Variety way of learning

Children learn in a variety of ways. Why are some more successful as learners than others?

Motivation has been conceptualised in varied ways including inner forces, enduring traits, behavioural responses to stimuli, and sets of beliefs and affects.

Many early views linked motivation with inner forces: instincts, traits, volition, and will. Behavioural (conditioning) theories view motivation as an increased or continual level of responding to stimuli brought about by reinforcement (reward). Contemporary cognitive views postulate that individuals' thoughts, beliefs, and emotions influence motivation.

Motivation is a process rather than a product. As a process, we do not observe motivation directly but rather we infer it from actions and verbalizations.

Motivation involves goals that provide impetus for and direction to action. Cognitive views of motivation are united in their emphasis on the importance of goals. Goals may not be well formulated and may change with experience, but the point is that individuals are conscious of something that they are trying to attain or avoid.

Motivation requires activity-physical or mental. Physical activity entails effort, persistence, and other overt actions. Mental activity includes such cognitive actions as planning, rehearsing, organising, monitoring, making decisions, solving problems, and assessing progress. Most activities that students engage in are geared toward attaining their goals.

Finally, motivated activity is instigated and sustained. Starting toward a goal is important and often difficult because it involves making a commitment and taking the first step. But motivational processes are critically important to sustain action. Many major goals are long term, such as earning a college degree, obtaining a good job, and saving money for retirement. Much of what we know about motivation comes from determining how people respond to difficulties, problems, failures, and setbacks they encounter as they pursue long-term goals. Such motivational processes as expectations, attributions, emotions, and affects help people surmount difficulties and sustain motivation.

In order to uncover the main currents which underlie and shape the field of motivation, there are challenges that researchers have been confronted with; consciousness versus unconscious, cognitive versus affect, reduction versus comprehensiveness, parallel multiplicity, context and time (D?rnyei, 2000).

The first challenge conscious versus unconscious asks the question how conscious the individual is of their actions. In a review of the conscious/unconscious issue, Sorrentino (1996) highlights the importance of non-conscious forces and argues that behaviour can happen without any reference to conscious thought. Furthermore, humans do a lot of things as a matter of routine, and such relatively automated or habitual actions are often not under direct motivational control (e.g. most people do not make a conscious decision, before brushing their teeth on a morning). However Bandura (1991) explaings that most human behaviour is activated and regulated over extended periods by cognitive mechanisms. I will take the view of Bandura in this essay that most of the significant thoughts and feelings that affect learning achievement in prolonged educational situations are conscious and known by the learner. I do, however acknowledge that this stance may 'suffer from a paucity of emotionality and a surfeit of rationality' (Berliner, 1989: 330).

The challenge of cognition versus affect, no longer views motivation as a reflection of certain inner forces such as instincts, drives and emotional states; nor is it viewed in strictly behavioural terms as a function of stimuli and reinforcement. Approaches now place the focus on the individual's thoughts, beliefs and interpretational processes that are transformed into action. Also emotional experiences play a very important role in shaping human behaviour, and most comprehensive overviews of motivation recognise this influence. There have been several attempts to account for affect and cognition in unified frameworks (e.g. most notably in attribution theory; Weiner, 1986); indeed as Ford (1992) summarises, the integration of emotional theories into the mainstream of motivational research is clearly one of the major priorities of motivational scholars.

A striking feature of all mainstream motivation theories is the lack of comprehensiveness. The number of potential determinants of human action is very extensive, a great deal of effort in motivation research has focused on drawing up reductionist models. Motivational psychologists have to decide which factors to assign a key role in their theories and what kind of relationships to specify between the selected factors. While the practice of mapping the multitude of motivational influences onto reductionist constructs may be appropriate from a theory-building perspective, the only way to do this effectively is by narrowing down the scope of behavioural events the theory is concerned with to a fairly homogenous set, which may be insufficient to address complex, real-world problems effectively. Whilst a specific theory may be perfectly adequate to explain the motivational basis of a certain, well defined set of behaviours, it may be inappropriate to account for the intricate motivational life of actual classrooms.

A particular motivation theory is successful in explaining and predicting a specific course of action, the typical implication is that the actional process in question occurs in relative isolation, without any interference from ongoing behaviours in which the actor is engaged. Unfortunately, real life is in discordance with such neat theories because this assumption of isolated action is rarely valid in the strict sense. Although it is true that people pursue only a limited number of actions at a time, such as a new action may be initiated while the success of the previous is still being evaluated. Student motivation and achievement is the product of a complex set of interacting goals and intentions. Therefore, a central issue in analysing student motivation is to account for the interplay of the learners' simultaneous focus on a number of different but interacting goals and activities. Very little research has been done to examine how people deal with multiple actions and goals, how they prioritise between them and how the hierarchies of superordinate and subordinate goals are structured. Boekaerts has proposed a pioneering action hierarchy framework for studying the complexity of student motivation, but, as she concludes (1998: 21), 'such research is still in a rather preliminary stage'.

