Sunday, November 30, 2008
Saturday, November 29, 2008
TED.com
Check out this website they bill themselves as "Inspired talks by the world's leading thinkers and doers" It's were I found the link to the interview with Mihaly Csikszentmihalyi
Web links
Here are a few web links that I found when surfing the web
This one includes a down loadable PDF dissertation on flow theory.
The next on is where Jenova Chen shares his research and ideas with gamers and game designers from all over the world who are interested in game design theories, processes and their innovations.
The site is currently focused on Jenova's MFA thesis research. It is about inventing new methodologies usable by the frontline game designers to help realizing dynamic Flow experiences for different individuals and more importantly for potential gamers who are still out of the current video game market.
This one includes a down loadable PDF dissertation on flow theory.
The next on is where Jenova Chen shares his research and ideas with gamers and game designers from all over the world who are interested in game design theories, processes and their innovations.
The site is currently focused on Jenova's MFA thesis research. It is about inventing new methodologies usable by the frontline game designers to help realizing dynamic Flow experiences for different individuals and more importantly for potential gamers who are still out of the current video game market.
Friday, November 21, 2008
Brinthaupt & Shin (2001)
Brinthaupt, T. and Shin, C.M. (2001). The relationship of academic cramming to the flow experience. College Student Journal, volume 35, 3 457-472
Problem
Brinthaupt, T. and Shin, C.M. (2001), noted that research has neglected to examine the experiential aspects of academic cramming. Most educators have a negative view of the efficacy of cramming; we lecture students telling them “don’t wait until the last minute.” Prior to Brinthaupt, T. and Shin, C.M. (2001), no one asked, what do the crammers get out of the experience? Brinthaupt, T. and Shin, C.M. (2001), asked whether was there a relationship between cramming and Csikszentmihalyi (1987), flow theory. The researchers hypothesized that for some students, procrastination and cramming can create a state of flow. When the researchers examined the art of cramming, they noticed similarities between cramming behavior and the flow state. To achieve a state of flow, the individual must engage in a challenging activity that requires skills, merge actions with awareness, have clear goals and receive feedback. During the flow experience, the individual experiences increased concentration, the loss of self-consciousness, and a transformation of time.
Brinthaupt, T. and Shin, C.M. (2001), note that when students self-selected texts to read for pleasure they were more likely to experience flow. However, assigned texts read for a specific class did not produce the same results. Brinthaupt, T. and Shin, C.M. (2001), suggest that the assigned texts are less engaging and possibly, even boring, thus contributing to the procrastination, which in turns leads to cramming. Csikszentmihalyi (1990, 1997) suggests that when students procrastinate, they are, intentionally or unintentionally, increasing the level of challenge. Therefore, a student who successfully crams for a test or writes a paper at the last moment may do so because the likelihood of experiencing a flow state is increased.
To explore the possible connection between cramming and flow, the authors created a study that simulated a cramming and testing situation. They predicted that the self-identified crammers would experience a flow-like state than the non-crammers. Additionally, they explored the relationship of typical study habits and academic procrastination to performance during the simulated cramming session.
Methods:
The sample consisted of 167 undergraduates students (75 male & 92 female) enrolled in Introductory Psychology at a large southeastern University. Students were given course credit for their participation in the study. The students participated in groups of 10 to 20, and data was collected approximately 2 weeks prior to the end of the term. Six of the participants were excluded due to missing data, making the final total 161 participants.
Study habits were assessed using a 5-point Likert scale questionnaire, which included questions on frequency of cramming for exams: how often the individuals crammed by choice; and how often the individuals gave themselves enough time to study. Participants also completed a 23-item Academic Procrastination State Inventory which was developed by Schouwenbury (1995). Participants read a short paragraph defining flow and then completed a questionnaire rating their experiences of flow across a wide variety of circumstances.
The students then participated in a simulated cramming session, in which they were given a chapter from a psychology text and directed to cram for a multiple choice test. The cramming session was timed by the researchers, and the multiple choice exam was administered immediately after the cramming session was completed. At the conclusion of the multiple choice test, the students were asked to complete a 5-point Likert scale rating their familiarity with the material presented. Then, they completed the Flow State Scale developed by Jackson and Marsh (1996). The Flow State Scale includes items related both to the flow state and as the autotelic experience.
