The Role of Motivation in Knowledge Acquisition

The Role of Motivation in Knowledge Acquisition

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Regina Vollmeyer and Falko Rheinberg

Abstract

As our research is based on so called dynamic systems or microworlds we first describe and discuss this paradigm. We give a short overview on the huge variety of tasks that are subsumed under this label. In particular, we reflect on advantages of our biology-lab task. Subsequently, we introduce our cognitive-motivational process model which specifies variables that help to describe self-regulated learning. Initial motivation (probability of success, interest, anxiety, and challenge) affects performance through mediating variables, for example strategies and motivation during learning. Metacognition especially planning could be included as a further mediating variable. This theoretical model has already been studied with our biology-lab task (Vollmeyer & Rheinberg, 2006). In this study, motivation influenced performance (initial motivation and motivation during learning could both predict knowledge acquisition). Initial motivation influenced which strategy was chosen (more motivated participants chose more systematic strategies and were more motivated during learning). Participants with a systematic strategy and more motivation during learning performed better. With the aid of this study we discuss which aspects of metacognition could be integrated into the model without risking an overlap with the construct of motivation.

R. Vollmeyer (*) Institute of Psychology, Johann Wolfgang GoetheUniversity Frankfurt, P.O. Box 11 19 32, Frankfurt 60054, Germany e-mail: [email protected] F. Rheinberg University of Potsdam, Postdam, Germany

In the beginning of the 1990s, I started my research on knowledge acquisition in cooperation with Bruce Burns (University of Sydney, Australia) and Keith Holyoak (University of California, Los Angeles, USA). The aim of our research was to investigate in how people learn about relations between variables in an unknown system. This question is very important, as people have to act in complex environments, like the

R. Azevedo and V. Aleven (eds.), International Handbook of Metacognition and Learning Technologies, Springer International Handbooks of Education 28, DOI 10.1007/978-1-4419-5546-3_46, © Springer Science+Business Media New York 2013

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Iron

+2

Oxygenation

+6

Carbon

Aluminum

-.5

+4

Chlorine

Temperature –10%

Fig. 46.1 Structure of a biology-lab system

world climate or an oil rig explosion in the Gulf of Mexico. Such complex systems have in common that people do not know exactly which variables are affected if one condition is changed. In real life, it is not possible to run experiments and watch what happens. However, researchers have created so-called microworlds which make it possible to study how people acquire knowledge and/or solve problems in artificial microworlds. After having started studying cognitive processes, Falko Rheinberg and I integrated motivation into this research in the end of the 1990s. In this chapter, we will argue that integrating motivation into a theory of problem-solving represents progress, as we think that with this variable we can predict the performance in problem-solving tasks more precisely. However, before we introduce our theoretical framework (see “The CognitiveMotivational Process Model”), we describe the technology, which we call “microworlds” or complex dynamic systems, and its advantages.

Microworlds Osman (2010) gives a review of research on human behavior in microworlds or complex dynamic systems. With these microworlds, researchers studied mainly cognitive processes, for example, decisionmaking, implicit learning, or planning. Microworlds are used not only in psychology but also in other domains (e.g., economics, management, or engineering). Figure 46.1 illustrates a simple microworld that we used in our research on problem-solving (e.g., Burns & Vollmeyer, 2002; Vollmeyer, Burns, & Holyoak, 1996).

