Keller, J & Bless, H. (2008). Flow and regulatory compatibility: an experimental approach to the flow model of intrinsic motivation. Personality and Social Psychology Bulletin, 34, 196-209.
Problem/Purpose
This article is of interest because (1) the authors investigated flow from an experimental perspective and (2) the authors included a personality measure to examine interaction effects between flow conditions and personality. The authors claim that flow studies have mostly consisted of correlational studies and there have been only two previous experimental studies on flow. Experimental methods allow the authors to present evidence for causal claims in flow theory. In the second of the experimental studies reported, the authors examined the role of individual differences with respect to volatility-persistence in flow/non-flow experiences.
Methods
Keller and Bless performed two experiments. In the first experiment, they manipulated conditions for subjects playing a modified version of the video game Tetris. Three conditions were used: the adaptive condition where game difficulty was matched to the subject’s ability level, the boredom condition where game difficulty was below ability level, and the overload condition where the game difficulty was above ability level. The subjects were 72 undergraduate students at the University of Mannheim (44 females and 38 males) who were paid 2 euros for participating in the study. Participants were randomly assigned to one of the three conditions and their success was measured based on how many lines they successfully filled while playing the game. Immediately after completing the game, participants took a questionnaire consisting of scales to assess their (1) perception of time, (2) feeling of control, (3) involvement and enjoyment, and (4) perceived fit of skills and task demands.
For the second experiment, 149 undergraduates from the same university participated (85 females, 5 unspecified, 59 males) and received 2 euros as payment. As in experiment one, subjects were randomly assigned to the same three conditions. For the questionnaire, feeling of control was replaced by degree of relaxation/agitation while the other three scales remained the same. In addition to the change in the post-game questionnaire, subjects completed a pre-game questionnaire that measured action-state orientation through a volatility-persistence measure.
Results
Keller and Bless found results consistent with flow model theory in their first and second experiments. Specifically, performance (measured as the number of lines completed in the game), involvement/enjoyment, and perceived fit scores were significantly higher for the adaptive condition than for the boredom and overload conditions. The scores for perceived time spent on task were lowest for the adaptive condition, which is also consistent with flow theory. Perceived control for those in the adaptive condition was higher than for those in the boredom condition and lower than for those in the overload condition, as expected by the researchers and consistent with flow theory.
In the second experiment, Keller and Bless found that the agitation/relaxation scores paralleled the perceived control scores from the first experiment. Agitation/relaxation for those in the adaptive condition was higher than for those in the boredom condition and lower than for those in the overload condition.
For volatility-persistence, an interaction effect was indicated by the data. The adaptive group data showed the largest increase in involvement/enjoyment for the high volatility-persistence subjects over the low volatility-persistence subjects. The boredom group data showed a smaller increase in involvement/enjoyment for the high volatility-persistence subjects over the low volatility-persistence subjects. In contrast, the overload group data showed a decrease in involvement/enjoyment for the high volatility-persistence subjects as compared to the low volatility-persistence subjects.
Discussion
The authors state that the data provide “strong support for the general assumptions of flow theory.” The authors concluded that their experimental data provide support for the causal assumption in flow theory that matching skills and task difficulty can result in a state of flow. They describe this assumption as having a crucial role in the theory. Then they bring up the issue of whether or not perceived fit of ability and task demands is a crucial issue and the limitations of their study—namely that their study does not and cannot address the role of perceived fit.
Keller and Bless also discuss that their research provides initial evidence that moderators, such as volatility-persistence, can affect flow. Other personality factors may or may not fit within the structural requirements of given tasks and are described as areas for future study. Finally, the authors suggest future contributions in flow research could investigate not only the roles of personality factors, but how flow experiences do (or do not) affect task involvement, persistence, and performance levels (including performance at later times separated from the initial flow experience).
Critique
This article is mostly well written. For the most part, Keller and Bless use understandable language. Areas for improvement would include better explanations of action-orientation, which is not well defined. Since action-orientation was measured as volatility-persistence, this deficit is not a huge problem for the article (as persistence is rather well-defined in standard English).
