Construing and testing explanations in a complex domain

摘要

Explanations were construed for an expert system in the domain of protein purification and based upon the multiple-explanation construction model (MEC model). Various explanations were construed covering different relevant aspects of the explanation space, expert-level explanations (quantitative representation), low-level explanations (qualitative representation), grounds explanations (background knowledge) and backing explanations (general abstract principles). These were tested on laboratory staff working at Pharmacia LKB Biotechnology AB in Uppsala, Sweden. The variables being tested were learning, understanding, usability, and novelty of the explanation types. The results indicate that the model is valuable in construing explanations with different “knowledge levels” with the purpose of fulfilling the needs of experts as well as “less-experts” covering different important aspects of the explanation space. In the context of learning, the results show that experts prefer expert-level explanations and low-level explanations whereas less-experts prefer a combination of all explanation types. A multiexplanation perspective has to be taken, where explanations covering different aspects of the explanation space on different levels have to be available to less-experts to facilitate learning from explanations in a specific complex domain. These results can have strong implications for learning, for example in the context of computer-supported education.