Motivational psychology has traditionally adopted an individualistic perspective in that it has typically concentrated on the individual in order to explain why the particular person behaves as he or she does. Humans are social beings and human action is always embedded in a number of physical and psychological contexts, which considerably affect a person's cognition, behaviour and achievement. The individualistic perspective of motivational psychology does not lend itself easily to account for the contextual influences stemming from the sociocultural environment. Meeting this challenge requires more than simply adding a few situational factors to existing theories; rather, it necessitates the combination of the individualistic and the societal perspectives.

Most theories imply that motivation is a relatively stable emotional or mental state, time is relevant to motivation constructs in at least two crucial areas. Motivation to do something usually evolves gradually, through a complex mental process that involves initial planning and goal setting, intention formation, task generation, action implementation, action control and outcome evaluation. These different subphases of the motivation process may be associated with different motives. Ignoring time can result in a situation when two theories are equally valid and yet contradict - simply because they refer to different phases of the motivation process. Secondly when we talk about sustained, long term activities, motivation does not remain constant during the course of months or years. Rather, it is characterised by regular (re)appraisal and balancing of the various internal and external influences to which the individual is exposed. Even within a single course, most learners experience fluctuation in their enthusiasm/commitment, sometimes on a day-to-day basis.


Keith Mitchell's perceptions of his students exemplify our intuitive understanding of the role of motivation in classroom learning and performance. Motivation can affect both new learning and the performance of previously learned skills, strategies, and behaviours. Activities such as drills and review sessions involve performance of previously learned skills, but most class time is spent learning facts, beliefs, rules, concepts, skills, strategies, algorithms, and behaviours.

As an example of the effect of motivation on performance, suppose that Keith tells his class to complete some review material and that the students, being less than enthusiastic about this assignment, work lackadaisically. To boost students' motivation Keith announces that they will have free time as soon as they complete the assignment. Assuming that the students value free time, we would expect them to quickly finish their work.

Such performance effects often are dramatic, but the role of motivation during learning is equally important. Motivation can influence what, when, and how we learn (Schunk, 1995). Students motivated to learn about a topic are apt to engage in activities they believe will help them learn, such as attend carefully to the instruction, mentally organise and rehearse the material to be learned, take notes to facilitate subsequent studying, check their level of understanding, and ask for help when they do not understand the material (Zimmerman, 2000). Collectively, these activities improve learning.

In contrast, students unmotivated to learn are not apt to be as systematic in their learning efforts. They may be inattentive during the lesson and not organised or rehearse material. Note taking may be done haphazardly or not at all. They may not monitor their level of understanding or ask for help when they do not understand what is being taught. It is little wonder their work suffers.

A key point is that motivation bears a reciprocal relation to learning and performance; that is, motivation influences learning and performance and what students do and learn influences their motivation (Pintrich, 2003; Schunk, 1995). When students attain learning goals, goal attainment conveys to them that they possess the requisite capabilities for learning. These beliefs motivate them to set new challenging goals. Students who are motivated to learn often find that once they go, they are intrinsically motivated to continue their learning.

Much research shows that students' beliefs about their capabilities relate to motivation. Students who feel self-confident about learning and performing well in school seek challenges, expand effort to learn new material, and persist at difficult tasks (Schunk, 1995). There are a few commonly used models in motivational research. Correlation research looks into the relations that exist between variables. Correlation findings often suggest directions for experimental research. The positive correlation obtained by Pintrich and De Groot (1990) between intrinsic value and academic performance suggests further research exploring whether increased intrinsic value leads to higher achievement. A disadvantage of correlational research is that it cannot identify intrinsic value and effect. The positive correlation between intrinsic value and academic performance could mean that (a) intrinsic value affects academic performance, (b) academic performance influences intrinsic motivation, (c) intrinsic motivation and academic performance affect each other, or (d) intrinsic value and academic performance are each influenced by other, unmeasured variables (e.g. home factors).