In a cover story, the participants were told that they were in a biology lab in which there is a tank with three water quality factors (oxygenation, chlorine, and temperature). These quality factors were the output variables of this system, affected by three input variables (iron, carbon, and aluminum). On each trial, a participant can change one, two, three, or none of the input variables. One output is relatively simple to manipulate because it is influenced by only one input (carbon → oxygenation). The other two outputs are more complex, because each is influenced by two inputs. One output (chlorine) is affected by two inputs, and the other (temperature) is affected by a decay factor (marked with a circle connected to the output) in addition to a single input variable. The decay factor was implemented by subtracting a percentage (10%) of the output’s previous value on each trial. Decay is a dynamic aspect of the system, because it yields state changes even if there is no input (i.e., all inputs are set to zero). The system is therefore complex in that it involves multiple input variables that must be manipulated to control multiple output variables. In the research in which microworlds are used to study cognitive processes, our biology lab is a rather simple system, as it includes six variables in total, three inputs and three outputs. There are even microworlds with more than 2,000 variables like the simulation of the town Lohhausen (Dörner, Kreuzig, Reither, & Stäudel, 1983). In this microworld, participants take over the role of the mayor of the town Lohhausen, and their goal was to take care of the future prosperity of the town over the short and long term. A 10-year period was simulated, and participants had eight 2-h sessions in total. However, it was not clear which variable was a good indicator for prosperity. Was it the town’s capital, the bank’s capital, or the factory’s capital? Were social indicators important (number of unemployed, number of apartments)? As there were so many variables, participants were in an uncertain situation. In smaller systems, it is possible to learn the structure of the whole system, whereas in microworlds with many variables and complicated relations between variables, participants are not able to detect all variables and their relations. These more complex microworlds have a high intransparency, and participants have to deal with uncertainty as

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699

Fig. 46.2 Screenshot of a biology-lab system

to how to solve problems with these systems. However, some microworlds mirror real-world problems (i.e., have higher ecological validity) as, for example, radar tracking (Kozlowski & Bell, 2006). It depends on the research question what kind of microworld researchers choose. An early overview of mainly European microworlds is presented by Funke (1991). Two different phases can be distinguished with such microworlds: (1) the exploration phase and (2) the application phase. If the microworld is presented with a nonspecific goal (e.g., “Learn how the inputs and outputs are connected”), learners explore the system. A screenshot of such a microworld is depicted in Fig. 46.2. Participants start with the actual state on Trial 1 in which they can choose numbers for the three inputs. The result of their manipulation is presented on Trial 2 under actual states. As the participants know that they have to find out the rules how the system works, they are free to choose values for the inputs. Through hypothesis testing, they need to formulate and test the rules. However, as soon as specific goals appear (e.g., “Bring oxygenation to 50”), learners need to apply their knowledge. If participants know the exact weights for each link, they can calculate the correct input. Another method is the means-ends analysis already described for problem-solving. Participants have

to enter values for the inputs and watch how close they are to the goals. They then push the inputs closer and closer to the goals. Therefore, with microworlds, it is possible to study different cognitive processes.

Advantages of Using Microworlds Microworlds have the advantage that they are presented on a computer, and therefore, it is easy to collect log files. These log files contain information about which inputs a participant manipulated. Microworlds also make it easy to exercise control over experimental procedures, as they can keep track of the amount of time each participant works with the system. Also, the presentation of questionnaires can be controlled by the system. Thus, it is unproblematic to compare participants in an identical situation. Another advantage is that prior knowledge about the content does not interfere with the learning behavior because no participant has ever learned how, for example, iron affects chlorine. Therefore, it is a complete new situation for a novice. Normally, prior knowledge is a good predictor for learning (Hattie, 2008). Thus, when running experiments with verbal material (e.g., as in a study with a hypermedia system on World


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War I, Vollmeyer & Burns, 2002), researchers need to take into account that learners with more prior knowledge acquire more knowledge about this material. In novel domains, control of prior knowledge is not necessary with microworlds. A third advantage is that besides log files other research methods can be used. For example, it is possible to use research methods to capture the participants’ thoughts with the thinking-aloud technique (Burns & Vollmeyer, 2002; Ericsson & Simon, 1993). This is particularly interesting because with think-aloud protocols researchers can study how participants plan and monitor their actions, which are aspects of metacognition (see below). The above-mentioned advantages render dynamic systems especially suitable for research questions that can be tested experimentally in the laboratory or in the classroom. To develop theories, such a system can be varied in small steps (e.g., task difficulty, goal specificity, implementing feedback). This may be the reason why researchers use them for many different research questions; one research question is self-regulated learning.

self-regulation explain the learning process, but each one specifies different variables. In line, we (Vollmeyer & Rheinberg, 1998, 2006) formulated a theoretical framework in which several psychological constructs describing learning can be put in order. The model emphasizes motivation and cognitive processes during learning, and therefore, we called it the cognitive-motivational process model.

