Self-Regulation Influence on Game Play Flow State

Computers in Human Behavior 54 (2016) 341e350

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Computers in Human Behavior

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Full length article Self-regulation inﬂuence on game play ﬂow state

* Li-Xian Chen , Chuen-Tsai Sun

Department of Computer Science, National Chiao Tung University, No.1001, University Road, Hsinchu 300, Taiwan article info abstract

Article history: The authors use the Music Flow digital game with 266 Taiwanese junior high school students to inves- Received 22 March 2015 tigate the influence of digital game player self-regulation (SR) on game flow state. Game data were used Received in revised form to probe various aspects of Bandura's (1986) SR learning model and related effects on game flow state as 17 August 2015 described in Csikszentmihalyi's (1990) flow theory to determine if game information can be used to Accepted 20 August 2015 measure SR during different flow states. A tool for recording stage selection, hit rate, and other real time Available online xxx data was created to monitor and measure flow state among players immersed in interactive play. Self- reaction capabilities were measured in terms of skill- and game-level difficulty during different states. Keywords: fi fl Game play Results indicate that SR exerted a signi cant and positive effect on ow state. Our main conclusions are fl fl Flow theory (a) ow state was continuously in uenced by self-reaction over time; (b) hit rate served as an indicator of Self-regulation self-judgment in terms of challenge, skill and flow state; and (c) flow states in players with distinct self- reaction capabilities were influenced by play stage selection. It is our hope that the method used in this study will help researchers in their efforts to measure and/or analyze player sense of fun in game-based learning environments. © 2015 Elsevier Ltd. All rights reserved.

