IntroductionAttention and emotion are critical for the occurrence of effective learning during childhood. Many studies have focused on developing strategies to help children maintain optimal attentional and emotional states, thereby fostering self-regulated learning1,2,3,4,5. It is important that these strategies align with the evolving social, emotional, and cognitive abilities of children2,6,7. One ability that has received wide attention is cognitive control, which helps individuals adjust behaviors and thoughts according to internally maintained goals8,9. Cognitive control is closely associated with the ability to regulate attention and emotion10,11. Students with a higher level of cognitive control are better at regulating their attentional and emotional states according to contextual demands, compared to their peers with a lower level of cognitive control12,13. However, since the maturation of cognitive control extends into late adolescence or early adulthood14,15,16,17, younger children may face challenges in effectively regulating attention and emotion during learning.Although laboratory studies have indicated the potential of certain strategies to enhance cognitive control in young learners17,18, it remains unknown whether these strategies affect young children’s cognitive and emotional states during actual learning activities. This gap is significant as it limits the application of theoretical knowledge in practical, educational settings. Bridging this gap is crucial, as it could lead to the development of more effective educational practices tailored to the evolving needs of children, thereby enhancing learning outcomes during a critical stage of their development. This study aimed to synergize the theoretical frameworks of cognitive control and self-regulated learning to investigate their practical application in real-world learning scenarios. We endeavored to make a significant contribution to the fields of child development and educational psychology, offering evidence-based strategies to optimize learning experiences in the crucial early years of childhood.The development of cognitive control in childhood has been extensively studied through the dual-mechanism framework, which dissociates cognitive control into proactive and reactive modes based on temporal dynamics19,20,21. Proactive cognitive control involves actively maintaining goals to facilitate early selection or response preparation before the arrival of target stimuli, while reactive cognitive control only allows the transient activation of goals after the occurrence of imperative events. As a result, proactive cognitive control is more effective than reactive cognitive control in enabling us to allocate cognitive resources, such as attention, in appropriate ways to monitor important events and respond efficiently19,20,21. The dual-mechanism framework of cognitive control, with its emphasis on proactive over reactive strategies, aligns closely with Zimmerman’s cyclical phase model of self-regulated learning (see examples in Fig. 1), highlighting the role of proactive strategies in effective learning processes22.Fig. 1: Illustrative examples of proactive and reactive control in Zimmerman’s self-regulated learning phases.This figure illustrates how, during self-regulated learning based on Zimmerman’s cyclical phase model, children’s learning strategies differ depending on whether they engage in proactive or reactive control. In proactive control, learners anticipate challenges, set goals, and adjust strategies in advance. In reactive control, learners respond to challenges as they arise without prior planning or strategy adjustment. The figure outlines the variations in control across the forethought, performance, and self-reflection phases of learning.Full size imageZimmerman’s model defines self-regulated learning as a process in which learners proactively manage their own learning through self-generated thoughts, feelings, and actions that are systematically oriented toward the attainment of their learning goals22. Furthermore, this model characterizes self-regulated learning by three cyclical phases: forethought, performance, and self-reflection. The forethought phase occurs before learning and involves processes such as goal setting, planning, and self-motivation, which enable learners to maintain goals. During the next performance phase, learners involve strategies like self-instruction and attentional focusing to monitor their performance and adapt their strategies accordingly. In the self-reflection phase, learners evaluate their performance against their goals and adjust and prepare for future learning tasks.Building on these theoretical frameworks19,20,21,22, the link between the development of cognitive control abilities in children and their capacity for self-regulated learning becomes evident through the alignment of proactive and reactive control with Zimmerman’s three phases. In the forethought phase, proactive control helps learners maintain goals, plan, and stay motivated, while reactive control, triggered by events, limits preparation. During the performance phase, proactive control enables real-time focus and strategy adjustment, whereas reactive control reacts only after issues arise, reducing efficiency. In the self-reflection phase, proactive control aids in goal evaluation and future planning, whereas reactive control focuses on immediate feedback. This understanding underscores the importance of fostering proactive control to enhance self-regulated learning in learning contexts.Children showed a progressive shift from only using reactive control to being able to use proactive control as age increases15,23,24. Studies often use the AX-CPT task to measure these control modes, where proactive control involves maintaining a cue (A) in memory to anticipate a target (X), while reactive control triggers a response only after seeing the probe (X)21. In a child-adapted AX-CPT, Chatham et al.25 found that eight-year-olds, when able to use proactive control efficiently, displayed a level like young