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The expertise reversal effect concerning instructional explanations

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Abstract

The expertise reversal effect occurs when learner’s expertise moderates design principles derived from cognitive load theory. Although this effect is supported by numerous empirical studies, indicating an overall large effect size, the effect was never tested by inducing expertise experimentally and using instructional explanations in a computer-based environment. The present experiment used an illustrated introductory text and a computer program about statistical data analyses with 93 students. Retention and transfer tests were employed as dependent measures. Each learner was randomly assigned to one condition of a 2 × 2 between subjects factorial design with the two factors expertise (novices vs. ‘experts’) and explanations (with vs. without). Expertise was induced by adding expository examples and illustrations to the introductory text to enhance text coherence and facilitate text comprehension. The expertise reversal effect was replicated for the dependent measure transfer, but not for retention. Results and implications for adaptive learning environments are discussed.

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Author information

Authors and Affiliations

  1. Department of Educational Psychology, Institute for Psychology, FernUniversität in Hagen, 58084, Hagen, Germany

    Günter Daniel Rey

  2. Department of Experimental and Theoretical Psychology, Institute for Psychology, Universität Heidelberg, 69117, Heidelberg, Germany

    Andreas Fischer

Authors
  1. Günter Daniel Rey
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  2. Andreas Fischer
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Corresponding author

Correspondence to Günter Daniel Rey.

Appendices

Appendix 1

Selected examples of the used explanations for the results presentation. Explanations were presented in the German language.

  1. (a)

    Linear regression: “The regression coefficient of a predictor x indicated how much the predicted value y increases if the predictor rises one point.”

  2. (b)

    Neural network without hidden-units (linear activity function): “The subsequent equation represents a nonlinear relationship between x and y (the higher b, the steeper is the graph and the stronger is the influence from x on y.”

  3. (c)

    Neural network with hidden-units: “The coefficients of a neural network with hidden-units cannot (under normal circumstances) be interpreted in a realistic manner. The relationship can only be described accurately through the relationship as a whole.”

Appendix 2

Selected examples of the retention and transfer pre-test questions presented immediately after inducing expertise. Questions were presented in the German language. The first three questions were retention questions, the last three questions were transfer questions.

  1. 1.

    Is a high RMSE measure good or bad?

    • Good

    • Depends on the data

    • Depends on the method

    • Bad

  2. 2.

    Please mark the statistical method, which allows capturing nonlinear relationships:

    • Linear regression

    • Polynomial regression

    • Neural network without hidden-units

    • Neural network with hidden-units

  3. 3.

    y = x2 + x + 1 is…

    • a polynomial of the first degree

    • a polynomial of the second degree

    • a polynomial of zero degree

    • a linear slope

  4. 4.

    The equation y = 10 * x + 5 is given. If x increases three points, by how much does y increase?

    • 5

    • 10

    • 15

    • None of the above

  5. 5.

    The equation y = b2 * x2 + b1 * x + b0 is given. Which statements about this function are correct?

    • If b0 does not equal zero, then it is not a linear slope

    • If b1 does not equal zero, then it is not a linear slope

    • If b2 does not equal zero, then it is not a linear slope

    • None of the above

  6. 6.

    Assume the output of a neural network output-unit is y = logistic(w1 * x + w0). Which of the statements are correct?

    • If the predictor increases by 1, y changes by w1

    • If the predictor increases by 1, y changes by w0

    • If the predictor increases by 1, y changes by x

    • None of the above

Appendix 3

Selected examples of the retention post-test questions presented after the statistical program was shown. Questions were presented in the German language.

  1. 1.

    What was the best procedure for gathering the interrelation between coffee consumption and alertness?

    • Linear regression

    • Polynomial regression of the fifth degree

    • Neural network without hidden-units

    • Neural network with four hidden-units

  2. 2.

    Which of the following graphs best describes the interrelation between coffee consumption and performance?

  3. 3.

    Were coefficients of a neural network without hidden-units (as it was in the default value in the program, i.e., with linear activity function) in accordance with the unstandardized coefficients of a linear regression?

    • Yes, all coefficients were nearly identical

    • Some, but not all, coefficients were nearly identical

    • No, no coefficient was nearly identical

  4. 4.

    Please tick the procedure, for those where the RMSE was nearly identical to the RMSE of a linear regression?

    • Polynomial regression of the first degree

    • Polynomial regression of the fifth degree

    • Neural network without hidden-units (linear activity function)

    • Neural network without hidden-units (nonlinear activity function)

    • Neural network with four hidden-units (linear activity function)

    • Neural network with four hidden-units (nonlinear activity function)

    • None of the above

Appendix 4

Selected examples of the transfer post-test questions presented after the statistical program was shown. Questions were presented in the German language.

  1. 1.

    Assume you computed a neural network with hidden-units as it was preset in the program. Assume further that the coefficient of predictor x is b1 = 4.56 and the coefficient of the bias-unit is b0 = 3.64. Do you know by which value the predicted value increases if the predictor x increases by 1?

    • Yes, namely…(please insert the result)

    • No

  2. 2.

    Assume a polynomial regression of the first degree with the predictor “coffee” (in cupfuls) and the criterion “performance” results in a coefficient b of 4.56. After a cup of coffee, a person achieves a performance value of 70. Do you know which performance should be expected after two cupfuls, according to this linear regression?

    • Yes, namely…(please insert the result)

    • No

  3. 3.

    If a linear regression is computed, which statement(s) is/are correct about the coefficient b of a predictor x?

    • The algebraic sign of b indicates if the prediction y becomes higher or lower with increasing x

    • The higher the amount of b, the higher the impact of the predictor x to the predicted value y

    • Neither answer is correct

  4. 4.

    If a linear activity function is chosen in a neural network with hidden-units for the output-units, which statement(s) is/are correct about the coefficient b of a predictor x?

    • If the predictor x increases by 1, the predicted value y increases by b

    • The algebraic sign of b indicates if the prediction y becomes higher or lower with increasing x

    • Neither answer is correct

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Rey, G.D., Fischer, A. The expertise reversal effect concerning instructional explanations. Instr Sci 41, 407–429 (2013). https://doi.org/10.1007/s11251-012-9237-2

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