Statistical Results and Research Proposal on Learner Control and Tablet-Based Learning
Abstract
<h2>Statistical Results</h2> <p>The paper researches the effects of learner control in the learning process, especially concerning learners’ performance in power circuits. Specifically, two groups were studied: the “free choice” condition, where the students were allowed to self-organize the workflow as they chose the order of the tasks, and the “task control” condition, where the workflow was pre-scheduled. In this research, the main concern was establishing various essential aspects for analysis, such as post-test academic performance, perceived effort, and perceived agency between these two groups.</p> <h3>Descriptive Analysis</h3> <p>Data at the nominal level offered a general perspective on group studies. The “Student Control” grade mean was 7.49 (SE = 0.351, SD = 3.142); however, the “System Control” group achieved a grade mean of 7.43 (SE = 0.328, SD = 2.969) for pretest scores. Results were slightly higher for the “Student Control” group, with a mean of 11.21 (SE = 0.569, SD = 5.088), while the “System Control” group had a mean of 11.45 (SE = 0.542, SD = 4.904) for post-test scores. Again, there was an almost similar effort rating across the groups, whereby the “Student Control” group gave a mean of 5.24 (SE = 0.287, SD = 2.567), while the “System Control” group had a mean of 5.13 (SE = 0.281, SD = 2.547). Agency ratings showed a more pronounced difference: the students who belonged to the “Student Control” group got a score of 3.58 (SE = 0.213, SD = 1.908), while the students belonging to the “System Control” group had a higher score of 5.55 (SE = 0.271, SD = 2.455). These descriptive statistics revealed some trends and were used for further inferential analyses.</p> <h3>Table 1: Descriptive Statistics</h3> <p><strong>Pretest</strong><br>Student Control – Mean: 7.49, SE: .351, SD: 3.142<br>System Control – Mean: 7.43, SE: .328, SD: 2.969</p> <p><strong>Post-test</strong><br>Student Control – Mean: 11.21, SE: .569, SD: 5.088<br>System Control – Mean: 11.45, SE: .542, SD: 4.904</p> <p><strong>Effort</strong><br>Student Control – Mean: 5.24, SE: .287, SD: 2.567<br>System Control – Mean: 5.13, SE: .281, SD: 2.547</p> <p><strong>Agency</strong><br>Student Control – Mean: 3.58, SE: .213, SD: 1.908<br>System Control – Mean: 5.55, SE: .271, SD: 2.455</p> <h3>Test for Normality</h3> <p>K-S and Shapiro-Wilk tests were applied to test for normality. The results showed that all the variables (Pretest, Post-test, Effort, and Agency) deviated from normality for both groups, with all p values < .05. For instance, Shapiro-Wilk values for the Pretest scores indicated .934 (p < .001) for the Student Control group and .923 (p < .001) for the System Control group. Despite these deviations, the large number of students (n = 163) allows the use of parametric analysis because, according to the Central Limit Theorem, the sampling distribution of the mean will be approximately normal regardless of the shape of the original population distribution.</p> <h3>Table 2: Tests of Normality</h3> <p><strong>Pretest</strong><br>Student Control – K-S: .126, p = .003; SW: .934, p = .000<br>System Control – K-S: .136, p = .001; SW: .923, p = .000</p> <p><strong>Post-test</strong><br>Student Control – K-S: .114, p = .012; SW: .938, p = .001<br>System Control – K-S: .106, p = .025; SW: .945, p = .002</p> <p><strong>Effort</strong><br>Student Control – K-S: .166, p = .000; SW: .917, p = .000<br>System Control – K-S: .157, p = .000; SW: .928, p = .000</p> <p><strong>Agency</strong><br>Student Control – K-S: .133, p = .001; SW: .927, p = .000<br>System Control – K-S: .146, p = .000; SW: .904, p = .000</p> <h3>Analysis of Variance</h3> <p>The independent samples t-test was used to compare the post-test scores in the two groups. As indicated by the figures, academic achievement between the two groups did not show a significant difference (mean difference = -4.44, t(161) = -0.34, p > 0.05). From this, we could deduce that task control did not play a role in post-test performance. The ANOVA test was used to measure effort and agency ratings since the independent variable had two levels. The analyses for effort yielded no significant difference between the two groups, F(1, 161) = 0.13, p = 0.72, suggesting that perceived effort was similar whether participants were in the student-controlled or system-controlled condition. However, the scores for agency ratings varied significantly, F(1, 161) = 32.11, p < 0.001. Participants in the “System Control” condition reported much higher perceived agency than those in the “Student Control” condition, with an average perceived agency of 5.55 (SD = 2.455) out of 10.