An integrated model of multimedia learning and motivation.
Evidence from multimedia research is far from being conclusive because of producing confounding effects or of neglecting parameters. Motivation plays a major role in this shortcoming as traditional multimedia theory is mainly based on cognitive factors, widely ignoring the fact that motivation significantly influences learning resources. Within this article, several theoretical models are critically reviewed and an integrated model of multimedia learning and motivation is presented, which is based on current research in the field of educational psychology. The model can stimulate research, as it represents an expansion of the popular cognitive theory of multimedia learning from Mayer (2001). It integrates additional variables, such as mental resources management and motivational processing. The model can also provide instructional designers with a summary of main factors that have to be considered when developing multimedia-based learning environments.
Motivating the academically unmotivated represents one of the critical issues for establishing a multimedia-based life-long learning platform (Hidi & Harackiewicz, 2000). However, multimedia research has not yet faced the motivational challenge. A fundamental characteristic of multimedia research is the fact that research studies testing similar instructional elements show positive effects, others null effects, with some even showing negative effects (see, for example, the meta-analysis from Dillon & Gabbard, 1998 or from Liao, 1999). This is because of many contingent factors (multimedia input, cognitive processing, learner dynamics, etc.) that have been shown to moderate multimedia learning effects. In order to be able to determine multimedia effectiveness, a theory of multimedia learning has to be established. Such a theory should integrate and distinguish as much as possible factors that contribute to multimedia learning, but especially motivational factors (e.g., Jonassen & Land, 2000). At the moment, there are several approaches dealing with motivation in multimedia learning, which have to be evaluated in their capacity to stimulate theory and research in the field. These approaches are widely unknown in the field of multimedia research and come from Malone and Lepper (1987), from a combination of a model of motivation in self-regulated learning and the ARCS-approach (Keller, 1983, 1997, 1999; Rheinberg, Vollmeyer, & Rollet, 2000), from a model of integrated multimedia effects by Hede (2002), and finally from a model of a motivational expansion of Mayer's (2001) cognitive theory of multimedia learning.
THE INSTRUCTIONAL DESIGN APPROACH FROM MALONE AND LEPPER (1987)
Malone and Lepper (1987) identified four major factors: challenge, curiosity, control, and fantasy, which make a multimedia learning environment motivating. To be challenging, activities should be kept continuously at an optimal level of difficulty to keep the learner from being either bored or frustrated. To elicit sensory or cognitive curiosity in activities, one can use audio-visual devices or present information that makes the learner believe that his/her current knowledge structure is incomplete, inconsistent, or un-parsimonious. Activities should also promote a sense of control on the part of the learner, that is, a feeling that learning outcomes are determined by the learner's own actions. Finally, one can engage the learner in make-believe activities or fantasy contexts that allow the learner to experience situations not actually present, but intrinsically motivating.
The approach from Malone and Lepper (1987) represents an instructional design approach which is, to some degree, based on motivational theory, but formulated primarily as prescriptions for instructional designers. These prescriptions are related to multimedia as they include, for example, audio-visual strategies, in addition, they are comprehensive, exclusive, and concern motivationally effective parameters. However, this model hardly stimulated empirical research (e.g., Amory, Naicker, Vincent, & Adams, 1999). Also, this approach does not represent a theory of how elements of a multimedia learning environment are related to different variables representing more or less valid motivational processes within a learner and their effects on learning, especially when considering recent developments in learning theory, that is, self-regulated learning. It represents one of the major paradigms in learning theory and needs special attention because of the problem resulting from lacking capacities in self-motivation. Above all, the approach from Malone and Lepper (1987) only has a descriptive function: It summarizes and categorizes motivationally relevant factors in multimedia learning. But these factors are not related to each other on a common psychological theory. Only such a theory could give an explanative and conclusive basis about the main and interaction effects of the identified factors.