Experimental research can clarify cause-effect relations. By systematically varying type of feedback and eliminating other variables as potential causes. Clarifying casual relations helps us understand the nature of motivation. At the same time, experimental research is often narrow in scope. Researchers typically vary only a few variables and try to hold all others constant, which is difficult to do and somewhat unrealistic. Schunk altered only one variable - attribution feedback. Classrooms are complex places where many factors operate simultaneously. To say that one or two variables cause outcomes is probably overstating their importance. It usually is necessary to replicate experiments and examine other variables to better understand effects.

Assessing motivation is an important task for research purposes. Some commonly employed indexes of motivation: choice of tasks, effort, persistence and achievement. The methods used to measure motivation are important too.

In an early study, Lepper, Greene and Nisbett (1973) employed choice of tasks as a motivational index. Preschoolers were observed during free play. Those who spent much time drawing were assigned to one of three conditions. In the expected-award group, children were offered a good player certificate if they drew a picture. Unexpected-award children were not offered the certificate, but unexpectedly received it after they drew a picture. No-award children were not offered the award and did not receive it. Two weeks later, children were again observed during free play when they could choose tasks to work on. Expected-award children chose to spend less time drawing following the study compared with the children in the other two conditions. The expectation of an award apparently decreased children's motivation as assessed by the amount of time they chose to draw during free time.

Despite the intuitive appeal of choice of tasks, choice is often not a useful index of motivation in school because in many classrooms students typically have few, if any, choices. A second index is effort. Learning often is not easy. Students motivated to learn are apt to expend effort to succeed. Students motivated to learn are likely to expend greater mental effort during instruction and employ cognitive strategies they believe will promote learning: organizing and rehearsing information, monitoring level of understanding and relating new material to prior knowledge (Pintrich, 2003; Pintrich & De Groot, 1990). The usefulness of effort as an index of motivation is limited by skill level because as skill increases one can perform better with less effort.

Salomon (1984) assessed students' mental effort and found that it related to self-efficacy. Children judged self-efficacy for learning from television or from written text, watched a televised film or read the comparable text, judged amount of mental effort necessary to learn, and were tested on the content. Students judged mental effort greater for text and demonstrated higher achievement scores form the text. For text, self-efficacy correlated positively with mental effort and achievement; for TV, it correlated negatively. Students who observed TV felt more efficacious about learning but expended less effort and achieved at a lower level. Schunk (1983a) assessed children's perceptions of how hard they worked during mathematics learning and found that providing children with feedback linking their performance to effort expenditure raised their perceptions.

A third motivational index is persistence, or time spent on a task. Students motivated to learn are more likely to persist, especially when they encounter obstacles. Persistence is important because much learning takes time and success may not readily occur. Persistence relates directly to the sustaining feature of motivation described earlier, and greater persistence leads to higher accomplishments.

Persistence is commonly used by researchers as a measure of motivation. Zimmerman and Ringle (1981) had children observe a model unsuccessfully attempt to solve a puzzle for either a long or short time while verbalizing statements of confidence or pessimism, after which children attempted to solve the puzzle themselves. Children who observed the high-persistent model worked longer on the task than the children exposed to the low-persistent model, and children who observed the confident model persisted longer than those who observed the pessimistic model.

As with effort, the usefulness of persistence as a motivational measure is limited by skill level. As students' skills improve, they should be able to perform well in less time. Persistence is most meaningful during learning and when students encounter obstacles.

Finally, student achievement may be viewed as an index of motivation. Students who choose to engage in a task, expend effort and persist are likely to achieve at higher levels (Pintrich & Schrauben, 1992; Schunk, 1995). Many research studies obtain positive relations between achievement and motivational indexes of choice, effort and persistence (Pintrich, 2003). Schunk (1983a) found that the more arithmetic problems children completed during class sessions (which reflected effort and persistence), the more problems they solved correctly on the posttest (a measure of achievement).

Dweck (1999) mentions, 'four beliefs and four truths about ability success, praise and confidence' and how they interact to promote adaptive motivation. Dweck (1999) talks about students who are highly skilled are worried about failure, and the most likely to question their ability and to wilt when they hit obstacles. The second 'truth' referred to by Dweck (1999) is that success in itself 'does little to boost students' desire for challenge or their ability to cope with setbacks. In fact... it can have quite the opposite effect'. The third 'truth' states that praise can lead students to fear failure, avoid risks, doubt themselves when they fail and cope poorly with setbacks. Finally the fourth 'truth' identifies that many of the most confident individuals do not want their intelligence too stringently tested, and their high confidence is all too quickly shaken when they are confronted with difficulty.

