turabian StyleMaija Nousiainen; Ismo T. Koponen. 2020. "Pre-Service Teachers’ Declarative Knowledge of Wave-Particle Dualism of Electrons and Photons: Finding Lexicons by Using Network Analysis." Education Sciences 10, no. 3: 76.
The lexical structure of language of science as it appears in teaching and teaching materials plays a crucial role in learning the language of science. We inspect here the lexical structure of two texts, written for didactic purposes and discussing the topic of wave-particle dualism as it is addressed in science education. The texts are analyzed as lexical networks of terms. The analysis is based on construction of stratified lexical networks, which allows us to analyze the lexical connections from the level of cotext (sentences) to context. Based on lexical networks, we construct lexicon profiles as they appear in two texts addressing the wave-particle dualism of electrons and photons. We demonstrate that the lexicon profiles of the two texts, although they discuss the same topic with similar didactic goals, nevertheless exhibit remarkable variation and differences. The consequences of such variation of lexicon profiles for practical teaching are discussed.
Ismo T. Koponen; Maija Nousiainen. Lexical Networks and Lexicon Profiles in Didactical Texts for Science Education. Econometrics for Financial Applications 2019, 15 -27.
AMA StyleIsmo T. Koponen, Maija Nousiainen. Lexical Networks and Lexicon Profiles in Didactical Texts for Science Education. Econometrics for Financial Applications. 2019; ():15-27.
Chicago/Turabian StyleIsmo T. Koponen; Maija Nousiainen. 2019. "Lexical Networks and Lexicon Profiles in Didactical Texts for Science Education." Econometrics for Financial Applications , no. : 15-27.
The cognitive and social aspects of students’ learning process in acquiring scientific, tiered system of knowledge are explored by using an agent-based-model. Cognitive aspects of learning are described as foraging for the best explanations on epistemic landscapes, whose tiered structures are set by instructional design. The sociodynamic aspects of learning are described as an agent-based model, where agents compare and adjust their proficiency through peer-to-peer comparisons. The results show that even in cases where social learning is unbiased, social learning has a substantial effect on learning outcomes.
Ismo T. Koponen. Agent-Based-Model of Students’ Sociocognitive Learning Process in Acquiring Tiered Knowledge. Communications in Computer and Information Science 2019, 82 -95.
AMA StyleIsmo T. Koponen. Agent-Based-Model of Students’ Sociocognitive Learning Process in Acquiring Tiered Knowledge. Communications in Computer and Information Science. 2019; ():82-95.
Chicago/Turabian StyleIsmo T. Koponen. 2019. "Agent-Based-Model of Students’ Sociocognitive Learning Process in Acquiring Tiered Knowledge." Communications in Computer and Information Science , no. : 82-95.
Students’ knowledge is often organized around relations and key concepts but it sometimes also resembles associative knowledge, where connections between knowledge elements are based on thematic resemblance without overarching organization based on substantiation or logical reasoning. Because it is known that associative knowledge, while important for learning too, may be very differently structured from more organized knowledge, a closer look on students’ thematically associated knowledge is warranted. In this study we model students’ thematically associative knowledge as a network of pairwise associative connections. The model is based on the assumption that associative knowledge is by a large degree governed by the intrinsic affinity of the knowledge elements that consists of the thematically associated knowledge base. The model introduced here makes minimal assumptions about the affinity distribution of such knowledge. The results show that in this case, under very general conditions, the network of associative knowledge is characterized by inverse power laws of degree, eigenvector, and betweenness centralities. These results agree with the empirically found properties of students’ associative networks.
Ismo T. Koponen. Modelling Students’ Thematically Associated Knowledge: Networked Knowledge from Affinity Statistics. First Complex Systems Digital Campus World E-Conference 2015 2019, 123 -134.
AMA StyleIsmo T. Koponen. Modelling Students’ Thematically Associated Knowledge: Networked Knowledge from Affinity Statistics. First Complex Systems Digital Campus World E-Conference 2015. 2019; ():123-134.
Chicago/Turabian StyleIsmo T. Koponen. 2019. "Modelling Students’ Thematically Associated Knowledge: Networked Knowledge from Affinity Statistics." First Complex Systems Digital Campus World E-Conference 2015 , no. : 123-134.