Results:
Students, on average, reported cramming more from necessity than from choice and cramming more often than they would prefer. Student scores on the Academic Procrastination state Inventory and the Flow State Scale were normally distributed. The scores on multiple choice tests, given at the conclusion of the simulated cramming session, were also normally distributed. The participants’ mean frequency of the flow experience was above the mid-point of the scale, indicating that it was a familiar experience for many of the participants.
The correlations among study habits showed several relationships. Frequency of cramming during the current semester was positively correlated with a preference for cramming and negatively correlated with allocating enough time to study. On average, students who reported a greater frequency of flow also reported a greater overall satisfaction with their study habits.
Students’ ratings of the simulated cramming session indicated, on average, that the simulation was moderately successful at approximating the cramming experience. However, students rated the similarity between the simulation and their own personal experiences of cramming below the midpoint of the flow scale. Correlations between students who rated themselves as crammers by choice versus crammers by necessity were not statistically significant.
Discussion:
Brinthaupt, T. and Shin, C.M. (2001), tested the hypothesis that one of the positive affects of academic cramming is that it increases the likelihood of flow or flow-like experiences. The researchers claim that, at the time, of this research this was the only study that had ever examined flow in this context. They found that self-described crammers by choice performed better on the task and reported a greater experience of flow than either non-crammers or crammers by necessity.
Brinthaupt, T. and Shin, C.M. (2001), state that while procrastination is negatively related to the creation of flow or flow-like experience, cramming by choice is positively related to flow. The authors suggest that some crammers might wait until the level of challenge matches their perceived level of skill, thus increasing the possibility of creating a flow-like experience and making the task more interesting and engaging. Brinthaupt, T. and Shin, C.M. (2001) state the differences between the students who cram by choice and those who cram by necessity is worthy of further investigation. They suggest that students who cram by choice may have a unique skill set that allows them to be more successful crammers than those who are forced to cram due to circumstances.
Critique:
The authors identified several limitations of their study. The simulated cramming sessions were only moderately similar to real-life for several reasons. First, the participants had no incentives to do well on the multiple choice exam. Next, the reading material and test material had little or no relevance to the participants. Also, the simulation was shorter, on average, than a real-life cramming session. Finally, flow was assessed in both the cramming and testing phases of the simulation.
Despite the limitations, I found that this study presented several interesting ideas. The idea that some students might choose procrastination as a means of making routine, required course work more challenging is intriguing. The authors proposed that students who procrastinated and crammed by choice might also have positive correlations between these behaviors and other sensation-seeking behaviors. It would be interesting to explore the connection between those who chose to cram and other risk taking behaviors. As I read this article, I thought about our in-class discussion about safe Sally and wondered about the personality types that, most often, accompany the cramming by choice behavior.
Problem
Brinthaupt, T. and Shin, C.M. (2001), noted that research has neglected to examine the experiential aspects of academic cramming. Most educators have a negative view of the efficacy of cramming; we lecture students telling them “don’t wait until the last minute.” Prior to Brinthaupt, T. and Shin, C.M. (2001), no one asked, what do the crammers get out of the experience? Brinthaupt, T. and Shin, C.M. (2001), asked whether was there a relationship between cramming and Csikszentmihalyi (1987), flow theory. The researchers hypothesized that for some students, procrastination and cramming can create a state of flow. When the researchers examined the art of cramming, they noticed similarities between cramming behavior and the flow state. To achieve a state of flow, the individual must engage in a challenging activity that requires skills, merge actions with awareness, have clear goals and receive feedback. During the flow experience, the individual experiences increased concentration, the loss of self-consciousness, and a transformation of time.
Brinthaupt, T. and Shin, C.M. (2001), note that when students self-selected texts to read for pleasure they were more likely to experience flow. However, assigned texts read for a specific class did not produce the same results. Brinthaupt, T. and Shin, C.M. (2001), suggest that the assigned texts are less engaging and possibly, even boring, thus contributing to the procrastination, which in turns leads to cramming. Csikszentmihalyi (1990, 1997) suggests that when students procrastinate, they are, intentionally or unintentionally, increasing the level of challenge. Therefore, a student who successfully crams for a test or writes a paper at the last moment may do so because the likelihood of experiencing a flow state is increased.
To explore the possible connection between cramming and flow, the authors created a study that simulated a cramming and testing situation. They predicted that the self-identified crammers would experience a flow-like state than the non-crammers. Additionally, they explored the relationship of typical study habits and academic procrastination to performance during the simulated cramming session.