These 'truths' are extremely generalised to all highly skilled students this cannot possibly be the case, that all students who were highly skilled are worried about failure, are not motivated after success or praise and that their confidence is knocked after being confronted with difficulty. Quite possibly a significant proportion of 'highly skilled' students may show a tendency to avoid failure, enough so, to promote further research, but not enough to make such a generalised comment. Dweck has also failed to mention how the research was conducted, putting great doubt on the validity of the statements. We are unaware of sample sizes, and therefore this asks the question if the sample size was sufficient and if it was randomly selected taking into account different types of catchment areas. Another frailty with Dwecks four 'beliefs and truths' is that only four indexes were developed to contain all students in relation to adaptive motivation. A serious question of validity needs to be asked here as I don't believe it is possible to categorise every individual under just four indexes, surely more would make the research more valid?

Two distinct reactions to failure have been identified, helpless and mastery-orientated patterns (Diener & Dweck, 1978, 1980). Seligman and Maier (1967) first identified helpless responses in animals. In their research, some animals failed to leave a painful situation because they believed, erroneously, that the circumstances were beyond their control. Diener & Dweck (1978, 1980) transferred this experiment to the classroom environment. They provided a sample of students with a set of problems and monitor their responses to the challenging questions. Before the completing the problems the students were divided into two groups using a questionnaire designed to predict who would show persistence versus non-persistence in the face of failure. Their results showed that more than a third of the group categorised as 'non-persistent' quickly began to denigerate their abilities and blame their intelligence for their failures compared to no one in the 'persistent' group. Students in the 'non-persistent' group lost faith in their ability so much so that over a third of them didn't believe they could complete the questions they answered successfully again, where as everyone in the persistent group believed they could.

Advantages of an experiment within a laboratory like Seligman and Maier's are that it offers a high degree of control over external factors, however within a school this is not possible there are continual distractions. Therefore generalization of laboratory findings to the field, is typically done with less confidence than it is with field research. Laboratory research has yielded many important findings on motivational processes, and researchers often attempt to replicate laboratory findings in the field (e.g. Diener & Dweck, 1978, 1980). In doing this external factors need to be taken into consideration; students and teachers walk by, bells ring, and fire drills are held. Because rooms may be used for other purposes, researchers typically must bring and set up their materials and equipment each time they work. Diener & Dweck (1978, 1980) have not specified over how many days they carried this experiment out for, which may allow external factors such as changes in student's mood and feelings on the day. An advantage of field research though is that the results can be generalised to similar settings because studies are conducted where students are present and interacting with the environment, unlike the laboratory. Deiner & Dwecks' research was only carried out with students from fifth- and sixth- grade, therefore restricting the ability to generalize the data to other year groups. There is also no record of the background of the pupils selected, which further restricts the validity of the data to be applied to other settings, as the results could be unique to a specific socio-economic background. Field research requires minimizing extraneous influences so that we can be more confident that our results are due to the factors we are studying as these extraneous influences can affect an experiment's results.

Rotter (1966) claims that if reinforcement is not perceived by children to be contingent upon their own behaviour, then it will not increase expectation that their behaviour will be reinforced in the future. In other words, if children believe that their successes and failures are contingent upon their own behaviour, then the children are deemed to hold an internal locus of control. On the other hand, if the children believe they are not contingent upon their own behaviour, then they are deemed to hold an external locus of control. Such generalised expectancies or beliefs influence the likelihood of academic success. Rotter (1975) clarified further his conceptualisation of a 'locus of control' by highlighting the importance of the value of the expected reinforcement. In effect, children can understand that they need to study to obtain high marks, but they might not value this potential reinforce.

Social learning

Measures of 'locus of control' have usually used qualitative data such as different types of questionnaires. Low reliability is reported in many of these questionnaires (Stipek & Weisz, 1981). Analyses have focuses mainly upon the relationship between children's scores on a questionnaire and global measures of achievement. However it remains speculative whether 'locus of control' is a cause or an effect of school achievement. Given that children tend to accept more responsibility for success than for failure (Butler, 1994), then those who are more successful at school are more likely to attribute it to themselves. Studies conducted within this theoretical framework offer little explanation of the underling psychology processes involved in children's learning. Neither do they illuminate development nor contextual issues. Because this approach is not concerned with the aggregation of usable knowledge for teaching practice, it is not a means for providing generalizable solutions for teaching problems (Shulman, 1986). Studies usually are conducted with few participants, which raises the issue of whether findings are reliable and representative of the population being studied (e.g. teachers, students). Another concern is that if researchers do not attempt to interpret data in light of a theoretical framework, findings may not be linked and interpretation will prove difficult.