A COMBINED MODEL OF MOTIVATION IN SELF-REGULATED LEARNING AND THE ARCS-APPROACH
Deficits of the approach from Malone and Lepper (1987) can be reduced by combining a current model of motivation in self-regulated learning by Rheinberg, Vollmeyer, and Rollet (2000) with the A(ttention) R(elevance) C(onfidence) S(atisfaction)-approach from Keller (1983, 1997, 1999). This combined model describes an iterative process (Figure 1). A self-regulated learner finds himself attracted to different goals, then, through given information from a multimedia learning environment, the learner compares the different goals in respect to their related expectancies or incentives and selects one goal as intention for acting. After an intention is given, the learner starts activities to accomplish the goal, which is linked with this intention. To be successful, action control processes accompany the transformation of the intention into action. During acting, new experiences produce a new indefinite goal status, and so on. The iterative process of motivation directed to self-regulated learning will stop, when all given goals are reached and/or when no new goals appear. Within this combination of models, it is assumed, that within a first stage, a self-regulated learner finds him/herself in an indefinite goal status. This status is transformed into an action-guiding intention, when low situation-outcome-expectancies (SOE), high action-outcome-expectancies (AOE), high outcome-consequence-expectancies (OCE), and high incentives (I) for a particular goal are given. In a second stage, the resulting intention is transformed into a certain action, whereby the intention must be supported by action control (i.e., attention, encoding, cognition, emotion, motivation, and environment control mechanism). The self-regulated learner can be supported within the different stages by instructional strategies which are given within multimedia environments and which are proposed by the ARCS-approach. Instructional strategies concerning the confidence-parameter can be linked with SOE (i.e., the instructional strategy of attribution modeling) and with AOE (i.e., expectancy for success and challenge setting). For example, if learners are told that effort is necessary to be successful in learning, then SOE are kept low, because learners will not think that a given situation will lead to a desired outcome without any action. AOE represents the concept of "probability of success," which can be influenced by making learners aware of evaluative criteria and by providing multiple achievement levels. OCE and incentives are related to satisfaction-focusing instructional strategies dealing with equity (realizing consistent outcome-consequence relationships), or natural and positive consequences (for stimulating the perception of incentives). Attention influencing instructional strategies (i.e., perceptional and inquiry arousal, or variability) can be linked to attention control. Goal orientation as part of the instructional strategies related to relevance should have an effect on encoding and cognition control, because it provides learners with information about what is important for understanding and learning. Motive matching represents an instructional strategy for supporting emotion and motivation control, because learners are pointed to concentrate on their personal needs and wishes. Finally, instructional strategies enhancing familiarity can be related to environmental control, because environments, which are familiar to learners, need less cognitive effort (and action control) for handling them in comparison with unknown environments.
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This combined model must be criticized especially as it relates two models from different theoretical backgrounds and as it does not include, in a significant way, elements of multimedia environments (e.g., audio-visual elements).
On the one hand, the model of motivation in self-regulated learning is mainly based on a cognitive theory of motivation, whereas the ARCS-approach has more in common with a behavioristic view of motivation. On the other hand and although, the given instructional strategies are related to the elements of a motivational process in a more or less plausible way, there are no statements or axioms indicating in detail and with a causal intention, which specific cognitive processes related to the arousal and maintenance of motivation, are influenced by which instructional strategies. This problem might come from the fact that the instructional strategies are more general compared to the elements of the motivational process. This higher degree of generality depends on the goal of the ARCS-approach, namely to represent a program for solving motivational problems in educational practice, what needs general strategies, which can more easily be adapted to educational problems than specific strategies.
The second problem with the combined model comes from missing explicitly anchored multimedia elements. Within the ARCS-approach, multimedia elements (i.e., audio-visual components of instructional materials) are considered, but they are only related to the parameter of attention. However, multimedia elements can also enhance the degree of reality of a learning environment, for example, by including video-sequences of real life situations or realistic animations. Degree of reality can be used to stimulate also other parameters than those proposed by the combined model. For example, the degree of reality enhances the familiarity of the learning contexts and therefore, according to the combined model, also influences environment control as another part of action control. Such influences are not postulated in the given combined model, which causes particular problems in respect to multimedia effects (Brunken, Plass, & Leutner, 2002).
AN INTEGRATED MODEL OF MULTIMEDIA EFFECTS
A model prominently including elements of multimedia environments was presented by Hede (2002). It consists of several groups of elements (see Figure 2).