Methods:
The sample consisted of 167 undergraduates students (75 male & 92 female) enrolled in Introductory Psychology at a large southeastern University. Students were given course credit for their participation in the study. The students participated in groups of 10 to 20, and data was collected approximately 2 weeks prior to the end of the term. Six of the participants were excluded due to missing data, making the final total 161 participants.
Study habits were assessed using a 5-point Likert scale questionnaire, which included questions on frequency of cramming for exams: how often the individuals crammed by choice; and how often the individuals gave themselves enough time to study. Participants also completed a 23-item Academic Procrastination State Inventory which was developed by Schouwenbury (1995). Participants read a short paragraph defining flow and then completed a questionnaire rating their experiences of flow across a wide variety of circumstances.
The students then participated in a simulated cramming session, in which they were given a chapter from a psychology text and directed to cram for a multiple choice test. The cramming session was timed by the researchers, and the multiple choice exam was administered immediately after the cramming session was completed. At the conclusion of the multiple choice test, the students were asked to complete a 5-point Likert scale rating their familiarity with the material presented. Then, they completed the Flow State Scale developed by Jackson and Marsh (1996). The Flow State Scale includes items related both to the flow state and as the autotelic experience.
Results:
Students, on average, reported cramming more from necessity than from choice and cramming more often than they would prefer. Student scores on the Academic Procrastination state Inventory and the Flow State Scale were normally distributed. The scores on multiple choice tests, given at the conclusion of the simulated cramming session, were also normally distributed. The participants’ mean frequency of the flow experience was above the mid-point of the scale, indicating that it was a familiar experience for many of the participants.
The correlations among study habits showed several relationships. Frequency of cramming during the current semester was positively correlated with a preference for cramming and negatively correlated with allocating enough time to study. On average, students who reported a greater frequency of flow also reported a greater overall satisfaction with their study habits.
Students’ ratings of the simulated cramming session indicated, on average, that the simulation was moderately successful at approximating the cramming experience. However, students rated the similarity between the simulation and their own personal experiences of cramming below the midpoint of the flow scale. Correlations between students who rated themselves as crammers by choice versus crammers by necessity were not statistically significant.
Discussion:
Brinthaupt, T. and Shin, C.M. (2001), tested the hypothesis that one of the positive affects of academic cramming is that it increases the likelihood of flow or flow-like experiences. The researchers claim that, at the time, of this research this was the only study that had ever examined flow in this context. They found that self-described crammers by choice performed better on the task and reported a greater experience of flow than either non-crammers or crammers by necessity.
Brinthaupt, T. and Shin, C.M. (2001), state that while procrastination is negatively related to the creation of flow or flow-like experience, cramming by choice is positively related to flow. The authors suggest that some crammers might wait until the level of challenge matches their perceived level of skill, thus increasing the possibility of creating a flow-like experience and making the task more interesting and engaging. Brinthaupt, T. and Shin, C.M. (2001) state the differences between the students who cram by choice and those who cram by necessity is worthy of further investigation. They suggest that students who cram by choice may have a unique skill set that allows them to be more successful crammers than those who are forced to cram due to circumstances.
Critique:
The authors identified several limitations of their study. The simulated cramming sessions were only moderately similar to real-life for several reasons. First, the participants had no incentives to do well on the multiple choice exam. Next, the reading material and test material had little or no relevance to the participants. Also, the simulation was shorter, on average, than a real-life cramming session. Finally, flow was assessed in both the cramming and testing phases of the simulation.
Despite the limitations, I found that this study presented several interesting ideas. The idea that some students might choose procrastination as a means of making routine, required course work more challenging is intriguing. The authors proposed that students who procrastinated and crammed by choice might also have positive correlations between these behaviors and other sensation-seeking behaviors. It would be interesting to explore the connection between those who chose to cram and other risk taking behaviors. As I read this article, I thought about our in-class discussion about safe Sally and wondered about the personality types that, most often, accompany the cramming by choice behavior.
Wednesday, November 19, 2008
Asakawa (2004)
Problem
Asakawa (2004) noted that the study of flow theory had primarily focused on populations in the United States and Italy. Some researchers believe that flow theory has the potential to foster psychological resilience and to improve the quality of life; therefore, more research is needed to establish the universality of Csikszentmihalyi’s theory to non-western cultures. In an effort to replicate and expand prior findings Asakawa conducted a 3-year two-part analysis of the flow experience and an exploratory examination of the autotelic personality in Japanese college students. Additionally he explored the relationship between Jujitsu-kan, the Japanese a sense of fulfillment and flow.
