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How should we go on with Cognitive Load Research, when considering all results, conclusions and implications of the present work? In sum, the implications for cognitive load research, for research on modality and seductive details effects as well as practical implications all point to the need to investigate the following three aspects. First, we should invest in the development of valid and sensitive methods to measure total cognitive load and its load types. Second, we should focus on measuring different levels of processing, for example by the differentiation of learning scales. Third, we should invest more in motivational aspects and the functions of arousal and affects. In addition, I recommend conducting Aptitude-Treatment-Interaction studies with possible relevant aptitude variables to

get further insights into all three aspects listed above. How we should invest in these three aspects and ATI-studies is presented in the following lines.

The development of valid and sensitive methods to measure total cognitive load and its load types has already begun with the first versions of Cognitive Load Theory (see Moreno et al., 2010). The options of measuring cognitive load classified by objectivity and causal relationship are summarized by Brünken et al. (2003; see Chapter 2.6), who recommend objective measures. However, the most frequently used method in Cogntive Load Research is still the subjective rating scale of Paas (1992). Disadvantages of this method are more and more obvious by the results of current studies. Especially the question if learners are able to estimate their mental effort. Moreover, if they are able to measure their mental effort, how can we compare subjective ratings in our experiments with between-subject designs? Many studies were conducted with other methods such as the dual-task method (Brünken et al., 2002; Brünken et al., 2003, 2004; Chandler et al., 1996; Marcus et al., 1996; Renkl, et al. 2003; van Gerven et al., 2002) or eye-tracking (Jarodzka et al., 2010; Recarte et al., 2003; Van Gog et al., 2010; Scheiter et al., 2009). The dual-task method is clearly associated with cognitive load in contrast to eye-tracking, which does not provide a strong causal relationship to cognitive load. Nevertheless, the recently developed method to measure beside the movement of pupils also its dilation, which is a more reliable and valid estimate of individual cognitive load (Clark et al., 2010; see also Chapter 2.6) should be the most frequently used method in future research. The questions about how we could determine extraneous load factors in a quantitative and/or qualitative way to be able to introduce sensitive measures for this specific load type could perhaps be answered by using this new method of pupillometry. The other questions about where do we estimate the limit of germane cognitive load, if there are any limits, and how we can clearly determine and measure cognitive overload could be investigated with this recently developed and highly recommended method as well.

Moreover, we should focus on measuring different levels of processing, for example by the differentiation of learning scales. The recommendation to measure not only retention performance as an indicator for learning success, but also transfer performance has been supported by many studies (see for example the metaanalysis of Ginns, 2005). However, we could gain more information by precisely differentiating between retention and transfer performance, as well as between the performance of remembering process and structural information and the according transfer performances. The options to differentiate between these performance levels is highly dependent on the given task. Thus, we should not only use complex tasks, defined by their element-interactivity (van Merrienboer, 1997), but also tasks

that include structural as well as process information, requiring mental animation for comprehension. In addition, the argument that retention is a prerequisite for transfer is not always valid, as in some cases it is possible to understand the whole process of a given task without explicit knowledge of certain terms, by which retention performance is often operationalized and measured. This is only one method of how we are able to take different levels of processing into account. Further research should invest in the development of other methods to clearly differentiate between qualitatively different processing levels. New methods of measuring total cognitive load or different load types in combination with reliable and valid methods of measuring different processing levels will provide further insights into the relation among the three load types, working memory capacity and learning.

In addition, in future research on cognitive load, we should consider motivational aspects. The individual level of motivation before, during and after the lesson provides information about how instructions should be designed to foster deep processing and the interest to learn more about the given task. Learners need to become motivated to make full use of their cognitive resources in productive ways to reach their best performance (Moreno, 2006). Thus, we should try to confirm that the instructions in use are not only resulting in a high learning performance, but also in higher motivation of the learner. By measuring arousal, affects, moods or motivation during learning, we will gain relevant information about the relation between motivation, cognitive load and learning.

Finally, Aptitude-Treatment-Interaction studies serve to uncover interactions between our found effects of factors such as modality, seductive details, support for coherence formation or mental animation and certain aptitude variables, which are associated with cognitive load. Associated variables are variables such as working memory capacity, memory capacity and specific memory skills. Seufert et al. (2009) give an example on how to do so and how to analyze these data by regression analysis with the “re-centering” method recommended by Aiken and West (1991). By conducting ATI-studies, we will gain more information about which aptitude variables are responsible for high cognitive load and intensive learning. In addition, this could be useful for practical implications, especially with respect to adaptive learning-systems that take prerequisites or characteristics of learners into account to foster positive cognitive load during learning.

Further research, which accounts for these three aspects of valid and sensitive methods to measure cognitive load, different levels of processing and motivational functions will contribute to moving Cognitive Load Research in a constructive direction. Such contributions and the investment in more ATI-studies will perhaps result in a more recent and less

simplistic version of Cognitive Load Theory, which is not based on the additivity hypothesis that has been falsified by the present work. As „…we should concede as cognitive scientists, that valid criticisms can be raised against any existing theory of cognition and that such criticism is essential to progress.” (Moreno, 2006, p. 179), I am closing the present work full of valid critiques with these lines and looking forward to observe and shape the next steps of progress in Cognitive Load Research.