Which of the following is not listed as one of the possible uses of positive psychology?

Introduction

Mihaly Csikszentmihalyi introduced flow theory in the 1970s based on research examining people who did activities for pleasure, even when they were not rewarded with money or fame. He considered artists, writers, athletes, chess masters, and surgeons – individuals who were involved in activities they preferred. He was surprised to discover that enjoyment did not result from relaxing or living without stress, but during these intense activities, in which their attention was fully absorbed. He called this state flow, because during his research, people illustrated their intense experiences using the metaphor of being carried by a current like a river flows.

Participants were motivated by the quality of the experience they had while they were engaged in the activity. The flow experience came when the activity was difficult and involved risk. It usually stretched the person's capacity and provided a challenge to his/her skills.

The concept of flow became the key element for the theory of optimal experience, since it provided the best user experience. Flow refers to a state of mind which brings together cognitive, physiological and affective aspects. Flow experience corresponds to an optimal psychophysical state: participants said it is like being in the zone, being on the ball, being in the groove. Flow also inspires peak performances so some use expressions such as ‘everything clicks’ and ‘experiencing a magic moment’.

Csikszentmihalyi reported that flow occurred more often during work than free time. It was easier to achieve the flow state in activities such as performing music, dance and writing since they had rules and required the learning of skills. In these activities people were deeply involved and motivated because they were participating in an enjoyable experience. While almost any active involvement can potentially lead to flow, activities which are passive, such as watching television, do not normally lead to flow. However in 2007 Steven Pritzker proposed that audience flow can occur if a television show is relevant to the viewers' life.

Flow is an interdisciplinary field of research, addressed by psychologists working in the fields of positive psychology, cognitive psychology, arts, sports, science, sociologists and by anthropologists interested in altered states of consciousness, spiritual experiences, and rituals in different cultures.

Looking Back, Looking Ahead

An examination of students’ educational experience from the perspective of flow theory is informative to both researchers and educators in that it helps us understand the personal and situational factors that promote students’ deep engagement in learning. There is enormous potential for students to experience flow in schools, but this potential is often thwarted because some key conditions for flow are largely missing from many of today's classrooms. While students generally experience two of the main conditions in that they are using their skills to address increased levels of challenges in the classroom, they often fail to experience flow. This may be because school environments generally offer few opportunities for choice. Moreover, the goals and feedback in classrooms may be focused on a target that is less immediate than the goals that are usually salient in flow experiences. Finally, classrooms may need to be structured in a way that helps students focus their attention on the tasks at hand.

Most encouraging is the fact that flow is consistently observed in a number of unique school contexts. Students often feel flow in their nonacademic classes, most of which provide a number of flow conditions like choice, autonomy, and focus that are typically absent in academic subjects. Likewise, students tend to experience flow when engaged in hands-on learning tasks. Additionally, a number of nontraditional schools and school programs are very successful in facilitating flow among their students – this appears to be attributable to greater focus on many of the conditions that produce flow. Teachers interested in increasing their students’ engagement in classrooms may learn a lot by examining the structure of nonacademic classes, as well as the nontraditional programs mentioned here, to take some cues about how to create more of the conditions for flow in students’ everyday experience.

Looking to the future of research on flow in education, there is still much work to be done. First, more research needs to be done linking the experience of flow in academic pursuits with a variety of learning outcomes, as the few studies that have addressed this issue have produced mixed results. Future research must further examine the nature of links between the experience of flow and specific learning outcomes. Additionally, more research is needed to understand the link between flow in a given subject area, and long-term commitment to that field.

Second, relatively little work has been done to examine the role of flow in learning environments beyond the classroom. One emerging application of flow theory in education concerns the use of computers and video games. Children and adolescents frequently experience flow when engaged in video and computer games (Bassi and Delle Fave, 2004). In recent years, researchers and educators alike have attempted to use the appeal of videogames to construct interactive computer technology for learning. A growing body of evidence suggests not only that these e-learning environments can be intensely engaging, but also that such engagement is linked to a variety of positive learning outcomes (Coller and Shernoff, 2006; Pearce, 2005; see Scoresby and Shelton, 2007 for a review). An examination of the e-learning experience from the perspective of flow theory will assist in the understanding and design of these increasingly used educational tools. Likewise, researchers should continue to examine the role of flow as it relates to learning in other extracurricular environments.