</p> <h3>Table 3: ANOVA (Agency)</h3> <p>Student Control – N: 81, Mean: 3.58, SD: 1.896, SE: .211, 95% CI: 3.16–4.00, Min: 1, Max: 9<br>System Control – N: 82, Mean: 5.55, SD: 2.455, SE: .271, 95% CI: 5.01–6.09, Min: 2, Max: 9<br>Total – N: 163, Mean: 4.57, SD: 2.401, SE: .188, 95% CI: 4.20–4.94, Min: 1, Max: 9</p> <h3>Discussion</h3> <p>The significance of the results discussed above is relevant to issues of instructional design and educational practice. While the motivational benefits of structured task sequences may not be sufficient to increase overall agency ratings, the presence of a structured sequence may help present students with clear goals and enable them to feel that their tasks are meaningful. However, pretest and post-test results, as well as effort ratings, show that increasing the instructional approach alone may not be enough to boost students’ academic performance. Other factors, such as instructional quality and individual differences, could be influential.</p> <p>As with most similar studies, the use of self-reported data has certain limitations because such data may be influenced by social desirability bias. It is recommended that future studies employ observational assessments or indices of physiological activity to confirm these findings. Moreover, due to the use of only one school, the study’s generalizability is limited; therefore, further research should be conducted in various educational contexts. Thus, even though in this study both academic performance and effort did not derive benefits from task control, the findings regarding the effect of agency on the learning motivation process are informative. These results can be helpful in ongoing discussions about improving educational methods and creating low-extrapolation teaching systems.</p> <h2>Research Proposal</h2> <h3>Introduction</h3> <p>Adopting educational technology has dramatically transformed teaching-learning practices in higher learning institutions, whereby tablets have become one of the tools that foster student engagement and motivation. Still, there is limited evidence on the efficacy of tablet learning, especially regarding long-term use and its benefits for persistence and achievement. This research proposal aims to study these dynamics, focusing on primary school students using tablets for learning. By analyzing the existence and determinants of the correlation between the use of tablets and academic achievement, this study will help fill identified gaps in the body of knowledge and provide practical recommendations. In this way, the research will further the knowledge base on how technology can influence learning environments and contribute to improved applications of technology in educational contexts.</p> <h3>Study Context and Participants</h3> <p>The proposed research will be carried out in a primary school, including students from first grade to upper grades, with a total of 200 students and 10 teachers. Participants will be divided into two groups: an experimental group working on tablets and integrating them into the learning process, and a comparison group working with traditional models of instruction. Each grade will have an equal number of students in each group, representing the entire school population. The increased number of students allows the study to gauge the effectiveness of tablets across various grade levels to build a broader understanding of their effectiveness. Teachers will be key participants in the research because they will assist students in the experimental group in navigating tablets and will assess their interactions and improvement. Conducting the research within a naturalistic classroom setting is expected to provide practical findings.</p> <h3>Tablet-Based Learning Applications</h3> <p>The tablet-based learning applications selected for this comparison are designed to provide features that enhance learner interaction and academic performance. Gameplay components will interact with conventional educational activities, converting them into motivating and sustained tasks for learners. For instance, students may receive points when they complete tasks or reach specific levels, thereby increasing motivation. Most exercises will require active participation, allowing students to manipulate variables, solve problems, and receive immediate feedback. Instant feedback components will guide students through the outcomes of their work, pointing out areas for improvement and enhancing confidence when mastering new knowledge. Altogether, these attributes are assumed to produce engaging interactions that encourage students to interact with content more effectively.</p> <h3>Research Design and Methodology</h3> <p>A quasi-experimental research design will be used to assess the effectiveness of tablet-based learning based on students’ performance and interest. One main benefit of a quasi-experimental design is that it is well suited for educational settings where random assignment may not be possible. Pre- and post-test results will be applied to determine the level of improvement in understanding throughout the three-month study period. During instruction, the experimental group will use tablet computers with the selected applications, while the control group will continue with standard methods. The incorporation of both quantitative and qualitative measures will ensure that multiple dimensions of the intervention are captured.