EXPLORING THE IMPACT OF DIGITAL CLASSROOM MANAGEMENT TOOLS ON STUDENT DISCIPLINE BEHAVIOR

International independent scientific journal №78/2025 14 PEDAGOGICAL SCIENCES EXPLORING THE IMPACT OF DIGITAL CLASSROOM MANAGEMENT TOOLS ON STUDENT DISCIPLINE BEHAVIOR ShenHuihui Al-Farabi Kazakh National University Faculty of Philosophy and Political Science, Department of Pedagogy and Educational Management https://doi.org/10.5281/zenodo.17311047 Abstract To investigate the practical role of digital classroom management tools in managing student discipline, a study was conducted with three middle school classes over one semester using a controlled experimental method combined with a questionnaire survey. The study analyzed the application of a smart classroom system in classroom discipline management. Results showed that in the experimental classes using the smart classroom system, the incidence of disciplinary violations decreased by 32.6% compared to the control class, and the proportion of time spent on focused behavior increased by 28.3%. Additionally, 89% of students in the experimental classes approved of the digital management tool, and 83% believed that the tool's real-time feedback helped them quickly correct their disciplinary issues. The core mechanisms for improving classroom discipline were identified as realtime feedback, behavior data visualization, and interactive incentive functions. The study demonstrated that the reasonable and restrained use of digital classroom management tools can enhance classroom focus without disrupting the teaching process. However, in high-load task scenarios, the marking frequency should be adjusted to avoid additional stress. These findings provide a reusable framework for time thresholds and frequency settings in middle school classroom discipline interventions. Keywords ： Digital classroom management tools; smart classroom system; classroom discipline behavior; educational technology Introduction With the advancement of digital transformation in education, digital tools such as smart classroom systems and interactive classroom platforms are increasingly integrated into primary and secondary school teaching. Their functions have expanded from "assisting knowledge delivery" to "managing classroom order." According to the "China Education Digital Development Report (2023)," over 65% of primary and secondary school teachers have attempted to use digital tools for classroom management. However, research on the correlation between tool application and discipline improvement remains limited. In traditional classroom management, teachers rely on methods such as "verbal reminders" and "behavioral criticism" to maintain discipline. These methods often suffer from "management delays" and difficulties in quantifying behavioral data, particularly in large-class settings where discipline management efficiency is low. Digital classroom management tools, with features such as real-time behavior recording, data analysis, and personalized incentives, offer new pathways for managing classroom discipline. This study used a smart classroom system as the research platform, combined with a questionnaire survey to analyze students' acceptance and experience of the tool. The study further validated the impact of digital tools on classroom discipline behavior, providing more comprehensive empirical evidence for their application in classroom management. Research Design Research Subjects The study selected three eighth-grade classes from a middle school, including two experimental classes (86 students: 45 boys and 41 girls) and one control class (43 students: 23 boys and 20 girls). Preliminary academic performance tests (average score differences P>0.05) and classroom discipline observations (disciplinary violation differences P>0.05) confirmed no significant differences in the baseline conditions of the experimental and control classes, ensuring the comparability of the study. Research Tools 1. Smart Classroom System: A mainstream educational technology company's smart classroom system was used, which includes a "classroom discipline management module" with the following features: • Real-time behavior marking: Teachers can use a tablet to mark eight common classroom behaviors, such as "focused listening," "whispering," "leaving seats arbitrarily," and "using mobile phones," with one click. • Behavior data visualization: The system automatically compiles class and individual discipline behavior data, generating a "daily discipline report" (including the number of violations, the proportion of focused time, and behavior improvement trends). • Interactive incentive function: A "discipline points" mechanism allows students to accumulate points that can be redeemed for rewards such as speaking opportunities, homework exemptions, and class honor badges. 