The Cognitive-Motivational Process Model The starting point of our cognitive-motivational process model is that people have a certain initial motivation when they encounter a certain task, for example, a dynamic system as our biology lab. The initial motivation has an impact on the learning outcome, not in a direct but in an indirect way. Between initial motivation and the learning outcome are mediating variables. To be more concrete, we will explain the different parts of the model in Fig. 46.3.

Initial Motivation

Self-Regulated Learning In the last 15 years, several theories that describe self-regulated learning were put forward in educational psychology, but Rheinberg, Vollmeyer, and Rollett (2000) defined a self-regulated learner as someone who learns without being forced or without external tutoring. Other authors add components to describe what exactly such a learner is regulating. In their model, Schunk and Zimmerman (1994) and Zimmerman (1995) specified self-regulated learning strategies, selfmonitoring of effectiveness, and self-motivation. Boekaerts (1996) and Pintrich (2000) described how learners use cognitive strategies, metacognition, volition, and motivation to monitor their learning process. In the literature on organizational psychology, researchers more often use Kanfer and Ackerman’s (1989) model of self-regulation, which comprises self-monitoring, self-evaluation, and self-reaction. Thus, different theories of

As there are many different motivational constructs in psychology, we first had to reduce the number of motivational constructs. We did this on a theoretical and empirical level (see Rheinberg, Vollmeyer, & Burns, 2001) and postulated four aspects of initial motivation: (1) probability of success, (2) anxiety, (3) interest, and (4) challenge. Probability of success is an aspect discussed as early as the models of Lewin, Dembo, Festinger, and Sears (1944), as well as that of Atkinson (1957, 1964). It is also part of newer theories such as those by Bandura (1997), Anderson (1993), and Wigfield and Eccles (2002). Learners at least implicitly calculate the probability of success in that they take into account their ability and the perceived difficulty of the task. The second aspect is anxiety, which we partly interpret as fear of failure in a specific situation (Atkinson, 1957, 1964). However, this aspect is not intended to be the opposite of high probability

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Initial Motivation

Mediators

Performance

Probability of success

Persistence

Knowledge acquisition

Anxiety

Strategy systematicity

Goal achievement

Interest

Motivational state

Challenge

Metacognition (planning)

Fig. 46.3 Variables of the cognitive-motivational process model

of success, because it can be high for learners who, for example, are in a social situation in which they do not want to fail even though they expect to succeed. The third aspect is interest. For learning, the topic of the learning material is important as has been shown in theories on interest (e.g., Krapp, Hidi, & Renninger, 1992). If learners are interested, they have positive affect and positive evaluations regarding the topic. The last aspect we included in the model is challenge. Challenge is experienced among others if learners accept the situation as an achievement situation in which they want to have success (value component from expectancy-value models).

Mediators for the Inﬂuence of Initial Motivation on Performance Researchers often study the relationship between motivation and performance. However, they seldom explain exactly how positive motivation leads to a good learning outcome. Does this effect occur because motivated learners persist longer on the task? Or do they put more effort into the task to process the material deeper? To answer these questions, we identified some potential mediators, but of course the list is not exhaustive. We first describe which mediators we have studied, and then we will discuss how metacognition could be integrated as a mediator. Our research

inspired by this model will be presented in the section entitled “Our Results on Self-Regulatory Behavior Gained with Microworlds.”

