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4UDC 378.1
A STUDENT-CENTERED FEEDBACK MODEL OF THE EDUCATIONAL PROCESS: QUALITY OF ACADEMIC ACHIEVEMENTS OF STUDENTS
MEKTEPBAYEVA ARUZHAN
1st year Master's degree student Department of Computer Engineering, Astana IT University
TLESHOVA ZHIBEK
Candidate of Pedagogical Sciences, Department of General Educational Disciplines, Astana IT
University
Abstract. This study addresses the needfor more effective feedback systems in higher education by developing a Student-Centered Feedback Model. The model integrates AI-driven feedback, peer assessments, and instructor interventions to create a personalized and continuous feedback loop throughout the learning process. To evaluate the model's effectiveness, a case study was conducted at Astana IT University involving two groups of first-year bachelor's students majoring in Computer Science and Software Engineering. The experimental group received continuous feedback via multiple channels, including AI systems, Moodle, and in-person interactions, while the control group followed a traditional feedback model. Over a 16-day period, data on academic performance and skill development were collected and analyzed. The results showed significant improvements in the experimental group's performance across various skills such as creativity, critical thinking, and problem-solving compared to the control group. The study concludes that the Student-Centered Feedback Model enhances learning outcomes and engagement, offering a practical solution for improving feedback systems in modern educational environments.
Keywords: Student-Centered Feedback, Higher Education, AI-driven Feedback, Personalized Learning, Academic Performance, Skill Development, Continuous Feedback
Introduction.
Higher education is increasingly shifting towards student-centered learning models, where the individual needs and learning styles of students are prioritized. Central to this shift is the evolving role of feedback, which has moved beyond simple evaluations of performance to becoming an essential tool for continuous learning and improvement. Effective feedback helps students not only understand their current academic standing but also encourages them to engage more deeply with their learning process through personalized, constructive guidance. This student-centered feedback model plays a critical role in enhancing academic outcomes and overall student satisfaction.
Existing research underscores the importance of feedback in driving academic success. Studies have shown that personalized feedback can accelerate student progress by up to eight months over the course of an academic year [1]. Additionally, 60% of students report that timely, individualized feedback is one of the key contributors to their academic achievements [2]. These statistics highlight the significant impact of tailored feedback in shaping positive learning outcomes.
Despite these advancements, there remain unresolved challenges in implementing effective feedback systems across higher education. Previous studies have identified gaps in the consistency, personalization, and timely delivery of feedback, which hinder its effectiveness in promoting academic success. Many institutions struggle with providing feedback that is both actionable and aligned with individual student needs, especially in digital or hybrid learning environments. Furthermore, current systems often lack the capacity to integrate feedback meaningfully into the learning process, leaving gaps in student engagement and academic development.
In response to these challenges, this article presents a new contribution by exploring a comprehensive student-centered feedback model that addresses these gaps. The model leverages digital tools and personalized feedback mechanisms to ensure that students receive timely, relevant, and constructive input that supports their academic growth. The article will demonstrate how this
model can fill existing gaps in feedback delivery and offer a more engaging, supportive learning environment for students.
The following sections of this article will provide a detailed review of relevant literature, outline the gaps and challenges in current feedback systems, and present the methodology for implementing a more effective feedback model. The final section will discuss the relevance and impact of this model, showing how it can lead to improved academic performance and student satisfaction in higher education.
Literature Review
The evolution of feedback models in higher education has garnered considerable attention over the past few decades, particularly with the shift towards student-centered learning environments. Seminal research by Black and Wiliam laid the groundwork for the concept of feedback as a formative assessment tool, which not only serves as an evaluation of student performance but also as a mechanism for improving learning outcomes. Their work emphasized that feedback should be more than a one-way communication from teacher to student; rather, it should foster an interactive dialogue that guides both teaching and learning [3]. This perspective marked a departure from the more behaviorist models of feedback, which viewed it solely as a way to correct student behavior.
Recent study [3] explored the further advanced the field by proposing a multi-level framework for feedback, emphasizing that feedback should not only address task performance but also help students develop self-regulation and meta-cognitive skills. Their research showed that feedback that reduces the gap between a student's current performance and their desired learning goals has a substantial effect on academic achievement, with clear and timely guidance being critical for its effectiveness [4]. This shift towards formative and process-oriented feedback models underscores the importance of making feedback a more integral part of the learning process, rather than a mere summative assessment.
Research [5] built upon these foundations by integrating cognitive and constructivist theories of learning into feedback models. They argued that feedback is most effective when it positions students as active participants in the learning process, capable of generating their own feedback and engaging in self-assessment. Their research highlighted the growing role of self-regulation and student agency in feedback systems, which are essential for developing lifelong learning skills. This more holistic approach reflects broader trends in education towards developing critical thinking and autonomy in students, rather than simply assessing their retention of information.