1. Introduction modes of information to form strategies such as choosing game stages as part of their efforts to achieve high levels of game com- Researchers are increasingly confirming the ways that digital petency (Chen & Law, 2015). Due to subjective characteristics such game-based learning (DGBL) environments help learners develop as SR, not all players feel pleasure or achieve effective learning cognitive operations skills (Papastergiou, 2009). Students in DGBL when they play the same games, raising questions regarding how settings have been described as intrinsically motivated to partici- they make adjustments in response to limitations and experiences pate in learning activities at high levels of concentration (Chen, during game play. DGBL environments give players opportunities Wigand, & Nilan, 1999; Klimmt, Schmid, & Orthmann, 2009; to continuously adjust playing processes and to apply SR, self- Vorderer & Bryant, 2006). DGBL environments have also been management, and other skills (Chen & Law, 2015). Our belief that described as supportive of spontaneous learning and explorative active management is a key factor in maintaining a sense of plea- skill development (Ke & Grabowski, 2007; Oliver & Herrington, sure and fun is our primary motivation for investigating player flow 2001; Raybourn & Bos, 2005). Kiili (2005) and Lancy (1987) are experiences. among researchers who believe that games are most successful at Flow experience, during which players are said to feel “carried attracting learners when they have clear, pre-established rules that away,” emerges from a mix of pleasure and satisfaction encourage gradual advancement to high levels of complexity, and (Csikszentmihalyi, 1975). Players in flow states become more when they provide immediate feedback that supports a sense of engaged in activities (Starbuck & Webster, 1991). A sense of flow is player satisfaction and achievement. also believed to exert long-term influences on player judgmentdan Motivation, pleasure, and player subjectivity are three topics of important point in light of Wu, Wang and Tsai's (2010) observation current interest to game researchers (Papastergiou, 2009). In DGBL of player satisfaction as an influential factor in game play. Other contexts, players tend to synthesize, analyze and evaluate multiple influences on flow experience include motivation (Wan & Chiou, 2006), metacognition (Shats & Solomon, 2002), personality (Faiola, Newlon, Pfaff, & Smyslova, 2013) and capability (Hong et al., 2013). * Corresponding author. National Chiao Tung University, Room 719, MIRC, No. Self-regulation, which Bandura (1986) first suggested as a pri- 1001, University Road, Hsinchu 30010, Taiwan. E-mail address: [email protected] (L.-X. Chen). mary means through which learners comprehend personal http://dx.doi.org/10.1016/j.chb.2015.08.020 0747-5632/© 2015 Elsevier Ltd. All rights reserved. 342 L.-X. Chen, C.-T. Sun / Computers in Human Behavior 54 (2016) 341e350 performance, is an important factor determining improvement in control (Shonkoff & Phillips, 2000) and self-management (Stright, DGBL environments (Erhel & Jamet, 2013). According to Bandura Neitzel, Sears, & Hoke-Sinex, 2001). (1986, 2001), SR consists of three stages: self-observation, during which learners analyze game environments, set goals, and monitor 1.2. Self-regulation and flow performance and the effects of specific actions; self-judgment, in which learners evaluate their current performance using personal The objective of any game is integrated into the game experi- standards or comparisons with others' performance; and self-re- ence and based on player perceptions (Piaget,1962). In other words, action, during which learners assess their satisfaction levels, met- games not only reflect, but also promote additional player cognitive acognitively evaluate their errors, and use the information to create development. When used in learning environments, properly new goals and strategies. applied digital games can impact learning effectiveness and learner Self-regulation is shaped by behavioral and environmental fac- motivation and concentration (Fabricatore, Nussbaum, & Rosas, tors (Zimmerman, 2008). Individuals with better SR skills tend to 2002) while providing entertainment via game rules, scenarios, have higher levels of positive flow affect (Barnett, 1991; and goal achievement (Prensky, 2001). However, although games Csikszentmihalyi, 1990) and more consistent interaction between can induce motivation and promote cognitive development, sense emotional experience and ability (Zimmerman, 2002). To match of pleasure and internal satisfaction require successful player en- expectations, individuals constantly make strategic decisions and counters with game challenges (Jarvinen,€ 2002). It is important to search for certain characteristics when choosing and playing games remember that players are likely to have dissimilar goals that can (Costkyan, 2002), and then make ongoing adjustments in response be affected by game design and interactive structure (Costkyan, to game difficulty in order to maintain their flow states (Hunicke & 2002). Providing choice within a game has the potential to Chapman, 2004). When goals cannot be achieved, at some point enhance a player's perception of autonomy, which has been shown players are likely to consciously decrease the difficulty level to increase intrinsic motivation (Ryan, Rigby, & Przybylski, 2006). (Bandura, 1991). Whereas good self-regulators tend to expand their Players regulate their goals, build a sense of achievement, and knowledge and cognitive competencies, poor self-regulators tend motivate themselves in the face of different challenges (Costkyan, to fall behind (Zimmerman, 1990). Results from a pilot study con- 2002). The mix of motivation, cognition, strategy, and behavior ducted for this research support the identification of players who produces a range of gaming experiences across different players. are capable of establishing goals and using immediate feedback to During game play, gaming experiences generate feeling expe- achieve short-term flow at different game levelsdtwo factors in riences via interaction between intrinsic motivation and SR. Feeling game play SR that exert positive effects on game flow. It is our experiences (e.g., playful and exploratory characteristics) are assertion that the higher the level of SR, the greater the potential associated with the flow experience identified by Csikszentmihalyi for player satisfaction. (1975). Individuals who become completely focused on situations For this study we analyzed the effects of SR on individuals and activities frequently fail to perceive their own initiatives while involved in aggressive game activities, using an original micro- engaged in subjective experiences that fill them with joy and analytical tool that records game behaviors and flow state self- reduce or eliminate anxiety. Such flow experiences increase the assessments. Data for investigating player self-judgment and self- potential for future engagement in the same activity reaction include the number of “strings hits” during a music- (Csikszentmihalyi, 1975, 1990; Webster, Trevino, & Ryan, 1993). oriented digital game, plus self-assessments of skills and chal- Individuals with this intrinsic trait tend to generate pleasure from lenges at different game difficulty levels. as well as add pleasure to their activities. An emotional flow state emerges when their skills match the challenges of a situation 1.1. Self-regulation (Csikszentmihalyi, 1975). To achieve such states, activities must have precise goals and explicit feedback mechanisms. Difficulty Self-regulation is defined as the ability to direct one's own be- levels should be higher than current skill levels so that individuals haviors, as opposed to being passively affected by external in- perceive a challenge but avoid feeling overmatchedda balance that fluences. According to Bandura's (1986) social cognitive theory, is thought to consistently produce positive feelings. personal cognition factors such as motivation and affect are recip- Other factors identified by Csikszentmihalyi (1990) as producing rocally determined by behavioral and environmental factors such or resulting from flow states include a sense of easy control, a as the number of successful hits in a game, scores, and rankings (see merging of action and awareness, concentration on the task at hand, also Zimmerman and Schunk (2001)). Three interactive stages are a loss of self-consciousness, a transformation in time perception, and usually involved in the self-regulatory process: self-observation, autotelic experiences. Chen et al. (1999) have established three self-judgment, and self-reaction. Individuals use self-judgment to categories of flow state characteristics: an antecedents stage strengthen positively evaluated behaviors and to weaken/eliminate involving clear goals, unambiguous feedback, and a balance be- actions that result in negative feelings. Social cognitive theoretical tween activity challenge and skill; an experience stage, during which frameworks generally suggest that SR is context-dependent, action and awareness merge, distractions are removed from con- therefore one's SR may be high in one situation or domain and sciousness, and worry of failure is eliminated; and a behavioral stage low in another (Zimmerman & Schunk, 2001). in which progress reflects internal and optimal experience such as Scholars have analyzed different aspects of SR from different the loss of self-consciousness, a distorted sense of time, and a sense perspectives. According to Zimmerman and Schunk (2001), self- of reward from the activity itself (Table 1). regulating learners actively participate in their learning processes Since they are based on personal perceptions of skills and metacognitively, motivationally, and behaviorally. Based on their challenges, flow experiences are considered subjective, with de- examinations of SR mechanisms in the context of Internet usage, terminations varying from person to person. Such subjective feel- LaRose and Eastin (2004) and LaRose, Lin, and Eastin (2003) ings can produce perceptions of optimal experiences or of anxiety established a “deficient Internet SR” construct for studying usage and/or boredom. Individuals must therefore clearly understand patterns and addiction. Puustinen and Pulkkinen (2001) built on their goals and maintain awareness of their progress so as to make Pintrich and Zimmerman's work to describe the details of SR as a immediate adjustments when necessary (Csikszentmihalyi, 1975). goal-oriented process. An emphasis on SR has also been noted in When self-perceived challenge and skill level achieves an equilib- discussions of self-generation (Zimmerman & Schunk, 1989), self- rium, the potential for entering a flow state increases, thus L.-X. Chen, C.-T. Sun / Computers in Human Behavior 54 (2016) 341e350 343