The first group of elements are those relating to the input of a learning process (i.e., the instructional material). The main input modalities are visual and auditory input. Visual input concerns text, pictures, diagrams, video, and animation. Auditory input consists of narration or commentary, instructions, cues, and music. Also, multimedia provides various degrees of learner control over these inputs by design features and links. It also provides learners with varying levels of interactivity controlled by the learner or the program.
The second group of factors refers to the processing of the information based on attention and working memory. Attention serves to focus the learner's concentration on the input. The processing of information occurs in working memory in different ways. First, dual-coding enables both auditory and visual inputs to be processed simultaneously. An additional factor is cognitive overload, which occurs when input exceeds the limited capacity of the working memory. Another factor is interference coming from one source disturbing semantic processing of information from other sources. The retention of information also depends on whether rehearsal takes place. Finally, an important factor in working memory is that of cognitive linking which connects verbal and visual representations. There are three factors relating to learner dynamics. The first is motivation with extrinsic and intrinsic motivational components. These factors influence cognitive engagement, which is the process whereby learners control by themselves their learning, which is related to time and effort. Also, learner style must be considered which influences the way people access multimedia (e.g., field dependence and field independence, surface processors or deep processors, or activity versus passivity of learners).
The final group of factors consists of four elements, (a) intelligence, (b) reflection, (c) long-term storage, and (d) learning. Intelligence is assumed multi-faceted involving many different forms of intelligence, which should be stimulated by a multimedia package. Reflection consists of evaluating the quality of thinking and learning. Long-term storage receives information from working memory, but also supplies working memory. In respect to stored knowledge, it can be distinguished between declarative, conditional, and procedural knowledge. Finally, learning comprises the comprehension of the material accessed through multimedia and the ability to recall and apply knowledge.
The integrated model from Hede (2002) represented a comprehensive model, which included many relevant factors in multimedia learning, especially motivational factors that are prominently anchored. However, this model is eclectic and also not parsimonious. The model from Hede (2002) is eclectic, because it integrates variables with different theoretical backgrounds without presenting innovative concepts that bring new perspectives into multimedia research. Such a concept could also help to reduce the many different factors to a number or a combination of factors that can more easily be tested within experimental multimedia research.
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THE MOTIVATIONAL EXPANSION OF MAYER'S (2001) COGNITIVE MODEL
The critical issues related to the approach from Hede (2002) can be avoided when considering a well-founded theory of multimedia learning and expand it with only few parameters. Such a basic theory is given by the cognitive theory of multimedia learning from Mayer (2001), which is based on the following assumptions: (a) working memory includes independent auditory and visual working memories; (b) each working memory store has a limited capacity, consistent with cognitive load theory; (c) humans have separate systems for representing verbal and nonverbal information, consistent with dual-code theory; and (d) meaningful learning occurs when a learner selects relevant information in each store, organizes the information in each store into a coherent representation, and makes connections between corresponding representations in each store. This theory stimulated a considerable amount of research leading to different relatively well-proven principles of multimedia learning (e.g., Brunken, Steinbacher, Schnotz, & Leutner, 2001; Chun & Plass, 1997; Mayer & Moreno, 1998; Moreno & Mayer, 1999; Plass, Chun, Mayer, & Leutner, 1998). The most important principles are: (a) split-attention principle (students learn better, when they do not split their attention between multiple sources of information); (b) modality principle (students learn better, when verbal information is presented auditorily rather than visually as onscreen text); (c) spatial contiguity principle (students learn better when onscreen text and visual materials are physically integrated rather than separated); or (d) temporal contiguity principle (students learn better, when verbal and visual materials are temporally synchronized rather than separated in time).