Third, the vast majority of research on flow in education has involved children and adolescents. While the experience of flow appears to be consistent across age groups, it is possible that certain conditions for flow, similar to challenge or autonomy, might be differentially salient to learners of different ages. Thus, examining a broader age range of learners, including adults, would be informative. In general, research on flow in schools should take into consideration the developmental stage of the students under investigation.

Finally, we must not forget that teachers play a key role in educational processes – an examination of teachers from the perspective of flow theory would be informative as well. Dissertation work by Di Bianca, in 2000, suggests that when teachers report the most flow, students generally report the least. In other words, those moments that are most engaging to teachers are least engaging to students. Further research is needed to corroborate and explain these findings.

The flow model and related research provides a solid base of knowledge regarding how students might become more engaged in their learning and how they feel when they are so engaged. This knowledge base is of practical use to educators interested in increasing student engagement. There is, however, much work remaining to be done in this field in order to more fully understand the complexities of flow's role in educational processes.

Introduction

There has been a lot of research since when Mihaly Csikszentmihalyi introduced flow theory in the 1970s. Flow theory was developed with the aim of understanding how people feel when they most enjoyed themselves, and why. Csikszentmihalyi came upon the concept of flow as a result of researching the question “What is enjoyment?” He studied people who did activities for pleasure even when they were not rewarded with money or fame. He considered artists, writers, athletes, chess masters and surgeons,—people who were involved in activities they preferred. It was surprising to discover that enjoyment did not result from relaxing or living without stress, but during these intense activities, in which their attention was fully absorbed. This state was called by Csikszentmihalyi flow, because during the research, people illustrated their intense experiences using the metaphor of being carried by a current like a river flows. Participants were motivated by the quality of the experience they had while they were engaged in the activity. The flow experience came when the activity was difficult and involved risk. It usually stretched the person's capacity and provided a challenge to his/her skills.

The concept of flow became the key element for the theory of optimal experience since it provided the best user experience. Flow refers to a state of mind which brings together cognitive, physiological and affective aspects. Flow experience corresponds to an optimal psychophysical state: participants said it is like being in the zone, being on the ball, being in the groove. Flow also inspires peak performances so some use expressions such as “everything clicks” (Jackson and Csikszentmihalyi, 1999; Martin and Jackson, 2008) and “experiencing a magic moment” (Kossak, 2009, p. 16).

Csikszentmihalyi reported that flow occurred more often during work than free time. It was easier to achieve the flow state in activities such as performing music, dance and writing since they had rules and required the learning of skills. In these activities people were deeply involved and motivated because they were participating in an enjoyable experience. While almost any active involvement can potentially lead to flow, activities which are passive, such as watching television, do not normally lead to flow. However, Pritzker (2007) proposed that audience flow can occur if a television show is relevant to the viewers' life. Flow is an interdisciplinary field of research, addressed by psychologists working in the fields of positive psychology, cognitive psychology, arts, sports, science, sociologists and by anthropologists interested in altered states of consciousness, spiritual experiences and rituals in different cultures.

Player Ricardo—A Competitor

Ricardo appears to be a competitor who came from behind to iteratively build a viable mental model and expertise, especially within the Surface Features module. Timed Report metrics, dashboards, and gameplay scores clearly document his persistence and improvement. Player logs suggest that Ricardo consulted the dashboard to build Selene expertise. He submitted 112 Flowometer reports. His subjective perceptions of experience during periods of learning did not categorize as flow. There is some evidence Ricardo experienced flow when he felt at the top of his game, controlling the game parameters. Patterns of high volume in player dashboard gestures during gameplay suggest Ricardo was building models of Selene expertise in applying Accretion and, especially, Surface Features concepts (see Table 11.1 and Appendix). Interleaving his learning trajectories, dashboard gestures, and Flowometer reports for Surface Features suggests Ricardo might have experienced eustress while using the dashboard to define and build expertise.