Table 1 Flow characteristic classiﬁcations.

Flow condition (Csikszentmihalyi, 1990) Flow stage (Novak et al., 1998) Present study

Clear goals. Antecedents. Required conditions between activity and individual. Immediate feedback. Balanced between challenges and skills. Easily controlled. Experience. Internal experience. Merging of action and awareness. Concentration on task at hand. Loss of self-consciousness. Behavioral. Time transformation. Autotelic experience. encouraging individuals to engage in more complex activities in the stand-alone rhythm game, and (b) clarify the relationship between pursuit of greater pleasure (Csikszentmihalyi & Csikszentmihalyi, SR and flow state. Study participants were asked to play Music Flow 1992; Moneta & Csikszentmihalyi, 1996). Boredom results when for 20 min, randomly selecting their own game levels. After each challenges are too low, while anxiety results when they are too high level of play, participants were instructed to respond to questions (Csikszentmihalyi, 1975; 1990). It is important to remember that regarding their flow states that were inserted into the game system. flow state is never staticdas challenges change, so do flow states After 20 min they were asked to fill out an SR questionnaire. Music (Fig. 1). Flow has a feature that records hit rates. Data for the first seven rounds of game play (1 round ¼ 1 min and 45 s of time) were collected and used to measure flow distance (FD), defined as the 1.3. Research questions distance between current emotional state and flow states. ANOVAs and correlation tests were used to analyze the relationship between We addressed five research questions: SR and flow state. The participant sample consisted of 110 male (41.4%) and 156 1. What are the influences of self-regulation during flow states? female (58.6%) eighth-grade students aged 14 and 15 (M ¼ 14.28, 2. Can self-regulation be determined from game data? S.D. ¼ .45) attending two randomly selected junior high schools 3. What is the relationship between game challenge self-judgment located in Taoyuan and Taichung in north and central Taiwan, and hit rate among players with different self-regulation respectively. All were confirmed as having basic (e.g., Web capabilities? browsing) computer skills. Since Music Flow was specifically 4. For players with different self-regulation abilities, what is the designed for this research project, the participants had no prior association between skill self-judgment and hit rate? experience playing it. 5. For players with different self-regulation abilities, what factors influence stage selection during flow states? 2.2. The game

2. Method Music Flow (hereafter abbreviated as MF) has ten difficulty levels, each with different required keystroke numbers (108e497) 2.1. Study design and participants and different bar dropping rates (50e83/per minute) (Figs. 2 and 3). Higher difficulty levels, which study participants were allowed to Our primary study goals were to (a) determine the influences of choose for themselves, had more keystroke numbers and faster bar SR on flow state among a group of eighth-grade students playing a dropping rates. MF meets the requirements of a simple and intuitive interface with easy navigation so that students can figure out how to play without written instructions or special technical skills. We also tried to inject the three factors identified by Csikszentmihalyi (1990) and Novak, Hoffman, and Yung (1998) as promoting student involvement in a gaming environment: clear goals, immediate feedback, and a balance between challenge and skill. Gaming records included information on player name, gender, age, score, and hit position. We coded each string hit as 1 and each miss as 0 when analyzing SR and flow state (Fig. 4). Performance during the final 30 s of each play round was used as a hit rate benchmark. Hit rate was calculated as

number of hit meters X 100%; All strings

where number of hit meters is the number of string hits during the ﬁnal 30 s of each play round.