A second part of the article that was unclear was the reasoning behind using the regression analysis for experiment two data instead of a potentially simpler method, like a two-way ANOVA. The extra dummy coding required for the regression seemed like it would have been unnecessary had the authors compared the three groups (adaptive, boredom, and overload) against volatility-persistence (low scores versus high scores). In spite of these two critiques, the article is otherwise clear and well directed.
Theoretical and Practical Implications
One theoretical implication of this study is that there is much work to be done to understand the role of affective traits and other individual differences with respect to flow experiences. The practical implications of this study include using experiments to study how these factors, as well as the assumptions within flow theory itself, affect flow experiences for individuals. The use of a videogame to manipulate subjects into flow and non-flow experiences also hints at the possible use of similar games and manipulations for future studies into flow.
Wednesday, October 29, 2008
Monday, October 20, 2008
flow-like conference
I'm submitting this post for Debrayh.
I (Debrayh) found this conference when I was doing some research on the web. Professor Csikszentmihalyi is one of the speakers (see below).
Psychology Conference - Jan 24, 2009
2009 Stauffer Symposium on Applied Psychology
Conference Web Page
January 24, 2009
8:45 am – 6:45 pm
Claremont Graduate University
Positive Psychology emerged at the beginning of the new millennium as a movement within psychology aimed at enhancing human strengths and optimal human functioning. This emerging area of scholarship, scientific research, and application has inspired leading scholars and practitioners from across the globe to rethink the fundamental nature of how we live, work, and educate; of our health and well-being; of how to design and lead positive institutions; and how to develop positive public policies. The ideas contained in the initial work in positive psychology have spread far and wide across the disciplines to form a broader movement, sometimes referred to as the positive social and human sciences.
Claremont Graduate University is proud to announce that it will host a day-long event to celebrate the emerging positive social and human sciences, and to push their boundaries. Leaders and leading scholars from across the positive science landscape will gather in Claremont on Saturday, January 24, 2009 to discuss Applying the Science of Positive Psychology.
I (Debrayh) found this conference when I was doing some research on the web. Professor Csikszentmihalyi is one of the speakers (see below).
Psychology Conference - Jan 24, 2009
2009 Stauffer Symposium on Applied Psychology
Conference Web Page
January 24, 2009
8:45 am – 6:45 pm
Claremont Graduate University
Positive Psychology emerged at the beginning of the new millennium as a movement within psychology aimed at enhancing human strengths and optimal human functioning. This emerging area of scholarship, scientific research, and application has inspired leading scholars and practitioners from across the globe to rethink the fundamental nature of how we live, work, and educate; of our health and well-being; of how to design and lead positive institutions; and how to develop positive public policies. The ideas contained in the initial work in positive psychology have spread far and wide across the disciplines to form a broader movement, sometimes referred to as the positive social and human sciences.
Claremont Graduate University is proud to announce that it will host a day-long event to celebrate the emerging positive social and human sciences, and to push their boundaries. Leaders and leading scholars from across the positive science landscape will gather in Claremont on Saturday, January 24, 2009 to discuss Applying the Science of Positive Psychology.
Sunday, October 19, 2008
Mihaly Csikszentmihalyi
FlowSome people become so deeply focused on an activity, they experience an almost euphoric state of joy and pleasure in the process. Time seems to stand still. They report feeling a sense of vitality and are highly alert and feel as if they are performing to the best of their ability.
According to psychologist and best-selling author Dr. Mihaly Csikszentmihalyi, they are most likely experiencing ‘Flow,’ a state of deep focus that occurs when the individual is engaged in a challenging task that demands concentration and commitment. But in order for ‘Flow’ to occur, the individual’s skill level must balanced to the challenge level of the task, and the challenge must have clear goals and provide immediate feedback.
Mihaly Csikszentmihalyi was born September 29, 1934, in Fiume, Italy and immigrated to the United States at the age of 22. He is noted for his work in the study of happiness, creativity, subjective well-being, and fun, but is best known for his research on Flow Theory.
He is currently a Distinguished Professor of Psychology and the director of the Quality of Life Research Center (QLRC) at Claremont Graduate University in Claremont California. The QLRC is a non-profit research institute that studies "positive psychology,” or human strengths, such as optimism, creativity, intrinsic motivation, and responsibility.
