Intelligence / The general ability, and Pedagogy:

Out of the many features of general intelligence, two most important characteristics are: (1) its general component which operates across all contexts and domains, (2) its plasticity, or amenability to enhancement, or acceleration, in response to appropriate environmental influences and this plasticity goes as far as the brain itself. When we accept the fact that the functioning of the general intellectual processor in the mind can be improved by education, then the construct of intelligence becomes more acceptable by the researchers. It no longer has ultimate control over our students’ ability to learn. Thus, the educators now have it in their power to raise their students’ general cognitive ability, and so raise all of their academic performance. Recently a group of researchers (Adey, et al, 2007) have recommended for action plan for raising general cognitive ability in terms of certain principles as well as approach. These principles are: 1 Learning activities must be generative of cognitive stimulation, that is they must have the potential to create challenge rather than being comfortably within the reach of the learner’s current processing capability. 2 Learning should be collaborative in the sense that learners learn to listen to one another, to changing their positions. 3 We need continually to raise awareness in students of what may be abstracted from any particular domain specific learning, such as: a) factors in the concept, such as the organization or the quantity of information that cause difficulties in representing and processing; b) connecting the present concept to others already in their possession, as they differentiate it from other concepts, and even as they decide that some concepts need to be unlearned; c) control of the thinking and learning processes as such, thereby transferring mental power from the teacher to the thinker. Gradually students become self-reliant and self regulating rather than depending on the teacher. 4 The present learning experience needs to be connected to the concept space and the learning space of the past. Considering the above principles the researchers (Adey et al, 2007) have advocated for “Powerful learning environments”. This approach emphasizes the metacognitive, self-regulated and motivational aspects of the learning environment with a special emphasis on the mastering of the mental operations and concepts related to a particular domain. In this learning environment, students are led to plan their learning or problem-solving acts from the start, reflect on what they know, what they can do, and what they do not know about the problem and the domain, build relations between the problem and their prior knowledge and systematically and continuously monitor and regulate the process from the start through to the end. The focus is to guide the students to make full use of their intellectual abilities for learning in specific domains. However, it does not seem to pay sufficient attention to either the processing or the developmental constraints of learning at particular phases of life by particular individuals. Moreover, teaching for cognitive stimulation is far more demanding.

Recent Trend in Learning and Instruction: Cognitive Load Theory

Over the years the teaching learning practice has been evolving and the recent findings in cognitive science suggest that knowledge acquisition is a constructing process of building coherent mental representations within conceptual frameworks, and that such representations may be built from verbal and pictorial symbols (Bransford, Brown, & Cocking, 2000). In this regard multimedia offer many possibilities to facilitate knowledge construction (Mayer, 2005a, 2005b). Constructive theories of learning assume that students use their prior knowledge to construct new knowledge from the presented information that is relevant for their individual goals and which is based on their individual experiences. This is a widespread belief that knowledge construction is facilitated by technology using multiple channels of communication (see Verhoeven & Graesser, 2008). Now the question is how students acquire new knowledge in multimedia environments with multimodal (visual & auditory) presentation of information when working on multiple tasks and how cognitive overload can be reduced by attuning instructional design to the characteristics of the student. Thus, the cognitive load theory emerged in the field of learning and instruction (through multimedia mode).

Cognitive load has been defined as the load that performing a task imposes on the learner’s cognitive system. The components of cognitive load theory have been explained by number of researchers (Pass, Renkl, & Sweller 2003; Sweller, 2005).

Aspects of Cognitive Load Theory

This theory explains that learning processes that lead to knowledge construction and automation are determined by the goal, the required mental representations, the learner’s inventory of cognitive schemata and processing strategies. Performance of learning tasks and the associated learning processes impose a cognitive load on the learner’s working memory. The main focus of this theory is the distinction between intrinsic load, which is due to the task, and extraneous load, which is due to sub-optimal instruction. Intrinsic load involves element activity which is determined by the nature of the task demands in relation to the expertise and motivation of the learner. Instructional design may result in extraneous load (which is ineffective for learning) and in ‘Germane’ load (which is effective for learning). Extraneous cognitive load is defined as unnecessary extra load due to poorly designed instruction. Germane load is defined as load that contributes to learning such as, self-explanation. Cognitive load theory primarily focuses on how constraints of working memory have to be taken into account in order to optimize learning processes. This is concerned with techniques of adopting cognitive load by optimizing the use of working memory capacity in order to facilitate changes in long-term memory associated with schema acquisition. This theory has many implications for instructional design, such as the learning materials should keep the students’ extraneous cognitive load at a minimum and germane load at a maximum during the learning process. A recent reconceptualization of cognitive load theory by Schnotz and Kürschner (2007) suggests that germane load should not simply be maximized, but rather adapted to the intrinsic load of the learning task within the constraints of working memory.

Now the important question is how cognitive load influences knowledge construction in interactive learning environments. Interactive knowledge construction is normally facilitated in an environment that stimulates meaningful, social and strategic learning processes (Bransford et al, 2000; Verhoeven et al, 2006). Meaningful learning presupposes that students attend to the essential aspects of the presented material, organize it into a coherent cognitive structure, and integrate it with what they already know. Social learning occurs when the students learn by observing teachers, tutors, or other students and when they receive feedback on their own activities. Strategic learning occurs when students can identify and apply, consciously or unconsciously, the intelligent procedures, processes, skills and strategies that help them to master the learning material and to transfer these strategies from one situation to another. Information Communication Technologies (ICT) are often expected to facilitate knowledge construction (Graesser, Chipman & King, 2008; Jonassen, 2004; Mayer, 2005a), but how learning environments can be designed to optimally facilitate students’ knowledge construction and elaboration. A common way to ground learning in meaningful context is to anchor the learning experience in an information rich, coherent, realistic, problem scenario (Leu & Kinzer, 2000). These environments with anchored problem-based learning provide an authentic context for students to identify and define problems, to execute strategies to solve the problems, to specify reasons for attempted solutions, and to observe results.