The main problem with the approach from Mayer (2001) comes from the fact, that this multimedia learning theory does not consider motivational aspects. Some elements of a multimedia environment can have also a noncognitive quality. For example, video information is evaluated as having a greater motivational value than audio information, because it integrates appealing dynamic pictures, colors, and so on, (e.g., Tang & Isaacs, 1993). Furthermore, audio or video information can have a motivational quality, because they give learning support and help therefore to reduce fear-of-failure (e.g., Cennamo, 1993). Also, Harp and Mayer (1997) distinguished between "cognitive interest" (based on structural coherence) and "emotional interest" (based on attention and curiosity), which can be triggered by different multimedia elements. In addition, approaches from Astleitner and Leutner (2000), Keller (1997), or Lee and Boling (1999) dealt with the motivational value of multimedia instructional elements. These motivational elements are important, because (a) motivation is influencing learning significantly; (b) motivational processes need memory resources and therefore inor decrease cognitive load; and (c) there is a more or less a direct connection between cognitive and motivational variables: especially attention represents an important element, both for cognitively and for motivationally driven models of learning. A possible solution to these problems might be to find and use a theory, which integrates cognitive and motivational aspects of memory usage and learning.
Evidence for Additional Motivational Parameters in Working Memory
An important assumption here is that not only words, pictures, their organization within mental models, and their integration with prior knowledge are part of the working memory, but also that there are other resources, which regulate or influence the learning process (e.g., Teasdale & Barnard, 1993; Wells & Matthews, 1994). Such elements are related to the concepts of costs and benefits analysis, action control, and noncognitive elements in memory.
Internal and external resources in learning are limited, so human learning is viewed as a process of resource management, which is controlled by motivational parameters (e.g., expectancies and incentives). Its objective is to optimize the use of available resources. Actions are optimally or maximally efficient, if spending of resources is minimized while gaining of resources is maximized (Klump, 1995). In general, failing to produce an intended action result does not entail any direct benefits. Necessary corrections may even entail additional resource consuming activities. Also, monitoring a task requires cognitive resources. The advantage of delegating control to lower-level (more automatic) processes means freeing capacity for planning and problem-solving. Automatic information processing is often closely related to motivational parameters, especially intrinsic motivation.
Kuhl (1985) postulated that not only words, images, and related structures are represented within the working memory, but also intentions, wishes, values, and so forth. Volition prevents competing intentions from becoming dominant before a current goal is reached. According to the model of action control by Kuhl (1985), volitional processes (or self-regulatory strategies) are based on the following components: (a) active attentional selectivity: it facilitates the processing of information supporting the current intention and inhibits the processing of information supporting competing tendencies; (b) encoding control: it facilitates the protective function of volition by selectively encoding those features of a stimulus that are related to the current intention; (c) emotion control: it inhibits emotional states that might undermine the efficiency of the protective function of volition; (d) motivation control: it refers to a feedback relation from self-regulatory processes to their own motivational basis; (e) environmental control: it describes a strategy that may develop from the more basic strategies dealing with emotion control and motivation control and consists of manipulating the environment; and (f) parsimony of information-processing: it is an aspect of volitional control that relates to rules for information processing.
Bower (1987) postulated that memory systems also contain emotion nodes, which are noncognitive and emotional. Once activated, such nodes influence the course of information-processing through the spreading of activation to associated nodes what can produce cognitive bias. Bias in memory can be accessible to consciousness, be under voluntary control, and affect attentional resource allocation, but, when automatic, that bias is unconscious, involuntary, and independent of the supply of resources of encoding. It should be emphasized that there may be a continuum of automaticity versus control, rather than a rigid dichotomy. Kluwe (1987) defined bias as (a) domain-specific knowledge was not available; (b) domain-specific knowledge was applied incorrectly; (c) domain-specific knowledge was not applied; (d) negative self-related thoughts in working memory; (e) inferences (thoughts irrelevant for problem solving), emotions (which can activate other memory contents, that is, state-dependent retrieval), and effort calculations (difficulties in solving a problem makes the learner to think about the relation of effort and expected outcome).
The Expanded Model
Cost and benefits analysis, action control, and noncognitive elements are part of the working memory processing and should therefore be considered within an expanded model of multimedia learning. To find an integrated and comprehensive model of multimedia learning and motivation, theoretical approaches especially from Mayer (2001, p. 44), which represents the main framework, and also from Brunken, Plass, and Leutner (2002), Keller (1983, 1997), Hoogeveen (1997), and Hede (2002) were considered.