2. Classroom Discipline Behavior Observation International independent scientific journal №78/2025 15 Scale: Based on the "Evaluation Standards for Primary and Secondary School Classroom Discipline," the scale was designed with two dimensions: "positive discipline behaviors" (e.g., focused listening, active participation, following classroom rules, assisting in maintaining order) and "negative discipline behaviors" (e.g., disruptive talking, fidgeting, tardiness, using electronic devices). Each dimension includes four specific behavior indicators, using the "frequency recording method" (recording the number of occurrences per class) and the "duration proportion method" (calculating the proportion of time positive behaviors persist during the total class time). Two researchers conducted simultaneous observation and recording, with a reliability coefficient of α=0.87, indicating good reliability. 3. Student Questionnaire: To understand students' acceptance, experience, and perceived behavioral impact of the digital classroom management tool, a "Digital Classroom Management Tool Application Survey" was designed, consisting of 15 questions across three dimensions: • Tool acceptance (5 questions): e.g., "Are you willing to continue using the smart classroom system for discipline management in class?" (Options: Very willing/Willing/Uncertain/Unwilling/Very unwilling). • Usage experience (5 questions): e.g., "Do you find the smart classroom system easy to operate?" (Options: Very convenient/Convenient/Average/Inconvenient/Very inconvenient). • Perceived behavioral impact (5 questions): e.g., "Does the real-time marking function of the smart classroom system help you quickly recognize your disciplinary issues?" (Options: Very much/Yes/Average/No/Not at all). A total of 86 questionnaires were distributed, with 80 valid responses collected (valid response rate: 92%). The questionnaire's validity was tested with a KMO value of 0.82, suitable for statistical analysis. Research Process The research period was from February 2025 to August 2025 (7 weeks), with the following steps: 1．Preparation Phase (Week 1): Teachers in the experimental classes received training on operating the smart classroom system (8 hours of cumulative training) to ensure proficiency in using the discipline management module. Baseline classroom discipline behavior observations were conducted for all three classes (3 lessons per class) to collect initial data. The experimental class students were introduced to the discipline management functions and points rules of the smart classroom system. 2．Experiment Implementation (Weeks 2–7): The experimental class applied the smart classroom system for discipline management across all subjects (Chinese, Mathematics, English, Physics, History). Teachers used the system daily to mark student behaviors, generate discipline reports, and adjust management strategies based on the data (e.g., conducting after-class discussions with students who violated rules more than three times per week). The control class adopted traditional classroom management methods (without digital tools), where teachers maintained discipline through verbal reminders, after-class criticism, and class agreements. 3．Data Collection (Throughout): Each week, two personnel were assigned to conduct random classroom observations for three classes (observing three lessons per class per week, totaling 63 lessons). They recorded the frequency and duration of students' positive and negative disciplinary behaviors. After the experiment, behavioral data generated by the smart classroom system for the experimental class was collected. Additionally, a "Digital Classroom Management Tool Application Survey" was distributed to students in the experimental class, and the collected questionnaires were organized and analyzed. Research Results and Analysis Differences in Classroom Discipline Behaviors Between the Experimental and Control Classes After the experiment, statistical analysis was conducted on the classroom discipline behavior data of the two groups, and the results are shown in the table below: Type of Discipline Behavior Experimental Class (Mean ± SD) Control Class (Mean ± SD) Significance (Pvalue) Difference (%) Negative behavior incidence (times per class) 1.2±0.5 1.8±0.7 <0.01 Decreased by 32.6% Positive behavior duration ratio (%) 78.5±6.2 61.2±7.8 <0.01 Increased by 28.3% Focused listening duration ratio (%) 72.3±5.8 55.1±6.9 <0.01 Increased by 31.2% Incidence of disruptive talking (times per class) 0.8±0.4 1.3±0.6 <0.01 Decreased by 38.5% The table shows that the incidence of negative disciplinary behaviors (e.g., talking out of turn, fidgeting) in the experimental class was significantly lower than in the control class, while the proportion of time spent on positive disciplinary behaviors (e.g., attentive listening, active participation) was significantly higher than in the control class (P < 0.01 for all). Among these, the proportion of time spent on attentive listening showed the greatest increase (31.2%), indicating that the application of the smart classroom system significantly improved students' classroom disciplinary behaviors, particularly in enhancing classroom focus. Analysis of Student Questionnaire Results Statistical analysis was conducted on 80 valid questionnaires, and the results for core questions in each dimension are as follows: International independent scientific journal №78/2025 16 Tool Acceptance Dimension 89% of students chose "very willing" or "willing" to continue using the smart classroom system for discipline management. 