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Abstract |
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![]() Figure 1. Desk view of an augmented reality interface. |
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![]() When the cognitive impact of two given interaction tools differs enough, different learning modes can be induced. Recent research shows that a user interface radically can influence the way people learn by means of a computer. Our research shows that these findings also apply to different interaction tools. An interaction tool, which induces a learning mode, also has consequences for the learning performance (Guttormsen Schär 1996; Guttormsen Schär, 1997b). For example, interaction tools that increase the cognitive load on students when learning (e.g. by demanding the users type commands in order to interact), can induce an explicit learning style. Interaction tools implying little cognitive load on the users, such as interaction by direct manipulation with graphical objects, tend to induce a more implicit, trial and error learning mode. The success of learning a certain task is closely linked to the chosen learning strategy, due to some strategies being more powerful than others (e.g. the success of implicit and explicit learning modes depends on the task saliency). Consequently, the interaction tool can play a major role in successful learning by supporting or inhibiting certain strategies. |
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![]() Table 1. Different forms and content of feedback. |
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![]() With increasing processing power, continuous feedback to the user is appealing. The effect of this kind of feedback was investigated in experiment 5, referred to below. An effect of feedback in this case not only would be related to aspects of the feedback itself, but also would indicate the degree to which users are influenced by apparently unimportant interface aspects in general. Two actual examples from commercial software illustrate different forms of subtle feedback. The two examples represent continuous and discontinuous methods of feedback respectively:
From a pedagogical point of view, different feedback content represents an important feature of a CAL system. Verbal forms of feedback communicate direct messages and are, therefore, closely linked to content feedback. Response feedback provides an evaluation of the appropriateness of an action. Approach feedback relates to the chosen task solving strategy. It is realistic for simple tasks with few parameters. It is more complex to design approach feedback for complex tasks. Motivation feedback is intended to increase the effort put into learning. Source of motivation is an individual matter; thus, many factors may have an influence. It is important to analyse carefully what motivates the target group of a learning program before deciding what to implement. Many effects can serve as cognitive feedback depending on the teaching strategy. Cognitive feedback is involved in all interactions with a system in which the students can observe the consequences of their own actions. In particular visual feedback in simulations can have great impact on the learning performance, by either explicitly or implicitly contributing to knowledge acquisition. Simulation promotes much indirect cognitive feedback; in fact, the idea is not based on instruction, but on letting the students implicitly unveil the learning goals. |
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Instruction:
Move the discs and discover the rules.
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![]() ![]() 3.2 Experiment 2 (Guttormsen Schär, 1996)
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![]() ![]() 3.3 Experiment
3 (Guttormsen Schär, Schierz, Stoll, & Krueger, 1997a)
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![]() Figure 3. Screen shot of ErgoLight. |
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![]() ![]() 3.4 Experiment
4 (Guttormsen Schär, 1997b)
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![]() Figure 4. Example of feedback by an extended history. |
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![]() ![]() 3.5 Experiment
5 (Guttormsen Schär, 1999a)
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4. Conclusions
Cognitive theory offers a background for explaining learning effects
in the HCI context. This perspective leads to a more differentiated
way of understanding HCI. A theoretical approach offers a model for
the impact of a given learning context. The human centred design approach
focuses more on direct cause-effect relationships measured by user preferences
or efficiency. In a CAL context, such measures alone are not sufficient
because they do not incorporate the quality of the achieved learning
performance. User-friendly interface design should, however, not be
neglected in CAL environments. In particular, experiment 5 showed that
an efficient and highly preferred interaction method (i.e. direct manipulation)
combined with discontinuous feedback resulted in the achievement of
both goals: high satisfaction and high learning performance.
The estimated usability of an interface depends on the selected measures.
A learning situation requires a special selection of measures. We have
demonstrated that "mono operation bias" is a serious threat to HCI research
related to CAL. Further, as an extension of the dominant research focus
on technology and multimedia, current CAL research should also take
the influence of the computer user-interface more into consideration.
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