Professor Csikszentmihalyi received his Ph.D. from the University of Chicago in 1965. He is a member of the American Academy of Education, the American Academy of Arts and Sciences, and the National Academy of Leisure Studies.
Quotes from Mihaly Csikszentmihalyi
To know one-self is the first step toward making flow a part of one's entire life. But just as there is no free lunch in the material economy, nothing comes free in the psychic one. If one is not willing to invest psychic energy in the internal reality of consciousness, and instead squanders it in chasing external rewards, one loses mastery of one's life, and ends up becoming a puppet of circumstances.
Flow is being completely involved in an activity for its own sake. The ego falls away. Time flies. Every action, movement, and thought follows inevitably from the previous one, like playing jazz.
It does not seem to be true that work necessarily needs to be unpleasant. It may always have to be hard, or at least harder than doing nothing at all. But here is ample evidence that work can be enjoyable, and that indeed, it is often the most enjoyable part of life.
“If the next generation is to face the future with zest and self-confidence, we must educate them to be original as well as competent”
If we agree that the bottom line of life is happiness, not success, then it makes perfect sense to say that it is the journey that counts, not reaching the destination.
Flow is being completely involved in an activity for its own sake. The ego falls away. Time flies. Every action, movement, and thought follows inevitably from the previous one, like playing jazz.
It does not seem to be true that work necessarily needs to be unpleasant. It may always have to be hard, or at least harder than doing nothing at all. But here is ample evidence that work can be enjoyable, and that indeed, it is often the most enjoyable part of life.
“If the next generation is to face the future with zest and self-confidence, we must educate them to be original as well as competent”
If we agree that the bottom line of life is happiness, not success, then it makes perfect sense to say that it is the journey that counts, not reaching the destination.
. . . Mihaly Csikszentmihalyi, Flow: The Psychology of Optimal Experience, 1990.
As you reflect on the words of Professor Csikszentmihalyi, please consider the following questions and post your response.
- Describe a time in your life when you experienced ‘Flow’ as it is described by Csikszentmihalyi.
- What circumstances contributed to the ‘Flow’ experience?
- Having experienced “Flow” do you feel that this is something that can be taught?
Saturday, October 18, 2008
Britte Cheng
Dr. Cheng completed her undergrad in Psychology at the University of Chicago. Britte worked on several research projects while at the University of Chicago, but the work with Csikszentmihalyi included interviewing adolescents at length about their upbringing and life to date. This was part of the effort to document the types of lives folks who were flow-prone had, what kind of families, experiences, education, etc. She managed data collection in several cities.
She received both her MA and PhD from Berkeley in Education in Cognition and Development/Education, Math, Science, and Technology.
Britte’s interest in the flow concepts centers more about the match between cognitive abilities and the challenge of the task. Britte elaborates,
For more information, check out the Learning in Informal and Formal Environments (LIFE) Center.
While working at SRI - Stanford Research Institute, Britte’s research strives to make help make homework that is engaging socially!
Britte chose the Rathunde & Csikszentmihalyi article because it connects flow to learning experiences -- as formalized in a pedagogical approach. She feels that teachers would see the relevance of the ideas in practice. She also feels that the connection between flow, motivation, and learning is fairly explicit.
Discussion Article
Rathunde, K. & Csikszentmihalyi, M. (2005). Middle school students' motivation and quality of experience: A comparison of Montessori and traditional school environments. American Journal of Education, 111, 341-371.
She received both her MA and PhD from Berkeley in Education in Cognition and Development/Education, Math, Science, and Technology.
Britte’s interest in the flow concepts centers more about the match between cognitive abilities and the challenge of the task. Britte elaborates,
"This is Vygotsky's ZPD. The point for me was about learning as a flow activity more than straight on motivation. Since then, I have continued to study how to connect to student's personal experiences, interests, etc. to create a more meaningful (and I guess arguably) motivating learning experience in classrooms. This is the mission of LIFE - the center that I am a part of."
For more information, check out the Learning in Informal and Formal Environments (LIFE) Center.
While working at SRI - Stanford Research Institute, Britte’s research strives to make help make homework that is engaging socially!
