Designing Winning Worked Examples 3 – Explanation Effects

Mirjam Neelen & Paul A. Kirschner

Worked examples come in many shapes and forms. They’re not only useful for simple, well-structured tasks or problems, but also for complex, ill-structured tasks or problems. In other words, tasks or problems with unknown elements and multiple acceptable solutions. They also possess multiple criteria for evaluating solutions and often require learners to make all kinds of judgments.

Although we know that in general worked examples were very effective for (complex) skills acquisition and tasks that involve problem-solving, we also know that we need to consider the three factors that moderate their effectiveness:

  • Intra-example features – how the example is designed and the way the example’s solution is presented.
  • Inter-example features – certain relationships among multiple examples and practice problems within one ‘session’ (or lesson).
  • Individual differences – the way learners process examples (aka explanation effects).

As often, the devil is in the detail!

In the first blog of this series, we discussed the role of intra-example features (how the example is designed and the way the example’s solution is presented) in worked examples.

Last week, we explored inter example features, or,  how to sequence and arrange the examples during instruction.

This week (the last blog in this series), we focus on explanation effects.

We have blogged about self-explaining before but note the difference! In our previous blog the self-explaining focuses on learners explaining solutions or concepts to themselves. In this context, it’s about learners explaining based on learning materials that are incomplete. Learning materials often include information gaps or omissions in the steps involved in worked-out problem examples (Roy & Chi, 2005).

Chi et al (1989) noted that learners often fail to fully understand the problem-solving model illustrated by the worked example. This lack of understanding might happen because worked examples are often incomplete (e.g., insufficiently explained solution steps). Because of this, learners are unable to generalise from the examples to problems that differ from the ones explained in the example. Micki Chi and her colleagues (1989) also noted that some learners are better than others at providing the missing explanations. They found that learners use qualitatively distinct strategies to balance the effect of poorly designed examples.

When discovering an unexplained step in an example, some learners generate their own justification for the actions described in the step. Chi et al. labeled this approach ‘the self-explanation’ effect and their study found that learners who self-explain while studying examples learn more effectively.

In addition, Alexander Renkl (1997) found that there are two successful ‘self-explanation styles’: anticipative reasoning (self-explaining by anticipating or predicting the next step in an example, followed by a self-check to determine if their prediction was correct) and principle-based explaining (seeking to articulate the conceptual structure of the problem by self-explaining the problem’s subgoal structure and making the domain principles on which the solutions were based explicit).

We now know that learners who self-explain outperform the ones who don’t, we need to encourage learners to not only self-explain, but also to do so as effectively as possible. This can be done in three ways.

  1. Fostering self-explanations through structural manipulations

Atkinson et al., (2000) discuss various strategies that improve self-explanations:

    • Subgoal labeling (see image for an example).

Margulieux & Catrambone (2016)

    • Incomplete examples, followed by feedback

This is part of a process called ‘fading’, moving from complete worked examples to increasingly more incomplete examples (all the way to ‘just problem-to-be-solved’).

Van Merriënboer and Kirschner (2017, p. 80) discuss five scaffolding techniques (the optimal level of support and guidance for the learner) and types of fading.

Modeling cognitive strategies by thinking aloud.First make all decision-making, problem-solving, and reasoning processes explicit in detail, but then reduce the level of detail as learners acquire more expertise.
Modeling cognitive strategies by eye-movement modeling examples.First give video examples with dynamic information showing the eye-movement patterns of the experts, but remove the eye-tracking information at a later stage.
Providing process worksheets, guiding questions, or checklists.Slowly reduce the amount of (sub) phases, questions, and rules-of-thumb that are given to the learner.
Applying performance constraints.First block all learner actions not necessary to reach a solution, and continuously make more actions available to the learner.
Examples or parts of the solution.Work from case studies or fully worked examples, via complete assignments, towards conventional tasks (problems-to-be-solved). This fading guidance is also known as the ‘completion strategy’.
  • Integrated examples (those who prevent the split-attention effect, as explained in the first blog on intra example features).

2. Training self-explanations

A study by Bielaczyc, Pirolli, and Brown (1995) showed that explicitly training learners in self-explaining works. In the training they a) introduced and motivated self-explanations, b) learned from a model through a video, and c) verified the learners’ ability to provide self-explanations.