Table 11.1. Ricardo’s replays and dashboard gestures by module, postgame, and round

The following figures document the story of his achievement and affective response:

Figures 11.14 and 11.15 illustrate his goal achievement within the Accretion module.

Figure 11.16 plots all his affective responses using the nine-channel format from flow theory.

Figure 11.17 provides a nine-channel format for comparison player Juana.

Figures 11.18–11.22 contain his Selene dashboards at critical incidents and the final dashboard for comparison player Juana.

Figure 11.23 displays the Selene leaderboard at the time Ricardo earned first place.

Figures 11.24 and 11.25 display a comparison between Ricardo’s Surface Features module achievement and goal states at the one-second unit.

2.12.3.7 Simulated Rotation

The rotation problem has been investigated using displays that simulate the retinal flow corresponding to a pursuit rotation, while the eye is actually stationary (see Figure 2(c)); any extraretinal signal thus indicates zero rotation. Initially, Warren W. H. and Hannon D. J. (1988; 1990) reported heading thresholds better than 1.5° during both simulated and real eye rotation (for rotation rates <1° s−1), as long as there was 3D structure in the scene, consistent with retinal flow theories based on motion parallax. With a frontal plane of dots, on the other hand (Figure 3), heading judgments were only accurate during real eye movements, consistent with extraretinal theories. Yet Grigo A. and Lappe M. (1999) reported accurate judgments during simulated rotation with a larger field of view, presumably due to global motion parallax from the periphery (Grigo, A. and Lappe, M., 1999). Subsequent research also supported some version of a retinal flow theory (Cutting, J. E. et al., 1992; van den Berg, A. V. 1993; 1996; Stone, L. S. and Perrone, J. A., 1997; Wang, R. F. and Cutting, J. E., 1999).

However, when Banks, M. S. and his colleagues (Royden, C. S. et al., 1992; 1994; Banks, M. S. et al., 1996) tested higher rotation rates (1–5 s−1), they found large heading errors of up to 15° in the simulated rotation condition, contrary to retinal flow theories. Whether the visual system can determine the direction of heading from retinal flow was thus cast into doubt.

Measuring Affect

The CyGaMEs Flowometer Report is a measure of affect: a player’s immediate subjective perceptions of current experience (see Figure 6.4). Although the Flowometer is integrated into a game system, it is not an embedded assessment because the Flowometer stops gameplay and asks the player to report, on a scale from 0 (low) to 100 (high), perceived level of skill and challenge (Reese, 2010). Reese derived the Flowometer, protocols implemented to administer it, and methodologies for analysis for Flowometer Report data (e.g., Reese, 2015) directly from flow theory and research methods (Csikszentmihalyi & Csikszentmihalyi, 1988b; Csikszentmihalyi & Larson, 1987; Delle Fave, Massimini, & Bassi, 2011; Hektner, Schmidt, & Csikszentmihalyi, 2007; Massimini & Carli, 1988; Moneta, 2012). Skill and challenge dimensions may be analyzed together using multilevel models. Data can be plotted as trajectories of player experience over time (Reese, 2015). CyGaMEs algorithms have been developed to categorize each skill-challenge dyad into sectors (see Figure 6.5). Based upon Flow literature (Csikszentmihalyi & Csikszentmihalyi, 1988a; Delle Fave et al., 2011; Hektner et al., 2007; Massimini & Carli, 1988) but adapted based upon Hatano and Inagaki’s (1986) work with adaptive and routine expertise, the nine sectors of affect are flow, arousal, anxiety, worry, apathy, intrinsic motivation, boredom, and routine expertise (replaces relaxation).

CyGaMEs protocols administer the Flowometer within every 5 minutes of gameplay at a randomly determined, preselected time such that each player within a game or nongameplay module receives the Flowometer on the same schedule. Flowometer Reports post with relevant paradata. Contextual paradata such as timestamp and location in the environment are the same variables as those posted with the Timed Report.