2.3. Flow state scale

We collected 1724 gaming records for 266 students. Average Fig. 1. Csikszentmihalyi's (1990) three-flow channel model. Individual actions cause number of play rounds was 7.4; data from 7 rounds were used to changes in feelings during different flow states. analyze the relationship between SR and flow state. We used 344 L.-X. Chen, C.-T. Sun / Computers in Human Behavior 54 (2016) 341e350

our observation that the majority needed 3 s or less to verbalize opinions regarding real-time skill and challenge levels, resulting in minimal interruption of the game-playing process. Our calculations utilized a symmetric flow line in which challenge ¼ skilldthat is, FD ¼ 0 at the flow state points (1,1), (2,2), (3,3), (4,4), (5,5). Maximum anxiety FD (þ1) was identified as (5,1) and maximum boredom FD (1) as (1,5). A transformation equation is expressed as

1 FD ¼ ðS CÞ; 4

where S denotes skill and C challenge. Using (2,3) as an example, the FD value is .25. Note that the more balanced the skill and challenge, the shorter the ﬂow distance. Anxiety-free players feel a complete balance of challenge and skill.

2.4. Self-regulation questionnaire

Microanalytic assessment tools and protocols such as self-report scales help researchers identify and examine real time SR-related behaviors. The questionnaire used in this research is based on the SR theory created by Bandura (1986) and further developed by Zimmerman and Schunk (2001) and Zimmerman (2002). The 25 questions were designed to measure students' SR ability in terms of. Fig. 2. Level selection screen shot for the game used in this study. 1. Self-observation, with players monitoring their performance and strategy during game play. An example item is “I concen- trate on the music during game play.” 2. Self-judgment, with players comparing their goals with actual performance. An example item is “I constantly try to determine whether or not I am achieving my goals during game play.” 3. Self-reaction, with players acknowledging their feelings and providing their own feedback. An example item is “I am satisﬁed with the performance during game play.”

All responses were recorded along a four-point scale from 1 (“definitely disagree”)to4(“definitely agree”). Item analysis was used to assess the quality of individual questions, and a combina- tion of reliability and factor analyses was used to evaluate overall reliability and construct validity. Cronbach's a coefficients (.75e.83) were regarded as acceptable. Summed scores for the three sub- scales represent degree of SR, with higher scores indicating greater self-regulating ability. However, such questionnaires are limited in their ability to measure causal links between SR processes and performance out- comes (Zimmerman, 2008), and of course are incapable of providing real-time data during game play. Therefore, we also made use of two other methods:

1. Post-event microanalysis, which was created to examine human beliefs and reasoning during actual performance (Agina, Kommers, & Steehouder, 2011a,b; Cleary, 2011; Kitsantas & Zimmerman, 2002). Agina (2008), Agina and Kommers (2008), and Agina et al. (2011a, b) have used this method to analyze Fig. 3. Screen shot from the percussion game used in this study. the ways that children think and talk aloud while alone (i.e., without external regulation). To analyze internal regulation, we asked players two questions about casual playing processes at ﬁ ﬂ Pearce, Ainley, and Howard (2005) modi ed ow state scale to end of each round: (a) what do you think is the challenge level of investigate the balance between player skill level and activity this game round? and (b) is your ability high enough to solve the & challenge level (Moneta Csikszentmihalyi, 1996)(Fig. 5). Study challenge of this game round? As Schunk and Swartz (1993a, b) participants were asked to assess their challenge and skill levels and many others have noted, learning processes are often more “ ” “ using a Likert-type scale ranging from 1 ( not at all )to5(very important than performance. As part of the SR subprocess, self- much”). We let the players make their own assessments, based on judgment focuses on the gap between player behavior and self- L.-X. Chen, C.-T. Sun / Computers in Human Behavior 54 (2016) 341e350 345

Fig. 4. Game data screen shot.

(Bandura, 1986; 2001). We predicted a relationship between stage choice and self-reaction ability.

3. Data analysis

3.1. Self-regulation ability

The high-to-low order of SR questionnaire scores was self- observation (M ¼ 3.03, SD ¼ .64), self-judgment (M ¼ 2.87, SD ¼ .72), and self-reaction (M ¼ 2.70, SD ¼ .68). The majority of students were found to have strong self-regulating abilities (M > 2.50) (Table 2).

3.2. Flow state

Fig. 5. Study participants were repeatedly asked two questions after each game round for purposes of assessing self-perceptions of challenge and skill: “What do you think is As stated above, we used data from each player's initial seven the challenge level of this game round?” and “Is your ability high enough to solve the rounds to analyze flow state conditions. At the end of the first challenge of this game round?” Responses were used to determine flow distances. round, 55 (20.7%), 116 (43.6%) and 95 study participants (35.7%) had scores indicating anxiety, flow state, and boredom, respectively. The data also show that 189 students (71.1%) chose the first established criteria (Bandura, 1986, 2001). We therefore (easiest) level to begin play, likely in support of their efforts to attempted to continuously record player self-judgments during become familiar with game rules and processes. Since they chose game play. Another reason for using this method was to test our higher levels as they gained experience, the percentage of partici- hypothesis that a relationship exists between playing process pants describing themselves as feeling anxiety increased from and self-judgment ability, especially toward the end of game sessionsdin this study, the final 30 s of each round of play. 2. Trace logs, which are frequently used to microanalyze gaming Table 2 processes (Winne & Jamieson-Noel, 2002). Players regularly Descriptive statistics for study participants' self-regulation capabilities.

monitor achievement and gaming tactics as part of SR. MF gives Factor N ¼ 266 players opportunities to monitor their tactics; we used this MSD feature to gather self-reported data about gaming tactics and achievement estimates. Self-reaction data were used to assess Self-observation 3.03 .64 player satisfaction levels that directly affect changes in behavior Self-judgment 2.87 .72 Self-reaction 2.70 .68 346 L.-X. Chen, C.-T. Sun / Computers in Human Behavior 54 (2016) 341e350

20.7% to 49.2% (Table 3). According to these data, the majority of 3.3. Comparison of game data and self-judgment players learned the MF melody well enough to not feel any need to return to easier game levels. As players chose more difficult levels, The students used the feedback provided by MF to make ad- their flow distances increased from .27 to .34 over several play justments to their game play actions. They were allowed to choose rounds, also indicating greater anxiety (Fig. 6). As shown in Fig. 7, their own difficulty levels, which we used as a self-measure of the percentage of study participants reporting a sense of boredom success. After each round of play, study participants used their own decreased from 35.7% to 8.5% over multiple rounds, also suggesting criteria to assess their performance. This raises questions about a a positive influence from SR ability over several rounds of play. possible correlation between self-judgment and hit rate (especially Participants were asked to report their flow state every 3 s in toward the end of game sessions), and whether or not the partic- order to measure and assess their challenge/skill balances. Since ipants were capable of making appropriate estimates of their skills flow state is a dynamic concept, we used a formula to transform and progress. We used overall game scores to address these ques- flow state scores into flow distance scores. According to our results, tions, and found a significant and positive correlation between none of the three SR dimensions influenced flow state during the score and challenge level, but not between score and skill level first three rounds of play, but a significant effect was noted starting (Table 12). In other words, the data indicate that scores did not with the fourth round. We also observed that self-reaction exerted accurately represent challenge and skill evaluations. significantly negative effects on flow distance starting at round Beat sequences were collected for the final 30 s of each play four, with correlation coefficients gradually declining thereafter round and recorded as 1 ¼ correct hit and 0 ¼ missed hit. The data (fourth round: r ¼.16, p < .01; fifth: r ¼.20, p < .05; sixth: were used to calculate and assess skill and challenge level. We r ¼.20, p < .01; seventh: r ¼.31, p < .05). In other words, greater assumed that study participants would perceive themselves as self-reaction capability resulted in shorter flow distance. Correla- more skilled as the number of correct hits increased, and vice versa. tion data are presented in Tables 4e10. Data for 266 students (1724 records) are shown in Table 13. Our results indicate that self-regulating ability exerted an in- A significant effect was noted for the correlation coefficient fluence on flow state as players progressed to higher levels. None of between hit rate and challenge (r ¼.41, p < .001), meaning that the students in the sample had played MF prior to their participa- perceptions of challenge decreased as correct hit rates increased, tion in this study, therefore SR was unlikely to exert any effect at the thereby confirming a correlation between level difficulty and self- beginning of play. However, over the full course of the study, SR judgment. A significant effect was also noted for the correlation ability exerted significant effects on flow distance. In other words, coefficient between hit rate and skill (r ¼ .11, p < .001). However, the data support the assumption of a significant correlation be- the statistical significance is considered low, suggesting that the tween player SR and flow distance. students were conservative in judging their skills. The correlation We also tried to determine if different SR levels exerted different between skill self-judgment and hit rate is in agreement with influences on flow distance in the sample, using the first factor as Moneta and Csikszentmihalyi's (1996) assertion that skill and an independent variable and the second as a dependent variable. challenge are the two most important variables influencing flow We found that study participants with scores higher than 49 had distance. the strongest self-regulating skills, and those with scores below 39 Next, we attempted to determine if any relationship exists be- had the weakest. Results from an independent sample t-test indi- tween self-judgment ability and hit rate. Study participants with cate a significant effect between the two groups (t ¼ 2.57, p < .05), self-judgment scores greater than 12.8 were categorized as high suggesting a clear difference in the effect of SR on flow distance. self-judgment and those with scores less than 8.8 as low self- Further, flow distance was significantly higher among those stu- judgment; each group was approximately one-third of the entire dents with high SR scores (M ¼ .32, SD ¼ .23 versus M ¼ .23, sample. For the high self-judgment group we noted a significant SD ¼ .20) (Table 11). and slightly negative correlation between hit rate and self- In summary, anxiety levels among the participants increased perceived challenge (r ¼.33, p < .001), and a significant and substantially over the course of game play, a result that can be slightly positive correlation with self-perceived skill (r ¼ .09, interpreted in several ways. One is that it indicates increasing p < .05). For the low self-judgment group, we also noted a signifi- participant familiarity with the main Music Flow game song, cant and slightly negative correlation between hit rate and self- therefore participants wanted to play at the same level or at levels perceived challenge (r ¼.44, p < .001) and a significant and considered more challenging in terms of staying on the right beat. slightly positive correlation with self-perceived skill (r ¼ .27, In such situations, SR would not affect flow distance at the begin- p < .001). Combined, the results indicate significant correlations ning of play, but as the players became accustomed to the MF between self-judgment and both skill and challenge, meaning that interface and game functions, SR exerted an increasingly significant the hit rate in the MF game could be used as a criterion for effect on flow distance. Accordingly the results indicate an affir- measuring the study participants' self-judging abilities. Accord- mative answer to the first research question. ingly, the answers to the second, third and fourth research questions are also affirmative (Tables 14 and 15).

3.4. Comparison of ﬂow distance and self-reaction

Table 3 fl Study participants' flow state distribution. As stated in Section 3.2, SR exerted an in uence on the study participants' flow states, which in turn impacted perceptions of Game round Anxiety (S < C) Flow (S ¼ C) Boredom (S > C) pleasure and satisfaction. We asked each student to briefly describe First 55 (20.7%) 116 (43.6%) 95 (35.7%) his or her feelings after each round of game play to help us deter- Second 59 (22.2%) 134 (50.4%) 73 (27.4%) mine the immediate impact of flow state on self-reaction (i.e., game Third 84 (31.6%) 136 (51.1%) 46 (17.3%) Fourth 75 (32.1%) 129 (55.1%) 30 (12.8%) level selection) among students with different SR values. We Fifth 80 (40.6%) 100 (50.8%) 17 (8.6%) analyzed 1724 flow distance records to determine level-choosing Sixth 67 (41.2%) 76 (46.6%) 20 (12.2%) strategies, using flow distance (anxiety, flow and boredom) as a Seventh 64 (49.2%) 55 (42.3%) 11 (8.5%) design variable and choice of level (harder, easier and the same) as a S ¼ skill, C ¼ challenge. reaction variable. As shown in the Chi-square independent test data L.-X. Chen, C.-T. Sun / Computers in Human Behavior 54 (2016) 341e350 347

Fig. 6. Average mean ﬂow distance (FD) values after seven rounds of game play.

Fig. 7. Flow state distributions of the study participants.

Table 4 Table 6 Correlations between self-regulation values and flow distance for first round of Correlations between self-regulation values and flow distance for third round of game play (n ¼ 266). game play (n ¼ 266).

123MSD 123MSD

1. Self-observation 21.22 4.47 1. Self-observation 21.22 4.47 *** *** 2. Self-judgment .66 11.47 2.86 2. Self-judgment .66 11.47 2.86 *** *** *** *** 3. Self-reaction .57 .54 10.78 2.72 3. Self-reaction .57 .54 10.78 2.72 ** *** *** 4. Flow distance .19 .23 .23 .27 .32 4. Flow distance .03 .09 .02 .24 .31

** *** *** p < .01, p < .001. p < .001.

Table 5 Table 7 Correlations between self-regulation values and ﬂow distance for second round of Correlations between self-regulation values and ﬂow distance for fourth round of game play (n ¼ 266). game play (n ¼ 234).

123MSD 123MSD

1. Self-observation e 21.22 4.47 1. Self-observation e 21.22 4.47 *** *** 2. Self-judgment .66 e 11.47 2.86 2. Self-judgment .69 e 11.48 2.93 *** *** *** *** 3. Self-reaction .57 .54 e 10.78 2.72 3. Self-reaction .61 .54 e 10.78 2.68 * * * * 4. Flow distance .13 .16 .05 .24 .31 4. Flow distance .01 .14 .16 .24 .33

* *** * *** p < .05, p < .001. p < .05, p < .001. presented in Table 16, a significant correlation was found between encouraged flow by making adjustments in terms of dynamic dif- flow state and level choice (c2 (4) ¼ 99.41, p < .05; contingency ficulty. Only a small number of students (40/279, or 14.3%) entered coefficient ¼ .23, p < .05). flow states when they felt anxious, indicating inability or lack of Next, we looked at the potential impact of self-reaction (in the willingness to move up to the next level of difficulty. According to form of level choice) on flow distance. As shown in Table 17, almost post hoc comparison results, there was a significant difference in one-third of the study participants (82/254, or 32.3%) selected a terms of choosing a harder or easier level when the participants felt more difficult level when they felt boreddin other words, they anxious (Table 18), with less anxious students choosing more 348 L.-X. Chen, C.-T. Sun / Computers in Human Behavior 54 (2016) 341e350

Table 8 Table 14 Correlations between self-regulation values and flow distance for fifth round of Correlations between hit rate, flow distance, skill and challenge for participants with game play (n ¼ 197). high self-judgment scores (n ¼ 78).

123MSD Variable 1 2 3 M SD

1. Self-observation e 21.22 4.47 1. Challenge 3.32 1.28 *** 2. Self-judgment .68 e 11.58 2.94 2. Skill .07 3.34 1.16 *** *** *** *** 3. Self-reaction .61 .57 e 10.71 2.70 3. Flow distance .72 .64 .01 .42 * *** * *** 4. Flow distance .08 .05 .20 .26 .32 4. Hit rate .33 .09 .32 .71 .27

* *** * *** p < .01, p < .001. p < .05, p < .001.

Table 9 Table 15 Correlations between self-regulation values and ﬂow distance for sixth round of Correlations between hit rate, ﬂow distance, skill and challenge for participants with game play (n ¼ 163). low self-judgment scores (n ¼ 78).

123MSD Variable 1 2 3 M SD

1. Self-observation e 21.53 4.61 1. Challenge e 3.32 1.28 *** *** 2. Self-judgment .69 e 11.68 2.98 2. Skill .21 e 3.34 1.16 *** *** *** *** 3. Self-reaction .64 .60 e 10.72 2.81 3. Flow distance .79 .77 e .01 .42 * *** *** *** 4. Flow distance .04 .08 .20 .31 .36 4. Hit rate .44 .27 .46 .71 .27

* *** *** p < .05, p < .001. p < .001.

Table 10 Our next task was to determine if there were any differences in Correlations between self-regulation values and flow distance for seventh round of level choice during different flow states among study participants game play (n ¼ 130). with different self-reaction levels. As shown in Tables 19 and 20, fi fl 123MSDthere was a signi cant difference between ow distance and game level choice among both high self-reaction (c2 (4) ¼ 19.81, p < .01; 1. Self-observation e 21.70 4.66 *** contingency coefficient ¼ .23, p < .01) and low self-reaction stu- 2. Self-judgment .67 e 11.66 2.98 *** *** c2 ¼ < fi ¼ 3. Self-reaction .63 .56 e 10.63 2.84 dents ( (4) 28.02, p .01; contingency coef cient .24, ** *** 4. Flow distance .23 .17 .31 .34 .37 p < .001), indicating a correlation between different flow states and ** *** p < .01, p < .001. level choice. Next, we looked at the impact of self-reaction (level choice) on flow state. Participants in the high self-reaction group reported that Table 11 when they were performing well during game play, they were not T-test results for high and low self-regulation groups of study participants. interested in moving to a lower level regardless of the anxiety-

Variable High self- Low self- t producing challenges they encountered. In contrast, students who regulation regulation described themselves as bored willingly moved to more difficult participants participants levels to achieve better matches between challenge and skill levels, fl (n ¼ 77) (n ¼ 78) thus improving their chances of entering ow states (Table 21). According to the post hoc comparison results shown in Table 22, MSDMSD the low-reaction students tended to choose more difficult levels * Flow distance .32 .23 .23 .20 2.57 when they felt anxious (95 instances, or 50%). However, the per- * p < .05. centage of those participants who entered flow states during the next stage of play was only 14.7%, suggesting that their skills and challenges did not match. These same participants also tended to Table 12 select more difficult stages when they felt bored (32.7%, or 18/55 ¼ Correlations between score, skill and challenge (n 266). reporting boredom). Combined, the data presented in this section Variable 1 2 M SD support an affirmative response to the fifth research question. 1. Challenge 3.33 1.18 *** 2. Skill .12 2.96 1.12 *** 3. Score .26 .03 461517 409936 4. Conclusions

*** p < .001. Individual players have their own ways of modifying games to make them more fun. When first learning a game, players spend Table 13 their time getting accustomed to game features, assessing game Correlations between hit rate, skill and challenge (n ¼ 266). situations, and establishing playing criteria, and make ongoing Variable 1 2 M SD adjustments in terms of SR, thus increasing their potential to enter flow states. After playing a new game for a period of time, self- 1. Challenge 3.33 1.18 *** fl 2. Skill .12 2.96 1.12 reaction gradually starts to in uence player feelings. Our data *** *** 3. Hit rate .41 .11 .75 .26 suggest that the study participants who had strong self-reaction

*** p < .001. capabilities were better at assessing their feelings, thus enhancing their sense of fun and satisfaction (Zimmerman, 2002). difficult game levels. The data also suggest that regardless of which Regardless of self-regulation ability, goal regulation is an fl level they chose when they were experiencing flow, they continued important factor determining ow state and an ongoing sense of to have fun. fun. Selecting a different level of play affects a player's sense of challenge, whether or not there is a close match with skill level. We L.-X. Chen, C.-T. Sun / Computers in Human Behavior 54 (2016) 341e350 349

Table 16 Results from Chi-square analysis of next game level choice for all participants during different ﬂow states.

Flow state Choice frequency (%) c2 Contingency coefﬁcient

Harder Easier Same

*** *** Anxiety 279 (47.6) 222 (37.9) 85 (14.5) 99.41 .23 Boredom 254 (81.2) 37 (11.8) 22 (7.0) Flow 515 (62.4) 225 (27.3) 85 (10.3)

*** p < .001.

Table 17 Next level choice frequencies during different ﬂow states.

Flow state Level choice Level choice frequency Frequency of entering ﬂow state

Anxiety Harder 279 40 Easier 222 78 Same 85 19 Boredom Harder 254 82 Easier 37 15 Same 22 7 Flow Harder 515 371 Easier 225 137 Same 85 73

Table 18 Table 21 Conﬁdence intervals for all study participants choosing the next game level Conﬁdence intervals for high self-reaction participants choosing their next game (n ¼ 266). levels.

Dependent variable Comparison group 95% CI Post hoc Dependent variable Comparison group 95% CI Post hoc

Anxiety Harder-Easier [.01, .20] Non-significant Anxiety Harder-Easier [.21, .24] Non-significant Harder-Same [.26, .41] Harder > Same Harder-Same [.03, .38] Harder > Same Easier-Same [.14, .33] Easier > Same Easier-Same [.01, .38] Non-significant Boredom Harder-Easier [.62, .77] Harder > Easier Boredom Harder-Easier [.37, .74] Harder > Easier Harder-Same [.68, .80] Harder > Same Harder-Same [.48, .77] Harder > Same Easier-Same [.11, .02] Non-significant Easier-Same [.22, .09] Non-significant Flow Harder-Easier [.25, .45] Harder > Easier Flow Harder-Easier [.06, .38] Non-significant Harder-Same [.45, .59] Harder > Same Harder-Same [.20, .53] Harder > Same Easier-Same [.25, .09] Non-significant Easier-Same [.02, .39] Easier > Same

Table 19 Table 22 Results from Chi-square analyses of next game level choice for high self-reaction Conﬁdence intervals for low self-reaction participants choosing their next game participants during different ﬂow states. levels.

Flow state Choice frequency (%) c2 Contingency coefﬁcient Dependent variable Comparison group 95% CI Post hoc

Harder Easier Same Anxiety Harder-Easier [.02, .44] Harder > Easier ** ** Harder-Same [.21, .49] Harder > Same Anxiety 46 (40.7) 44 (38.9) 23 (20.4) 19.81 .23 Easier-Same [.08, .31] Non-significant Boredom 56 (72.7) 13 (16.9) 8 (10.4) Boredom Harder-Easier [.65, .91] Harder > Easier Flow 82 (50.9) 56 (34.8) 23 (14.3) Harder-Same [.70, .89] Harder > Same ** p < .01. Easier-Same [.13, .10] Non-significant Flow Harder-Easier [.25, .64] Harder > easier Harder-Same [.48, .73] Harder > Same fi Table 20 Easier-Same [ .33, .02] Non-signi cant Results from Chi-square analyses of next game level choice for low self-reaction participants during different flow states.

Flow state Choice frequency (%) c2 Contingency coefﬁcient satisfy the various requirements of players with different SR skills

Harder Easier Same in order to enhance their ongoing sense of fun.

*** *** Experiences toward the end of gaming sessions can strongly Anxiety 95 (52.8) 53 (29.4) 32 (17.8) 28.02 .24 inﬂuence player satisfaction. Our data indicate that the study par- Boredom 56 (72.7) 13 (16.9) 8 (10.4) Flow 82 (50.9) 56 (34.8) 23 (14.3) ticipants' self-judgment skills, based on current game play experi-

*** ences, affects motivation to continue play. As Wu, Wang, and Tsai p < .001. (2010) note, player satisfaction depends a great deal on how well a game satisﬁes the needs of individual players. Game factors such found that high self-reaction players had appropriate adjustment as scores, hit rate, levels, and rewards also affect player satisfaction, mechanisms. Concurrently, low self-reaction players in the sample but not to the same degree as self-performance. In the game used in appeared to be less capable, perhaps even incapable, of making this study, a good hit rate was clearly the most important factor in changes and adjustments to ongoing situations. Accordingly, game terms of player satisfaction, therefore hit rate could be used to designers may be interested in modifying game learning goals to assess self-reaction ability. 350 L.-X. Chen, C.-T. Sun / Computers in Human Behavior 54 (2016) 341e350

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