Figure 3 represents the resulting model of motivation and multimedia learning. It is assumed that there are mental resources management and motivational processing influencing Mayer's (2001) mental activities (i.e., selection, organization, and integration) and mental models (i.e., verbal and pictorial). Mental activities depend on mental resources management which itself is influenced by motivational processing. Mental resources management is related to attention, engagement, and monitoring. Motivational processing consists of goal setting and action control.
Attention represents the capacity of the working memory that is devoted to a certain task within a given period of time. Engagement concerns the number of mental activities in relation to a certain task within a given period of time. Monitoring has the function of changing attention and engagement based on standard-related evaluations of the success of mental activities in relation to a certain task within a given period of time. Goal setting concerns thinking about the expectancies and values related to a certain task and selecting that task as intention for carrying out, which shows the most favorable combinations of expectancies and values. Action control can shield an actually given intention (for fulfilling a certain goal and the corresponding task) from alternative intentions.
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Based on the given assumptions of the expanded model, the following relationships between mental activities, mental resources management, and motivational processing can be expected: Available working memory capacity, the number of working memory activities, and monitoring the quality of working memory activities are related to the success of mental activities in different ways.
If there is no attention to a certain task, then no information processing concerning this task is possible. When attention shifts from one task to task-irrelevant aspects of a learning situation, then information processing concerning this task is interrupted or stopped, and given provisional results might been forgotten partly or completely. As a result of lacking attention, mental activities will not be completed (effect on completion).
If there is attention devoted to a certain task, but no or only little engagement, then the capacity of the working memory is not used sufficiently for information processing. It happens little or nothing within the working memory. None or only few mental activities are carried out, none or only few concepts or rules are generated or acquired, knowledge is not sufficiently structured or related to other parts of the memory, and so on. As a result of lacking engagement, a mental activity takes more time to be carried out (effect on time).
If attention and engagement are given, but only little or no monitoring, then the efficiency of the working memory activities is low. Monitoring should guarantee that the success of mental activities is continuously evaluated for what should result in changing attention and engagement in a way that working memory activities are optimized for achieving a given intention. As a result of lacking monitoring, a mental activity will not be carried out with high efficiency (effect on efficiency).
Goal setting has the function to start mental resources management in respect to a certain task which corresponds to a particular goal. If the checking of expectancies and values concerning a certain task leads to the intention to work on that task, then attention, engagement, and monitoring for fulfilling that task are activated. Goal setting is permanently taking place during learning using information from monitoring results. Action control has the function to keep mental resources management active for a certain task over time. Action control differs between action-oriented and state-oriented subjects. Action-orientated subjects carry out a greater proportion of their intentions, or facilitate decision-making by actively increasing their own motivation for the tentatively chosen alternative (e.g., Kuhl, 1987).
PREDICTIONS FROM THE MODEL
The presented model can stimulate different areas of multimedia research. First, research dealing with the motivational quality of multimedia elements has to be considered. For example, research from Malone and Lepper (1987), Means, Jonassen, and Dwyer (1997), Shellnut, Knowlton, and Savage (1999), Chang and Lehman (2001), or Song and Keller (2001) dealt with motivating features of multimedia environments. This research can be connected with the presented model in a way that elements of a multimedia environment are related to the postulated theoretical parameters. Such motivating features contain, for example, statements of the utility of the content (related to goal setting) or variability in audio and visual effects (related to attention).
Second, personality characteristics linked with the theoretical components can be considered and aptitude-treatment-interaction (ATI)-research can be undertaken on the basis of the presented model. Goal setting is, for example, related to success- or failure-oriented learners: Success-oriented in comparison with failure-oriented learners are more focused on realistic and reachable goals (Covington, 2000). Action control is different, based on the concept of action- or state-oriented learners: action-oriented learners use selective attention to focus on intention supporting parts of the environment, whereas state-oriented learners also focus on other parts of the environment (Kuhl, 1985). Both, goal orientation and action-control, influence the type of acquired knowledge (goal-related or incidental) from multimedia-based learning environments.