When players of the CyGaMEs instructional science game Selene: A Lunar Construction GaME actively strive toward expertise, they often perceive gameplay challenge to be greater than their gameplay skill level (Reese, 2015). Players who tenaciously strive toward gameplay achievement—hour after hour—are certainly invested and engaged in their gameplay. But the research methods and measures described find that these players’ self-perceptions during gameplay learning do not typically categorize as flow. Rather, gameplay inducing and exhibiting persistence and problem-solving (targeted as twenty-first century skills) may quantify as worry, anxiety, and arousal. Since these players persevere to success, such gameplay experience would categorize as eustress (positive stress during adaptation) rather than distress (negative stress that debilitates); that is, emotions of striving (to learn more about the distress/eustress distinction, see Selye, 1974, 1975, 2013).

What effect, if any, do multisensory representations contribute to subjective self-perceptions of experience during gameplay learning? Integration of flow theory, the Flowometer, and analyses techniques will support exploration of the three-way interaction among multisensory effects, game-based learning, and affect.

Conclusion

Knowledge is key to sustainable competitive advantage, but different kinds of knowledge affect competitive advantage differently, and they exhibit qualitatively different dynamic properties and behaviours. This pertains in particular to tacit and explicit knowledge. Tacit knowledge is rich and powerful, enabling rapid, expert-level action in many circumstances, but it tends to be highly situated and to flow slowly and narrowly across people, organisations, places and times. In contrast, explicit knowledge tends to be more generally applicable and to flow broadly and rapidly, but it is diluted and less powerful than its tacit counterpart.

Mobile network technologies are becoming increasingly powerful and ubiquitous, but like most information technologies, they maintain a predominate focus on explicit knowledge. In contrast, advancing capabilities to facilitate remote and multiparty collaboration offer increasing potential to support tacit knowledge flows as well. We describe a multilayer, ad-hoc, mobile networking architecture that enables both tacit and explicit knowledge flows through our MIO work. Indeed, building upon Knowledge Flow Theory, we illustrate an approach to accelerating tacit knowledge flows through such mobile network technologies. The ability of our ad-hoc, mobile, MIO networks to connect boarding parties with radiological experts (with positive identifications obtained in minutes) represents a vivid example of how tacit knowledge can flow – virtually, and only while the IT networks connect people – across situations and distances. As suggested above, perhaps a term such as virtual tacit knowledge flows would characterise this phenomenon more clearly.

This chapter offers a theoretical contribution by illustrating how network technology, which is associated generally and broadly with explicit knowledge and information flow, can enable virtual, tacit knowledge flows as well. Such support of tacit knowledge as well as explicit knowledge flows elucidates an exciting path for continued research along these lines. One could, for instance, identify and examine different combinations of network architectures and knowledge types to examine their comparative efficacy; research to reveal and understand the limitations of such architectures with respect to different knowledge domains would also lead to important discoveries.

This work highlights practical application as well. We show, for instance, how network technologies can be leveraged to accelerate tacit knowledge flows in the very practical domain of homeland security. Maritime interdiction represents a fundamental aspect of homeland security, and even a small competitive advantage that a nation can gain and maintain over terrorist organisations can save many lives. More research along these lines can employ similar network architectures in other areas of practical application (e.g. human trafficking, drug smuggling, illegal immigration) at the international, national and regional levels, and practical results can be used in turn to guide the development and refinement of more advanced and tailored technologies. The link between mobile network technologies and tacit knowledge flows is only just emerging now, but it offers huge potential and opens up a substantial area for continued research and technological development.

3.1 Inflow and outflow

When we touch a seen object, organizing the arm movements requires appreciating its location with respect to some body-image reference frame. This requires knowing the position of the eye in the head and the head with respect to the body image. Since we reach with reasonable accuracy, we must know the position of the eye in the head fairly accurately. Two theories have been advanced for the source of this signal.

The first, proposed by Helmholtz and others (see McCloskey, 1981, for an historical review), suggests that we know eye position by the “effort of will”; that is, by monitoring the efferent signal, E', in Fig. 2C. This is called the outflow theory. The in-flow theory, proposed by Sherrington (1906), suggests that muscle proprioception provides this information. This idea may include any extraretinal sensory signal but so far there has been little evidence for other types of mechanoreceptors.