Duration and Frequency of the Learning Activity An indicator for high motivation is a high persistence for a task, which is measured as time on task (i.e., duration). If initial positive motivation prolongs time on task, then people might acquire more knowledge. Indeed, researchers (e.g., Fisher, 1996; Helmke & Schrader, 1996; Volet, 1997) have found that the longer students study a certain topic at school the higher is their level of academic achievement. As it is not clear what people exactly do when they learn longer, time on task is a vague measure. However, the use of microworlds makes available a second indicator of persistence, namely, the total number of times with which participants manipulate the inputs. We studied persistence with the help of the microworld biology lab (Vollmeyer & Rheinberg, 2000). Systematic Learning Strategies Learning strategies are regarded as an important predictor of learning outcomes. Craik and Lockhart (1972) described why deep processing of the learning material leads to better knowledge than shallow strategies. However, it seems to be a problem to find indicators of deep processing or good strategies. For example, Artelt (2000) and

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Jamieson-Noel and Winne (2003) showed that there is no relationship between learners’ selfreported strategies and their actual use. Thus, researchers need other methods than self-reported questionnaires. More specifically, they need objective data to describe learners’ strategies. As an example, we describe how we operationalized strategy systematicity when using a microworld (e.g., Vollmeyer et al., 1996). Based on Tschirgi’s (1980) classification, participants who vary only one input at a time and hold the other inputs constant are called systematic (e.g., iron 10, carbon 0, aluminum 0). In contrast, participants could change all inputs haphazardly which is highly unsystematic (e.g., iron 10, carbon 10, aluminum 10). Using systematic strategies leads to more knowledge acquisition.

Motivational State During Learning As a third mediator, Vollmeyer and Rheinberg (1998) suggested the motivational state of the learner. Whereas the already described initial motivation refers to participants’ appraisals, affect, and interpretations of the whole situation before starting to learn, the motivational state refers to the participants’ motivation during the exploration phase. In our questionnaire, we ask how much fun people are having during learning and whether or not people clearly know what to do next. The latter aspect refers to expectancies: If learners do not know how to handle a task, they are less motivated and may give up. As people experience success and/or failure during learning, their motivational state can vary over the learning period, and therefore, it is informative to measure it several times. When using microworlds, it is possible to interrupt the learners after manipulating the system for a certain number of trials. Metacognition In most self-regulation theories, metacognition is an important variable (see “Self-Regulated Learning”). Although metacognition is a “fuzzy concept” (Flavell, 1981), in our own work on learning with microworlds, we followed Simon’s idea (1996) that metacognition is mainly used for executive control like planning (Vollmeyer & Rheinberg, 1999), a cognitive process which is mainly used in the exploration phase. In the area


of problem-solving, Davidson, Deuser, and Sternberg (1994) instead described four metacognitive processes: problem identification, representation, planning how to proceed, and solution evaluation. This definition is similar to the one used in Winne’s model of self-regulated learning (e.g., Winne, 2001; Winne & Hadwin, 1998). To point out that different researchers use the term metacognition to mean different cognitive processes, we want to refer to a recent study by Güss, Tuason, and Gerhard (2010). They studied how different cultures (the USA, Brazil, India, Germany, Philippines) solve problems with two microworlds. Therefore, they asked their participants to think aloud while they were a commanding officer of a fire brigade (microworld: WINFIRE) or a supermarket manager controlling the temperature of a cold storage depot (microworld: COLDSTORAGE). In the coding system for their thinking-aloud protocols, they had one category for metacognition. This category was used when general goals or strategies were expressed (“I’m going to prioritize the towns over the forest”). Hence, in this study on problemsolving, metacognition was restricted to planning. Another aspect of metacognition is monitoring, that is, controlling one’s learning. Nelson and Narens (1994) divided monitoring into three categories: (1) ease of learning, (2) judgments of learning, and (3) feeling of knowing. However, as Weinert (1984) had discussed earlier, metacognition, or more concretely, monitoring, and motivation are sometimes defined and operationalized the same way (e.g., ease of learning maps to probability of success, feeling of knowing maps to “motivational state”). Therefore, if both constructs (motivational state and metacognition) were included in one study, we would expect a correlation between these two mediators, because their operationalization overlaps. The same problem occurs with the construct of flow by Csikszentmihalyi (1975). Flow is a pleasant state, in which the following characteristics occur: (1) a challenge-skill balance, (2) merging of action and awareness, (3) unambiguous feedback, (4) concentration on the task at hand, (5) time transformation, and (6) fluency of action. We added the construct flow as mediating variable

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into our cognitive-motivational process model (Vollmeyer & Rheinberg, 2006). However, as motivational state and flow overlap in their definition as well as in their operationalization (i.e., among other items both questionnaires ask for self-reported ability), it is unclear how to disentangle these concepts. As flow is theoretically better defined, we decided to use only flow in further investigations. Adding metacognition (especially monitoring) into the cognitive-motivational process model would even enlarge the problem because all measures are self-reported and some questionnaire items express similar meaning. The aspect of planning, however, should capture new information about the cognitive process.