We examine students’ representations of their conceptions of the interlinked nature of science history and general history, as well as cultural history. Such knowledge landscapes of the history of science are explored by using the knowledge cartographic, network-based method of analysis to reveal the key items, landmarks, of the landscapes. We show that Katz centrality and Katz centrality efficiency are robust and reliable measures for finding landmarks. It is shown that landmarks are most often persons but include also colligatory landmarks, which refer to broader sets of events or ideas. By using Katz centrality we study how landmarks depend on periodisation of the networks to see what kinds of changes occur by changing the time window on history. The community structure of the networks is studied by using the Louvain method, to reveal the strong thematic dependence of the communities. When landmarks are studied in relation to community structure, it is found that colligatory landmarks gain importance in relation to person-centred landmarks. Network-based cartography thus reveals many features about landmarks, how communities emerge around them and how they depend on periodisation, which traditional methods can only detect or identify with difficulty. Such knowledge has direct impact on the design and planning of education and courses which could better address the need to facilitate a deeper understanding of the related nature of science history and history in general.
Henri Lommi; Ismo T. Koponen. Network cartography of university students’ knowledge landscapes about the history of science: landmarks and thematic communities. Applied Network Science 2019, 4, 6 .
AMA StyleHenri Lommi, Ismo T. Koponen. Network cartography of university students’ knowledge landscapes about the history of science: landmarks and thematic communities. Applied Network Science. 2019; 4 (1):6.
Chicago/Turabian StyleHenri Lommi; Ismo T. Koponen. 2019. "Network cartography of university students’ knowledge landscapes about the history of science: landmarks and thematic communities." Applied Network Science 4, no. 1: 6.
Relational interlinked dependencies between concepts constitute the structure of abstract knowledge and are crucial in learning conceptual knowledge and the meaning of concepts. To explore pre-service teachers’ declarative knowledge of physics concepts, we have analyzed concept networks, which agglomerate 12 pre-service teacher students’ representations of the key elements in electricity and magnetism. We show that by using network-based methods, the interlinked connections of nodes, locally and globally, can be analyzed to reveal how different elements of the network are supported through their connections to other nodes in the network. Nodes with high global connectivity initialize contiguous concept patchworks within the network and are thus most often found to be abstract, general, and advanced concepts. Locally cohesive concepts, on the other hand, are nearly always auxiliary supporting concepts, related to specific textbook-type experiments and model-type conceptional elements. Comparisons of group-level knowledge and individual pre-service teacher students’ knowledge in the form of networks shows that while in group-level the aggregated knowledge is expert-like, at the individual level pre-service teacher students possess only a fraction of that knowledge.
Ismo T. Koponen; Maija Nousiainen. Pre-Service Teachers’ Knowledge of Relational Structure of Physics Concepts: Finding Key Concepts of Electricity and Magnetism. Education Sciences 2019, 9, 18 .
AMA StyleIsmo T. Koponen, Maija Nousiainen. Pre-Service Teachers’ Knowledge of Relational Structure of Physics Concepts: Finding Key Concepts of Electricity and Magnetism. Education Sciences. 2019; 9 (1):18.
Chicago/Turabian StyleIsmo T. Koponen; Maija Nousiainen. 2019. "Pre-Service Teachers’ Knowledge of Relational Structure of Physics Concepts: Finding Key Concepts of Electricity and Magnetism." Education Sciences 9, no. 1: 18.
Concept maps, which are network-like visualisations of the inter-linkages between concepts, are used in teaching and learning as representations of students’ understanding of conceptual knowledge and its relational structure. In science education, research on the uses of concept maps has focused much attention on finding methods to identify key concepts that are of the most importance either in supporting or being supported by other concepts in the network. Here we propose a method based on network analysis to examine students’ representations of the relational structure of physics concepts in the form of concept maps. We suggest how the key concepts and their epistemic support can be identified through focusing on the pathways along which the information is passed from one node to another. Towards this end, concept maps are analysed as directed and weighted networks, where nodes are concepts and links represent different types of connections between concepts, and where each link is assumed to provide epistemic support to the node it is connected to. The notion of key concept can then be operationalised through the directed flow of information from one node to another in terms of communicability between the nodes, separately for out-going and in-coming weighted links. Here we analyse a collated concept network based on a sample of 12 original concept maps constructed by university students. We show that communicability is a simple and reliable way to identify the key concepts and examine their epistemic justification within the collated network. The communicabilities of the key nodes in the collated network are compared with communicabilities averaged over the set of 12 individual concept maps. The comparison shows the collated network contains an extensive set of key concepts with good epistemic support. Every individual networks contain a sub-set of these key concepts but with a limited overlap of the sub-sets with other individual networks. The epistemically well substantiated knowledge is thus sparsely distributed over the 12 individual networks.