In addition to these theoretical advancements, there has been increasing interest in the application of digital technologies to enhance feedback processes. Recent studies have demonstrated the efficacy of learning management systems (LMS) and AI-driven assessment tools in providing personalized, real-time feedback. For example, research has shown that digital platforms like Moodle can significantly improve student engagement and academic outcomes by facilitating timely and interactive feedback between instructors and students [6]. This trend towards digitalization was further accelerated by the COVID-19 pandemic, which forced many institutions to adopt online and hybrid learning models. Studies during this period highlighted the challenges and opportunities of providing effective feedback in virtual environments, with many educators reporting difficulties in maintaining the same level of interaction and personalization as in face-to-face settings [7].
In the context of Kazakhstan, Kerimbayev et al. explored the implementation of student-centered feedback models in higher education through the use of Moodle. Their study found that integrating digital tools to provide feedback not only increased student engagement but also fostered better communication between students and instructors. This, in turn, had a positive impact on academic performance, suggesting that digital feedback mechanisms can be particularly effective in environments where face-to-face interaction is limited. However, the study also pointed to significant challenges in ensuring the consistency and quality of feedback, particularly in regions where digital infrastructure may be lacking.
Despite the progress made, several gaps remain in the literature. One of the primary issues is the uneven implementation of student-centered feedback models across different educational settings. While much of the research has focused on Western contexts, there is a need for more studies that examine how these models can be adapted to diverse cultural and technological environments, particularly in Central Asia and other regions with less developed digital infrastructures. Additionally, there is a lack of longitudinal studies that investigate the long-term impact of student-centered feedback on academic achievement and student development. Future research should aim to fill these gaps by exploring how digital and traditional feedback systems can be more effectively integrated to support diverse student populations.
The literature on feedback in higher education reveals a clear shift towards student-centered models that prioritize interactive, formative feedback as a tool for learning. The integration of digital technologies into feedback systems has further enhanced the potential for personalization and timely interventions, although challenges remain in ensuring equitable access and consistent implementation. As the field continues to evolve, future research should focus on refining these models to better meet the needs of diverse student populations across various educational contexts.
Methods and materials
In this section, we outline the methodology used to develop and implement the Student-Centered Feedback Model. By employing a mixed-methods approach, we integrate both qualitative and quantitative data to assess the model's effectiveness. This methodology is designed to address the limitations identified in traditional feedback systems, ensuring that the proposed model can adapt to diverse student needs and learning environments.
To establish the foundation for the Student-Centered Feedback Model, we first identified the current gaps and shortcomings in traditional feedback systems, as outlined in Table 1. This table highlights key issues such as the lack of personalization, delayed feedback, and the limited impact of feedback on the learning process, which our model seeks to address and improve upon.
Table 1. Problems and Shortcomings of the Traditional Feedback System
Problem Description Impact on the Learning Process
Generalized and Non-Personalized Feedback Instructors often provide generalized comments that are not tailored to the individual needs of students. Students struggle to deeply understand their mistakes and improve performance due to the lack of personalization.
Delayed Feedback Feedback is provided too late, when students have already moved on to new assignments or topics. Recommendations lose relevance, and students cannot apply them to their current learning process.
One-Way Feedback Process Feedback is one-way: students cannot ask questions or clarify the instructor's comments. Students lack opportunities to fully comprehend their mistakes and correct them effectively.
Low Student Engagement Many students take a passive approach to feedback and do not see its practical value. Students fail to utilize feedback to improve their performance, which diminishes the quality of their learning
Limited Use of Technology Modern tools such as AI and LMS are not fully leveraged for automating and personalizing the feedback process. The quality of feedback is reduced, making it less effective, especially in large student groups.
Figure 1. Traditional Feedback Model in Education
Figure 1 illustrates a Traditional Feedback Model, which remains one of the most prevalent systems in education. This model emphasizes the role of instructors, computers, and peers as primary sources of feedback. The feedback message, typically delivered after a student's performance on a task, consists of various elements such as timeliness, level of detail, comprehensibility, accuracy, tone, focus, and function.
Feedback is often centered on current performance and is expected to result in improved learning outcomes (e.g., grades, mastery of a subject). However, this process is largely influenced by the individual characteristics of the learner, such as their ability, motivation, self-efficacy, and receptivity to feedback.
The model also includes the learner's cognitive processing, where the student must first understand the feedback before engaging in affective processing (emotional reactions) and behavioral processing (actions taken based on the feedback). However, the flow of feedback often lacks dynamic interactions and is limited by its delayed nature, which can hinder timely adjustments.
Limitations of the Traditional Feedback Model
While the traditional model has been foundational in education, several limitations restrict its effectiveness in fostering deeper learning and continuous improvement:
Delayed Feedback. Feedback is usually delivered after the task has been completed, making it less effective in allowing students to make adjustments during the learning process. This delay often causes feedback to lose relevance by the time the student is able to apply it.