In contrast with the generalised expectancy model of 'locus of control' outlined above, attribution models emphasise the perceptions of cause and the importance of situational variables. Attribution theorists place children's casual perceptions about their learning outcomes at the heart of motivational process (Weiner, 1992). An important distinction is drawn in attribution theory between contingency and control since children who perceive failure as resulting more from a shortfall in ability than from effort are likely to respond differently in achievement situations. In contrast, in social learning theory attributions to ability and to effort signify an internal locus of control. In attribution theory, attributing failure to lack of ability is likely to be more devastating to future success because ability is often perceived as stable, whereas 'there is always room for more effort'. For Weiner, then, differences in children's motivational patterns result from differences in their attributions.

Students in the lower elementary grades are generally highly motivated to learn mathematics. They believe that they are competent and that working hard will enable them to succeed. Many first and second graders do not distinguish between effort and ability as causes of success in mathematics (Kloosterman, 1993). However, there is considerable evidence that some students begin to differentiate ability for different content domains as early as kindergarten or first grade (Wigfield et al., 1992). By the middle grades, many students begin to perceive mathematics to be a special domain in which smart students succeed and other students merely "get by" or fail. They begin to believe that success and failure are attributable to ability and that effort rarely results in a significant change in their success patterns (Kloosterman& Gorman,1990).

When students attribute their successes to ability, they tend to succeed; when they attribute their failures to lack of ability, they tend to fail. Gender studies have shown that girls tend not to attribute their successes to ability but do tend to attribute their failures to lack of ability, exactly the attributional style that leads to failure. For example, Meyer and Fennema (1985) studied the relation-ship between students' attributions of success in mathematics in the 8th grade and their subsequent achievement in 11th grade. This study was a departure from most attribution research, at least as it related to mathematics education, in that it assessed the relationship between attributions and future success in mathematics instead of current success. The authors found that attribution of success to ability was the most consistent correlate of Grade 11 achievement. Conversely, attribution of failure to lack of ability was the most consistent correlate of lack of achievement for both males and females. For girls in particular, when ability was controlled for, attributing failure to lack of ability was associated with lower achievement. However, attributing failure to lack of effort was also a significant predictor of lack of achievement on computation problems and high-level, conceptual mathematics tasks. Boys' attributions were not as pronounced as girls' for these variables. The authors concluded that attributions may be more important as predictors of success in mathematics for females than for males.

Kloosterman (1988) studied how seventh graders perceived the role of successes and failures in influencing their motivational attributions, their mathematical self-confidence, and their beliefs about effort as a mediator of mathematical ability and failure as an acceptable phase in learning mathematics. He found that attributional style (a combined score, scaled in the direction of internal, stable attributions) was the best predictor of mathematical self-confidence. The belief that effort is a mediator of ability and that failure is an acceptable phase in learning mathematics also contributed to students' self-confidence in mathematics. Although girls, more often than boys, felt that failure was an acceptable phase in learning mathematics, the fact that girls also thought about their failures more than boys did may have contributed to differential effects like those reported by Meyer and Fennema (1985).

These findings are significant in that when students conceive of ability as amenable to change or augmentation through effort, they tend to expend more effort in mathematics and, thus, are better achievers than students who believe that ability is fixed. Because the belief that occasional failure is acceptable in learning mathematics predicts mathematical self-confidence, the practice of allowing children to struggle with challenging problems, even in the elementary grades, is supported. When children who have not experienced difficult problems in mathematics encounter a problem that cannot be solved in a routine fashion, they may have their confidence shattered unless they believe that occasional mistakes are a part of learning mathematics.

By the time they reach college, students generally have formed stable attributions regarding their successes in mathematics. Because the attributional patterns of students in mathematics-related majors tend to focus on ability and effort as the causes for success and lack of effort for failure, females, who tend to attribute their failures to ability, may be systematically excluded from mathematics majors as a result of their prior mathematics education (Amit, 1988; Bassarear, 1986). In addition, because students with unstable attributions for the causes of failure in mathematics tend to dislike mathematics greatly (Lehmann, 1986), these students may also be filtered out of mathematics-related majors.

Amit (1988) studied the attributions of university students in five major areas and found that, overall, females tend to attribute their successes in mathematics to external and unstable causes, whereas males attribute their successes to ability, an internal and stable factor. When attributions of success were analyzed taking academic major into account, however, students tended to attribute their causes of success and failure the same way regardless of gender. Students choosing mathematics as a major tended to attribute their successes to ability and their failures to other factors. In fact, as the mathematical requirements for participation in college majors increased, so did the attribution of success to the internal factor of ability. Students who attribute their failures in mathematics to internal factors and their successes to external factors are unlikely to choose a college curriculum with substantial mathematics content.


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