Ismo T. Koponen; Maija Nousiainen. Concept networks of students’ knowledge of relationships between physics concepts: finding key concepts and their epistemic support. Applied Network Science 2018, 3, 14 .
AMA StyleIsmo T. Koponen, Maija Nousiainen. Concept networks of students’ knowledge of relationships between physics concepts: finding key concepts and their epistemic support. Applied Network Science. 2018; 3 (1):14.
Chicago/Turabian StyleIsmo T. Koponen; Maija Nousiainen. 2018. "Concept networks of students’ knowledge of relationships between physics concepts: finding key concepts and their epistemic support." Applied Network Science 3, no. 1: 14.
We present a method to analyse how pre-service science teachers relate events, ideas, characters and deeds in history of science and in cultural and general history. A group of 25 students presented their views they deemed to be of importance in history of science, culture, society and politics in era between 1550 and 1850. The sample is based on students’ study reports and analysed by using network analysis. We show how students’ knowledge of history of science and history in general are organised around certain famous characters, ideas, events and institutions, thus revealing the phenomenon of accumulation of fame; the Mathew effect in action.
Ismo Koponen; Maija Nousiainen. University students’ associative knowledge of history of science: Matthew effect in action? European Journal of Science and Mathematics Education 2018, 6, 69 -81.
AMA StyleIsmo Koponen, Maija Nousiainen. University students’ associative knowledge of history of science: Matthew effect in action? European Journal of Science and Mathematics Education. 2018; 6 (2):69-81.
Chicago/Turabian StyleIsmo Koponen; Maija Nousiainen. 2018. "University students’ associative knowledge of history of science: Matthew effect in action?" European Journal of Science and Mathematics Education 6, no. 2: 69-81.
Learning scientific knowledge is largely based on understanding what are its key concepts and how they are related. The relational structure of concepts also affects how concepts are introduced in teaching scientific knowledge. We model here how students organise their knowledge when they represent their understanding of how physics concepts are related. The model is based on assumptions that students use simple basic linking-motifs in introducing new concepts and mostly relate them to concepts that were introduced a few steps earlier, i.e. following a genealogical ordering. The resulting genealogical networks have relatively high local clustering coefficients of nodes but otherwise resemble networks obtained with an identical degree distribution of nodes but with random linking between them (i.e. the configuration-model). However, a few key nodes having a special structural role emerge and these nodes have a higher than average communicability betweenness centralities. These features agree with the empirically found properties of students’ concept networks.
Ismo T. Koponen; Maija Nousiainen. Modelling students’ knowledge organisation: Genealogical conceptual networks. Physica A: Statistical Mechanics and its Applications 2018, 495, 405 -417.
AMA StyleIsmo T. Koponen, Maija Nousiainen. Modelling students’ knowledge organisation: Genealogical conceptual networks. Physica A: Statistical Mechanics and its Applications. 2018; 495 ():405-417.
Chicago/Turabian StyleIsmo T. Koponen; Maija Nousiainen. 2018. "Modelling students’ knowledge organisation: Genealogical conceptual networks." Physica A: Statistical Mechanics and its Applications 495, no. : 405-417.
Ismo T. Koponen; Maija Nousiainen. An Agent-Based Model of Discourse Pattern Formation in Small Groups of Competing and Cooperating Members. Journal of Artificial Societies and Social Simulation 2018, 21, 1 .
AMA StyleIsmo T. Koponen, Maija Nousiainen. An Agent-Based Model of Discourse Pattern Formation in Small Groups of Competing and Cooperating Members. Journal of Artificial Societies and Social Simulation. 2018; 21 (2):1.