adults, in contrast to three-and-a-half-year-olds who primarily exhibited reactive control. These findings were replicated by another study, which found that four-to-seven-year-old children showed a progressive shift from reactive to proactive control, with an estimated turning point between five and six years of age25. Furthermore, researchers studied the age differences in using proactive control in different task contexts. When proactive control had to be used, children aged five years used it as nine-year-old children did26. In contrast, when the use was merely possible, only the older-age group used proactive control. Therefore, younger children may be able to use proactive control, but not be able to use it as efficiently and voluntarily as older children and adults.Proactive control ability is involved in self-regulated learning22,27. Although this assumption has not been empirically tested, previous studies have indicated that proactive cognitive control is significantly related to students’ academic achievement. First, one study has shown that proactive control can act as an indicator of academic achievements, demonstrating its predictive value for reasoning skills, as well as mathematics and reading performance in children aged 6–10 years28. Furthermore, research involving children as young as 7 years old has revealed their capability to employ both proactive and reactive control strategies effectively in both laboratory and daily life tasks. This suggests that proactive cognitive control extends beyond the confines of standardized laboratory settings, playing a crucial role in everyday learning activities, such as the processing of Arabic numerals29.Despite significant age differences in using proactive control, a recent study indicates that preschoolers can use proactive control, but they usually prioritize reactive control when tackling tasks in daily lives15. This argument has been supported by another study, which found that preschoolers could use proactive control when they were explicitly asked to do so23. Therefore, it is proposed that younger children can be promoted to use proactive cognitive control as older children through educational strategies.A core component of proactive control is planning ability, which is critical for building explicit representations of goals before the arrival of targets (see examples in Fig. 1). To determine whether kindergarteners and first graders could use proactive control through planning strategies, researchers encouraged them to perform tasks by thinking about goals, naming the next task loud, or pressing a pictorial representation of the next task silently20,24. The results indicate that these planning strategies can encourage young children to use proactive control to support task performance.Furthermore, Hadley et al.17 investigated whether incorporating strategies like monitoring and reflection (as illustrated in Fig. 1) could enhance the use of proactive cognitive control in children aged five to seven. In their study, “standard feedback” was implemented by providing children with accurate, direct feedback on their performance (i.e., monitoring). In contrast, the “estimated feedback” condition combined monitoring with reflection strategies. This approach encouraged children not only to monitor but also to critically evaluate their own performance. The findings of the study revealed that proactive control was utilized more effectively in the ‘estimated feedback’ condition. This suggests that integrating both monitoring and reflection strategies plays a vital role in fostering cognitive control during early childhood.The above planning, monitoring, and reflecting strategies are crucial metacognitive processes in self-regulated learning, illustrated in Fig. 1’s integration into each phase. Meta-analyses have established a robust link between these strategies and improved academic performance among elementary and secondary school students30. For example, a study found the significant impact of metacognitive strategies on the science achievement of seventh-grade students in Turkey31. Moreover, recent research has extended these findings to preschool education, revealing the effectiveness of self-regulated learning with metacognitive strategies for younger children32. This study highlights that self-monitoring and control are key predictors of early writing self-efficacy in preschoolers, while strategies such as planning and goal setting strongly predict early writing performance. Furthermore, qualitative analyses revealed that preschoolers employed eleven different self-regulated learning strategies, with planning, goal setting, self-monitoring, and self-evaluation being key to enhancing their writing quality.Cognitive control can modulate emotional and attentional states33,34,35,36. Recent studies have differentiated between proactive and reactive control in terms of their influence on modulating attention and emotion. For example, emotional regulation can be enacted in a proactive or reactive way, but the efficiency differs37. Researchers created a proactive condition, in which instructional cues were shown before emotional stimulus to help participants regulate emotions in a proactive way; in contrast, the reactive condition was characterized by the simultaneous presentation of instructional cues and emotional stimulus. The results indicated that less cognitive effort was required to decrease negative emotions in proactive conditions than in reactive conditions35.Proactive control also maintains attention by using early cues to focus on relevant information while ignoring irrelevant details38,39. In contrast, when the reactive control mode is activated, the allocation of attentional resources is mainly driven by stimulus in a just-in-time manner. A recent study indicates that while adults can regularly avoid distractions, six to ten-year-olds are learning to do so. In contrast, four to five-year-olds remain highly prone to distraction34. Such age difference is in line with the developmental trajectory of cognitive control, suggesting