Methods
Asakawa (2004) collected data from a private Japanese university over the course of 3 academic years. The sample consisted of 102 Japanese student volunteers (48 males and 54 females). The students were enrolled in a beginning psychology course and received extra course credit for their participation in the study. The study used the “Experience Sampling Method”, developed by Csikszentmihalyi (1987). The students were given preprogrammed wrist watches which would signal them 8 times a day at random intervals, for a total of 56 signals in a week. The students would then stop what they were doing to complete the “Experience Sampling Form (ESF)”. The ESF included questions related to daily locations, activities, companions, and psychological states.
Students completed a total of 4197 ESFs. To maintain consistency and reliability ESFs that contained incomplete responses and/or were completed more than 15 minutes after the signal were discarded. Additionally only participants who completed at least 15 ESF were included in the data base.
Seven experiential items on the ESF: concentration, enjoyment, happiness, activation, satisfaction, perceived control of the situation, and perceived importance for the future were used to measure quality of experience. Concentration, enjoyment, satisfaction, perceived control of the situation, and perceived importance for the future was measured using a 10-point rating scale ranging from “not at all” to “very much.” Jujitsu-kan, the Japanese sense of fulfillment, was evaluated by asking the students, to rate the level of Jujitsu-kan they were you experiencing on 9-point rating scale ranging from “low” to “high”. Jujitsu-kan was measured only in the third year of the study. The types of activities in which a participant was engaged were measured by using an open-ended question: “What where you doing when you were signaled.” Perceived challenges, perceived skills, anxiety, relaxation, and apathy were measured on 9-point rating scale ranging from “low” to “high.”
Results
The study examined the applicability of flow theory to a Japanese sample. In concordance with flow theory, as flow increased, so did the students concentration, enjoyment, happiness, activation, satisfaction, perceived control of the situation, and perceived importance of future value. Additionally, as flow increased the students reported an increase in feelings of Jujitsu-kan. Flow theory assumes a positive correlation between the time spent in flow and the quality of experience. The Japanese students, who reported more time in the flow, also reported higher levels of concentration, more enjoyment, were more active, experienced higher levels of satisfaction, felt more in control, and felt a stronger sense of Jujitsu-kan than the students who spent less time flow.
To expand the examination of flow theory, Asakawa conducted an exploratory investigation on the autotelic personality. The first analysis examined the type of activities students engaged in during the week. The results indicated no significant difference between the two groups, except for two marginal differences found for schoolwork (t (50) =1.79, p = 0.080) and overall active leisure (t (50) = 1.77, p = 0.082). On average the autotelic students spent more time doing schoolwork and active leisure than their non-autotelic counterparts. Asakawa concluded that except for two slight differences being autotelic or non-autotelic did not significantly influence how Japanese college students spent their time. The next analysis examined the overall quality of experience between the autotelic and non-autotelic groups of students. On average the autotelic students rated their concentration, enjoyment, satisfaction, sense of control, higher than the non-autotelic students. These differences between the two groups were in agreement with flow theory and not unexpected.
Discussion
Asakawa states that these findings provide evidence of flow theory’s applicability to the Japanese population and suggest the universality of flow experience. Additionally the research suggests that flow has a positive effect on the psychological well-being of individuals who experience high levels of flow in their daily lives. Contrary to the previous studies conducted in western societies Asakawa’s research did not obtain clear differences between the autotelic and non-autotelic groups of Japanese college students’ usage of time. Both groups seemed to have similar college student life. Being autotelic or non-autotelic did not significantly influence how the Japanese students spent their time. However, when the two groups were asked to rate the quality of experience autotelic Japanese students reported significantly more positive experiences than the non-autotelic students.
Critique
Multiple tables and statistics embedded in the text, made this article very challenging to read. The study used a convenience sample of volunteers, all of whom received extra credit for their participation in the research. While the sample size was large, it did consist of 3 separate groups of student volunteers over the three year course of the study. The researcher did not address the possible issue of differences between the groups during the three year time frame. Additionally, the question in relationship to Jujitsu-kan, the Japanese a sense of fulfillment was added to the study during the third and final year. This research was exploratory and supports earlier work completed by Csikszentmihalyi and others; however, I am not sure that it would be generalizability to all non-western populations. I personally did not find this research as useful as I was hoping. Asakawa did replicate prior work and there was the finding of similarities in activity choices and between the autotelic and non-autotelic students. Leading me to question the role cultural, parental, and societal expectations play in the behavior patterns of this student population. I feel that flow theory does have the potential to contribute the psychological well-being of individuals and therefore is a topic worthy of further investigation.
