The worked example effect indicates that learning by studying worked examples is more effective than learning by solving the equivalent problems. The expertise reversal effect indicates that this is only the case for novice learners; once prior knowledge of the task is available problem solving becomes more effective for learning. These effects, however, have mainly been studied using highly structured tasks. This study investigated whether they also occur on less structured tasks, in this case, learning to reason about legal cases. Less structured tasks take longer to master, and hence, examples may remain effective for a longer period of time. Novice and advanced law students received either a description of general process steps they should take, worked examples, worked examples including the process steps, or no instructional support for reasoning. Results show that worked examples were more effective for learning than problem-solving, both for novice and advanced students, even though the latter had significantly more prior knowledge. So, a worked example effect was found for both novice and advanced students, and no evidence for an expertise-reversal effect was found with these less structured tasks.

Nievelstein, F., Van Gog, T., Van Dijck, G., & Boshuizen, H. P. A. (2010). Instructional support for novice law students: Reducing search processes and explaining concepts in cases. Applied Cognitive Psychology. DOI: 10.1002/acp.1707Reasoning about legal cases is a complex skill that imposes a high working memory load, especially for novice students. Not only do novices lack necessary conceptual knowledge, searching through the information sources that are used during reasoning can also be assumed to impose a high additional working memory load that does not contribute to learning. Therefore, this study investigated the effects of supporting novice law students’ learning by (a) providing the meaning of important concepts in the case and (b) reducing the search process by providing a condensed (relevant articles only) rather than a complete civil code. Results show that performance on a test case (for which they had to use the complete civil code) was significantly better for participants who had used the condensed civil code during learning. Performance on a conceptual knowledge post-test was significantly enhanced when students had received the concept explanations during learning

Quantitative recidivism risk assessment can be used at the pretrial detention, trial, sentencing, and / or parole stage in the justice system. It has been criticized for what is measured, whether the predictions are more accurate than those made by humans, whether it creates or increases inequality and discrimination, and whether it compromises or violates other aspects of fairness. This criticism becomes even more topical with the arrival of the Artificial Intelligence (AI) Act. This article identifies and applies the relevant rules of the proposed AI Act in relation to quantitative recidivism risk assessment. It does so by focusing on the proposed rules for the quality of the data and the models used, on biases, and on the human oversight. It is concluded that legislation may consider requiring providers of high-risk AI systems to demonstrate that their solution performs significantly better than risk assessments based on simple models, and better than human assessment. Furthermore, there is no single answer to evaluate the performance of quantitative recidivism risk assessment tools that are or may be deployed in practice. Finally, three approaches of human oversight are discussed to correct for the negative effects of quantitative risk assessment: the optional, benchmark, and feedback approach.

is senior onderzoeker bij het Nederlands Studiecentrum Criminaliteit en Rechtshandhaving (NSCR) te Amsterdam. Prof. dr. F.L. Leeuw is directeur van het Wetenschappelijk Onderzoek-en Documentatiecentrum van het ministerie van Veiligheid en Justitie en daarnaast hoogleraar Recht, òpenbaar bestuur en sociaal-wetenschappelijk onderzoek aan de Universiteit Maastricht. Mr. drs. M.P.C. Scheepmaker is hoofdredacteur van Justitiële verkenningen.

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