that as children shift from reactive to proactive control around the age of five years, their ability to shield themselves against distraction may improve34.The impact of attentional and emotional states on learning outcomes is significant across various age groups1,40,41,42,43,44. A study focusing on disadvantaged children revealed that attention skills in kindergarten play a crucial mediating role between preschool emotion knowledge and first-grade academic competence, independent of factors like maternal education, family income, and children’s demographic and vocabulary skills42. Similarly, Reyes et al.41 employed a multimethod, multilevel approach to investigate the relationship between classroom emotional climate and academic achievement in fifth and sixth graders. The results highlighted that student engagement mediated the positive link between classroom emotional climate and academic grades, irrespective of teacher characteristics and classroom organizational and instructional climates. These outcomes remained consistent across different grades and genders. These findings highlight the crucial role of cultivating a positive emotional environment and enhancing attentional focus in classrooms to improve academic performance among elementary school students.To summarize, previous studies have indicated the significant development of the ability to use proactive over reactive cognitive control in early childhood and the significant relations between proactive control and academic achievements23,28,29,34. Additionally, the planning, monitoring, or reflecting strategies have been suggested to motivate the use of proactive cognitive control, modulate attention and emotion, and benefit learning17,23,24,33. However, most of these studies were conducted in strictly controlled laboratory settings, leading to a gap in understanding how cognitive control strategies impact learning in real-world settings. To address this gap, this study aimed to test how cognitive control strategies influence young children’s emotional and attentional states and learning outcomes during coding learning, which is a more ecologically valid setting compared to laboratory testing.During coding lessons, we recorded video footage of children in small groups, each containing two or three children. From these recordings, we selected three lessons, representing the early, middle, and late stages of the course, to evaluate the attentional and emotional states of each child every three seconds. Using these high-resolution data, we tested whether the integration of cognitive control strategies would influence the attentional and emotional states of young children during learning. Based on previous studies11,23,35,36, we hypothesized that incorporating cognitive control strategies would enable children to stay in the attentional state, maintain a positive emotional state for longer periods, and achieve better learning outcomes.ResultsAttentional stateThe results indicated that the experimental group, which received integrated cognitive control strategies, stayed in the attentional state statistically longer than the control group (t(82.221) = 3.174, p = 0.002, d = 0.671, see details in Table 1). This suggests that children in the experimental group were more focused than children in the control group. The difference observed across different lessons was not significant (p = 0.346), and the Group × Lesson interaction was not significant (p = 0.155). Despite the nonsignificant interaction, we continued conducting two types of analyses driven by the hypotheses. First, we examined the differences of lessons for each group. The difference observed across different lessons was significant in the control group (ß = 0.200, SE = 0.070, t(26.501) = 2.841, p = 0.009), while such difference was not significant in the experimental group (p = 0.353). Further analyses indicated that children in the control group were more focused during Lesson 2 compared to Lesson 12 (t(10) = 2.945, p = 0.015, d = 0.560).Table 1 Group differences in the proportion of time staying in each attentional and emotional stateFull size tableIn addition, we compared the group differences in the proportion of time staying in the attentional state in each lesson. For Lessons 2 and 6, the group difference was not statistically significant (ps > 0.05). However, such difference was statistically significant in Lesson 12 (t(30) = −2.975, p = 0.006, d = 1.086), suggesting that the experimental group was more focused than the control group. Figure 2a shows the averaged proportion of time that children stayed in the attentional state in each lesson.Fig. 2: The proportion of time children stayed in the attentional state and each emotional state by group and lesson.Solid lines represent the experimental group, while dashed lines represent the control group. Error bars denote standard errors. a shows the proportion of time children were in the attentional state. b shows the proportion of time children showed positive emotions. c shows the proportion of time children showed negative emotions. d shows the proportion of time children exhibited neutral emotions.Full size imagePositive emotional stateThe results indicated a significant difference between the experimental and control groups in the proportion of time showing positive emotion (t(32.815) = 2.995, p = 0.005, d = 0.752, see details in Table 1), suggesting that children in the control group stayed longer in positive emotional state than children in the experimental group. Such significant difference persisted when we computed the proportion of time showing positive emotion during the attentional state (ß = 0.040, SE = 0.018, t(33.168) = 2.239, p = 0.032, d = 0.490). The differences observed across different lessons in the proportion of time showing positive emotion was not statistically significant (ß = −0.026, SE = 0.019, t(61.194) = 1.362, p = 0.178). Furthermore, we compared the group differences in the proportion of time showing positive emotion in each lesson. In Lesson 2, children in the control group