One-Way Feedback Flow. The model relies on a one-way communication channel where the feedback primarily flows from the source (e.g., instructor or peer) to the learner. There is limited opportunity for students to ask clarifying questions or seek additional guidance, which can lead to misunderstandings or incomplete corrections.
Limited Personalization. Feedback in this model tends to be general and not tailored to the unique needs of each student. This lack of personalization fails to address specific areas of improvement, leaving students without clear action plans for how to enhance their performance.
Emphasis on Outcome Over Process. The traditional feedback model heavily emphasizes the outcome (e.g., grades, performance) rather than focusing on the process of learning. Feedback is often
used to validate performance after the fact, rather than guiding students throughout the learning journey.
Learner Dependency on External Feedback. Students are dependent on external feedback from instructors or peers, rather than developing the capacity for self-assessment and self-regulation. This lack of focus on fostering self-feedback skills hinders the development of autonomous learners who can continuously improve.
To address the shortcomings of the Traditional Feedback Model, Figure 2 presents an innovative solution through the Student-Centered Feedback Model.
Figure 2. The Student-Centered Feedback Model
Figure 2 shows the Student-Centered Feedback Model, which is designed to integrate various sources of feedback, personalized interventions, and dynamic adaptations to support academic achievement. The model operates through a series of interconnected stages. Unlike the traditional approach, the Student-Centered Feedback Model prioritizes timely, adaptive, and multi-source feedback that engages students in active reflection and promotes self-regulation. This dynamic process ensures that students can act on feedback in real-time, adjust their learning strategies, and continuously improve their performance.
1. Learning Environment. The process begins in the broader learning environment, which could be blended, online, or traditional. This context shapes how feedback is collected and delivered, depending on the tools and methods used.
2. Multi-Source Feedback Collection. Feedback is gathered from various sources.
- Automated Feedback: Generat ed by AI and digital tools based on student performance metrics from the LMS.
- Instructor Feedback: Comes from more personalized interactions with students, where the instructor assesses their progress and offers tailored insights.
- Peer Feedback System: Students review each other's work, providing collaborative input that helps with self-reflection and deeper learning.
3. Data Collection, Segmentation, and Adaptive Analysis.
The collected data is segmented and analyzed using AI-driven insights and instructor observations. This adaptive analysis helps to identify patterns in student performance, participation, and engagement, allowing for more personalized and relevant feedback.
4. Intelligent Personalization of Feedback _ for Each Student.
- After analyzing the data, feedback is tailored to the individual needs of each student. This personalization can take the form of:
- Automated Feedback: Quick responses to specific areas needing improvement.
- Instructor Feedback: Contextualized advice that helps address more complex or nuanced challenges.
- Peer Input: Recommendations based on group dynamics, collaboration, and peer assessment.
5. Feedback Delivery Channels.
- LMS Digital Delivery: Automated feedback is delivered directly through the LMS, providing real-time updates and continuous support.
- Face-to-Face or Virtual Conferences: Instructors offer more personalized, deep feedback through meetings, allowing for a thorough discussion of challenges and potential improvements.
- Peer-to-Peer Digital Interaction: Students engage with their peers through forums or group work, refining their understanding of the material and improving their performance based on collaborative efforts.
6. Cognitive and Emotional Processing. Once feedback is received, students process it both cognitively and emotionally. This processing stage is crucial as it determines how students react to the feedback—whether they feel motivated, frustrated, or encouraged. The emotional component also plays a role in the student's decision-making process.
7. Student Action on Feedback. Based on their understanding and emotional response, students take action on the feedback. This could involve revising assignments, seeking clarification from instructors, or implementing improvements suggested by peers.
8. Instructor-Guided and Collaborative Improvements. Instructor-Guided Adjustments: Instructors offer further guidance, helping students correct mistakes and advance their learning.
Collaborative Improvements: Peers work together to help each other refine their work, encouraging a collaborative learning environment where students support one another's growth.
9. Outcome Monitoring and Progress Analysis. As students take action on the feedback, their progress is continuously monitored through the LMS and AI tools. This allows both the system and the instructors to track improvements and identify any areas where additional support might be needed.
10. Continuous Monitoring and Progress Analytics. The model integrates continuous monitoring, ensuring that feedback is not static but evolves with the student's performance. AI and LMS systems provide real-time analytics to both students and instructors, facilitating timely interventions.
11. Dynamic Adjustment Loop for Feedback. Feedback is part of a dynamic loop, where the results of the previous feedback session influence the next one. The system adjusts continuously, ensuring that feedback remains relevant and supports ongoing academic improvement.
