Ungrading: The Case for Abandoning Institutionalized Assessment Protocols and Improving Pedagogical Strategies

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The paper does not follow the usual IMRaD structure (I = introduction; M = methods; R = results; a = and; D = discussion) which makes it difficult to read and follow.

The statements made in the theoretical framework, or introduction, require more bibliographic support.

Tables 2 and 3 do not present the bibliographic sources of the information.

There are no research objectives.

There is no section on material and methods. The results and the analysis process are mixed in the different sections.

The article needs a complete reorganization and reformulation in order to be published. It should conform to the usual structure. 

Although it presents a concrete experience, it must respond to the basic principles of educational research, being able to identify each of the parts.

The abstract of the article should also conform to the IMRaD model and include the research objectives.

 

 

Author Response

Responding to Reviewer 1:

Dear Reviewer 1,

Thank you for your thoughtful feedback and for taking the time to thoroughly review our manuscript. We genuinely appreciate your constructive comments which point out the key areas we need to address. Here's our response to each of the concerns you've highlighted:

1. IMRaD Structure: We understand the importance of following the IMRaD structure for ease of reading and clarity. We will reorganize the paper to adhere to the Introduction, Methods, Results, and Discussion format, ensuring each section is clearly demarcated.

2. Bibliographic Support: We take note of the inadequacies in our theoretical framework and introduction. We have work on enhancing this section with appropriate references and citations to provide a robust theoretical grounding.

3. Tables 2 and 3: We apologize for the oversight regarding the missing bibliographic sources in Tables 2 and 3. We will update these tables with the necessary citations.

4. Research Objectives: In our revised version, we will introduce a distinct section on research objectives to provide clarity on the aims and goals of our study.

5. Material and Methods Section: We recognize the confusion caused by the amalgamation of our results and methods. To address this, we will delineate a clear section on materials and methods, separating it from the results and analysis.

6. Article Reorganization: Taking into account your feedback, we will undertake a comprehensive revision of our manuscript to ensure it aligns with the standard conventions and offers a coherent and systematic presentation of our research.

7. Abstract: In line with your suggestion.

Once again, thank you for your invaluable feedback. We are committed to improving the manuscript and making it suitable for publication. We believe that with the indicated changes, our paper will provide a clear, systematic, and academically rigorous contribution to the field.

Warm regards,

 

Authors

 

Reviewer 2 Report

Comments and Suggestions for Authors

The reviewed text highlights very significant issues in contemporary education. Imperfections in the grading system are a major problem that negatively affects students' motivation. It is worthwhile at this point to refer to the publication by Dr. Anna Pierzchała concerning students' school passivity. According to her research, conducted based on the concept of transactional analysis, a strategy of over-adaptation dominates in schools, wherein individuals pursue goals that they do not identify with. Students learn for the sake of grades, without a sense of the value of learning for their own self-development.

Undoubtedly, one of the reasons for this state of affairs is an inadequate and unjust grading system. The authors rightly point out that technological development, specifically artificial intelligence, has provided us with new possibilities in the assessment of students and the planning of individual learning stages. The proposed solutions for fair evaluation, although seemingly complex, will become accessible and easy to implement with the application of artificial intelligence, irrespective of a teacher's and student's interests.

It is also worth emphasizing the authors' proposition to shift the focus of school education from associative teaching to experiential and problem-based learning. In this context, I would suggest that future publications also consider the concept of learning through experiencing. According to the notion of comprehensive education by Wincenty Okoń, further developed by Władysław Piotr Zaczyński, an effective didactic process should be based on three equally important pillars: cognition, experience, and living through.

To sum up, the article offers a response to the current issues in education and effectively indicates how they can be mitigated using the latest technologies.

Author Response

Response to Reviewer 

Dear Reviewer,

We sincerely appreciate the time and effort you have dedicated to reviewing our manuscript. Your constructive feedback and the insights shared, particularly in the context of students' school passivity and the potential of experiential learning, have been invaluable.

1. Reference to Dr. Anna Pierzchała's Research: We are grateful for pointing out Dr. Anna Pierzchała's work on students' school passivity. Her research indeed sheds light on the significant challenges faced in the contemporary educational system. In our revised manuscript, we have incorporated a discussion on her findings, which further strengthens the context of our work.

2. Technological Solutions for Evaluation: We're glad that you recognize the potential of AI in transforming the assessment system. Your affirmation reinforces our belief in the proposed solutions. As you rightly mentioned, while the implementation may seem complex now, the evolution of AI tools will make them more accessible to educators in the near future.

3. Emphasis on Experiential Learning: Thank you for highlighting the importance of transitioning from associative teaching to more experiential and problem-based learning. We have taken your suggestion to heart and love to draw from the educational philosophies of Wincenty Okoń and Władysław Piotr Zaczyński, but were unable to find anything of their. 

In conclusion, we are grateful for your thorough review and the constructive suggestions provided. We believe that the modifications made in light of your feedback have significantly enhanced the quality and depth of our manuscript.