Britte chose the Rathunde & Csikszentmihalyi article because it connects flow to learning experiences -- as formalized in a pedagogical approach. She feels that teachers would see the relevance of the ideas in practice. She also feels that the connection between flow, motivation, and learning is fairly explicit.
Discussion Article
Rathunde, K. & Csikszentmihalyi, M. (2005). Middle school students' motivation and quality of experience: A comparison of Montessori and traditional school environments. American Journal of Education, 111, 341-371.
Friday, October 17, 2008
Lee 2005
Lee, E. (2005). The relationship of motivation and flow experience to academic
procrastination in university students. Journal of Genetic Psychology, 166, 5-14.
Problem/Purpose
I found this study particularly interesting because I see many of my students displaying a lack of self-regulation, a lack of motivation, and a general displeasure of learning. The study by Eunju Lee provides some important practical and theoretical implications that seem rather meaningful and especially practical. Besides, who would not want to review a study about academic procrastination!
Lee investigated the implications of academic procrastination on student motivation and flow experiences. Lee examined whether flow was related to procrastination when motivation was considered. He expected that students with high self-determined extrinsic motivation would be less likely to procrastinate. He also believed that flow would be associated with high self-determined motivation, but also with low procrastination.
Methods
Lee began with 277 South Korean college students enrolled at two small universities. The participants were educational psychology students. However, 15 students had missing data, so the researcher dropped these students from the analyses. The study ended up with 138 males and 124 females. The students had an average age of 20.02 years, and a standard deviation of 1.20 years. Eighty four percent of the students were sophomores; 12% were freshman, and 4% were seniors.
Students completed written questionnaire packets using three different measures. All measures needed to be translated into Korean. The first measure was the 16-item Procrastination Scale developed my Tuckman in 1991. The Procrastination Scale uses a 4-point Likert-type format ranging from very true (4) to not at all true (1). The reliability is .83.
The second measure is the Flow State Scale developed my Jackson and Marsh in 1996. Lee only used five of the nine subscales. He incorporated challenge-skill balance, clear goals, unambiguous feedback, concentration on task at hand, and loss of self-conscientiousness. These five subscales have a 5-point Likert-type response pattern that ranges from high agreement (5) to disagreement (1). The reliabilities for these five scales ranged from .77 to .84.
Lastly, Lee used the Academic Motivation Scale to assess students’ learning motivation. Students rated a 7-point Likert-type scale from very true (7) to not at all true (1). Reliability data ranged from .83 to .93.
Results
Lee presented correlations to address the first research question of whether or not a relationship existed between academic procrastination and motivation and flow. Procrastination had a .28 positive correlation with amotivation. Lee also obtained a negative correlation between procrastination and self-determined extrinsic motivation (-.40) and intrinsic motivation (-.50). Procrastination also negatively correlated with all five of the flow subscales.
Lee also conducted a hierarchical multiple regression analysis investigating the contribution of motivation and flow to predict procrastination and to predict academic procrastination. For the first step of the regression, the researcher included amotivation, non- self-determine extrinsic motivation, self-determined extrinsic motivation, and intrinsic motivation. Amotivation produced a significant positive effect, and intrinsic motivation produced a significant negative effect at the .05 level.
In the second step of the regression, Lee added challenge-skill balance, clear goals, unambiguious feedback, concentration on the task, and loss of self-consciousness. Clear goals, concentration on the task, and loss of self-consciousness produced significant negative effects at least the .01 level. These results suggested that procrastination was best predicted by flow experience rather than by motivation.
Discussion
Lee concluded that his findings were consistent with previous research. Tasks that are self-determined do not elicit procrastinating behaviors. Lee also concluded that students who procrastinate are more likely not to experience flow state in the learning process. Students without clear goals, did not concentrate on the task, and had high self-consciousness show high procrastination tendencies.
Critique
This article is very well written. Lee uses language that is very easy to understand. Reliabilities for the three measures were relatively strong too. However, Lee did not use a random sample for this study. Another weakness included the fact that this study is not very generalizeable because Lee used a limited selection of Korean college students, and Lee acknowledges this as a limitation. Lee also acknowledges that some of the results obtained from this study are modest at best.