Performance The cognitive-motivational process model leaves open how many indicators for performance should be conceptualized. In terms of validity, it is better to use more indicators. When using microworlds, researchers can measure what the participants learned of the system’s structure in the exploration phase. Knowledge acquisition is gathered in that a diagram as in Fig. 46.1 without the links and weights is presented to the participants. After every round, they have to fill in which link exists and which weight they assume. The more they know about the links between the inputs and the outputs, the better is their knowledge acquisition. If specific goals are presented in the application phase (e.g., “Oxygenation should be on 50”), then goal achievement could measure the transfer of knowledge. The latter is true for participants who discovered the system’s links and the weights. They only have to use their knowledge to calculate the inputs.

Our Results on Self-Regulatory Behavior with Microworlds In a prior article (Vollmeyer & Rheinberg, 2006), we summarized our results of how motivational effects influence self-regulated learning. Therefore, a test of our cognitivemotivational process model has been published

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in detail. The published study was based on 109 students. In the current chapter, we present a short version with more emphasis on the results’ limitations and challenges. The general aim of our research was to demonstrate the importance of motivation in the learning process. Even before participants start acquiring knowledge about a microworld, positive initial motivation (high probability of success, high interest, and high challenge) should help in choosing a more systematic and maybe a more effortful strategy. Positive initial motivation should also support motivation during learning. A systematic strategy and positive motivation during learning should foster knowledge acquisition. We exploited the microworld’s advantages in that we could use the log files to define strategies. Thus, we had a category system consisting of three categories: the highly systematic strategy (change one input variable at a time, Tschirgi, 1980), the highly unsystematic strategy (change all inputs at a time), and a category in between (change two variables). We interrupted our participants three times (Rounds 1 to 3) after six trials to measure their motivation with a questionnaire. Finally, we had two measures of performance, namely, knowledge acquisition and goal achievement. For knowledge acquisition, we asked our participants to fill in the links and weights in the empty structure diagram (see Fig. 46.1) (measured three times during participants’ activity with the microworld). We then counted how many correct links and weights the participant had discovered. Goal achievement was the score calculated as difference between the goal state and the actual score of each output variable. With the help of a path analysis, we could support these theoretical assumptions through an empirical model (see Fig. 46.4). The structural equation model (see Fig. 46.4) shows that with high motivation (high interest, high challenge, and high probability of success), participants chose a more systematic strategy and experienced more positive motivation during learning. A highly systematic strategy and positive motivation during learning led to better knowledge acquisition. When participants had to apply their knowledge, good knowledge of the system’s structure and high motivation helped them reach goal states more accurately (high goal achievement).


704 Round 1

.42

interest

systematicity

Round 2 .58

systematicity

Round 3 .74

systematicity

.68 .46 challenge

probability of success

.81

.53

.66

.19

motivat. state

.51

motivat. state

.34 knowledge

.76

motivat. state

.68

knowledge

.22

.17

.18 .72

knowledge .63

goal achievement

Fig. 46.4 Path analysis of the task-specific model for the microworld biology lab