stayed longer in the positive emotional state than children in the experimental group (t(27) = 2.384, p = 0.024, d = 0.931). Such group difference was only marginally significant for Lesson 6 (t(28) = 2.030, p = 0.052, d = 0.783) and not significant for Lesson 12 (t(28) = 1.513, p = 0.141, d = 0.573). Figure 2b shows the averaged proportion of time that children in each group showed positive emotion in each lesson.Negative emotional stateWe did not conduct statistical analyses on this emotional state as children in both control and experimental groups had a small proportion of time showing negative emotions. Only four children in the experimental group showed negative emotions in Lesson 2, while only one child in the experimental group and one child in the control group showed negative emotions in Lesson 12. In Lesson 6, children in the two groups did not show negative emotions at all. Figure 2c shows the averaged proportion of time that children in each group displayed negative emotions in each lesson.Neutral emotional stateThe results indicated that the experimental and control groups showed a significant difference in the proportion of time showing neutral emotions (t(32.126) = −3.142, p = 0.004, d = 0.794, see details in Table 1). The differences observed across different lessons were not significant (ß = −0.003, SE = 0.015, t(55.920) = -0.220, p = 0.826). These results stayed the same when we computed the proportion of time showing neutral emotions during the attentional state. Furthermore, we compared group differences in each lesson. In Lesson 2, the experimental group had a larger proportion of children showing neutral emotions than the control group (t(27) = 2.166, p = 0.039, d = 0.846). Such difference was marginally significant for Lessons 6 and 12 (t(28) = 2.065, p = 0.052, d = 0.779; t(28) = 1.987, p = 0.057, d = 0.573). Figure 2d shows the averaged proportion of time showing neutral emotions for each group and each lesson.Coding abilityThere was significant Timing × Group interaction in predicting the Function skill (ß = 5.022, SE = 2.381, t(41.586) = 2.109, p = 0.041). Further tests indicate that the Function skill improved significantly from pre-test to post-test in the experimental group while such change was not significant in the control group (Table 2). Additionally, although the Timing × Group interaction was not significant for the total scores as well as the Assignment, Type, Conditional, Loop, Decomposition, and Algorithm (ps > 0.05), we tested the main effect of Timing for each group. The results indicated that only the experimental group showed significant improvement in the total scores as well as the Assignment, Type, Loop, and Decomposition (Table 2). Furthermore, the proportion of time staying in the attentional state was significantly related to the post-test Decomposition, Function and Algorithm, after controlling for group and the pre-test scores (see Table 3 and Fig. 3).Table 2 The changes of coding ability from pre- to post-testFull size tableTable 3 Relations between learning outcomes and collaborative performanceFull size tableFig. 3: Relations between attention and learning outcomes.This figure illustrates that the proportion of time staying in the attentional state during learning process exhibits a significant correlation with the post-test sub-dimensions of coding ability, while controlling for pre-test coding ability and group. The black dots represent each child’s data points, while the slope indicates the direction and strength of the correlation. a shows the relationship between the proportion of time staying in the attentional state and post-test performance in Decomposition. b shows the relationship between the proportion of time staying in the attentional state and post-test performance in Function. c shows the relationship between the proportion of time staying in the attentional state and post-test performance in Algorithm.Full size imageDiscussionThis study, fundamentally guided by the dual-mechanism framework of cognitive control and Zimmerman’s cyclical model of self-regulated learning, aimed to test the effect of cognitive control strategies on the emotional and attentional states of young children in a learning environment with high ecological validity. To reach this goal, we designed a coding course that incorporated cognitive control strategies (i.e., planning, monitoring, and reflecting) for the experimental group but omitted them for the control group. We selected three lessons representing the early, middle, and late phases of the course, during which we rated children’s emotional and attentional states every three seconds. With many codes generated and analyzed, we had two key findings. First, cognitive control strategies enabled children in the experimental group to stay more focused and remain in the neutral emotional state longer than children in the control group. Additionally, we found that the experimental group had a better learning outcome compared to the control group. The learning outcome was significantly related to the attentional state. These findings are discussed below.As we hypothesized, cognitive control strategies enabled children to be more focused. These findings were consistent with previous research suggesting that the use of proactive control mode is associated with the improved ability to shield against distractions and maintain the attentional state34. As suggested by previous studies15,22,24, children in this study could not use proactive control voluntarily. Accordingly, to help children shift from the reactive to the proactive mode of cognitive control, we integrated cognitive control strategies, such as planning, monitoring, and reflecting, into the coding course provided to the experimental group. For example, during the Group Activity Phase, children in the experimental group were asked to make plans and perform the coding tasks accordingly. These integrated strategies may have helped children in the experimental group not to be distracted by the goal-irrelevant information, thereby maintaining