Example-based learning: should learners receive closed-book or open-book self-explanation prompts?

In learning from examples, students are often first provided with basic instructional explanations of new principles and concepts and second with examples thereof. In this sequence, it is important that learners self-explain by generating links between the basic instructional explanations’ content and the examples. Therefore, it is well established that learners receive self-explanation prompts. However, there is hardly any research on whether these prompts should be provided in a closed-book format—in which learners cannot access the basic instructional explanations during self-explaining and thus have to retrieve the main content of the instructional explanations that is needed to explain the examples from memory (i.e., retrieval practice)—or in an open-book format in which learners can access the instructional explanations during self-explaining. In two experiments, we varied whether learners received closed- or open-book self-explanation prompts. We also varied whether learners were prompted to actively process the main content of the basic instructional explanations before they proceeded to the self-explanation prompts. When the learners were not prompted to actively process the basic instructional explanations, closed-book prompts yielded detrimental effects on immediate and delayed (1 week) posttest performance. When the learners were prompted to actively process the basic instructional explanations beforehand, closed-book self-explanation prompts were not less beneficial than open-book prompts regarding performance on a delayed posttest. We conclude that at least when the retention interval does not exceed 1 week, closed-book self-explanation prompts do not entail an added value and can even be harmful in comparison to open-book ones.

Weighting features by the value displacement rebound

Learning from examples draws on similarity, a concept which formalisation leads to the notion of instance space. Continuous spaces are easier to embrace since, unlike discrete, they often can be seen as hyper-constructs of 3D. Unsurprisingly, the instance-based learning methods are more developed for continuous domains than for discrete ones. The value difference metric (VDM) is one of the few examples of metrics for discrete spaces. Mixed reports about utility of VDM exist. In this paper VDM is compared with another approach where data features are weighted by the Information Gain. Some vulnerabilities of VDM are identified. A weighting method, nothing like VDM, although inspired by the former, is proposed. The results are in favour of the new weighting scheme with illustration of utility for health diagnostics.

Illiberal Norm Diffusion: How Do Governments Learn to Restrict Nongovernmental Organizations?

Recent decades have witnessed a global cascade of restrictive and repressive measures against nongovernmental organizations (NGOs). We theorize that state learning from observing the regional environment, rather than NGO growth per se or domestic unrest, explains this rapid diffusion of restrictions. We develop and test two hypotheses: (1) states adopt NGO restrictions in response to nonarmed bottom-up threats in their regional environment (“learning from threats”); (2) states adopt NGO restrictions through imitation of the legislative behavior of other states in their regional environment (“learning from examples”). Using an original dataset on NGO restrictions in ninety-six countries over a period of twenty-five years (1992–2016), we test these hypotheses by means of negative binomial regression and survival analyses, using spatially weighted techniques. We find very limited evidence for learning from threats, but consistent evidence for learning from examples. We corroborate this finding through close textual comparison of laws adopted in the Middle East and Africa, showing legal provisions being taken over almost verbatim from one law into another. In our conclusion, we spell out the implications for the quality of democracy and for theories of transition to a postliberal order, as well as for policy-makers, lawyers, and civil-society practitioners.