Renkl et al (1998) conducted a similar experiment and also showed that an explicit ‘self-explaining’ training intervention had a strong effect on self-explanation activities and learning outcomes (assessed through performance and solving transfer problems).

3. Use of social incentives

Perhaps counter-intuitively, Atkinson et al., (2000) describe multiple studies that found that, when learners are asked to (prepare for) teaching others, this doesn’t improve learning (so much for the stubborn Dale’s cone that refuses to go!). This doesn’t mean that teaching others isn’t useful (also see our blog here), but that there seem to be two factors that moderate the effect of teaching others and those are experience in teaching others (duh!) and prior content knowledge (double duh).

There’s one other variation of self-directed explanation that seems even more powerful than self-explanations. Siegler (2002) studied young children who were asked to self-explain either their own or another’s (the experimenter’s) answers to solve number conservation and mathematical equality problems. Interestingly, children who showed the most success in explaining the experimenter’s reasoning were also the ones who showed the greatest increases in generating correct answers on their own. Apparently, there’s an advantage to having to explain a variety of performance models (yours and others).

Roy and Chi (2005) suggest two ways to interpret Siegler’s results. One way is to say that his results are completely consistent with Chi’s previously discussed ‘repair view’. Explaining someone else’s reasoning, especially a more correct one, provides opportunities for comparing and contrasting the other person’s reasoning with your own. When you then spot a ‘conflict’, you will likely repair your own representation (Chi, 2000). A second interpretation is that explaining another person’s correct reasoning is similar to explaining a text sentence or passage. However, the advantage over explaining a text passage might be that a peer’s reasoning might be more transparent than a text’s. Roy and Chi conclude that “in any case, exposing a learner to multiple perspectives on a problem (or perhaps even multiple representations of a problem solution), either from a text or from another peer’s reasoning, seems to support effective explaining and thereby learning” (p. 14).

As we’ve seen in the last three weeks, there’s a lot to designing good worked examples but because they’re so effective, it’s worth the effort!


Atkinson, R. K., Derry, S. J., Renkl, A., & Wortham, D. (2000). Learning from examples: Instructional principles from the worked examples research. Review of educational research70(2), 181-214.

Bielaczyc, K., Pirolli, P. L., & Brown, A. L. (1995). Training in self-explanation and self-regulation strategies: Investigating the effects of knowledge acquisition activities on problem solving. Cognition and instruction13(2), 221-252.

Chi, M. T., Bassok, M., Lewis, M., Reimann, P., & Glaser, R. (1989). Learning from examples via self-explanations. Knowing, learning, and instruction: Essays in honor of Robert Glaser, 251-282.

Chi, M. T. (2000). Self-explaining expository texts: The dual processes of generating inferences and repairing mental models. Advances in instructional psychology5, 161-238.

Margulieux, L. E., & Catrambone, R. (2016). Improving problem solving with subgoal labels in expository text and worked examples. Learning and Instruction42, 58-71.

Renkl, A., Stark, R., Gruber, H., & Mandl, H. (1998). Learning from worked-out examples: The effects of example variability and elicited self-explanations. Contemporary educational psychology23(1), 90-108.

Renkl, A. (1997). Learning by Explaining–Or Better by Listening?. Paper presented at the Annual Meeting of the American Educational Research Association (Chicago, IL, March 24-28, 1997).

Renkl, A., Atkinson, R. K., & Maier, U. H. (2000). From studying examples to solving problems: Fading worked-out solution steps helps learning. In Proceeding of the 22nd Annual Conference of the Cognitive Science Society (pp. 393-398).

Roy, M., & Chi, M. T. (2005). The self-explanation principle in multimedia learning. The Cambridge handbook of multimedia learning, 271-286.

Siegler, R. S (2002). Microgenetic studies of self-explanation. In N Granott & J. Parziale (Eds.), Microdevelopment: Transition processes in development and learning (pp.31-58). Cambridge University Press.

Van Merriënboer, J. J., & Kirschner, P. A. (2017). Ten steps to complex learning: A systematic approach to four-component instructional design. New York, NY: Routledge.