Chicago/Turabian StyleIsmo T. Koponen; Maija Nousiainen. 2018. "An Agent-Based Model of Discourse Pattern Formation in Small Groups of Competing and Cooperating Members." Journal of Artificial Societies and Social Simulation 21, no. 2: 1.
Concept maps are used in teaching and learning as representations of students’ understanding of conceptual knowledge. Concept maps are basically networks of interlinked web of concepts. A long-standing problem in educational research is identifying the key concepts of importance in such networks. Here we use network analysis to examine students’ representations of the relatedness of physics concepts in the form of concept maps, and suggest how key concepts and their epistemic support can be identified. The concept maps are analysed as directed and weighted networks, where nodes are concepts and links represent different types of connections between concepts. The notion of key concept is operationalised through the communicability, separately for out-going and in-coming weighted links. Using a collated concept network based on a sample of 12 original concept maps constructed by university students we show that the communicability is a simple and reliable way to identify the key concepts and examine their epistemic justification within the network.
Ismo T. Koponen; Maija Nousiainen. Concept Networks in Learning and the Epistemic Support of Their Key Concepts. Econometrics for Financial Applications 2017, 759 -769.
AMA StyleIsmo T. Koponen, Maija Nousiainen. Concept Networks in Learning and the Epistemic Support of Their Key Concepts. Econometrics for Financial Applications. 2017; ():759-769.
Chicago/Turabian StyleIsmo T. Koponen; Maija Nousiainen. 2017. "Concept Networks in Learning and the Epistemic Support of Their Key Concepts." Econometrics for Financial Applications , no. : 759-769.
We discuss here conceptual change and the formation of robust learning outcomes from the viewpoint of complex dynamic systems (CDS). The CDS view considers students’ conceptions as context dependent and multifaceted structures which depend on the context of their application. In the CDS view the conceptual patterns (i.e. intuitive conceptions here) may be robust in a certain situation but are not formed, at least not as robust ones, in another situation. The stability is then thought to arise dynamically in a variety of ways and not so much to mirror rigid ontological categories or static intuitive conceptions. We use computational modelling to understand the generic dynamic and emergent features of that phenomenon. The model is highly simplified and idealized, but it shows how context dependence, described here by an epistemic landscape structure, leads to the formation of context dependent robust states that can be viewed as attractors in learning, and how owing to the sharply defined nature of these states, learning appears as a progression of switches from one state to another, giving thus the appearance of conceptual change as switches from one robust state to another. Finally, we discuss the implications of the results in directing attention to the design of learning tasks and their structure, and how empirically accessible learning outcomes might be related to these underlying factors.
Ismo T. Koponen; Tommi Kokkonen; Maiji Nousiainen. Complex Dynamic Systems View on Conceptual Change: How a Picture of Students’ Intuitive Conceptions Accrue From Dynamically Robust Task Dependent Learning Outcomes. Complicity: An International Journal of Complexity and Education 2017, 14, 1 .
AMA StyleIsmo T. Koponen, Tommi Kokkonen, Maiji Nousiainen. Complex Dynamic Systems View on Conceptual Change: How a Picture of Students’ Intuitive Conceptions Accrue From Dynamically Robust Task Dependent Learning Outcomes. Complicity: An International Journal of Complexity and Education. 2017; 14 (2):1.
Chicago/Turabian StyleIsmo T. Koponen; Tommi Kokkonen; Maiji Nousiainen. 2017. "Complex Dynamic Systems View on Conceptual Change: How a Picture of Students’ Intuitive Conceptions Accrue From Dynamically Robust Task Dependent Learning Outcomes." Complicity: An International Journal of Complexity and Education 14, no. 2: 1.
I.T. Koponen; T. Kokkonen; Maija Nousiainen. Modelling sociocognitive aspects of students’ learning. Physica A: Statistical Mechanics and its Applications 2017, 470, 68 -81.
AMA StyleI.T. Koponen, T. Kokkonen, Maija Nousiainen. Modelling sociocognitive aspects of students’ learning. Physica A: Statistical Mechanics and its Applications. 2017; 470 ():68-81.
Chicago/Turabian StyleI.T. Koponen; T. Kokkonen; Maija Nousiainen. 2017. "Modelling sociocognitive aspects of students’ learning." Physica A: Statistical Mechanics and its Applications 470, no. : 68-81.