Learning by writing explanations: Is explaining to a fictitious student more effective than self-explaining?

Explaining Effective Learning by Analogical Reasoning

Machine learning algorithms are usually considered as explicit learning strategies requiring large data samples. Contrary to these accounts, cognitive learning seems to be based on significantly less amounts of training data and occurs often in the form of implicit learning. In order to close this gap we propose to explain these discrepancies by a form of analogical learning, bridging these two...

Explaining Preferences, Preferring Explanations?

In this paper we study the possibility of providing causal explanations for preferred answer sets, as those obtained from logic programs with ordered disjunction (LPODs). We use a recently defined multi-valued semantics for answer sets based on a causal algebra and consider its direct application to LOPDs by several illustrating examples. We also explain the limitations of this simple approach ...

Learning by Explaining to Oneself

Are Self-explaining and Coached Problem Solving More Effective When Done by Pairs of Students Than Alone?

Although cognitive science has discovered several methods for increasing the learning of complex skills, such as physics problem solving, detailed examination of verbal protocols suggests there is still room for improvement. Basically, students do not always apply the meta-cognitive strategies that the instruction invites. For instance, when prompted to self-explain, students may still choose t...

Explaining Simulations Through Self Explaining Agents

Several strategies are used to explain emergent interaction patterns in agent-based simulations. A distinction can be made between simulations in which the agents just behave in a reactive way, and simulations involving agents with also pro-active (goal-directed) behavior. Pro-active behavior is more variable and harder to predict than reactive behavior, and therefore it might be harder to expl...