7% of students chose "uncertain," and only 4% chose "unwilling" or "very unwilling" (main reasons: "concerned about being frequently flagged, feeling pressured," "finding the points rewards insufficiently attractive"). User Experience Dimension 82% of students found the smart classroom system "very convenient" or "convenient" to operate (teachers only need to click on the terminal icon to complete marking). 15% of students found the operation "average," and 3% found it "inconvenient" (main reasons: "the screen brightness during marking distracts students," "occasional system lag causes marking delays"). Perceived Behavioral Impact Dimension 83% of students stated that the real-time marking feature of the smart classroom system "very much" or "somewhat" helped them quickly recognize disciplinary issues. 76% of students felt that the "disciplinary points reward system" was "very motivating" or "motivating" in encouraging them to follow rules. 68% of students reported that the personal discipline reports generated by the system helped them "clearly understand their behavioral changes" and "proactively adjust their classroom performance." Overall, most students held a positive attitude toward the digital classroom management tool. The tool's real-time feedback and motivational features had a significant positive impact on guiding students' disciplinary behaviors, with only a small number of students expressing concerns about operational experience or psychological pressure. Mechanisms of the Digital Classroom Management Tool Over seven consecutive weeks of logs, we observed three reproducible "tool → behavior" pathways. The first is the convergence effect of immediate prompts: when the system provided visible marking within one minute, related low-intensity disciplinary behaviors returned to baseline within 3–5 minutes. The second is the visualization effect on teacher decisionmaking: the "time period heatmap" in the weekly report allowed teachers to move routine reminders to the 3rd– 5th minute of class. The third is the sustainability of points-based incentives: when rewards were more aligned with students' motivations (e.g., priority to speak, homework reduction), the duration of rule adherence was longer. Combining system data with student questionnaire results, the digital classroom management tool influenced classroom disciplinary behaviors primarily through the following three mechanisms: 1. Real-Time Feedback Mechanism: 83% of students in the experimental class recognized the role of the real-time marking feature. When teachers used the system to mark disciplinary behaviors with one click, students could immediately perceive their issues (e.g., when a teacher marked "whispering," students could notice the prompt icon in the corner of the classroom screen). This shortened the time gap between "occurrence of disciplinary behavior" and "teacher intervention," preventing the spread of disciplinary issues (e.g., from individual talking to group chatting). 2. Data Visualization Mechanism: Teachers in the experimental class reported that the "weekly discipline report" generated by the system clearly showed trends in student behavior changes (e.g., a student reduced disciplinary incidents from five in the first week to one in the eighth week). It also identified common class issues (e.g., concentration of disciplinary behaviors in the first 10 minutes of a specific class), shifting discipline management from "experience-based judgment" to "data-driven decision-making," making it more targeted. 3. Incentive Reinforcement Mechanism: 76% of students recognized the role of the points-based incentive feature. Rewards such as "opportunities to speak in class" and "homework reduction" aligned with middle school students' needs for "gaining attention" and "reducing academic burden," forming a positive cycle of "following rules → earning points → redeeming rewards → continued rule adherence." Additionally, the display of class honor badges (the system scrolling the top 10 students in the points ranking on the classroom screen) enhanced students' self-discipline awareness. Discussion and Suggestions Research Discussion The results of this study confirm that digital classroom management tools (smart classroom systems) can effectively improve students' classroom discipline behaviors, consistent with the conclusions of scholars such as Zhang Li (2023) and Wang Mingyuan (2022). It is worth noting that during tasks requiring sustained attention, such as listening or mental arithmetic, overly frequent prompts may compound the cognitive load of the task itself, leading some students to exhibit a "surface calm, inner anxiety" substitution effect. Compared to traditional classroom management, the advantages of digital tools are mainly reflected in three aspects: 1. Improved Management Efficiency: Teachers can record behaviors with a single click on the terminal without "interrupting teaching to correct discipline," reducing disruptions to the teaching process. This is particularly evident in large classes with over 60 students (Li Juan, 2021). 