Asakawa (2004) noted that the study of flow theory had primarily focused on populations in the United States and Italy. Some researchers believe that flow theory has the potential to foster psychological resilience and to improve the quality of life; therefore, more research is needed to establish the universality of Csikszentmihalyi’s theory to non-western cultures. In an effort to replicate and expand prior findings Asakawa conducted a 3-year two-part analysis of the flow experience and an exploratory examination of the autotelic personality in Japanese college students. Additionally he explored the relationship between Jujitsu-kan, the Japanese a sense of fulfillment and flow.
Methods
Asakawa (2004) collected data from a private Japanese university over the course of 3 academic years. The sample consisted of 102 Japanese student volunteers (48 males and 54 females). The students were enrolled in a beginning psychology course and received extra course credit for their participation in the study. The study used the “Experience Sampling Method”, developed by Csikszentmihalyi (1987). The students were given preprogrammed wrist watches which would signal them 8 times a day at random intervals, for a total of 56 signals in a week. The students would then stop what they were doing to complete the “Experience Sampling Form (ESF)”. The ESF included questions related to daily locations, activities, companions, and psychological states.
Students completed a total of 4197 ESFs. To maintain consistency and reliability ESFs that contained incomplete responses and/or were completed more than 15 minutes after the signal were discarded. Additionally only participants who completed at least 15 ESF were included in the data base.
Seven experiential items on the ESF: concentration, enjoyment, happiness, activation, satisfaction, perceived control of the situation, and perceived importance for the future were used to measure quality of experience. Concentration, enjoyment, satisfaction, perceived control of the situation, and perceived importance for the future was measured using a 10-point rating scale ranging from “not at all” to “very much.” Jujitsu-kan, the Japanese sense of fulfillment, was evaluated by asking the students, to rate the level of Jujitsu-kan they were you experiencing on 9-point rating scale ranging from “low” to “high”. Jujitsu-kan was measured only in the third year of the study. The types of activities in which a participant was engaged were measured by using an open-ended question: “What where you doing when you were signaled.” Perceived challenges, perceived skills, anxiety, relaxation, and apathy were measured on 9-point rating scale ranging from “low” to “high.”
Results
The study examined the applicability of flow theory to a Japanese sample. In concordance with flow theory, as flow increased, so did the students concentration, enjoyment, happiness, activation, satisfaction, perceived control of the situation, and perceived importance of future value. Additionally, as flow increased the students reported an increase in feelings of Jujitsu-kan. Flow theory assumes a positive correlation between the time spent in flow and the quality of experience. The Japanese students, who reported more time in the flow, also reported higher levels of concentration, more enjoyment, were more active, experienced higher levels of satisfaction, felt more in control, and felt a stronger sense of Jujitsu-kan than the students who spent less time flow.
To expand the examination of flow theory, Asakawa conducted an exploratory investigation on the autotelic personality. The first analysis examined the type of activities students engaged in during the week. The results indicated no significant difference between the two groups, except for two marginal differences found for schoolwork (t (50) =1.79, p = 0.080) and overall active leisure (t (50) = 1.77, p = 0.082). On average the autotelic students spent more time doing schoolwork and active leisure than their non-autotelic counterparts. Asakawa concluded that except for two slight differences being autotelic or non-autotelic did not significantly influence how Japanese college students spent their time. The next analysis examined the overall quality of experience between the autotelic and non-autotelic groups of students. On average the autotelic students rated their concentration, enjoyment, satisfaction, sense of control, higher than the non-autotelic students. These differences between the two groups were in agreement with flow theory and not unexpected.
Discussion
Asakawa states that these findings provide evidence of flow theory’s applicability to the Japanese population and suggest the universality of flow experience. Additionally the research suggests that flow has a positive effect on the psychological well-being of individuals who experience high levels of flow in their daily lives. Contrary to the previous studies conducted in western societies Asakawa’s research did not obtain clear differences between the autotelic and non-autotelic groups of Japanese college students’ usage of time. Both groups seemed to have similar college student life. Being autotelic or non-autotelic did not significantly influence how the Japanese students spent their time. However, when the two groups were asked to rate the quality of experience autotelic Japanese students reported significantly more positive experiences than the non-autotelic students.