The experimental results favor the outflow theory (e.g., Carpenter, 1988; Hansen and Skavenski, 1977; McCloskey, 1981). Subjects seem unaware of the position of their eyes when it is changed passively by forceps (Brindley and Merton, 1960). Subsequently, Skavenski (1972) found that comfortable subjects, devoting full attention, could sense that their eyes were being passively deviated in the dark but only beyond about 7 deg and could tell the direction only about 75% of the time. This indicates that while muscle proprioception can be perceived (Goodwin et al., 1972)—this was long doubted—it is not useful in practice for telling where the eye is.

When the eye is moved passively, the subject perceives the visual scene to move, not the eye. Since the subject did not instruct the eye to move, retinal image motion is attributed to motion of visual objects. In the terminology of Fig. 2C, an object's perceived position is the sum of its retinal error, e, and the eye position command, E' (outflow), as proposed by Von Holst and Mittelstaedt (1950). The usual demonstration is to close one eye and poke the other through the upper lid; the visual surround will appear to move. A more controlled way is to displace the eye with a suction contact lens in which case the subject believes that the target (a small light in a dark room) had been displaced, not the eye (Skavenski, 1972). To dissociate intended and actual movement, one may hold the eye mechanically (Brindley and Merton, 1960; Skavenski et al., 1972). When an eye movement is attempted, the subject again perceives visual objects to move. When the subject told the eye to move, it is presumed to have obeyed; if images did not move on the retina the visual scene must have moved along with the eye. Thus, the experiments of Skavenski et al. (1972) seemed to confirm the outflow theory of Helmholtz. The localization of target lamps was accounted for by assuming that the subject thought the eye was pointing where it had been told to go, not where it actually was.

There are, however, bits of evidence suggesting that the situation may be more complicated. Steinbach and Smith (1981) found that when a patient's eye was rotated surgically, the patient did not mislocalize targets (by an amount predicted by the angle of surgical rotation) when first seen with that eye. On the other hand, Bock and Kommerell (1986) could not confirm this observation. Another problem was the observation that when the eye muscles were pharmacologically paralyzed, attempted eye movements led to the subjective impression that the eyes would not move, not that the visual environment had shifted position as predicted by outflow (e.g., Stevens et al., 1976; see also McCloskey, 1981, for a review). When only one viewing eye was paralyzed and a subject tried to point to eccentric targets, he pointed in accord with the outflow theory (Stevens et al., 1976), yet the subjective impression was not of a problem in visual localization. Matin et al. (1982) reinvestigated this phenomenon using a systemic paralyzing agent. When only a small target lamp, known to be moveable, was visible, an effort to move the paralyzed eyes resulted in an apparent movement of the target in the same direction. When the room lights were turned on, this illusion was destroyed presumably because the subject knew that the room could not move and so felt only that his eye was paralyzed. As might be expected, subjective impressions depend heavily on context. In summary, the concept of outflow has not been seriously challenged.

1.2 Common theories underpinning gamification

In this section, we describe briefly several theories (goal-setting, self-efficacy, self-determination, social comparison, flow theory, and operant conditioning theory) that have been frequently applied in the gamification literature (Huang & Hew, 2018; Ritcher et al., 2015; Sailer et al., 2017), and suggest how these theories may relate to the various game elements.

Goal-setting theory: A goal gives an individual a purpose, focus, and measurable outcomes that can be used to define what needs to be accomplished (Kapp, 2014). According to goal setting theory, goals that are immediate, specific, and moderately challenging are more motivating than goals that are long-term, vague, and too easy or too difficult (Locke, Shaw, Saari, & Latham, 1981). In addition, when immediate feedback is provided to individuals, as in the case of many gamified practices (e.g., using badges), participants can measure their progress in relation to their goals. In this way, individuals know if they need to adjust their directions or strategies to better pursue their goals (Locke, 1996; Locke & Latham, 2002).

