Exploring the affordances of Bayesian networks for modeling usable knowledge and knowledge use in teaching

In this article we propose the use of Bayesian networks as a potentially promising way to model usable knowledge. Using the Classroom Video Analysis (CVA and CVA-M) assessments as a lab model for studying teachers’ usable knowledge, we first explored whether we can identify the knowledge (pieces) underlying teachers’ written responses. In the CVA approach we ask teachers to respond to short video clips of authentic classroom instruction based on different prompts that are designed to simulate common teaching tasks. We then explored the affordances of Bayesian networks to functionally model usable knowledge as an interconnected dynamic knowledge system consisting of different knowledge pieces and connected pathways weighted by situation-specific relevance and applicability. We explore the implications of these models for studying the development and growth of usable knowledge and propose the use of Bayesian networks as a novel and potentially promising way to model usable knowledge and for understanding how knowledge is used in teaching.

[1]  Hala Ghousseini,et al.  Getting Inside Rehearsals , 2016 .

[2]  L. Shulman.  Knowledge and Teaching: Foundations of the New Reform , 1987 .

[3]  H. Mandl,et al.  Inert knowledge: Analyses and remedies , 1996 .

[4]  D. Ball.  Bridging Practices , 2000 .

[5]  J. Tenenbaum,et al.  Special issue on “Probabilistic models of cognition , 2022 .

[6]  J. Tenenbaum,et al.  Bayesian Networks, Bayesian Learning and Cognitive Development , 2022 .

[7]  John R. Anderson.  The Architecture of Cognition , 1983 .

[8]  D. Ball,et al.  Developing Measures of Teachers’ Mathematics Knowledge for Teaching , 2004, The Elementary school journal.

[9]  Deborah Loewenberg Ball,et al.  Content Knowledge for Teaching , 2008 .

[10]  Marco Scutari,et al.  Learning Bayesian Networks with the bnlearn R Package , 2009, 0908.3817.

[11]  Radhakrishnan Nagarajan,et al.  Bayesian Networks in R , 2013 .

[12]  Gary Klein,et al.  Deliberate performance: Accelerating expertise in natural settings , 2010 .

[13]  Robert A Jacobs,et al.  Bayesian learning theory applied to human cognition. , 2011, Wiley interdisciplinary reviews. Cognitive science.

[14]  A. Pouget,et al.  Probabilistic brains: knowns and unknowns , 2013, Nature Neuroscience.

[15]  Anne Reilley Freese,et al.  The role of reflection on preservice teachers’ development in the context of a professional development school , 1999 .

[16]  Radhakrishnan Nagarajan,et al.  Bayesian Networks in R: with Applications in Systems Biology , 2013 .

[17]  C. Lewis,et al.  Lesson Study to Scale Up Research-Based Knowledge: A Randomized, Controlled Trial of Fractions Learning , 2017 .

[18]  James W. Stigler,et al.  Measuring Usable Knowledge , 2012 .

[19]  J. Tenenbaum,et al.  Probabilistic models of cognition: exploring representations and inductive biases , 2010, Trends in Cognitive Sciences.

[20]  L. Shulman.  Those Who Understand: Knowledge Growth in Teaching , 1986 .

[21]  Nicole B. Kersting,et al.  Further exploration of the classroom video analysis (CVA) instrument as a measure of usable knowledge for teaching mathematics: taking a knowledge system perspective , 2016 .

[22]  R. Santagata,et al.  The role of perception, interpretation, and decision making in the development of beginning teachers’ competence , 2016 .

[23]  J. Gustafsson,et al.  Beyond dichotomies: Competence viewed as a continuum , 2015 .

[24]  S. Krauss,et al.  Teachers’ Mathematical Knowledge, Cognitive Activation in the Classroom, and Student Progress , 2010 .

[25]  Hyman Bass,et al.  Interweaving content and pedagogy in teaching and learning to teach: Knowing and using mathematics. , 2000 .

[26]  Rosemary S. Russ,et al.  What constitutes teacher learning , 2016 .

[27]  James W. Stigler,et al.  Teachers’ Analyses of Classroom Video Predict Student Learning of Mathematics: Further Explorations of a Novel Measure of Teacher Knowledge , 2010 .