Cognitive abilities - Technology enhanced learning

Individual learners play a central role in a technology enhanced learning environment. Each learner has individual characteristics such as different Technologies linking Learning, Cognition & Instruction cognitive abilities, learning style preferences, prior knowledge, motivation, and so on. These individual differences affect the learning process and are the reason why some students find it easy to learn in a particular course whereas others find the same course difficult (Jonassen & Grabowski, 1993).

The context in which learning takes place also plays an important role. This learning context includes learning objectives, learning activities, learning assessments, the used technology or tools, information resources, and teachers, tutors or assistants. The learner with his/her individual differences as well as the mentioned aspects of the learning context can be seen as components of a learning system. Each of these components and especially the interaction between these components influences the learning process. For example, Gagné (1985) argued that an interaction between the learning objectives and the learning activities exists and that different conditions on the structure and kind of learning activities are necessary for different types of learning objectives. He identified five categories of learning, namely verbal information, intellectual skills, cognitive strategies, motor skills, and attitudes. For learning attitudes, persuasive arguments or a kind of role model are necessary. In contrast, to learn motor skills, an important condition of learning is to practise these skills. On the other hand, when learning verbal information like facts, no practices, arguments or role models are necessary.

Another example is the interaction between information resources and the individual differences of learning styles. The information resources can be presented in different forms such as text, images, animations, simulations, graphs, Technologies linking Learning, Cognition & Instruction and so on, and therefore matches or mismatches with each learner’s preferred way of receiving information. The better these aspects match, the better learning can take place. Furthermore, the information might be comprised of concrete material such as facts and data or the information might deal about more abstract material like concepts and theories. Again, matching or mismatching influence the learning process.

Many other links between the above mentioned components are investigated and influences on learning are elaborated. In this chapter, we focus on research dealing with the link between aspects of individual learners, in particular cognitive abilities and learning styles, how instruction can be designed in order to match or mismatch with these needs, and how these instructions can be supported by technology.

Concerning individual differences, a lot of research has been done about prior knowledge and its influence on learning. Jonassen and Grabowski summarized that prior knowledge is one of the strongest and consistent individual difference predictors of achievement (Jonassen & Grabowski, 1993). Although prior knowledge seems to account for more variance in learning than other individual differences, more recently educational researchers have focused on aspects of cognitive abilities and learning styles, their influence on learning, and also how they can be incorporated in technology enhanced learning.

Cognitive abilities and learning styles play an important role in education. For example, cognitive overload may hinder the process of learning and yield to poor performance. Regarding learning styles, Felder pointed out that learners with Technologies linking Learning, Cognition & Instruction strong preference for a specific learning style may have difficulties in learning if the teaching style did not match with their learning style (Felder & Silverman, 1988; Felder & Soloman, 1997). From theoretical point of view, we can therefore

argue that incorporating the cognitive abilities and learning style of students makes learning easier and increases the learning efficiency of the students. On the other hand, learners who are not supported by the learning environment may experience problems in the learning process.

Although these hypotheses seem to be intuitive and supported by educational theories, inconsistent results are obtained by studies dealing with investigating the effects on achievement when providing matched and mismatched instructions for learners with different abilities and preferences. As Jonassen and Grabowski (1993, p. 28ff) summarized, several reasons for such inconsistent results are known in the field of aptitude-treatment interaction (ATI) research. Limitations might include “small samples size, abbreviated treatments, specialized aptitude constructs or standardized tests, and a lack of conceptual or theoretical linkage between aptitudes and the information-processing requirements of the treatment”. An example for a supporting study is the study performed by Bajraktarevic, Hall, and Fullick (2003) showing that students attending an online course that matches with their preferred learning style (either sequential or global) achieved significantly better results than those who received a mismatched course. Another supporting example is the study by Ford and Chen (2001) where they investigated the performance of students attending a course that either matches or mismatches with their cognitive style (field-dependency or field-independency). Also in this  case, students who undertook the matched course achieved significantly better results than those who attended the mismatched course.

 In contrast, the study by Brown, Brailsford, Fisher, Moore and Ashman (2006) focused on the visual and verbal preference of learners. As a result they concluded that “it did not seem to matter whether a student was a visual or bimodal learner, nor if they were presented with visual, verbal or mixed representations of data” (Brown et al., 2006, p. 333). Another example for a study that did not yield significant results was described in Tillema (1982) and dealt with the serial and holistic cognitive style. These inconsistent results show that more future work is necessary. However, a lot of recent research has been done dealing with aspects of incorporating cognitive abilities and learning styles in technology enhanced learning systems. This chapter aims at providing an overview on these aspects. First, an introduction into cognitive traits and learning styles is provided, taking also into account instructional strategies to support specific cognitive traits and learning styles of students in educational systems. The next section discusses and gives examples of how cognitive traits and learning styles can be identified. Subsequently, the relationship between cognitive traits and learning style is discussed.