Case Study: Implementation of the Student-Centered Feedback Model at Astana IT University
This case study was conducted at Astana IT University, focusing on first-year bachelor's students majoring in Computer Science and Software Engineering. The study involved two groups, each consisting of 30 students, over a period of 16 days. The aim was to assess the impact of continuous, multi-source feedback on student performance compared to the traditional method of delayed feedback.
Participants:
• Control Group: 30 students who followed a traditional educational model. Feedback was provided only after the completion of assignments. These students attended lectures and worked on tasks independently without receiving real-time guidance.
• Experimental Group: 30 students who received continuous feedback through multiple channels, including.
- Moodle: Instructors provided digital feedback on ongoing tasks.
- In-person feedback: Continuous interaction with instructors during and after lectures.
- AI-powered systems: Instant feedback on coding assignments and technical projects.
- Peer feedback: Regular collaboration and input from fellow students.
Procedure:
- Experimental Group: Feedback was given at every stage of their assignments. AI tools corrected coding syntax and logic issues in real-time, while peers and instructors helped students refine their ideas and projects during group tasks and individual work. Personalized feedback via Moodle was also utilized for tracking student progress.
- Control Group: Feedback was given only at the end of each task, after the students had submitted their assignments. This reflected a more traditional, instructor-centered model, where feedback served primarily as an evaluation tool rather than a formative process.
Results and Discussion.
The results of this study were measured through two key metrics: academic performance and skill improvement.
Academic Performance: The results, shown in Figure 2, demonstrate a clear difference in grade improvement over the 16-day period. The experimental group saw their average grades rise steadily from 65% at the start to 95% by the end of the study. In contrast, the control group experienced slower improvement, reaching an average of 80% by the conclusion of the study. This data underscores the importance of continuous feedback in helping students to gradually improve their work overtime (Figure 3).
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Figure 3. Grade Improvement Over time: Experimental and Control Group
The Figure 3 represent the enhanced Explanation of the Case Study with Statistical Insights (Figure 3).
In this small-scale case study, we examined the impact of the Student-Centered Feedback Model on academic performance over a short period of 16 days. This case study involved two groups of undergraduate students:
• Experimental Group: Received continuous, personalized feedback through a combination of AI, peer interactions, and instructor comments.
• Control Group: Received traditional feedback methods at the end of each task without the iterative, personalized feedback loops.
The results, depicted in the graph, demonstrate a significant difference in performance:
• The experimental group saw a steady increase in average grades, with an improvement from 65% on Day 1 to 95% by Day 16.
• The control group, by contrast, experienced a slower improvement in grades, starting from 65% and only reaching 80% by the end of the study.
The personalized feedback model clearly provided the experimental group with more actionable insights into their performance, allowing them to make more rapid and effective adjustments.
Figure 4. Skill Improvement across Different Learner Types
Skill Improvement: Figure 4 highlights the progress made by three types of learners: Visual Learners, Collaborative Learners, and Analytical Learners. Before the feedback model was implemented (represented by the red lines), students displayed lower skill levels in areas such as creativity, problem-solving, and critical thinking. After the continuous feedback model was applied (represented by the blue shaded areas), all three learner types showed marked improvements across these competencies, demonstrating the effectiveness of the feedback model in enhancing a variety of critical skills (Figure 4).
This case study at Astana IT University demonstrates that continuous, multi-source feedback significantly improves academic performance and skill development among first-year bachelor's students. The students in the experimental group, who received ongoing feedback from instructors, peers, and AI systems, showed faster and more significant progress compared to those in the control group who followed a traditional feedback system. These findings suggest that real-time, personalized feedback is essential for fostering student success in an increasingly technology-driven educational environment.
Conclusion.
The implementation of the Student-Centered Feedback Model at Astana IT University demonstrated the significant benefits of continuous, personalized feedback in improving student learning outcomes. The study revealed that first-year students who received ongoing feedback through AI systems, peer assessments, and instructor interactions showed notable progress in both academic performance and key skills such as creativity, critical thinking, and problem-solving compared to those who followed traditional feedback methods. These findings suggest that integrating multi-source feedback into educational practices can foster deeper learning and enhance student engagement, making it a valuable approach for modern, technology-driven learning environments.
REFERENCES
1. Education Endowment Foundation, "The Impact of Feedback on Learning," 2016.
2. Instructure and Hanover Research, "State of Student Success Report 2023," 2023.
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4. J. Hattie and H. Timperley, "The Power of Feedback," Review of Educational Research, vol. 77, no. 1, pp. 81-112, Mar. 2007. doi: 10.3102/003465430298487.
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6. Z. Kerimbayev, Z. Kultan, Z. Abdykarimova, and N. Akramova, "Learning Management Systems in Distance Learning: Case Study of Kazakhstan," Education and Information Technologies, vol. 25, no. 6, pp. 5831-5847, Dec. 2020. doi: 10.1007/s10639-020-10296-0.
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