Theoretical and Practical Implications
One theoretical implication of this study is that there is very little research investigates procrastination. The practical implications of this study include using these findings to develop interventions for teachers and parents to help students reach flow states. Teachers should also balance the students’ skills with the task challenges. Teachers and parents should also help students set clear goals, to learn to concentrate, and not be overly self-conscientious.
procrastination in university students. Journal of Genetic Psychology, 166, 5-14.
Problem/Purpose
I found this study particularly interesting because I see many of my students displaying a lack of self-regulation, a lack of motivation, and a general displeasure of learning. The study by Eunju Lee provides some important practical and theoretical implications that seem rather meaningful and especially practical. Besides, who would not want to review a study about academic procrastination!
Lee investigated the implications of academic procrastination on student motivation and flow experiences. Lee examined whether flow was related to procrastination when motivation was considered. He expected that students with high self-determined extrinsic motivation would be less likely to procrastinate. He also believed that flow would be associated with high self-determined motivation, but also with low procrastination.
Methods
Lee began with 277 South Korean college students enrolled at two small universities. The participants were educational psychology students. However, 15 students had missing data, so the researcher dropped these students from the analyses. The study ended up with 138 males and 124 females. The students had an average age of 20.02 years, and a standard deviation of 1.20 years. Eighty four percent of the students were sophomores; 12% were freshman, and 4% were seniors.
Students completed written questionnaire packets using three different measures. All measures needed to be translated into Korean. The first measure was the 16-item Procrastination Scale developed my Tuckman in 1991. The Procrastination Scale uses a 4-point Likert-type format ranging from very true (4) to not at all true (1). The reliability is .83.
The second measure is the Flow State Scale developed my Jackson and Marsh in 1996. Lee only used five of the nine subscales. He incorporated challenge-skill balance, clear goals, unambiguous feedback, concentration on task at hand, and loss of self-conscientiousness. These five subscales have a 5-point Likert-type response pattern that ranges from high agreement (5) to disagreement (1). The reliabilities for these five scales ranged from .77 to .84.
Lastly, Lee used the Academic Motivation Scale to assess students’ learning motivation. Students rated a 7-point Likert-type scale from very true (7) to not at all true (1). Reliability data ranged from .83 to .93.
Results
Lee presented correlations to address the first research question of whether or not a relationship existed between academic procrastination and motivation and flow. Procrastination had a .28 positive correlation with amotivation. Lee also obtained a negative correlation between procrastination and self-determined extrinsic motivation (-.40) and intrinsic motivation (-.50). Procrastination also negatively correlated with all five of the flow subscales.
Lee also conducted a hierarchical multiple regression analysis investigating the contribution of motivation and flow to predict procrastination and to predict academic procrastination. For the first step of the regression, the researcher included amotivation, non- self-determine extrinsic motivation, self-determined extrinsic motivation, and intrinsic motivation. Amotivation produced a significant positive effect, and intrinsic motivation produced a significant negative effect at the .05 level.
In the second step of the regression, Lee added challenge-skill balance, clear goals, unambiguious feedback, concentration on the task, and loss of self-consciousness. Clear goals, concentration on the task, and loss of self-consciousness produced significant negative effects at least the .01 level. These results suggested that procrastination was best predicted by flow experience rather than by motivation.
Discussion
Lee concluded that his findings were consistent with previous research. Tasks that are self-determined do not elicit procrastinating behaviors. Lee also concluded that students who procrastinate are more likely not to experience flow state in the learning process. Students without clear goals, did not concentrate on the task, and had high self-consciousness show high procrastination tendencies.
Critique
This article is very well written. Lee uses language that is very easy to understand. Reliabilities for the three measures were relatively strong too. However, Lee did not use a random sample for this study. Another weakness included the fact that this study is not very generalizeable because Lee used a limited selection of Korean college students, and Lee acknowledges this as a limitation. Lee also acknowledges that some of the results obtained from this study are modest at best.
Theoretical and Practical Implications
One theoretical implication of this study is that there is very little research investigates procrastination. The practical implications of this study include using these findings to develop interventions for teachers and parents to help students reach flow states. Teachers should also balance the students’ skills with the task challenges. Teachers and parents should also help students set clear goals, to learn to concentrate, and not be overly self-conscientious.