Critique
Multiple tables and statistics embedded in the text, made this article very challenging to read. The study used a convenience sample of volunteers, all of whom received extra credit for their participation in the research. While the sample size was large, it did consist of 3 separate groups of student volunteers over the three year course of the study. The researcher did not address the possible issue of differences between the groups during the three year time frame. Additionally, the question in relationship to Jujitsu-kan, the Japanese a sense of fulfillment was added to the study during the third and final year. This research was exploratory and supports earlier work completed by Csikszentmihalyi and others; however, I am not sure that it would be generalizability to all non-western populations. I personally did not find this research as useful as I was hoping. Asakawa did replicate prior work and there was the finding of similarities in activity choices and between the autotelic and non-autotelic students. Leading me to question the role cultural, parental, and societal expectations play in the behavior patterns of this student population. I feel that flow theory does have the potential to contribute the psychological well-being of individuals and therefore is a topic worthy of further investigation.
Thursday, November 6, 2008
Bassi et al. 2007
Bassi, M., Steca, P., Delle Fave, A., & Caprara, G. V. (2007). Academic self-efficacy beliefs and quality of experience in learning. Journal of Youth and Adolescence, 36, 301-312.
Problem/Purpose
This article is of interest because the authors investigated learning activities and looked specifically at Bandura’s self-efficacy theory and Czikszentmihalyi’s flow theory. The authors chose these two areas because they are both important theories in active individual intrinsic motivation, but self-efficacy focuses on individuals’ expectations of success or failure and because flow studies have focused on task value and individuals’ reasons for engaging in different achievement tasks. Previous empirical findings have shown that self-efficacy beliefs strongly mediate skill effects and other beliefs as they pertain to performance and accomplishment in academic tasks. Meanwhile flow studies have indicated that the perceived quality of task experiences is related to challenges and personal capabilities. The authors sought to investigate the interplay between daily experiences and self-efficacy beliefs in learning activities.
Methods
Bassi et al conducted an experience sampling method (ESM) study with 130 Italian secondary school students, adolescents aged 15 to 19 years (mean 17.25 years). The data for analysis was collected in 2000 as part of a longitudinal research program in Italy. Participants completed a self-efficacy scale with one domain to measure individuals’ perceived capabilities at mastering different curricular areas. A second domain referred to the perceived capacity for self-regulating learning activities. In addition, there were items to measure value placed on academic pursuits and academic aspirations. For the ESM portion of the study, participants carried digital diaries and forms to complete when the diaries sent random signals throughout the day. These diaries and forms allowed for determinations of daily time budgets and the associated quality of experience at those times.
In the data analysis, the top and bottom quartiles of respondents with respect to self-efficacy were categorized into high and low self-efficacy groups for comparison. (The middle 50% of respondents were not analyzed.) The researchers found an expected and statistically significant gender disparity between the high and low self-efficacy groups, consistent with previous research studies. Within the groups however, there were no other significant gender differences. T tests were used for all comparisons, and Cronbach’s alpha values for the measure of self-efficacy at three different times ranged from alpha = 0.85 to 0.88.
In addition, quality of students’ experiences were analyzed using the experience fluctuation model (EFM), which is an elaborated version of the traditional flow theory model. The traditional flow theory model consists of four states: optimal experience (flow, with high levels of challenge and skill), anxiety (where challenges are greater than skill level), relaxation (where skill level is greater than challenges), and apathy (where skill and challenge levels are both low). EFM includes four additional categories: (1) arousal (between anxiety and flow), (2) worry (between apathy and anxiety), (3) boredom (between apathy and relaxation), and (4) control (between relaxation and flow).
Results
Bassi et al found many expected results using t-tests as their primary statistical method. Bonferroni adjustments were made to account for the large number of t-tests and to reduce the likelihood of Type I errors.
High self-efficacy students placed more importance on academic attainments, aspired to a higher educational level, and reported higher beliefs in the academic efficacy than how self-efficacy students did. The high self-efficacy students also obtained higher teachers’ evaluations.
With respect to time distributions for the two groups, high self-efficacy students spent more time in learning tasks while low self-efficacy students spent more time in maintenance activities (such as grooming, hygiene, eating, and sleeping). The primary factor in these differences was the amount of time spent in homework. High self-efficacy students spent twice as much time on homework.
High self-efficacy students mostly associated all learning experiences with flow, although they also reported high frequencies of relaxation and apathy in school work. In contrast, low self-efficacy students associated class work mostly with anxiety, but homework equally with flow and relaxation.