Self-efficacy theory: Self-efficacy is a person's belief in how well he or she can deal with prospective situations (Bandura, 1982). It also determines the effort and the persistence that one exerts to overcome obstacles (Bandura, 1982). Self-efficacy can be increased when an individual experiences success completing a series of tasks that gradually increase in difficulty. Gamified practices that start with smaller, less difficult tasks can thus help build users' self-efficacy. The use of point systems, badges, and progress bars can also stimulate self-efficacy by measuring progression and providing users with direct feedback on their performance (Gnauk, Dannecker, & Hahmann, 2012).

Self-determination theory: The self-determination theory posits that humans possess three innate psychological needs that can motivate them to engage or not to engage in an activity: autonomy, relatedness, and competence (Ryan & Deci, 2000). Gamified practices that offer individuals the autonomy to choose which activities they prefer to complete (e.g., by offering varying levels of difficult tasks) can address this need. Having a sense of autonomy can increase participants' behavioral and emotional engagement (E. Skinner, Furrer, Marchand, & Kindermann, 2008). Relatedness refers to individuals' need to connect or interact with other people (Ryan & Deci, 2000). Gamified practices that allow participants to compete or collaborate with other people serve this need. A heightened sense of relatedness helps promote feelings of enjoyment; it can also motivate participants to further participate in the activity (Skinner et al., 2008). Competence refers to the desire to master one's pursuits or learning. Gamified practices that provide indicators of participants' progression (e.g., progress bars, levels) and immediate feedback (e.g., points, badges) can help foster users' sense of competency (Sailer et al., 2017).

Social comparison theory: According to social comparison theory, human beings evaluate their opinions and abilities by comparing them with those of others (Festinger, 1954). Two types of social comparison may be found: upward-identification, which occurs when individuals compare themselves with more competent people and believe that they can be as good as them, and downward-identification, which occurs when individuals compare themselves with people who are worse off (Molleman, Nauta, & Buunk, 2007; Suls, Martin, & Wheeler, 2002). Social comparison theory can help explain the motivational aspect of badges, points, levels, and leaderboards. For example, the total number of points collected or the ranking an individual achieves in a leaderboard can help drive upward-identification comparisons. Upward-identification can positively influence students to become more engaged in learning (Chen & Chen, 2015).

Flow theory: Flow is often used to describe the experience of being fully focused on an activity (Csikszentmihalyi, 1990; Nakamura & Csikszentmihalyi, 2009, pp. 195–206). Some of the conditions that can promote flow include clear and proximal goals, immediate feedback on performance and progress, and an appropriate level of challenges (Nakamura & Csikszentmihalyi, 2009, pp. 195–206; Shernoff, Csikszentmihalyi, Schneider, & Shernoff, 2003). Gamified practices such as using badges to provide feedback can help promote flow (Hamari & Sjöblom, 2017). The use of different levels, which allow users to choose the appropriate level of challenge, can also help promote flow.

Operant conditioning theory: Behavior can be motivated or impeded by the consequences of the behavior (B. F. Skinner, 1950). One of the most frequently used types of reinforcement is positive reinforcement. Positive reinforcement occurs when a new stimulus, presented as a consequence of a behavior, strengthens the original behavior (B. F. Skinner, 1953; Woolfolk, 1998). Certain behaviors can be reinforced or maintained using a schedule of reinforcement (B. F. Skinner, 1953, 1989). In the first stage, when people are learning a new skill or behavior, a continuous reinforcement schedule can be adopted (Skinner, 1953, 1989; Woolfolk, 1998). Gamified practices that give a reward (e.g., points or badges) for every correct response or for the completion of every task are examples of a continuous reinforcement schedule. Although badges cannot be used to buy tangible materials such as food, these virtual rewards reinforce desirable behavior within the gamified practice (Landers, Bauer, Callan, & Armstrong, 2015). However, after people have mastered a new skill, a random reinforcement schedule can be used to maintain the particular skill or behavior (Skinner, 1953, 1989; Woolfolk, 1998). Gamified practices that provide badges intermittently when a certain number of points have been accumulated can help retain an individual's interest in the activity. Table 1 summarizes the above discussion.

Table 1. Theories and related game elements (Huang & Hew, 2018; Ritcher et al., 2015; Sailer et al., 2017).