</p> <h3>Data Collection Methods</h3> <p>Quantitative measures will include standardized tests and Likert-scale questionnaires. Standardized tests will measure academic achievement aligned with subject content and problem-solving skills, while questionnaires will measure levels of engagement, motivation, and satisfaction from the students’ perspective. For example, students will respond to statements such as, “How often did you feel engaged while doing the learning tasks?” using a scale ranging from strongly disagree (1) to strongly agree (5). Secondary data will be collected through semi-structured interviews to obtain students’ and teachers’ views on the efficiency of tablet use in their classes. Questions such as, “Which aspect of using tablets did you like most?” or “Did you experience any difficulties?” will guide these interviews. Observations of interaction patterns between students and teachers, as well as general classroom learning environments, will provide qualitative data to complement quantitative findings.</p> <h3>Data Analysis Techniques</h3> <p>Quantitative data will be analyzed using statistical techniques, while qualitative data will be examined through thematic analysis. Frequency distributions will present demographic information and initial insights into academic achievement and activity patterns. Group comparisons will be conducted using paired t-tests to determine whether significant improvements occurred during the intervention. ANOVAs will compare the results of experimental and control groups. For example, the analysis may reveal whether students in the tablet-based group performed better in problem-solving tasks than those in the traditional group. Qualitative thematic analysis will identify recurring patterns and emerging themes, such as increases in motivation or technological challenges, providing deeper insight into the effects of tablet use on learners.</p> <h3>Ethical Considerations</h3> <p>Ethical integrity will be a priority in this research project. Parental consent will be obtained for all participants, and students will be informed about the study’s objectives, procedures, and voluntary nature. Participation or withdrawal will not affect students’ academic performance. Students’ assent will also be sought to respect their autonomy. Participants will be assigned unique identifiers to ensure anonymity, and data will be stored securely on encrypted devices. Regular breaks will be permitted to prevent stress, and teachers will assist students experiencing difficulties with tablets. All identifying information will be deleted six months after completion of the research to comply with ethical principles and the Data Protection Act.</p> <h3>Strengths of the Study</h3> <p>The study adopts both quantitative and qualitative data collection methods, offering a comprehensive perspective on the impact of tablet-based learning. By incorporating multiple data sources and analysis methods, the study aims to capture the richness of students’ educational experiences. Conducting the research in a real-world classroom setting enhances ecological validity, meaning the findings are more applicable to everyday educational contexts. The diversity of data collection methods also allows comparison between objective test results and subjective experiences of engagement and motivation.</p> <h3>Limitations and Future Directions</h3> <p>The study has several limitations. The sample is restricted to one school, which may limit generalizability to other educational contexts. Schools in different regions or with different resources may produce different outcomes, suggesting that future research should be conducted across various settings. Self-report measures of engagement may introduce response bias. Future studies could incorporate objective measures such as eye-tracking or time-on-task assessments. Additionally, the three-month duration of the intervention may not capture long-term effects of tablet-based learning. Future research could extend the intervention period to examine prolonged impacts on student performance.</p> <h3>Conclusion</h3> <p>This study will provide valuable insights into implementing tablet-based learning in primary schools. By evaluating the effects of tablet-assisted learning on student engagement and academic achievement, the research aims to help educators and policymakers understand the strengths and limitations of integrating tablets into instruction. The findings may inform future research and the development of educational technologies that enhance learning quality across diverse educational environments. Given the practical and theoretical relevance of the study, it offers a timely contribution to discussions on technology-enhanced learning. By addressing gaps in current literature, the research seeks to support informed decision-making regarding tablet use in promoting student achievement and lifelong learning.</p>