Both groups associated homework with below average values of wish to be doing the activity (they would rather have been doing something else). T-test comparisons showed significant differences between the groups with respect to concentration, control, and satisfaction. However, both groups showed an increase in most of the values when engaged in flow experiences.
Discussion
The authors state that their findings confirm the strong links between self-efficacy beliefs and performance and attainment. They identified crucial differences in time allotment between the groups, especially with respect to homework. High self-efficacy students associated school work and homework with flow, although school work was also associated with relaxation and apathy. Low self-efficacy students did not perceive many flow opportunities in learning tasks, associating class work with anxiety and noting that tests and exams often exceeded their personal skills. These students also reported a high degree of apathy. However, both groups significantly wished to be engaged in a different activity when doing school work.
The authors posit that low self-efficacy students rarely experiences flow states during learning activities and this may account for their much lower perceptions of concentration, control, involvement, wish to do the activity, and satisfaction during school work.
The authors indicate that their data stress a crucial role for self-efficacy in motivating students and that since self-efficacy beliefs develop early, timely intervention is needed. They also indicate that there is a possibility that flow experiences could be fostered for low self-efficacy students in order to sustain long-term perseverance that could result in feedback to self-efficacy perceptions. They state that over time such feedback could provide evidence for students to alter their self-efficacy beliefs and develop into a virtuous cycle promoting skill development, satisfaction, and goal setting.
Limitations noted in the article are all related to sample size. Due to limited sample size (explained as necessary for cost reasons and also because of the large number of data recorded and analyzed per participant), there are limitations in statistical power and the ability to examine gender differences.
Critique
This article is mostly well written. For the most part, Bassi et al use language that is understandable and jargon is avoided when possible and explained where necessary. The reliance on such a large number of t-tests suggests that other statistical methods, such as linear regression or multivariate ANOVA could have accounted for type I errors more effectively than the Bonferroni technique. A second shortcoming was language that implied causal relationships between self-efficacy and motivation. There may be such a relationship, but the evidence in this research paper do not provide it and the citations are not described in ways that make it clear that such a relationship has been demonstrated.
Theoretical and Practical Implications
A major implication of this article is the potential to use flow experiences to alter a normally stable trait, perception of self-efficacy. If this is a causal trait that affects other desirable factors, such as time spent on homework, perseverance, performance, teacher perception, etc. then being able to improve self-efficacy is indeed a worthwhile goal. Unfortunately the collection of data to demonstrate this potential would require longitudinal data on a large number of subjects and the combined effort of many different teachers to modify their curricula to be sure that students were engaged in flow activities most of the time. To my knowledge, there are few if any curricula designed around flow activities available for such studies.
Problem/Purpose
This article is of interest because the authors investigated learning activities and looked specifically at Bandura’s self-efficacy theory and Czikszentmihalyi’s flow theory. The authors chose these two areas because they are both important theories in active individual intrinsic motivation, but self-efficacy focuses on individuals’ expectations of success or failure and because flow studies have focused on task value and individuals’ reasons for engaging in different achievement tasks. Previous empirical findings have shown that self-efficacy beliefs strongly mediate skill effects and other beliefs as they pertain to performance and accomplishment in academic tasks. Meanwhile flow studies have indicated that the perceived quality of task experiences is related to challenges and personal capabilities. The authors sought to investigate the interplay between daily experiences and self-efficacy beliefs in learning activities.
Methods
Bassi et al conducted an experience sampling method (ESM) study with 130 Italian secondary school students, adolescents aged 15 to 19 years (mean 17.25 years). The data for analysis was collected in 2000 as part of a longitudinal research program in Italy. Participants completed a self-efficacy scale with one domain to measure individuals’ perceived capabilities at mastering different curricular areas. A second domain referred to the perceived capacity for self-regulating learning activities. In addition, there were items to measure value placed on academic pursuits and academic aspirations. For the ESM portion of the study, participants carried digital diaries and forms to complete when the diaries sent random signals throughout the day. These diaries and forms allowed for determinations of daily time budgets and the associated quality of experience at those times.
In the data analysis, the top and bottom quartiles of respondents with respect to self-efficacy were categorized into high and low self-efficacy groups for comparison. (The middle 50% of respondents were not analyzed.) The researchers found an expected and statistically significant gender disparity between the high and low self-efficacy groups, consistent with previous research studies. Within the groups however, there were no other significant gender differences. T tests were used for all comparisons, and Cronbach’s alpha values for the measure of self-efficacy at three different times ranged from alpha = 0.85 to 0.88.
