认知网络分析法及其应用案例分析
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摘要
认知网络分析法(ENA)是在教育大数据与学习分析快速发展的大背景下产生的一种日益重要的表征学习者认知网络结构的研究方法。研究采用文献研究法和案例研究法,从概念、理论基础、分析过程、支持工具、研究案例和特征等方面对认知网络分析法进行系统介绍。研究发现,该方法是一种以认知框架理论为基础,通过建构动态网络模型对学习者个体和群体的认知元素间的网络关系进行可视化表征、分析的方法。该方法包括"基于节的编码"和"创建动态模型"两个阶段和八个具体操作环节。ENA Webkit是一个重要的支持认知网络分析的工具。当前,该方法在协作学习、实践社区以及学习评价中被广泛应用,并通过与其他方法的深度融合对学习者的认知网络进行深层次表征、分析和比较。它具有以下特征:对要素间共现关系的关注是其核心;可多层次、动态化表征个体和群体的认知网络;是一种思维工具,可基于多个理论框架多维表征学习者的认知发展;还是一种基于证据的深度学习评价方式。
With the rapid development of educational big data and learning analysis, epistemic network analysis(ENA) is increasingly becoming an important method to characterize learners' cognitive network structure. This study adopts literature research and case study to introduce epistemic network analysis systematically from the perspectives of concept, theoretical basis, analysis process, supporting tools, research cases and features. It is found that this method can visually represent and analyze the network relationship between individual and group cognitive elements of learners by constructing a dynamic network model based on the theory of cognitive framework. There are two phases of "stanza-based coding" and "creating dynamic model" and eight specific operational links in this method. ENA Webkit is an important tool to support epistemic network analysis. ENA has been widely used in collaborative learning, community of practice and learning evaluation, which can deeply characterize, analyze and compare learners' cognitive network through deep integration with other methods. ENA focuses on the cooccurrence of elements and can represent the cognitive network of individuals and groups at multiple levels and dynamically. Moreover, it is a thinking tool that can represent learners' cognitive development based on multiple theoretical frameworks, and an evidence-based evaluation method of deep learning as well.
With the rapid development of educational big data and learning analysis, epistemic network analysis(ENA) is increasingly becoming an important method to characterize learners' cognitive network structure. This study adopts literature research and case study to introduce epistemic network analysis systematically from the perspectives of concept, theoretical basis, analysis process, supporting tools, research cases and features. It is found that this method can visually represent and analyze the network relationship between individual and group cognitive elements of learners by constructing a dynamic network model based on the theory of cognitive framework. There are two phases of "stanza-based coding" and "creating dynamic model" and eight specific operational links in this method. ENA Webkit is an important tool to support epistemic network analysis. ENA has been widely used in collaborative learning, community of practice and learning evaluation, which can deeply characterize, analyze and compare learners' cognitive network through deep integration with other methods. ENA focuses on the cooccurrence of elements and can represent the cognitive network of individuals and groups at multiple levels and dynamically. Moreover, it is a thinking tool that can represent learners' cognitive development based on multiple theoretical frameworks, and an evidence-based evaluation method of deep learning as well.
引文
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[2]SHAFFER D W,COLLIER W,RUIS A R. A tutorial on epistemic network analysis:analyzing the structure of connections in cognitive,social,and interaction data[J]. Journal of learning analytics,2016,3(3):9-45.
[3]CAI Z,EAGAN B,DOWELL N, PENNEBAKER J,GRAESSER A C,SHAFFER D W. Epistemic network analysis and topic modeling for chat data from collaborative learning environment[C]//Proceedings of the 10th International Conference on Educational Data Mining.Wuhan:EDM Society, 2017.
[4]SHAFFER D W,RUIS A R.Epistemic network analysis:aworked example of theory-based learning analytics[M/OL]//LANG C,SIEMENS G,WISE A,et al.Handbook of learning analytics,2017:175-185[2019-04-04].https://solaresearch.org/hla-17/.
[5]RUPP A A,CHOI Y,GUSHTA M,et al.Modeling learning progressions in epistemic games with epistemic network analysis:principles for data analysis and generation[C]//Proceedings from the Learning Progressions in Science Conference.Iowa City:IA,2009:24-26.
[6]SHAFFER D W. Formatting data for epistemic network analysis[R]//Games and Professional Simulation(Gaps)Technical Report Series.Wisconsin Center for Education Research, University of Wisconsin-Madison,2014:1-15.
[7]GAˇSE VICˊD,JOKSIMOVICˊS,EAGAN B R,et al.SENS:network analytics to combine social and cognitive perspectives of collaborative learning[J].Computers in human behavior,2018,7(3):1-16.
[8]FOUGT S S,SIEBERT-EVENSTONE A,EAGAN B,et al. Epistemic network analysis of students'longer written assignments as formative/summative evaluation[C]//Proceedings of the 8th International Conference on Learning Analytics and Knowledge. New York:ACM,2018:126-130.
[9]张敏霞,刘霜.教师在线实践社区中的知识管理行为研究[J].中国电化教育,2014(9):120-125.
[10]王陆.教师在线实践社区的研究综述[J].中国电化教育, 2011(9):30-42.
[11]CHESLER N C,RUIS A R,COLLIER W,et al. A novel paradigm for engineering education:virtual internships with individualized mentoring and assessment of engineering thinking[J]. Journal of biomechanical engineering,2015,137(2):1-8.
[12]ARASTOOPOUR G,SHAFFER D W,SWIECKI Z,et al. Teaching and assessing engineering design thinking with virtual internships and epistemic network analysis[J]. International journal of engineering education,2016,32(2):1492-1501.
[13]CHESLER N C,ARASTOOPOUR G,D'ANGELO C M, et al. Design of a professional practice simulator for educating and motivating first-year engineering students[J]. Advances in engineering education,2013,3(3):1-29.
[14]徐刘杰,陈世灯.学习者知识建构的社会认知网络[J].开放教育研究,2017,23(5):102-112.
[15]吴忭,王戈,盛海曦.认知网络分析法:STEM教育中的学习评价新思路[J].远程教育杂志,2018,36(6):3-10.
[16]MISLEVY R J,GENEUA H,RICONSCENTE M,et al.Evidence-centered assessment design[M]//MISLEVY R J,GENEUA H,RICONSCENTE M,et al.Assessing Model-Based Reasoning using Evidence-Centered Design. Cham:Springer,2017:19-24.
[17]MISLEVY R J. Issues of structure and issues of scale in assessment from a situative/sociocultural perspective[J/OL]. National Center for Research on Evaluation Standards&Student Testing,2006(1):1-37[2019-04-04].https://files.eric.ed.gov/fulltext/ED518674.pdf.