their attention on the task. From an educational standpoint, these findings underscore the importance of incorporating cognitive control strategies into curriculum design to enhance children’s focus and engagement, especially in tasks requiring sustained attention and strategic thinking.Additionally, our results showed that children in the experimental group using cognitive control strategies maintained attention significantly longer than controls, even as course difficulty increased. This outcome suggests that the strategies may have enabled the experimental group to develop consistent attentiveness over time. Notably, this finding is significant considering previous research indicating that a high mental workload can impair selective attention45. We propose that cognitive control strategies may have motivated children to prioritize and selectively focus on goal-relevant tasks, even under substantial cognitive demands. Furthermore, although prior studies have investigated the effect of interventional strategies on attentional states in controlled laboratory settings46,47, our findings contribute to this field by addressing how cognitive control strategies improve the attentional state of young children in a real-world learning environment. This emphasis on ecological validity offers practical insights for enhancing attentional control and learning outcomes in young learners.As we expected, the experimental group demonstrated significant improvement from pre-test to post-test in the total scores as well as the Assignment, Type, Conditional, Loop, Decomposition, and Function skills, whereas the control group did not show such changes. This suggests that cognitive control strategies enhanced multiple coding skills for children in the experimental group. Additionally, there was a significant positive correlation between the post-test Decomposition, Function and Algorithm and the proportion of time that students stayed in the attentional state during learning. This result supports previous findings48,49,50, suggesting that higher academic achievement is positively related to more attention resources engaged in classes. The implication of this for educational practice is that cognitive control strategies, which enhance attentional states, may be crucial for improving learning outcomes.Consistent with our hypotheses, cognitive control strategies influenced the emotional states of children. However, in contrast with our initial prediction, the strategies led children to spend more time in a neutral emotional state but less time in a positive emotional state. This finding diverges from previous findings51,52, which have established positive associations between positive emotions and outcomes such as intrinsic motivation, on-task attention, and academic achievement51,52,53. However, our findings are supported by other studies54,55,56, which suggest alternative interpretations. For example, Liu et al.56 found that positive activating emotions, such as enjoyment, hope, and pride, had a detrimental effect on academic performance in Chinese students, whereas positive deactivating emotions, such as relaxation and relief, were not significantly related to academic outcomes. They suggested that positive activating emotions could distract students from their work, while positive deactivating emotions might lead to a decrease in both effort and motivation. Similarly, cognitive control strategies in our study may have helped children avoid the distracting effects of positive activating emotions, instead maintaining a neutral emotional state that was more conducive to sustained attention and task completion. This interpretation needs to be empirically tested in future studies.The attentional and emotional states of children during coding learning were evaluated in three lessons every three seconds, which generated many codes. While such high-resolution analyses posed challenges, the findings of this study warrant further validation with a larger sample size. To address concerns regarding statistical power due to the small sample, we conducted post-hoc comparisons regardless of the presence of significant interactions. Additionally, this study defined three emotional states (i.e., positive, neutral, and negative). Future studies can analyze emotions in a more complex manner, such as quantifying the intensity of each emotional state57. Furthermore, while this study’s assessment of attentional and emotional states has relied primarily on behavioral observations and facial expressions, these external measures may not fully capture internal cognitive-affective processes. Future research should incorporate multi-method approaches (e.g., physiological measures, eye-tracking, or neural correlates) to improve the validity and precision of such assessments. Finally, although we propose that the learning activities became more challenging as the courses progressed, the difficulty level of each lesson was not explicitly defined in this study. Future studies should statistically test whether the difficulty of learning content modulates the effect of cognitive control strategies on the emotional and attentional states of children.Despite the limitations, the contribution of this study lies in validating and extending the dual-mechanism framework20,21 and Zimmerman’s model22 by demonstrating their relevance and applicability to young children’s learning processes. As guided by these theories, this study provides empirical evidence that the integration of proactive control strategies can enhance children’s attentional focus, emotional stability, and learning gains. The findings have immediate implications for educational practice, suggesting that proactive control strategies should be systematically incorporated into early educational practices.MethodsThis study examined the influence of cognitive control strategies on young children’s attention, emotions, and coding skills during practical coding lessons. Involving 46 children from a Hangzhou kindergarten, the research compares an experimental group, engaging in