In addition, quality of students’ experiences were analyzed using the experience fluctuation model (EFM), which is an elaborated version of the traditional flow theory model. The traditional flow theory model consists of four states: optimal experience (flow, with high levels of challenge and skill), anxiety (where challenges are greater than skill level), relaxation (where skill level is greater than challenges), and apathy (where skill and challenge levels are both low). EFM includes four additional categories: (1) arousal (between anxiety and flow), (2) worry (between apathy and anxiety), (3) boredom (between apathy and relaxation), and (4) control (between relaxation and flow).
Results
Bassi et al found many expected results using t-tests as their primary statistical method. Bonferroni adjustments were made to account for the large number of t-tests and to reduce the likelihood of Type I errors.
High self-efficacy students placed more importance on academic attainments, aspired to a higher educational level, and reported higher beliefs in the academic efficacy than how self-efficacy students did. The high self-efficacy students also obtained higher teachers’ evaluations.
With respect to time distributions for the two groups, high self-efficacy students spent more time in learning tasks while low self-efficacy students spent more time in maintenance activities (such as grooming, hygiene, eating, and sleeping). The primary factor in these differences was the amount of time spent in homework. High self-efficacy students spent twice as much time on homework.
High self-efficacy students mostly associated all learning experiences with flow, although they also reported high frequencies of relaxation and apathy in school work. In contrast, low self-efficacy students associated class work mostly with anxiety, but homework equally with flow and relaxation.
Both groups associated homework with below average values of wish to be doing the activity (they would rather have been doing something else). T-test comparisons showed significant differences between the groups with respect to concentration, control, and satisfaction. However, both groups showed an increase in most of the values when engaged in flow experiences.
Discussion
The authors state that their findings confirm the strong links between self-efficacy beliefs and performance and attainment. They identified crucial differences in time allotment between the groups, especially with respect to homework. High self-efficacy students associated school work and homework with flow, although school work was also associated with relaxation and apathy. Low self-efficacy students did not perceive many flow opportunities in learning tasks, associating class work with anxiety and noting that tests and exams often exceeded their personal skills. These students also reported a high degree of apathy. However, both groups significantly wished to be engaged in a different activity when doing school work.
The authors posit that low self-efficacy students rarely experiences flow states during learning activities and this may account for their much lower perceptions of concentration, control, involvement, wish to do the activity, and satisfaction during school work.
The authors indicate that their data stress a crucial role for self-efficacy in motivating students and that since self-efficacy beliefs develop early, timely intervention is needed. They also indicate that there is a possibility that flow experiences could be fostered for low self-efficacy students in order to sustain long-term perseverance that could result in feedback to self-efficacy perceptions. They state that over time such feedback could provide evidence for students to alter their self-efficacy beliefs and develop into a virtuous cycle promoting skill development, satisfaction, and goal setting.
Limitations noted in the article are all related to sample size. Due to limited sample size (explained as necessary for cost reasons and also because of the large number of data recorded and analyzed per participant), there are limitations in statistical power and the ability to examine gender differences.
Critique
This article is mostly well written. For the most part, Bassi et al use language that is understandable and jargon is avoided when possible and explained where necessary. The reliance on such a large number of t-tests suggests that other statistical methods, such as linear regression or multivariate ANOVA could have accounted for type I errors more effectively than the Bonferroni technique. A second shortcoming was language that implied causal relationships between self-efficacy and motivation. There may be such a relationship, but the evidence in this research paper do not provide it and the citations are not described in ways that make it clear that such a relationship has been demonstrated.
Theoretical and Practical Implications
A major implication of this article is the potential to use flow experiences to alter a normally stable trait, perception of self-efficacy. If this is a causal trait that affects other desirable factors, such as time spent on homework, perseverance, performance, teacher perception, etc. then being able to improve self-efficacy is indeed a worthwhile goal. Unfortunately the collection of data to demonstrate this potential would require longitudinal data on a large number of subjects and the combined effort of many different teachers to modify their curricula to be sure that students were engaged in flow activities most of the time. To my knowledge, there are few if any curricula designed around flow activities available for such studies.
Wednesday, October 29, 2008
keller & bless 2008
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.
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.
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.
Subscribe to:
Posts (Atom)