Thursday, October 2, 2008
Vollmeyer & Rheinberg 2006
Vollmeyer, R. & Pheinberg, F. (2006). Motivation effects on self-regulated learning with different tasks. Educational Psychology Review, 18, 239-253.
Problem
Vollmeyer and Rheinberg presented a cognitive-motivational process model that presents factors of initial motivation, collected mediators that influence initial motivation on performance, and found evidence to support different learning outcomes. Four factors influence initial motivation: probability of success, anxiety (fear of failure), interest, and challenge (importance or task value). In the first study, research consisted of a comparison between learning in a linear design setting and learning in a hypermedia environment. In the second study, researchers measured the performance using data from university students.
They hypothesized that initial motivation (interest, challenge, probability of success, and anxiety) would influence performance on four mediating variables, specifically Flow.
Vollmeyer and Rheinberg used several measurement tools. They constructed the Questionnaire on Current Motivation or QCM, designed to capture learners’ current motivation after being instructed to complete a task. The QCM produced Cronbach’s alphas that fell between 0.68 - 0.90 for probability of success, interest, anxiety, and challenge.
The researchers identified four mediators: time on task, learning objective, motivation, and flow.
Flow state balances challenge and skill and merges action and awareness. Flow provides unambiguous feedback and time transformation. Flow includes concentration and fluency of action. The Flow Short Scale (FKS) that considers all six characteristics of Flow. This instrument uses only ten items and takes about 40 seconds to complete. The FKS’s reliability is 0.90. The FKS can be combined with the Experience Sampling Method (ESM). Participants are paged in ESM studies, and are asked to stop what activity they are doing and answer scales.
Methods
Vollmeyer and Reheinberg used small real classrooms. Learners participated in two phases: a learning phase and an application phase. Participants changed an input variable and observed what happened to an output variable. Learners had to demonstrate knowledge about the system and answer the motivational questionnaire.
A path analysis was used to analyze the process through a structural equation model. The researchers combined three (interest, challenge, probability of success) of the four variables into a latent variable, eliminating anxiety because it does not share a relationship with other motivational factors.
The second study used university students. In a French class, students reported their initial motivation with all four factors before class, and reported their FKS during class. At the end, the students subjectively rated how much they learned. Students took an exam at the end of the semester.
Researchers collected students’ math grade and age from Psychology students. Students reported the strength of their intention to learn statistics. Near the end of the semester, researchers told to solve a statistics task. They administered the QCM and FKS. Number correct on the final exam served as a performance measure too.
Results
Learners with high initial motivation had a more systematic strategy, higher motivation, and consequently larger knowledge acquisition. Feedback was provided at regular intervals. The path from Round 3 to goal achievement was 0.63, and the path from motivation state to goal achievement was 0.22.
After learning of a topic, participants had rated interest, probability of success, challenge, and anxiety. Participants rated their motivation during their time with the hypermedia materials. Researchers found that the motivation in the hypermedia did not vary much.
In the French study, the path from Flow to final exam was 0.31, and in the statistics study, the path from Flow to final exam was 0.21.
Discussion
Students interested, challenged, and believing that they will be successful, come up with better strategies. They enjoy learning more too. Participants became annoyed with the interruptions when prompted to rate the QCM. Researchers noticed scores varied more on the initial motivation measure than on the during activity measure, indicating that the experience or feedback can influence motivation. knowledge and motivational state influence goal achievement.
Critique
This article seemed to jump around a bit. They spent a lot of time setting up the research, while simultaneously presenting their own, making it difficult to follow. Sections are clearly defined for constructs and ideas. The authors also assume that the reader has a good understanding of path analysis.
A few problems occurred in the hypermedia environment. Because participants did not follow a linear line of events, researchers had to find a way to create accurate comparisons with the other group. They tried using linking between sequential pages and time per page. But this did not provide an accurate picture of what participants read.