2. Precision Management: Behavior data can be used to develop personalized management strategies (e.g., individual conversations with frequently disruptive students, adjusting classroom processes for common issues), avoiding a "one-size-fits-all" approach and aligning with the "student-centered" educational philosophy (Liu Min, 2020). 3. Enhancing Student Agency: Through incentive mechanisms, students shift from "passively following rules" to "actively maintaining discipline," fostering self-management awareness. This aligns with the Ministry of Education's 《China Education Digitalization Development Report (2023)》, which emphasizes that "digital tools should empower students' autonomous development." International independent scientific journal №78/2025 17 However, the study also identified potential issues: 3% of students reported system lag, and 30% of experimental class teachers noted that "system data recording takes 1-2 minutes of class time" and that "overreliance on data may overlook the emotional needs behind student behaviors" (e.g., disciplinary issues caused by emotional fluctuations due to family conflicts, which data cannot directly reflect). This suggests that digital tools cannot fully replace the "humanistic care" in traditional classroom management and must be integrated with teachers' emotional communication and psychological counseling. As Chen Jie (2024) stated, "The core of digital classroom management is 'tools assisting humans,' not 'humans relying on tools.'" Practical Suggestions 1. For Schools: Increase resource investment in digital classroom management tools, prioritizing systems that are user-friendly and highly stable (e.g., avoiding products prone to frequent lag). Additionally, conduct specialized teacher training that includes not only tool operation but also "data interpretation skills" and "emotional communication techniques," such as using case studies to guide teachers on "how to combine data with conversations to analyze the deeper reasons behind student misbehavior," to prevent the application of tools from becoming superficial (Zhao Gang, 2023). 2. For Teachers: Control the frequency of digital tool usage, recommending no more than three behavior markings per class to avoid "over-monitoring" that may provoke student resistance. For students flagged by the data as disruptive, prioritize one-on-one conversations after class to understand the reasons behind their behavior (e.g., whether they are distracted due to not understanding the lesson content) before formulating intervention strategies. This balances "data management" with "emotional communication" (Sun Meng, 2022). Additionally, allow students to participate in designing the points reward system (e.g., voting on the types of rewards), enhancing their sense of involvement. 3. For Tool Developers: Optimize system functionality by adding a "behavior reason annotation module" (allowing teachers to quickly record potential reasons for student misbehavior, such as "poor mood" or "lack of understanding") and simplifying operational processes (e.g., introducing a "one-click batch marking" feature to mark multiple students for group misbehavior). This reduces teachers' operational burden. Developers could also add a "student self-recording feature," enabling students to submit weekly "discipline reflections" through the system, further strengthening their self-management awareness (Zhou Yang, 2021). Conclusion This study, through controlled experiments and questionnaire surveys, confirms that digital classroom management tools (smart classroom systems) can significantly reduce the incidence of student disciplinary issues and increase the proportion of positive disciplinary behaviors. Real-time feedback, data visualization, and interactive incentives are identified as the core mechanisms. 89% of students in the experimental classes expressed approval of the tool, indicating a high level of acceptance. The study demonstrates that digital classroom management tools can serve as effective auxiliary means for classroom management in primary and secondary schools, providing technological support for innovative classroom management models. However, it is important to note that digital tools are not a "panacea" and must be combined with the humanistic care and emotional communication inherent in traditional management to achieve optimal classroom discipline management outcomes. Future research could expand the sample scope (e.g., including students from primary and high school levels) or compare the application effects of different types of digital classroom management tools (e.g., smart classroom systems, classroom interaction apps, AI behavior recognition cameras) to further enrich the research on digital classroom management. References: 1．Zhang Li. Innovative Research on Classroom Management Models in Primary and Secondary Schools under the Background of Education Digitalization [J]. Education Exploration, 2023(5): 4549.(https://kns.cnki.net/kcms2/article/abstract?v=3uoq IhG8C44YLTlOAiTRKibYlV5Vjs7iJTKGjg9uTdeTsOI_6qSxnfFg UvD1hN7nXt1XrAT-hsQn3R8X7u5DqQ&uniplatform=NZKPT) 2．Wang Mingyuan. 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