Research on multimedia comprehension has evolved various models in which cognitive, memory-based, and constructive views are integrated. The memory based view considers comprehension as a product of processing of explicitly presented information, whereas constructivist theories emphasize the roles of world knowledge and inferences (Verhoeven & Perfetti, 2008). The point of debate is how multimedia processing takes place and how students learn to develop multimedia comprehension skills. The comprehension of multimedia demands specific strategies of information utilization and is highly vulnerable to goal competition and task difficulty (De Stefano & LeFevre, 2007). The cognitive theory of multimedia learning (Mayer, 2005b) is based on the idea that there are separate processing systems for both kinds of information, such as verbal and visual channels in the working memory with limited capacities. The integrative model of text and picture comprehension assumes channels on two different levels. On the perceptual level, the model includes sensory channels (auditory & visual), and on the cognitive level, it assumes representational channels, namely a descriptive channel and a depictive channel in working memory (Schnotz, 2005). Mayer (2005a, 2005b) argues that a higher quantity of learning (i.e., capacity to transfer what is learned to new situations) is attained when text and pictures are presented in an auditory – visual mode as opposed to a visual-visual mode (Schnotz, 2005). The additive learning effect of pictures accompanying oral or written text is referred to as the multimedia effect (Mayer, 2005b). For hypermedia comprehension, the student must combine the meaning of each unit with the message accumulated up to that point on the basis of prior units and their mutual links. Knowledge integration involves a large number of component skills that are not always adequately covered in instructional design. One such skill is the chunking of multiple information elements into a single unit or into cognitive schemas that can subsequently be automated and stored in long-term memory. The information that becomes integrated may stem from different information sources such as text and pictures. These integrative processes may impose high working memory load on the student’s working memory (Paas et al, 2003). The review of recent studies concludes that the knowledge base in learner long-term memory (LTM) provides executive guidance in the process of knowledge elaboration. Accordingly, the role of external instructional guidance could be described as providing a substitute for missing LTM knowledge structures in a schema-based framework for knowledge construction and elaboration. It is also argued that adaptive learning environments based on rapid diagnostic methods could provide instructional support at different stages of knowledge elaboration in order to optimize cognitive load. Continuous balancing of executive function is seen as essential for optimizing cognitive load by presenting required guidance at the appropriate time and removing unnecessary redundant support as learner proficiency in a domain increases.

Moreover, learning outcomes are dependent on personal characteristics also such as prior knowledge, motivation, and perspective-taking. Furthermore, it appears to be mediated by task demands, which are the result of instructional design. Concept map structures and interactive animations in the instructional design can be seen to impact the learning outcomes. Finally, the success of learning is also clearly related to environmental factors such as interactivity, locus of control, opportunities for collaboration etc.. Thus, in order to optimize cognitive load adaptive learning environments should be implemented in which task demands and instructional support levels are attuned to the expertise and memory capacities of the individual learner (Salden, Paas, & Van Merriënboer, 2006) Based on the re-analysis of these above factors, Schnotz and Kürschner (2007) have also identified the need for research on more sensitive ways of assessing learner characteristics, both prior to and during instruction, in order to understand learning processes and outcomes. The same learning environment is differentially demanding and produces different results depending on characteristics of the learners, most importantly their knowledge in the task domain. Goldman (2009) has also indicated that to optimize learning outcomes, theories of instructional design and learning need to be more adaptive and reflect the nuances of interactions among learners, tasks and instructional supports. Researchers have extensively worked on ‘Cognitive Load Theory’ (CLT) in order to contribute to a global understanding of how individual, task and environment variables interact in shaping the learners’ activity and the associated cognitive load. These messages have lots of implications and guidelines for instructional designers. Such as, Kalyuga (2009) demonstrates that task instructions have to be carefully tailored to fit the learner’s level of prior knowledge. Segers and Verhoeven (2009) suggest that “a layer of structure between the child and the Web is a useful addition to education”. Amadieu et al.’s (2009) results point to the need to design content representations that are easy to interpret and to use (as apposed to complex / confusing network concept maps). Similarly, Schnotz and Rasch (2009) show the importance of designing visualizations that facilitate the processing of contents in a way that is consistent with task demands. Scheiter et al. (2009) illustrate the need for flexible environments that will accommodate students’ strategies. Moreno’s (2009) conclusions support the view that collaborative scenarios should be kept simple and should not bypass students’ individual work on the subject-matter. Another important aspect of this research domain is the novelty and versatility of the tools, representations and learning contents that are presently being investigated.

Beyond the level of knowledge construction, knowledge elaboration is a process of using prior knowledge to continuously expand and refine new material based on the processes like organizing, restructuring, interconnecting, integrating new elements of information, identifying relations between them and relating the new material to the learner’s prior knowledge. These processes are essential for meaningful learning as they allow the learners to organize knowledge into a coherent structure and integrate new information with existing knowledge structures. According to cognitive load theory, two key functional components of our cognitive architecture are responsible for these processes (Sweller, 2003, 2004; Van Merriënboer & Sweller, 2005). One is our long-term memory (LTM) the permanent store of organized information and the other is working memory (WM), the immediate storage of information, at hand and its processing. The knowledge structures in LTM are essential for preventing working memory (WM) overload and for guiding cognitive processes. Accordingly, the role of external instructional guidance in the process of knowledge elaboration could be described as providing a substitute for missing LTM structures. Thus, knowledge elaboration processes require executive guidance that is shared between the learner and instructional means (or another expert). Specifically, three processes are identified: