Learning Progress in a Chinese Order of Stroke Game: The Effects of Intrinsic Cognitive Load and Gameplay Interest Mediated by Flow Experience

Abstract

When learning to write Chinese characters, it is essential for students to learn and maintain the correct order of the strokes. Chinese teachers often use computer-supported drill and practice to develop students' ability to write in the correct order, but such devices are rarely designed to stimulate learners' memory-manipulation in cognitive processes. To enhance the effect of the stimuli, a computer game called Chinese order of strokes was designed for students to practice their sensorimotor skill by providing a different color (i.e., red) to evoke learners' memory-manipulation cognitive processes. To understand the effect of this game, third-grade students from an elementary school in Taipei were invited to play Chinese order of strokes, and the correlates between their intrinsic cognitive load, gameplay interest, and flow experience were examined. The results showed that intrinsic cognitive load was negatively related to gameplay interest and flow, gameplay interest was positively related to flow, and flow was positively related to learning progress. The results imply that teachers can utilize a digital Chinese order of strokes to implement characters based on their own teaching materials and to facilitate the students' learning of the correct order of strokes.

Keywords

Chinese order of strokes flow gameplay interest intrinsic cognitive load primary education

Introduction

Learning environments designed to help users discover materials completely on their own are generally harder to use and ineffective because they provide limited guidance or hints (Mayer, 2004). In a learning environment, guidance is important (Baydas et al., 2015). In the context of learning to write Chinese characters, if students make errors in relation to the order of strokes, their learning progress could improve if they receive visual–spatial guidance (Hsiung, Chang, Chen, & Sung, 2017). In line with this learning, we designed a computer game called the Chinese order of strokes (COS). In COS, the color red is used to indicate the incorrect order of strokes and as a form of guidance to evoke learners' memory-manipulation cognitive processes. In line with this design, students are allowed to continuously adjust the order of the strokes as a memory-manipulation cognitive process (i.e., a cognitive process that can covertly alter one's memory by manipulating the information stored in the memory; Gilbert, 2015). Based on the practice of COS, we explored how visual–spatial guidance can influence players' cognitive and affective factors.

In line with technology-enhanced learning, Moreno (2006) proposed the cognitive–affective theory of learning with media (CATLM), reporting how the design of multimedia learning materials can induce positive emotions in learners, and how these positive emotions then facilitate the cognitive processing and improve cognitive and affective outcomes (Plass et al., 2013). As a cognitive factor, the cognitive load theory highlights “the manner in which cognitive resources are focused and used during learning” (Chandler & Sweller, 1991, p. 294). This implies that cognitive load can be defined by cognitive aspects, which affect both learning efficiency and the use of the available but limited cognitive capacity that learners bring to learning tasks (İliċ & Akbulu, 2019; Schrader & Bastiaens, 2012). Accordingly, this study explored how COS design can raise players' cognitive load when they practice stroke writing.

Previous studies have indicated that interest is classified as an affective variable (Mayer & Moreno, 2002) that strongly influences and plays a positive role in learning (Hidi & Renninger, 2006; Hong, Lin, Hwang, Tai, & Kuo, 2015). For instance, flow is an affective factor; it refers to one's mental state when one has filtered out irrelevant emotions and is fully immersed in an activity (Csikszentmihalyi, 1975). Particularly, there are many affective factors that influence players' performance in game-based learning; for example, Kiili (2005) proposed that flow could positively facilitate the user's experience to maximize the game's impact. Another affective factor influencing learners' immersion in game playing is interest (Hong, Hwang, Liu, Lin, & Chen, 2016; Hong et al., 2015). Hong et al. found that gameplay interest can positively predict game or learning performance. However, research on the impact of action in gameplay has revealed performance advantages of perceptual and cognitive tasks, in particular, enhancement of sensorimotor learning (Gozli, Bavelier, & Pratt, 2014). How cognitive and affective factors affect sensorimotor learning (i.e., by rebuilding the learner's mental model with error stroke perception) in Chinese stroke writing is unclear. Therefore, this study focused on understanding the interrelatedness among intrinsic cognitive load (ICL), gameplay interest, flow experience, and learning progress in the context of the COS game.

Literature Review

Cognitive Load

Three different types of cognitive load can be distinguished in learning settings: ICL, extraneous cognitive load, and germane cognitive load (Sweller, van Merrienboer, & Paas, 1998). The ICL, or the natural load, is induced by the complexity of the information that must be processed to understand the learning task and content to be able to carry them out. Extraneous cognitive load is induced by the instructional design of the learning material and may be detrimental to learning. Germane cognitive load refers to the working memory resources required to process the acquired learning information into more complex schema. It is imposed by activities that are believed to determine successful learning.

The ICL depends on the interaction between the complexity of the educational content and learners' level of expertise (Sweller et al., 1998) and cannot be altered by instructional design. On the other hand, extraneous and germane cognitive load can both be manipulated. Efforts have therefore been made to develop design principles for reducing extraneous cognitive load (Mayer & Moreno, 2003), while instructional designers have tried using germane load-inducing methods such as self-explaining (Paas, Renkl, & Sweller, 2003). Kalyuga and Plass (2009) suggest that ICL affects electronic gaming. Considering the mechanism of COS (i.e., if a player makes a mistake in the order when writing a stroke, the previous stroke will appear in red before proceeding to the next stroke), learners' perceptive ability may affect their learning emotion states; thus, ICL was adopted in this study.

Gameplay Interest

Two types of interest described in the motivation literature are topic interest and situational interest (Ainley, Hidi, & Berndorff, 2002). Topic interest is interest elicited by a word or paragraph describing the subject matter or the content of material and is content-specific. Situational interest appears to arise from novelty, curiosity, or salient informational content (Schraw & Lehman, 2001). Flowerday, Schraw, and Stevens (2004) found a positive interplay between the topic and situational interest. That is, situational and topic interest appeared to significantly affect learning task processing (Linnenbrink-Garcia, Patall, & Messersmith, 2013). Hong et al. (2015) conceptualized gameplay interest as an individual psychological state and compared the interest maintained along with the number of game practice sessions; they found the two factors to be positively dependent. However, with behaviorally identified subjective gaming, players' emotional state is associated with higher level visual processing networks (Ju & Wallraven, 2019). Thus, how gameplay interest affected students playing COS was explored in this study.

Flow

Flow is defined as a state of optimal experience in which a person derives pleasure from focusing on a task, regardless of the extrinsic rewards (Csikszentmihalyi, 1990). Flow is more likely to occur in autotelic individuals who have a tendency to find intrinsic motivation rather than extrinsic motivation in their daily activities (Asakawa, 2004). Thus, flow occurs when individuals feel that a given task is challenging, and she or he has the level of skill with which to meet the challenge (Choe, Kang, Seo, & Yang, 2015). Flow also emphasizes the positive aspects of learning because individuals can experience enjoyable moments when they are immersed in a task (Tobert & Moneta, 2013). It is important for students to experience flow during learning as it allows them to feel a sense of enjoyment, excitement, and fun (Kaye, Monk, Wall, Hamlin, & Qureshi, 2018). In an online learning environment, learners' flow experience has been shown to be effective in improving academic achievement (Esteban-Millat, Martínez-López, Huertas-García, Meseguer, & Rodríguez-Ardura, 2014). Accordingly, this study focused on understanding the flow experience when students played COS.

Learning Progress

Learning progress assessment is one prominent tool in the field of formative assessment that can help teachers, as well as students, to optimize learning and instruction (Black & Wiliam, 2009; Clark, 2012). Learning progress can be defined as a series of gradual or sudden changes in a learner's understanding. The theory of mental models accounts for these conceptual changes (Kim, 2015). Conceptual changes in a learning situation are transformations that lead learners closer to their predefined goal. A number of experimental studies have demonstrated that qualitatively conceptual changes occur when learners encounter a problem (Siegler, Thompson, & Opfer, 2009; Vosniadou & Vamvakoussi, 2008). Learning progress assessments in school usually provide information with pre- and posttests of achievement (Förster & Souvignier, 2014). Thus, in this study, the difference between the scores of the first and the final trials embedded in the COS system was investigated.

Research Hypotheses and Model

Hypotheses

When attending to a learning task, cognitive pressure can substantially undermine one's inherent enjoyment (Lepper, Corpus, & Iyengar, 2005). However, with less mental effort devoted to external contingencies, learning behavior will be in accordance with interest in a particular task (Malmberg, Pakarinen, Vasalampi, & Nurmi, 2015). That is, cognitive load researchers have highlighted how the design of learning information and its form of presentation can overwhelm children's limited working memory capacity (Kirschner, Ayres, & Chandler, 2011; van Merrienboer & Sweller, 2005), thus restricting students' ability and emotion in acquiring and demonstrating their emerging academic competencies (Howard et al., 2015). Accordingly, the correlation between ICL and gameplay interest was hypothesized as follows:

Hypothesis 1 (H1): ICL is negatively related to gameplay interest.

Flow is the holistic experience felt when individuals act with total involvement. It is likely associated with attention, emotion, and reward processing (Yoshida et al., 2014). Inal and Cagiltay (2007) stated that the difficulty and complexity of learning tasks influence students' flow due to the mental effort expended. However, mental effort is negatively associated with a highly immersive gaming environment if it leads to flow but decreased learning outcomes (Schrader & Bastiaens, 2012). In a context-specific website, flow state is an underlying mechanism which ICL responds to. Thus, the correlation between ICL and flow was hypothesized as follows:

Hypothesis 2 (H2): ICL is negatively related to flow.

Gameplay interest is a manifestation of individual interest defined as a deep personal interest in a field or activity based on preexisting knowledge, personal experiences, and emotions (Ainley et al., 2002). Gameplay interest has been found to increase the positive attitudes toward promoting personal engagement in tasks (Flowerday et al., 2004; Flowerday & Shell, 2015) and increased the understanding of text (Flowerday & Shell, 2015). When playing COS, it is likely that a higher level of interest would result in greater intention to play (Hong, Hwang, Liu, Ho, & Chen, 2014) and a greater flow state (Matthews, 2015). This reasoning drives the following hypothesis:

Hypothesis 3 (H3): Gameplay interest is positively related to flow.

Csikszentmihalyi (1990, 1998) stated that flow effects are mainly based on individuals' internal perceptions, but many existing studies have focused the effects of flow on digital game learning and long-term outcomes (Bressler & Bodzin, 2013; Colliver & Veraksa, 2019). Previous studies on flow and game performance have demonstrated that flow is positively correlated with learning performance (Hong, Hwang, Hsu, Wong, & Chen, 2011). In particular, flow occurs when learners concentrate on and experience enjoyment in the activity, the effect of which is learning achievement (Admiraal, Huizenga, Akkerman, & Dam, 2011). Accordingly, the hypothesis between flow and learning progress in the context of COS was proposed as follows:

Hypothesis 4 (H4): Flow is positively related to learning progress.

Research Model

According to CATLM (Moreno, 2006), during gameplay, learners' affective aspects can be raised, and there can be interplay between these aspects and their cognitive learning (Leutner, 2014). In line with this, the research model for this study was proposed as follows and the model shows in Figure 1.

Figure 1.

Research model.

Research Design

COS game design

A formal game is one that can be played in rule-governed manipulations of symbolic states and cognitive process under time pressure (Gomila & Calvo, 2008). Hong et al. (2009) suggested that drill and practice with time pressure is one type of game that could be effectively used to encourage players to work on applying knowledge. Drill and practice gives players fewer chances to exercise game strategies but more chances for memorization. Accordingly, this study designed COS as a drill and practice game for elementary school students.

The aim of COS is to train players to write Chinese characters in the correct order of strokes. In this game, the administrator can add Chinese characters into the game. To add a character into the COS database, the administrator must write the strokes in the correct order. Figure 2 shows a screenshot of how to add a new character to the database. The numbered boxes below the main character show the correct order of strokes. For this study, the research team added the characters from 8 lessons (i.e., 112 characters in total) based on the third-grade participants' Chinese textbook. Example characters were 苗(miáo)、晨(chén)、芽(yá)、壯(zhuàng)、嫩(nèn)、築(zhú)、虔(qián)、誠(chéng)、杜(dù)、鮮(xiān)、耀(yào)、夢(mèng)、翅(chì)、翔(xiáng) in Lesson 1.

Figure 2.

How to add a new character in the COS game. COS = Chinese order of strokes.

Students can play COS on a computer or on a tablet. They can either use their finger or a mouse to write the character's order of strokes. When the character first appears, it is white. As the player writes a correct stroke, it turns black. If the player makes an error in the stroke order, the system will display the stroke in red. Figure 3 shows a game screenshot of the character苗(miáo) from Lesson 1 of the third-grade Chinese textbook.

Figure 3.

Character 苗(miáo) in the COS game. COS = Chinese order of strokes.

Procedure and participants

Purposive sampling was used in this study, and third-grade students from one elementary school in Taipei were invited to participate in this experiment. The experiment consisted of six sessions, with one session each week. In the first session, the students had 3 minutes to familiarize themselves with COS. For each experiment session, the students were given 10 minutes to play. The students' performance in the first session was recorded in the computer as their pretest score, and their performance in the sixth session was recorded as their posttest score. After the sixth session, a questionnaire on ICL, gameplay interest, and flow was distributed to the students. A total of 149 questionnaires were returned, and after the deletion of 9 incomplete questionnaires, 140 were used for the statistical analysis, giving an effective questionnaire return rate of 94%. The sample was comprised of 79 males (56.8%) and 61 females (43.2%) participants.

Questionnaire and measurement

The questionnaire items were adapted from previous theories or studies and were obtained by professionally translating the original items into Chinese using the back translation method, which allows one to verify the accuracy and clarity of the translation to ensure face validity. The questionnaire items were assessed on a 5-point Likert-type scale ranging from 1 (disagree strongly) to 5 (agree strongly).

Intrinsic cognitive load

Items were adapted from Paas and van Merrienboer (1993) for measuring ICL in terms of invested mental effort, representing the aspects of perceived level of difficulty to access the specific mental demands on working memory. The ICL experienced was measured using eight items in the questionnaire.

Gameplay interest

Topic interest has a direct effect on situational interest and an indirect effect on engagement through situational interest (Flowerday & Shell, 2015). In game-based learning, gameplay interest is the basic factor in developing and maintaining an internal motivation to learn (Hidi & Renninger, 2006). Accordingly, the COS gameplay interest construct was developed with seven items that referred to the psychological state arising from specific characteristics of the COS game environment.

Flow

Csikszentmihalyi (1975) identified nine factors related to the flow experience: clear goals, immediate feedback, personal skills well suited to given challenges, merging action and awareness, concentration on the task at hand, a sense of potential control, a loss of self-consciousness, an altered sense of time, and an experience that becomes autotelic. The COS game gives learners 10 minutes to complete a trial; thus, the merging of action and awareness, concentration on the task at hand, a loss of self-consciousness, an altered sense of time, and attention paid to reducing errors were considered in the design of the questionnaire items.

Learning progress

We used participants' first and sixth session scores and counted the difference as their learning progress. The increase in their score was used for testing the fitness of the measuring model.

Data Analysis

Item analysis

The internal validity of original items in each construct was subjected to first-order confirmatory factor analysis (CFA). If the factor loading (FL) is less than 0.5 (see Table 2) and those values in the first-order CFA do not pass these CFA thresholds (see Table 1), then they should be deleted from the original questionnaire (Hair, Black, Babin, & Anderson, 2014). Therefore, the results revealed that the number of items for ICL was reduced from eight to five, those for flow were reduced from seven to four, and those for gameplay interest were reduced from six to five.

Table 1.

Summary of Confirmatory Factor Analysis.

Measurement index	Threshold value	ICL	Gameplay interest	Flow
χ²	–	1.20	4.83	2.95
df	–	5	5	2
χ²/df	<5	0.02	0.97	1.47
RMSEA	<0.08	0.00	0.00	0.06
GFI	>0.80	0.99	0.98	0.99
AGFI	>0.80	0.99	0.96	0.95

Note. df = degrees of freedom; ICL = intrinsic cognitive load; RMSEA = root mean-square error of approximation; GFI = goodness of fit; AGFI = adjusted goodness of fit index.

Table 2.

The Item Internal and External Validity Analysis.

Items of each construct	M	SD	FL	t
ICL
1. It is very hard for me to write the correct order of strokes based on the initial white-colored character that appeared on the screen.	2.81	.91	.71	18.31
2. It is very difficult for me to practice COS in the limited time given.	2.63	.94	.84	19.31
3. When playing the game, I am not used to the mode of how a wrong order of stroke is displayed.	2.59	.95	.87	19.61
4. I was frustrated when practicing COS.	3.09	.96	.87	19.61
5. I was annoyed when it showed that I had made an error in the order of strokes.	2.62	.92	.62	16.91
Flow
1. When I play COS, I am aware of the strokes I missed.	3.24	.90	.66	10.74
2. When I play COS, I am totally focused on what I am doing.	3.95	.91	.75	14.07
3. After I finished playing COS, I felt time passed quickly.	3.94	.94	.74	13.75
4. COS inspires me to keep playing.	3.16	.96	.78	14.24
Gameplay interest
1. I like to play COS to learn the correct order of strokes.	4.78	.91	.88	13.52
2. I feel very excited when I play the COS game.	4.73	.95	.81	12.47
3. I feel very happy when I play the COS game.	4.68	.96	.80	12.12
4. I enjoy playing the COS game.	4.64	.98	.92	15.92
5. I hope to play COS many more times.	4.70	.91	.91	15.90

Note. M = mean; SD = standard deviation; FL = factor loading; ICL = intrinsic cognitive load; COS = Chinese order of strokes.

The external validity of the questionnaire items is determined by comparing the upper 27% and lower 27%. If the t-test value (critical ratio) is larger than 3 (***p < .001), every item is capable of being included in this questionnaire. Table 2 indicates that the t value for each item is greater than 12.12 (***p < .001), which means that the external validity of every item has the degree of response of different samples (Green & Salkind, 2004).

Reliability and validity of constructs

The present analysis aimed to examine the reliability and validity of the questionnaire. First, reliability can be evaluated from the aspects of internal consistency and composite reliability (CR). Table 2 shows the Cronbach's α values, which were all above .64. According to Hancock and Mueller (2013), a Cronbach's α value above .60 indicates an acceptable level of reliability. Moreover, to determine the CR of the constructs, all of the CR values were above .82, which surpassed the suggested threshold value of 0.70 (Hair et al., 2014).

Second, the convergent validity in this study evaluated and verified whether (a) the average variance-extracted values were greater than .5 (Fornell & Larcker, 1981) and (b) the FLs of all of the items were significant and greater than 0.5. If these conditions are met, it indicates that construct validity is acceptable (Hair et al., 2014). Table 3 shows that the average variance-extracted values were above .54 and the FL values were above .62; as all of the conditions were met, it indicated an acceptable convergent validity.

Table 3.

The Construct Reliability and Validity Analysis.

	Cronbach's α	CR	AVE
ICL	.89	.89	.62
Flow	.64	.82	.54
Gameplay interest	.94	.94	.75

Note. CR = composite reliability; AVE = average variance-extracted; ICL = intrinsic cognitive load.

Learning progress analysis

To identify the difference between the pre- and posttest scores, we performed a paired samples t test. Table 4 shows that for these third-grade students, t = –6.77, p < .001, indicating that there was a significant difference between the pre- and posttest, and that the mean score of the posttest (M = 1,819.85) was higher than that of the pretest (M = 1,464.27).

Table 4.

The Difference Between the Pre- and Posttest.

Sample	Test	Number	Mean score	t test
Third graders	Pretest	76	1,464.27	–6.77***
Third graders	Posttest	76	1,819.85	–6.77***

Note. ***p < .001.

Results

Goodness-of-Fit for Research Model

According to Hair et al. (2014), the model fit indexes for the absolute fit measures include the χ², root mean-square error of approximation (RMSEA), goodness of fit (GFI), and adjusted goodness of fit index (AGFI). The overall absolute fit measures in this research were χ² = 212.55, and the df were 131, indicating that the χ²/df = 1.62; a ratio of less than 3 is considered to be indicative of a good fit. For RMSEA, a threshold value of less than 0.08 constitutes a good fit. For GFI, Hair et al. indicated that a threshold value above 0.80 is acceptable; for AGFI, Hair et al. suggested that a threshold value above 0.80 is acceptable. In this study, RMSEA = 0.06, GFI = 0.86, and AGFI = 0.87 indicating that all values fit the required threshold.

The model fit indexes for Incremental Fit Measures include the Normed Fit Index, Non-Normed Fit Index, Incremental Fit Index, Comparative Fit Index, and Relative Fit Index. Values of model fit indexes greater than .90 are considered a good fit (Schumacker & Lomax, 2004); however, Ullman (2001) suggested that the threshold could be lessened to greater than .80. Table 5 shows the model fit indexes. The Incremental Fit Measures in this research were Normed Fit Index = 0.90, Non-Normed Fit Index = 0.94, Incremental Fit Index = 0.95, Comparative Fit Index = 0.95, and Relative Fit Index = 0.91. The Parsimonious Adjusted Measures include the Parsimony Comparative Fit Index (PCFI), the PCFI Parsimonious Normed Fit Index (PNFI), and the Parsimonious Goodness-of-Fit Index (PGFI). According to Mulaik (2009), PCFI, PNFI, and PGFI should be above .5 for a good model fit. The Parsimonious Fit Measures indexes of this study were as follows: PCFI = 0.81, PNFI = 0.75, and PGFI = 0.66. Overall, according to Hair et al. (2014), the model fit indexes above show that the model for this research is acceptable.

Table 5.

Model Fit Indexes of Confirmatory Factor Analysis.

Index	Threshold value	Confirmation data	Result
χ²/df	<5	1.62	Supported
RMSEA	<0.08	0.04	Supported
GFI	>0.80	0.91	Supported
AGFI	>0.80	0.87	Supported
NFI	>0.90	0.90	Supported
NNFI	>0.90	0.94	Supported
CFI	>0.90	0.95	Supported
IFI	>0.90	0.95	Supported
RFI	>0.90	0.91	Supported
PNFI	>0.50	0.75	Supported
PGFI	>0.50	0.66	Supported

Note. df = degrees of Freedom; NFI = Normed Fit Index; NNFI = Non-Normed Fit Index; CFI = Comparative Fit Index; IFI = Incremental Fit Index; RFI = Relative Fit Index; PNFI = Parsimonious Normed Fit Index; PGFI = Parsimonious Goodness-of-Fit Index; RMSEA = root mean-square error of approximation; GFI = goodness of fit; AGFI = adjusted goodness of fit index.

Path Analysis

Structural equation modeling has evolved into a mature and popular methodology to investigate model-derived structural hypotheses (Hershberger, 2003). Figure 4 shows the results of the path relationship among the hypotheses, revealing that H1 1 through H4 were supported. Figure 2 shows that ICL was significantly related to gameplay interest (β = −.50, t = −3.47***), ICL was negatively related to flow (β = −.51, t = −4.15***), gameplay interest was positively related to flow (β = .43, t = 2.83**), and flow was positively related to learning progress (β = .67, t = 7.69***). To examine the indirect effect of ICL and gameplay interest, the bootstrap method was employed via Amos. The analytical results indicated that the indirect effect between ICL and learning progress was significant (β = −.22, standard error = .041, p < .01) with a 95% confidence interval (CI) [−.26, −.12]. The indirect effect between gameplay interest and learning progress was significant (β = .19, standard error = .048, p < .01) with a 95% CI [.43, .58]. The 95% CIs did not include zero, which revealed that actually there was a mediator effect for flow in the relationship between ICL, gameplay interest, and learning progress. Moreover, the test of the research model demonstrated that this model accounted for 45% of the variance in students' learning progress. According to the above results, it can be clearly seen that the addition of ICL and gameplay interest is beneficial to its predictive power.

Figure 4.

Verification of the research model. *p < .05. **p < .01. ***p < .001.

Discussion

Based on CATLM, there can be interplay between cognitive and affective factors and they can influence learning performance; this study employed ICL, gameplay interest, and flow to explore their interrelatedness for third graders. Returning to the hypotheses proposed in the literature review of this study, it is now possible to claim that ICL, gameplay interest, and flow are the key antecedents of learning progress. The research findings show that there were significant negative correlations between ICL, gameplay interest, and flow; however, there were significant positive correlations between gameplay interest, flow, and learning progress.

In examining H1, the results indicated that the correlation between ICL and gameplay interest was negative. As the form of presentation affects ICL and can overwhelm children's limited working memory capacity (Kirschner et al., 2011; van Merrienboer & Sweller, 2005), Mutlu-Bayraktar, Cosgun, and Altan (2019) suggested that with less mental effort exerted toward external contingencies, learning behavior will be in accordance with interest in a particular task. The results of this study were consistent with their argument, and H1 was supported.

In examining H2, the result indicated that the correlation between ICL and flow was negative. Yoshida et al. (2014) stated that flow is likely associated with attention, emotion, and reward processing. Flow in a context-specific website is the underlying mechanism by which ICL responds, and COS was designed to evoke a memory-manipulation cognitive process, which allowed students to use try-out strategies and to continuously adjust the order of strokes in their working memory. Mental effort in playing COS is strongly associated with a highly immersive gaming environment (Ju & Wallraven, 2019), and increasing ICL in playing COS leads to a decrease in flow. Consequently, H2 was supported.

In examining H3, the results indicated that the correlation between gameplay interest and flow was positive. Gameplay interest is a manifestation of individual interest, which is defined as a deep personal interest in a field or activity based on preexisting knowledge, personal experiences, and emotions (e.g., Ainley et al., 2002; Ju & Wallraven, 2019). Gameplay interest has been found to increase positive attitudes toward promoting personal engagement in tasks (Flowerday & Shell, 2015; Flowerday et al., 2004; Schraw & Lehman, 2001). It is likely that higher interest in the game could result in greater intention to play (Hong et al., 2014) and greater flow state (Matthews, 2015). The result of this study was consistent with the above assertions, and so H3 was supported.

In examining H4, the results indicated that the correlation between flow and learning progress was positive. Previous studies on flow and game performance have demonstrated that flow is positively correlated with learning performance (Colliver & Veraksa, 2019; Hong et al., 2011). In addition, Kiili (2007) suggested that flow, as an in-depth reflective process, is the key to successful game-based learning. When playing COS, learners must take in their internal perceptions and practice a memory-manipulation cognitive process; thus, the effects of flow on academic achievement are positive, indicating that increasing flow when playing COS will increase participants' learning progress; hence, H4 was supported.

Conclusions

To conjure the memory-manipulation cognitive process, this study used the color red as a perception stimulus to trigger the participants' recognition of error in the order of strokes and examined the students' learning progress when practicing stroke order while playing COS. Taken together, the findings of this study suggest two principal implications. First, this study introduces a memory-manipulation cognitive process to study learning effectiveness for third graders; the results lend further support to relational perspectives on gameplay in drill and practice games. Where prior research has not established the importance of using color to stimulate learners' ability in error recognition and correction, this study advances our understanding of how ICL, gameplay interest, and flow interact when students play COS; it found that cognitive and affective factors can promote or inhibit learning progress.

Second, the results reveal that the advantage of COS is likely due to the sensorimotor learning of the repetitive pattern of motion through drill and practice, which leads to an important implication for teaching. The findings from this study suggest that teachers could benefit from paying closer attention to how use of COS can benefit students with a high level of gameplay interest, particularly in contexts such as using color to represent mistakes in writing the correct order of strokes. Teachers may use COS in their teaching, and add additional content based on their lessons. Utilizing COS means students' cognitive processes can become more effective, and their learning of COS will be enhanced.

Limitations and Future Research

Many studies have shown that for elementary school-aged children, writing skills are associated with reading ability (Chan, Ho, Tsang, Lee, & Chung, 2006). Chinese characters have a very complicated orthographic structure that makes it difficult for children to master reading skills. Therefore, it is possible that for young Chinese children, the early emergence of word expertise requires a longer developmental period (Zhao, Zhao, Gaspar, & Weng, 2015). Accordingly, the learning effectiveness of COS may be analyzed based on age difference, and future research may compare the cognitive and affective levels in relation to playing COS so as to understand how age affects COS gameplay.

Due to the complexity of the Chinese writing system, we expect that visual working memory rather than phonological working memory exerts a unique influence on learning Chinese characters (Opitz, Schneiders, Krick, & Mecklinger, 2014). Possible gender differences for cortical interactions during cognitive tasks require the generation and manipulation of two- or three-dimensional spatial imagery; it was previously reported that male and female subjects employed slightly different cortical areas to solve tasks. This issue was not explored in this study; future studies could therefore employ a larger number of trials and recruit more subjects to investigate gender differences in this area.

Flow is viewed in this study as a psychological state (of immersive pleasure) that gives rise to a form of mental energy, empowering motivation, and enabling optimal experience (Nakamura & Csikszentmihalyi, 2014). In line with this conceptualization, this study aimed to investigate the interaction between the experience of gameplay flow and learning performance, but how the gameplay flow affects the players' actual motivation to continue playing COS has not been explored. Future studies may examine the correlates between flow and empowering motivation related to learning progress.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by National Taiwan Normal University.

Author Biographies

Jon-Chao Hong is a professor at National Taiwan Normal University with an interest in computer game-based learning. He is also a member of the Chinese Language and Technology Center in National Taiwan Normal University.

Ming-Yueh Hwang is a professor in the Department of Adult and Continuing Education at National Taiwan Normal University with an interest in education. She is also a member of the Chinese Language and Technology Center in National Taiwan Normal University.

Kai-Hsin Tai is a postdoctoral fellow of National Taiwan Normal University and a member of the Chinese Language and Technology Center.

Pei-Hsin Lin is a PhD student at National Taiwan University and a research assistant to Professor Jon-Chao Hong at National Taiwan Normal University.

Pei-Chun Lin is a research assistant to Professor Jon-Chao Hong at National Taiwan Normal University.

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