Theoretical and Practical Implications
The analysis combined all three variables into a latent variable. Analyzing variables separately would have been more interesting. Patterns of motivational factors need to be taken into account too. This study also suggests combining factors to make accurate comparisons. This information is useful for teachers because they can design lessons with timely feedback. Teachers also have a better knowledge of anticipating when motivation will fall.
Problem
Vollmeyer and Rheinberg presented a cognitive-motivational process model that presents factors of initial motivation, collected mediators that influence initial motivation on performance, and found evidence to support different learning outcomes. Four factors influence initial motivation: probability of success, anxiety (fear of failure), interest, and challenge (importance or task value). In the first study, research consisted of a comparison between learning in a linear design setting and learning in a hypermedia environment. In the second study, researchers measured the performance using data from university students.
They hypothesized that initial motivation (interest, challenge, probability of success, and anxiety) would influence performance on four mediating variables, specifically Flow.
Vollmeyer and Rheinberg used several measurement tools. They constructed the Questionnaire on Current Motivation or QCM, designed to capture learners’ current motivation after being instructed to complete a task. The QCM produced Cronbach’s alphas that fell between 0.68 - 0.90 for probability of success, interest, anxiety, and challenge.
The researchers identified four mediators: time on task, learning objective, motivation, and flow.
Flow state balances challenge and skill and merges action and awareness. Flow provides unambiguous feedback and time transformation. Flow includes concentration and fluency of action. The Flow Short Scale (FKS) that considers all six characteristics of Flow. This instrument uses only ten items and takes about 40 seconds to complete. The FKS’s reliability is 0.90. The FKS can be combined with the Experience Sampling Method (ESM). Participants are paged in ESM studies, and are asked to stop what activity they are doing and answer scales.
Methods
Vollmeyer and Reheinberg used small real classrooms. Learners participated in two phases: a learning phase and an application phase. Participants changed an input variable and observed what happened to an output variable. Learners had to demonstrate knowledge about the system and answer the motivational questionnaire.
A path analysis was used to analyze the process through a structural equation model. The researchers combined three (interest, challenge, probability of success) of the four variables into a latent variable, eliminating anxiety because it does not share a relationship with other motivational factors.
The second study used university students. In a French class, students reported their initial motivation with all four factors before class, and reported their FKS during class. At the end, the students subjectively rated how much they learned. Students took an exam at the end of the semester.
Researchers collected students’ math grade and age from Psychology students. Students reported the strength of their intention to learn statistics. Near the end of the semester, researchers told to solve a statistics task. They administered the QCM and FKS. Number correct on the final exam served as a performance measure too.
Results
Learners with high initial motivation had a more systematic strategy, higher motivation, and consequently larger knowledge acquisition. Feedback was provided at regular intervals. The path from Round 3 to goal achievement was 0.63, and the path from motivation state to goal achievement was 0.22.
After learning of a topic, participants had rated interest, probability of success, challenge, and anxiety. Participants rated their motivation during their time with the hypermedia materials. Researchers found that the motivation in the hypermedia did not vary much.
In the French study, the path from Flow to final exam was 0.31, and in the statistics study, the path from Flow to final exam was 0.21.
Discussion
Students interested, challenged, and believing that they will be successful, come up with better strategies. They enjoy learning more too. Participants became annoyed with the interruptions when prompted to rate the QCM. Researchers noticed scores varied more on the initial motivation measure than on the during activity measure, indicating that the experience or feedback can influence motivation. knowledge and motivational state influence goal achievement.
Critique
This article seemed to jump around a bit. They spent a lot of time setting up the research, while simultaneously presenting their own, making it difficult to follow. Sections are clearly defined for constructs and ideas. The authors also assume that the reader has a good understanding of path analysis.
A few problems occurred in the hypermedia environment. Because participants did not follow a linear line of events, researchers had to find a way to create accurate comparisons with the other group. They tried using linking between sequential pages and time per page. But this did not provide an accurate picture of what participants read.
Theoretical and Practical Implications
The analysis combined all three variables into a latent variable. Analyzing variables separately would have been more interesting. Patterns of motivational factors need to be taken into account too. This study also suggests combining factors to make accurate comparisons. This information is useful for teachers because they can design lessons with timely feedback. Teachers also have a better knowledge of anticipating when motivation will fall.
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