Limitations and Challenges Comparing the theoretical model in Fig. 46.3 and the empirical model in Fig. 46.4 makes it obvious that we could not include all possible mediating variables. To study the role of persistence, we would need a different design, in which it is left open how often a participant wants to manipulate our microworld. We realized such a design (Vollmeyer & Rheinberg, 2000) and found that indeed motivation affects persistence: Whereas initial motivation had an influence on persistence (i.e., more motivated participants were more persistent), the relationship between persistence and learning was disrupted because learners with more knowledge stopped earlier. However, learners with low knowledge but high motivation were more persistent and hence accumulated more knowledge over time. Thus, motivation had its most measurable impact on the learning outcomes of slow learners. More problematic is the inclusion of the mediating variable metacognition. Although it would be possible to have participants think aloud to measure their problem identification, planning, and so on, adding these aspects of metacognition into the path analysis would lead to methodological problems. For each additional variable, we need more participants to have enough power to run the path analysis. Another limitation of the path analysis is that it is based on correlations, and thus, we cannot claim causality. The model in Fig. 46.4 is based on the procedure that initial motivation was measured before participants entered their first change to an input. Strategy systematicity was coded from the first trial to the first interruption, and knowledge acquisition and motivational state were both measured during the

interruption. Therefore, the path model could be criticized on the grounds that the relation between knowledge acquisition and motivational state could be turned around. Even if we added only a single indicator for metacognition, we would enlarge the problem of causality. As the verbal protocols would be measured at the same time as the strategy systematicity, it could be argued that metacognition in the exploration phase (i.e., planning, solution evaluation) influences strategy systematicity, or vice versa. Depending on how we choose the direction of the arrows, we will obtain different effects on the dependent variables (i.e., knowledge acquisition and goal achievement). Even if we compared all plausible models and their model fits, we might find the best empirical model but still be left with doubts as to whether the best theoretical model was detected. To integrate metacognition into our model, it is necessary to first study all aspects that Davidson et al. (1994) mentioned for problem-solving via verbal protocols. Then maybe we could find a way to reduce the four aspects if they are correlated. Thus, one aggregated score for metacognition in the exploration phase could be added into our model. However, monitoring will be even more difficult to integrate methodologically because this concept is already close to motivational state (see above) and it is measured through a questionnaire at the same time point as motivational state and knowledge acquisition (problem of causality). Analyzing our data through structural equations offers another interesting question: What happens during the learning process? With the help of our microworld, we can study, with significant precision and in a longitudinal way, what participants feel after they manipulated the

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inputs for a certain number of trials. Did some participants lose interest? Or was there an increase in some participants’ belief that they could manage the task (i.e., probability of success)? For these questions, we also need more participants to study subpopulations. Up to now, we have not really analyzed our data as a process model for self-regulated learning. Self-regulation means that learners supervise their own learning and change their behavior and/ or their motivation if, for example, a strategy does not work or an unexpected result occurs. In the path analysis in Fig. 46.4, systematicity and motivational states between Rounds 1 and 2 are not as high as between Rounds 2 and 3. Between the first rounds it seems that some learners changed their behavior and/or their motivation. Although theoretically we expect a feedback loop, the empirical model cannot depict these assumptions. More analyses are needed to explain this self-regulation process.

Final Remarks So far we used microworlds only for research questions as opposed, for example, to diagnostic questions (Wirth & Klieme, 2003). We gained insight into how motivation affects the learning process. However, previously we started manipulating single variables of the model. For example, Vollmeyer and Burns (1996) presented correct, wrong, or no hypotheses about the structure of the system. The result was that even a wrong hypothesis helped students better predict the outcomes for the output variables. In a study by Vollmeyer, Püttmann, and Imhof (2009), we increased the (self-reported) probability of success through instruction related to stereotype threat (Schmader, Johns, & Forbes, 2008). In this study, we did not use a microworld but a physics task because we manipulated girls’ stereotype threat. (Instruction: “It is important to keep in mind that if you are feeling anxious while working with the program, this anxiety could be the result of these negative stereotypes that are widely held in society and have nothing to do with your actual ability to do well on the task.”) As expected,

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the girls’ probability of success increased. As a consequence, the girls started with better learning strategies and experienced more flow during learning than the girls without this instruction. Finally, they even acquired the same amount of knowledge as males did. If even such small manipulations can foster the formulation of hypotheses (Vollmeyer & Burns, 1996) or probability of success (Vollmeyer et al., 2009), the next step in our research could be to develop a program to support students’ self-regulated learning. Our results have demonstrated that a more positive motivation before starting to learn and more systematic strategies improved learning.

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