planning, monitoring, and reflecting, with a control group. The impact is assessed through video analyses and pre- and post-tests.ParticipantsA total of 47 children were initially recruited from two kindergarten classes in Hangzhou, China. However, due to health issues, one child was excluded, resulting in a final sample of 46 children for statistical analyses. Among all families in this study, 93.48% of fathers and 89.13% of mothers received a level of education equal to full-time college or higher, and the others received a level of education equal to high school or lower. All children were reported by parents and teachers as being in good physical and mental health. Additionally, most families (67%) had an annual income greater than 200,000 RMB, 20% below 100,000 RMB, and 13% in the range of 100,000 to 200,000 RMB. According to data from the National Bureau of Statistics of China, the per capita disposable income of residents nationwide in 2023 was 39,200 RMB per person, indicating that the economic power of most participants in this study is above the average level.This study used a quasi-experimental design. One kindergarten class was selected as the experiment group, comprising 24 children (Mean age = 5.25 years, SD = 0.44, 14 boys, 10 girls), while another class served as the control group that was comprised of 22 children (Mean age = 5.18 years, SD = 0.50, 14 boys, 8 girls). The Chi-square test and independent-samples t test indicated no significant differences in gender (χ2 = 0.136, p = 0.713) and age (t = −0.487, p = 0.628) between the experimental and control groups. Therefore, these factors were not included as covariates in subsequent statistical analyses.The two classes were taught by the same team of educators, including a primary teacher and four teaching assistants, with guidance from the head teachers of the two classes. The head teachers, with several years of experience in the kindergarten, brought practical knowledge and understanding of early childhood education. The primary teacher and teaching assistants, all graduate students from the Faculty of Education at a local university, have been thoroughly trained in early education, including but not limited to teaching methodologies, curriculum design, and educational psychology. This study was approved by the Internal Review Board of Zhejiang University. Parents signed informed consent and children were verbally assented before attending this study.Study designBoth the experimental and control groups participated in the coding course twice a week for six weeks. All courses were video recorded. Children in both groups were invited to participate in the pre- and post-test. In each course, children were assigned to small groups, each of which contained two or three children.The coding course was taught with Matatalab, a coding robot set (https://matatalab.com/zh-hans). This set included command blocks, a control board, a control tower, and a robot. Using Matatalab, we designed a coding course involving 12 lessons. Specifically, the basic functions of Matatalab, such as moving forward, were taught in the first lesson. The middle eight lessons taught the Assignment, Type, Conditional, Loop, Decomposition, and Function skills. The last three lessons focused on the Algorithm and Debugging skills. More details can be found in a previous publication58.Each lesson lasted about an hour and included three phases, as shown in Fig. 4. In the first phase (Instruction Phase), the primary teacher reviewed what was learned in the previous course and taught new skills. In the second phase (Group Activity Phase), two or three children worked as a small group to solve coding problems. They separately played the operator, helper, or monitor roles in turn. In the third phase (Summary Phase), the primary teacher made a brief summary of the lesson.Fig. 4: Design of the coding course.Each lesson comprised three phases: the Introduction Phase, Group Activity Phase, and Summary Phase. In the experimental group, cognitive control strategies—such as planning, monitoring, and reflection—were embedded into each phase of every lesson. This contrasts with the control group, where these strategies were not systematically integrated.Full size imageIntegration of cognitive control strategiesExclusively in the experimental group, the cognitive control strategies—planning, monitoring, and reflecting—were integrated into the first two phases of each coding course (see the schedule for integration in Fig. 4).During the first phase, the primary teacher played a crucial role in setting the stage for the experimental group. This involved clearly articulating the learning objectives for the lesson, a key component of the Planning strategy. By outlining these objectives, the teacher aimed to provide a roadmap for the lesson, enabling the children to anticipate the kinds of tasks and challenges they would encounter. This planning phase was crucial for aligning the children’s expectations with the lesson’s aims and for preparing them to engage proactively with the upcoming activities. However, the teacher did not introduce the teaching objectives in the control group at the beginning of the class. Instead, they taught new skills immediately after reviewing the content of the previous class.During the Instruction Phase, the primary teacher in the experimental group demonstrated how to make problem-solving plans on the mini-maps (Planning). These mini-maps were the same as the real maps, with the only difference in size. Children in the experimental group were then encouraged to make plans on mini-maps using marker pens and stickers. The planning process was expected to help children build an explicit representation of their goals. In contrast, the primary teacher in the control group merely put the commands on the control board to address the problems, without utilizing the mini-map planning activity.At the beginning of the second phase (Group Activity Phase), each child in the experimental group independently created plans on mini-maps (Planning). Subsequently, teaching assistants prompted these children to engage in group discussions about their plans to formulate a unified strategy, which would be used to guide the subsequent manipulation of command blocks. Then, each member in a small group chose their role as the operator, monitor, or helper. Meanwhile, the children in the control group directly chose their roles without making plans.The three roles had different responsibilities in the experimental group and the control group. In the experimental group, the operator and the helper were responsible for selecting appropriate command blocks based on the pre-made plans. The operator put the right commands on the control board. The monitor was responsible for monitoring and reflecting whether any wrong command was used and how to fix it (Monitoring and Reflecting). In addition, the monitor was also responsible for putting a sticker on the mini-map each time a subtask was completed. In contrast, the operator and helper in the control group selected command blocks without reference to the pre-made plans. The monitor in the control group did not monitor each move and only collected stickers at the end of each task.The evaluation schemes for the emotional and attentional states of young childrenTo measure children’s emotional and attentional states, we recorded video footage of each small group during the group activity phase. The videos of Lessons 2, 6, and 12, representing the early, middle, and late stages of our coding course, were selected to be coded and analyzed. These videos involved 17 children in the control group and 21 children in the experimental group.Based on previous studies58,59, we generated a coding scheme separately for rating the emotional and attentional states. Table 4 lists the descriptions and examples for each item in the coding schemes. We evaluated four types of emotions: positive, neutral, negative, and unable to judge. The positive type refers to any positive emotion, such as amusement, contentment, and happiness59. The negative type refers to any negative emotion, such as sadness, anger, disgust, frustration, and fear59. The neutral type refers to the absence of observable positive and negative emotions. Finally, the type of “unable to judge” refers to situations where it was difficult to assess the emotional states of children, such as when their faces were obscured.Table 4 The coding schemes for emotional and attentional statesFull size tableThere were three attentional states: attentional, inattentional, and unable to judge. Facial cues (e.g., gaze cues) were used to determine the attentional states of children, as suggested by previous studies58,60. Children were judged to be in the attentional state if they were working on solving task-related problems, as evidenced by their gaze being focused on objects related to coding tasks. In contrast, being in the inattentional state indicated that children were occupied with activities unrelated to coding tasks, such as playing with water bottles. The category of “unable to judge” applied to instances where determining the child’s attentional state was not possible, such as when the face of a child was not visible in the videos.Procedure for assessing emotional and attentional statesThe videos were rated by two research assistants, who were trained through two sessions. In the first session, one researcher trained two assistants about rating schemes in a group meeting. Then, two assistants rated a five-minute video independently. Later, the two assistants discussed and resolved discrepancies with the guidance of the researchers. In the second session, the two assistants independently rated a 40-minute video. Then, they compared their ratings and discussed their discrepancies until an agreement was reached. Finally, they carried out independent rating and reached good reliability in both the emotional (Kappa = 0.846) and attentional (Kappa = 0.899) dimensions.After training, the two research assistants independently rated the videos of Lessons 2, 6, and 12, representing the early, middle, and late stages of our coding course. The video for each lesson and each small group lasted about 40 min. During these video sessions, the two assistants independently evaluated the attentional and emotional states of each child every three seconds while they worked on coding problems. However, evaluations were not carried out during interruptions, such as teacher check-ins. Each rater produced a total of 110,024 codes across all lessons and for all participants. Initially, the two research assistants reached good reliability in both the emotional (Kappa = 0.781) and attentional (Kappa = 0.724) dimensions. Then, they compared their ratings and resolved inconsistencies. Each assistant could choose to keep their ratings if inconsistencies could not be resolved. Finally, they reached good reliability in both emotional (Kappa = 0.957) and attentional (Kappa = 0.953) assessments.For our analyses, we first calculated the duration of each emotional and attentional state for every child across groups, based on the average ratings provided by the two raters. This approach ensured a balanced representation of each rater’s observations. To account for the variability in the time taken by different groups to solve coding problems, we then adjusted these durations relative to the total time required to solve a problem for each group. Specifically, the duration of a particular emotional state, such as positive emotion, was determined by averaging the number of corresponding codes (e.g., code 1 for positive emotion) assigned by the two raters. We then applied a normalization process to these durations. This process involved scaling the duration of each emotional and attentional state in proportion to the overall time taken to solve the problem. The mathematical details presented in Eqs. (1)–(5). This method of correction ensures that our analysis accurately reflects the relative proportion of each emotional and attentional state in the context of the varying problem-solving times across different groups.$${Positive}=\frac{{number\; of\; codes}1}{{number\; of\; codes}1\,+{number\; of\; codes}2+{number\; of\; codes}3\,}$$(1)$${Neutral}=\frac{{number\; of\; codes}2}{{number\; of\; codes}1\,+{number\; of\; codes}2+{number\; of\; codes}3\,}$$(2)$${Negative}=\frac{{number\; of\; codes}3}{{number\; of\; codes}1\,+{number\; of\; codes}2+{number\; of\; codes}3\,}$$(3)$${Attention}=\frac{{number\; of\; codes}5}{{number\; of\; codes}5+{number\; of\; codes}6}$$(4)$${Inattention}=\frac{{number\; of\; codes}6}{{number\; of\; codes}5+{number\; of\; codes}6}$$(5)Note. In these equations, codes 1–3 represent positive, negative, and neutral emotions, respectively. Additionally, codes 4–5 separately represent attentional and inattentional states.Methods for assessing children’s coding skills and reasoning abilityWe evaluated children’s coding ability using the revised Coding Ability Test (CAT)61 during pre and post-test sessions. The CAT, a card- and game-based tool, is designed to test young children’s coding ability. It includes one baseline game and nine formal testing games. The baseline game helps children learn basic rules. The nine formal testing games measure the ability to use the Assignment, Type, Conditional, Loop, Decomposition, Function, and Algorithm. The definitions of coding skills are presented in Table 5. There was an instruction video at the beginning of each game. At the end of each game, the listed commands were photographed. These photos were used by raters to evaluate children’s performance according to the scoring manual. The raters evaluated all data after reaching good interrater reliability (r > 0.90). The detailed design and scoring strategies can be found in the previous study58. Additionally, we employed the Raven’s Standard Progressive Matrices to assess children’s reasoning ability, which was used as a control variable in data analyses for its potential influence on learning outcomes.Table 5 The definitions of coding skillsFull size tableStatistical analysesThe Linear Mixed Model within IBM SPSS Statistics 22 (IBM Corp., Chicago, IL, USA) was used to examine the effect of cognitive control strategies on children’s emotional state, attentional state, and coding ability. This model has the flexibility to analyze data with repeated variables and missing values62.To test the group differences in collaborative performance, the measure of each emotional or attentional state was included as the dependent variable, the Group (Experimental vs. Control), Lesson (Lesson 2 vs. Lesson 6 vs. Lesson 12), and their interaction were included as independent variables. We compared the models that only included the main effects to the models that included both the main effects and interactions. When an interaction effect was included in a model, it was retained only if the AIC value decreased by more than 263,64,65,66. The final selected models and the AIC values of different models are shown in Supplementary Table 1. Additionally, the independent-sample t test was performed to examine group differences in attentional and emotional states in each lesson.To examine the group differences in coding abilities, the independent variables in the models included the Group (Experimental vs. Control), Timing (pre- vs. post-test), and their interaction, with reasoning ability included as a covariate. Additionally, we tested the Timing effect separately for the experimental and control groups, with reasoning ability included as a covariate. Finally, we computed the correlations between the corrected length of emotional and attentional states and the post-test coding ability with the pre-test coding ability and group as covariates.Data availabilityThe datasets that were generated and/or analyzed in the current study are not publicly available but can be made available upon reasonable request to the corresponding author, F.G.ReferencesAhmed, W., van der Werf, G., Kuyper, H. & Minnaert, A. 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This work was supported by the National Natural Science Foundation of China (62477041), Fundamental Research Funds for the Central Universities, the MOE (Ministry of Education in China) Project of Humanities and Social Sciences (20YJA190002), and Zhejiang University Education Foundation Global Partnership Fund.Author informationAuthors and AffiliationsDepartment of Curriculum and Learning Sciences, Zhejiang University, 866 Yuhangtang Road, Zijingang Campus, 310058, Hangzhou, ChinaChanjuan Fu, Xinyue Shi, Shanyun He & Fengji GengDepartment of Psychology and Behavioral Sciences, Zhejiang University, 866 Yuhangtang Road, Zijingang Campus, 310058, Hangzhou, ChinaXiaoxin HaoCollege of Education, Renmin University of China, No. 59, Zhongguancun Street, Haidian Dist., 100872, Beijing, ChinaHanxin QianChildren’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, 310052, Hangzhou, ChinaFengji GengAuthorsChanjuan FuView author publicationsSearch author on:PubMed Google ScholarXiaoxin HaoView author publicationsSearch author on:PubMed Google ScholarXinyue ShiView author publicationsSearch author on:PubMed Google ScholarHanxin QianView author publicationsSearch author on:PubMed Google ScholarShanyun HeView author publicationsSearch author on:PubMed Google ScholarFengji GengView author publicationsSearch author on:PubMed Google ScholarContributionsC.F.: Conceptualization, Formal analysis, Investigation, Data Curation, Writing—Original Draft, Visualization. X.H.: Investigation, Writing—Review & Editing. X. S.: Investigation. H.Q.: Investigation. S.H.: Writing—Review & Editing, Supervision. F.G.: Conceptualization, Methodology, Resources, Writing—Review & Editing, Supervision, Project administration, Funding acquisition.Corresponding authorsCorrespondence to Shanyun He or Fengji Geng.Ethics declarationsCompeting interestsThe authors declare no competing interests.Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary informationSupplementary materialRights and permissionsOpen Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. 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