## Key Ideas > [!abstract] Core Concepts > > - **Expertise blinds teachers to student perspective**: Having knowledge makes it difficult to imagine not having that knowledge > - **Automatic retrieval masks cognitive effort**: Expert fluency makes complex processes seem effortless, underestimating student difficulty > - **Requires deliberate compensation**: Teachers must systematically identify prerequisites and over-explain rather than assume understanding ## Definition **Curse of Knowledge**: Cognitive bias where having knowledge of a topic makes it difficult to understand the perspective of someone without that same knowledge (Hinds, 1999). ## Overview Expert teachers possess a blindspot: their [[Fluency|fluency]] in mathematics makes retrieving and applying knowledge feel effortless, creating the illusion that the tasks they assign require minimal cognitive work (Hinds, 1999). When solving $4a = 12$ feels trivially simple, teachers struggle to imagine the vast network of chunked knowledge this "basic" problem actually demands (understanding equality symbols, algebraic notation, inverse operations, and maintaining balance) (Miller, 1956; Cowan, 2001). This **curse of knowledge** (the cognitive bias where expertise makes it difficult to adopt a novice's perspective) leads teachers to underestimate time needed for mastery, skip essential prerequisites, rush through explanations, and provide insufficient practice (Hinds, Bernstein, & Loewenstein, 1988). The phenomenon mirrors the [[Dunning-Kruger Effect]] in reverse: beginners overestimate their competence due to ignorance, experts underestimate the competence required of beginners due to their automated knowledge (Kruger & Dunning, 1999). Effective teaching requires deliberate compensation strategies (identifying all [[Prior Knowledge|prior knowledge]] requirements, making invisible thinking processes visible, and over-explaining rather than under-explaining) to counteract this bias that makes expert teachers inadvertently inaccessible to novice learners (Rosenshine, 2012). ## Connected To [[Experts and Novices Think Differently]] | [[Prior Knowledge]] | [[Explicit Teaching]] | [[Fluency]] | [[Surface and Deep Structure]] | [[Dunning-Kruger Effect]] --- ## How the bias manifests in teaching Teacher expertise creates this bias through [[Fluency|fluency]] in retrieving information from long-term memory effortlessly (Hinds, 1999; Ericsson & Kintsch, 1995). Teachers can immediately recognise the [[Surface and Deep Structure|deep structure]] of a question without consciously realising how they do so (Chi, Feltovich, & Glaser, 1981). This automated expertise makes it difficult to remember what it was like not to understand the mathematics being taught, reducing empathy with students struggling to learn (Hinds, 1999). The bias appears in several teaching behaviours. When teachers provide rapid explanations, they assume content is obvious whilst students face multiple new concepts simultaneously (Hinds, 1999; Cowan, 2001). Teachers skip steps in worked examples believing "they should know this", yet students lack the prerequisite knowledge being assumed (Chi, Feltovich, & Glaser, 1981). Complex examples that seem to "demonstrate the principle clearly" to expert teachers create cognitive overload from multiple variables for novice learners (Sweller, 1988). Teachers provide minimal practice after one or two examples, assuming students understand, whilst learners need extensive repetition to achieve automation (Ericsson, Krampe, & Tesch-Römer, 1993). The phenomenon relates inversely to the [[Dunning-Kruger Effect]], where lack of knowledge makes people overestimate their competence just as having knowledge makes experts underestimate the competence required by novices (Kruger & Dunning, 1999). ## Compensation strategies Teachers can counteract the curse of knowledge through systematic analysis of prerequisites before teaching new skills. This requires identifying all [[Prior Knowledge|prior knowledge]] required, listing every component skill and concept students need, and testing what students actually know rather than what curriculum documents suggest they should know (Rosenshine, 2012). Instructional delivery requires adjustments to compensate for expert blindness. [[Explicit Teaching|Explicit teaching]] with clear, step-by-step instruction makes the invisible thinking processes of experts visible to novices (Rosenshine, 2012). Teachers should over-explain rather than under-explain, providing more detail than feels necessary to their expert perspective, and show all working steps that automated expertise might otherwise skip (Hinds, 1999). Practice requirements differ from expert intuitions. Students need practice until they achieve [[Fluency|fluency]], not just initial understanding (Ericsson & Kintsch, 1995). Teachers should use formative assessment to monitor actual comprehension rather than assumed understanding, and provide multiple examples since single examples prove insufficient for novices to recognise patterns (Rosenshine, 2012). ## Practical implications Expert teachers consistently underestimate the time students need for mastery (Hinds, 1999). Problems that seem "simple" to experts often require vast networks of automated knowledge invisible to conscious awareness (Chi, Feltovich, & Glaser, 1981). When students show confusion, this may indicate the curse of knowledge affecting teaching rather than student deficiency (Hinds, 1999). Curriculum pacing frequently reflects expert assumptions about learning speed rather than the reality of novice learning trajectories (Rosenshine, 2012). Examples reveal the hidden complexity of apparently simple tasks. Solving 4a = 12 seems trivial to mathematics teachers but requires chunked knowledge of equality symbols, algebraic notation, inverse operations, and maintaining balance (each element that once required conscious effort) (Cowan, 2001). Similarly, instructions to "just factor the quadratic" assume students have automated their multiplication tables, perfect squares, factor pairs, distributive property, and trinomial patterns (Miller, 1956). In literacy, asking students to "find the main idea" presumes vocabulary knowledge, relevant background knowledge, text structure awareness, and inferential reasoning skills that novice readers may lack (Chi, Feltovich, & Glaser, 1981). ## Teaching applications > [!tip] Practical guidance > > Teachers should carefully delineate the [[Prior Knowledge|prior knowledge]] required when teaching a new skill (Rosenshine, 2012). Instruction should be [[Explicit Teaching|explicit]], with teachers over-explaining rather than under-explaining (Hinds, 1999). Students need practice until they achieve [[Fluency|fluency]] before moving to new content (Ericsson & Kintsch, 1995). ## References Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. *Cognitive Science*, 5(2), 121–152. https://doi.org/10.1207/s15516709cog0502_2 Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. *Behavioral and Brain Sciences*, 24(1), 87–114. https://doi.org/10.1017/S0140525X01003922 Ericsson, K. A., & Kintsch, W. (1995). Long-term working memory. *Psychological Review*, 102(2), 211–245. https://doi.org/10.1037/0033-295X.102.2.211 Ericsson, K. A., Krampe, R. T., & Tesch-Römer, C. (1993). The role of deliberate practice in the acquisition of expert performance. *Psychological Review*, 100(3), 363–406. https://doi.org/10.1037/0033-295X.100.3.363 Hinds, P. J. (1999). The curse of expertise: The effects of expertise and debiasing methods on predictions of novice performance. *Journal of Experimental Psychology: Applied*, 5(2), 205–221. https://doi.org/10.1037/1076-898X.5.2.205 Hinds, P. J., Bernstein, E., & Loewenstein, J. (1988). Unpacking the curse of knowledge. In *Proceedings of the Academy of Management*. Kruger, J., & Dunning, D. (1999). Unskilled and unaware of it: How difficulties in recognizing one's own incompetence lead to inflated self-assessments. *Journal of Personality and Social Psychology*, 77(6), 1121–1134. https://doi.org/10.1037/0022-3514.77.6.1121 Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. *Psychological Review*, 63(2), 81–97. https://doi.org/10.1037/h0043158 Rosenshine, B. (2012). Principles of instruction: Research-based strategies that all teachers should know. *American Educator*, 36(1), 12–19. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. *Cognitive Science*, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4