COGNITIVE TRAITS - Reasoning Ability

With respect to reasoning abilities, we can distinguish between inductive, deductive and abductive reasoning. In the following discussion, we will focus on inductive reasoning, since this ability is the most important one regarding learning. We shall also provide some discussion on deductive reasoning. Inductive reasoning skills relate to the ability to construct concepts from examples. When a student faces a complicated problem, he/she looks for known patterns, and uses them to construct a temporary internal hypotheses or schema in which to work (Bower & Hilgard, 1981). It is easier for students who possess better inductive reasoning skill to recognize a previously known pattern and generalize higher-order rules. As a result, the load on working memory is reduced, and the learning process is more efficient. In other words, the higher the inductive reasoning ability, the easier it is to build up the mental model of the information learned. According to Harverty, Koedinger, Klahr, & Alibali (2000) inductive reasoning ability is the best predictor for academic performance. For simulation based discovery learning, students are asked to infer characteristics of a model through experimentations by using a computer simulation and thus are asked to use their inductive reasoning skills. According to Veermans and van Joolingen (1998) simulation based discovery learning results in deeper rooting of  the knowledge, enhanced transfer, the acquisition of regulatory skills, and would yield better motivation. However, discovery learning does not always yield to better learning results. One of the reasons is that students have difficulties in performing the required processes. To improve the learning progress and support learners with low inductive reasoning abilities, Veermans and van Joolingen have designed a mechanism that provides advices based on the performed experiments in the simulation. This mechanism is integrated in SimQuest, an authoring system for simulation based discovery (van Joolingen & de Jong, 2003).

Considering again exploratory learning and the Exploration Space Control elements, for learners with low inductive reasoning skills, many opportunities for observation should be provided. Therefore, learning systems can support these learners by providing a high amount of well-structured and concrete information

with many paths. For learners with high inductive reasoning skills, the amount of information and paths should decrease to reduce the complexity of the hyperspace and hence enable the learners to grasp the concepts quicker. Moreover, information can be presented in a more abstract way (Kinshuk & Lin, 2003). Deduction is defined as drawing logical consequences from premises. An application for deductive reasoning is, for instance, naturalistic decision making (Zsambok & Klein, 1997), which deals with examining what people do in real world situations. It has been observed that experienced decision makers recognize the situation and associate an appropriate solution whereas unexperienced decision makers perform an unorganized and almost random search of alternatives. When it comes to complex situations, humans often fail in finding appropriate solutions. According to Dörner (1997) several reasons exist for such failures, for example, humans tend to oversimplify the mental model of the complex system, tend to be slow in thinking when it comes to conscious thoughts, or tend to ignore the possibility of side-effects. However, Dörner’s experiments showed that leaders from business and industry tend to make more effective decisions in complex situations. Therefore, he argued that the necessary behavior and skills can be acquired and learnt.