Third, empirical studies should especially focus on the question that considering motivational features could influence cognitive load within multimedia learning. One might expect, that implementing motivational features within multimedia environments increases cognitive load and therefore decreases learning relevant information processing. Multimedia learners who are confronted with motivational elements may be distracted from information processing activities related to cognitive learning. Negative motivational effects can be prevented with, for example, embedding information or with limiting information resources, exploration paths, and reducing information to be presented (Kashihara, Kinshuk, Oppermann, Rashev, & Simm, 2000).
Specific Predictions in Respect to Seductive Details and Motivational Adaptivity
Two main aspects will be important for future research and instructional design, one with questions dealing with the phenomenon of "seductive details" (Harp & Mayer, 1998), and one dealing with "motivationally adaptive" mechanisms (Song & Keller, 2001). "Seductive details," that are, interesting, but irrelevant adjuncts in instructional materials, distract a learner or disrupt the coherence of a learning process. It has to be clarified in future research to what extent motivational strategies in multimedia are seductive and how strategies can be implemented in multimedia without producing the risk of being seductive. There are different predictions (see Harp & Mayer, 1998): motivationally relevant instructional activities might increase motivation, but decrease learning by taking the learner's selective attention away from important information (distraction hypothesis), by interrupting the transition from one main idea to the next (disruption hypothesis), and by building a coherent mental representation but not of structurally important ideas (diversion hypothesis).
A second main research question should deal with the issue of how multimedia can be made "adaptive" to different types of learners and their needs. An aspect of adaptivity within multimedia can relatively easily be realized by using exercises and questions with different task difficulties (Astleitner & Keller, 1995). However, task difficulty is also an important variable in supporting learning and knowledge acquisition. In that respect, strategies for presenting and selecting tasks with varying difficulties have to be found, which assist each other in a complementary manner: Both, supporting cognitive learning and stimulating motivation must be achieved. Another open question concerns the aspect of the intensity of motivation-related actions within multimedia environments. Here, many different motivational tactics can be implemented to offer a broad range of motivating facilities (motivationally saturated multimedia), in a way that every learner can find something that fits to his needs. Also, another method of being adaptive might be to undertake pretests and deliver different types of motivating multimedia instruction based on such tests (motivationally adaptive multimedia). A too high intensity of motivational strategies can lead to negative effects on cognitive load and learning. Therefore, a multimedia learning environment with only few motivational strategies (motivationally minimized multimedia) can be suggested to be investigated by multimedia research.
The presented model has accommodated a wide range of research results that gives the model a more classificatory than an explanatory and unifying character. However, there are specific predictions from the model possible, which can be used as hypotheses for further multimedia research.
Information flow in the presented model is without arrows that indicate there are no clear causal linkages. Most of the linkages between components are assumed reciprocal. For example, motivational processing is assumed to influence attention. When there is a chance to achieve a certain goal with an action, then the attention is concentrated on that action. Also, attention plays a major role in action control and therefore attention influences motivational processing. Of course, all components and linkages are open to further conceptual refinement and experimental tests.
For some of the components of the model, it has to be stated whether they represent stable personality characteristics and/or instable parts of information-processing within the working memory. Attention is, on the one hand, something that needs some information-processing resources; on the other hand, it allocates such resources with the working memory. Cognitive engagement and monitoring take part within the working memory and do not represent a more or less stable personality characteristic. The main components of motivation, that is, goal setting and action control, are, on the one hand, embedded within processes which use working memory capacity, and are, on the other hand, closely connected to personality characteristics.
The presented model should stimulate research, as it has expanded the model from Mayer (2001) by five additional variables, such as attention, engagement, monitoring, goal setting, and action control. However, it should also provide instructional designers with a summary of main factors that have to be considered when designing multimedia-based learning environments. In a further step, elements of multimedia presentations have to be linked to the additional variables, so that multimedia effects on learning can be optimized.
This work was supported from Cornelsen-Stiftung fur Lehren und Lernen (Germany) within the project "Virtual Thinking School" (T066/11261/2001). This project was situated within the Center for Teaching-Learning-Research (ZLB) at the University of Erfurt (Germany).
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HERMANN ASTLEITNER AND CHRISTIAN WIESNER
University of Salzburg
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|Publication:||Journal of Educational Multimedia and Hypermedia|
|Date:||Mar 22, 2004|
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