Time-On-Task

In cognitive research, the factor of time has a special function, as Kliegel, Mayr and Krampe (1994) describe by time-accuracy functions. The authors explain that these functions specify how much presentation time is needed to achieve a particular accuracy level. Two different time-accuracy functions based on negatively accelerated exponential functions in a diagram, where time is illustrated on the x-axis and correct response in percent is illustrated on the y- axis, can help visualize different purposes. Imagine two of these functions showing one function where time-accuracy is reached very early (a very rapid rise of the exponential function) in contrast to one function with a slower rise. The rapid rising curve would reflect less resources-demanding performance because less time is needed to reach the same accuracy. Differences between these curves would illustrate the differences between two tasks due to the type or complexity of cognitive processing involved, the differences between two persons or groups due to characteristics such as ability, and the changes within a person or

group due to learning or using a cognitive strategy, for instance. This paradigm developed by Kliegel et al. (1994) overcomes the traditional schism between performances assessed with latency and accuracy measures. This paradigm is especially useful in cognitive research, when minimalistic tasks in experimental conditions are introduced encompassing cognitive processes of scanning, episodic memory and figural reasoning.

In complex learning, for example learning from a multimedia presentation which includes pictures and texts presenting process and structural information of the content to be learned, time is an important component of the learning process. However, there exist different ways to reach a certain level of performance in complex learning, and the qualitative aspect of learning would be disregarded by just relativating the performance by time-on-task. An attempt to illustrate the relation between time-on-task and performance could be realized by adapting the concept of instructional efficiency developed by Paas and van Merriënboer (1993). Learning instructions are highly efficient if high performance was reached by little mental effort. This concept could be enhanced by the assumption that efficient instructional design should evoke high performance and low time-on-task values. Thus, the higher the performance and the least amount of time-on-task, the more efficient the instructional design is. This relative condition efficiency could also be represented in a diagram categorizing instructional conditions into high and low efficient designs by using a formula for distance: E = │R-P│/ √2, whereas E stands for Efficiency, R for mental effort and P for performance. The R could be replaced by T for Time-on-task and the thereby developed T, P coordinate system illustrates categorized efficient instructional designs.

However, by treating time-on-task as the only decisive variable to reject or adopt instructional designs for practical implications, would be a very hard criterion because the qualitative aspect of learning is not accounted for. In the case of the original instructional efficiency related to mental effort, the implication of Paas et al. (1993) is that multimedia designers should develop instructions, which engage the learner in conducive cognitive learning processes directing the learner to a higher learning outcome with minimal mental effort. However, Koch, Seufert and Brünken (2008) argue that this implication does not include the fact that complex learning sometimes needs deep processing which would produce a medium to high level of mental effort. This argument holds for time-on-task because deep processing sometimes needs more time. It is the goal of learning to reach a high level of understanding even when it is necessary to overcome some difficulties that are perhaps mentally more costly and time-consuming and also meaningful. This should be a new perspective on efficiency of instructional designs.

In a self-paced instructional design, the decision about how to use time seems to be completely dependent on the learner. However, one aspect of time could be influenced by instructional designers that is time as an economic resource. With the construction of learning material, a time-related decision has to be made by presenting the material successively or simultaneously. This decision has consequences for learning as Mayer (2001a) reports from many studies confirming the temporal contiguity principle, which says that learners perform better when corresponding words and pictures are presented simultaneously rather than successively. This effect is found for complex learning material, but does not hold for material that is based on very short segments. Thus, time should also be taken into account from instructional designers as an economic resource, which could be used efficiently.

In sum, time-on-task is one yet another factor, which influences complex learning especially when controlled by system-paced learning that seems to be an artificial condition, if the main interest is to investigate complex learning processes. This is why more and more experiments are driven under self-paced conditions and the interesting effects of multimedia research like for example the modality effect seem to disappear (Ginns, 2005). Some of the authors argue that this pacing effect could be due to the use of memory strategies like replaying or rehearsal (Harskamp, Mayer, & Suhre, 2007; Tabbers, 2002). A counter- argument could be found in seductive details research where the phenomenon appears that even under self-paced condition the learners do not overcome the power of seductive details showing the highest time-on-task values in combination with significantly lower performance in contrast to learners who did not receive seductive details. Overall, the literature review confirms that there are more system-paced studies than self-paced studies and no systematic comparison by one material was realized until now. Thus, further research is needed to investigate the power of time-on-task. Learning time is only one component to describe the outcome and conclusions from the factor of time on task and should be carefully drawn upon. However, it has already been concluded that time-on-task influences the learning process like prior knowledge, memory skills and spatial ability do.

2.5.5 Resume and Conclusions

The above selected factors: prior knowledge, memory skills, spatial ability, and time-on-task were described in order to prepare relevant issues for an examination of the additivity hypothesis of Cognitive Load Theory. Attention should be paid to all these factors in the operationalization phase of the studies of the present work. What could be concluded from the previous summary of these four factors is that they are related to each other. Imagine for

example a learner with high spatial ability. This learner also would show certainly a high level in working memory skills at least for working memory tasks requiring processes associated with the visuospatial sketchpad and therefore would not need as much time to process spatially complex information in an efficient way. In contrast, we could assume that a learner with very low values on a spatial ability scale would not be as successful in working memory tasks requiring processes associated with the visuospatial sketchpad and would therefore need more time to select, organize and integrate spatially complex information. Thus, the overall influencing power of prior knowledge and the two basical cognitive abilities memory skills and spatial ability that are required in multimedia learning as well as the variable time-on-task have been highlighted in multimedia research as main covariables for studies investigating instructional designs.