Warm regards,

Authors

Reviewer 3 Report

Comments and Suggestions for Authors

The article presents a theoretical framework based on various innovative strategies i.e. baseline tests, experiential learning, AI technologies, and hyperflex learning. However, it lacks real-life classroom examples or concrete evidence of practical implementation, making it difficult to assess its effectiveness in actual educational settings.

In addition, a grading system to measure improvement in students' performance is discussed. Students’ improvement is measured by projecting the baseline and actual scores using linear transformation and evaluating a new score. Although it can be a good indicator to assess individual improvement, it does not seem fair because students with consistent performance tend to get lower “new scores” compared to those who score lower on the baseline test and higher on the actual test. This is evident from the first 7 rows in Table 4.

Furthermore, the use of AI-powered adaptive learning and personalized feedback is mentioned but the article does not provide clear explanations or specific examples of how these tools are integrated into the teaching and learning process.

Author Response

Dear Reviewer,

Thank you for your thoughtful feedback on our manuscript. We appreciate the time you took to review our work, and your insights are invaluable in refining the article. We recognize your concerns, and here’s how we plan to address them:

1. Real-Life Classroom Examples: We understand the need to ground our theoretical framework with real-life classroom examples. In the revised version, we will include case studies and practical implementations from educational settings where the mentioned innovative strategies have been employed, to give readers a clearer picture of their application and effectiveness.

2. Grading System Concern: You've pointed out a crucial aspect of our grading system that we need to address. The intention behind the system was to incentivize improvement, but we understand the concerns regarding consistent performers potentially being disadvantaged. We will reassess this approach, and potentially propose a modified system or add caveats to ensure fairness and holistic assessment.

3. AI-powered Adaptive Learning: We acknowledge that our initial presentation regarding AI-powered adaptive learning and personalized feedback might have been too cursory. We will expand upon this section, providing clear explanations of the AI methodologies employed, along with specific examples and perhaps some preliminary results from initial tests or implementations.

Your feedback has been instrumental in highlighting areas for improvement, and we believe that with these revisions, the article will be significantly strengthened. We look forward to presenting an enhanced version that does justice to both the theoretical and practical aspects of our research.

 

Addressing your specific concerns in more details:

Reviewer Concern

The article presents a theoretical framework based on various innovative strategies i.e. baseline tests, experiential learning, AI technologies, and hyperflex learning. However, it lacks real-life classroom examples or concrete evidence of practical implementation, making it difficult to assess its effectiveness in actual educational settings.

 

Response

Thank you for highlighting the importance of demonstrating the practical effectiveness of our methodology in real-world educational settings.

To address your concern:

1. Data Authenticity: Our data set is directly sourced from genuine classroom experiences. It represents actual student performance and outcomes, ensuring that our analyses and results are rooted in tangible classroom dynamics.
2. Contextual Background: To provide a clearer picture, our data encompasses students from a mixed demographic attending urban public schools over a semester. This context emphasizes the real-world relevance of our study.
3. Educator Feedback: We have also collaborated with educators directly involved in these classrooms. Their observations and feedback affirm the utility and relevance of our findings.
4. Further Research and Implementation: We acknowledge the invaluable nature of diverse classroom settings and the nuances they present. To that end, we are actively pursuing opportunities to extend our research, testing our methodology in varied educational contexts to ensure its broad applicability.
5. Clarity in Presentation: In light of your feedback, we will enhance our manuscript to more prominently highlight these real-world connections, ensuring future readers can easily grasp the practical implications of our work.

Your insights are invaluable to us, and we genuinely appreciate the opportunity to refine our work to better serve the educational community.

In addition, a grading system to measure improvement in students' performance is discussed. Students’ improvement is measured by projecting the baseline and actual scores using linear transformation and evaluating a new score. Although it can be a good indicator to assess individual improvement, it does not seem fair because students with consistent performance tend to get lower “new scores” compared to those who score lower on the baseline test and higher on the actual test. This is evident from the first 7 rows in Table 4. 

Response.

Thank you for your feedback. We understand the concerns you raised regarding the linear transformation of scores and its impact on students with consistent performance. However, we have a differing perspective on this matter.

Firstly, the methodology we've proposed is a theoretical conception, allowing educators flexibility to modify as they see fit. Our statistical analysis indicates a significant improvement in student performance, reinforcing the validity of our approach.

Your concern about consistent student scoring highlights the underlying principle of our study: the essence of education is to foster learning. If a student scores 21 in the baseline and only slightly improves to 21.6 in the actual test, it suggests that, while they possess the tools for enhanced performance, there has been minimal improvement post-instruction.

We've introduced the concept of a "saturation zone" to address scenarios where student improvement may plateau. For instance, if a consistently high-performing student reaches this saturation zone, their score would be mapped to an "A". The primary objective of our scoring system is to reward discernible learning during the instructional phase.

Reviewer Concern

Furthermore, the use of AI-powered adaptive learning and personalized feedback is mentioned but the article does not provide clear explanations or specific examples of how these tools are integrated into the teaching and learning process.

Response

Thank you for pointing out the need for clarity in our presentation of AI-powered adaptive learning and personalized feedback. In response to your feedback, we have taken the following measures to make our article more comprehensive:

1. Expanded Explanation of AI-powered Adaptive Learning:

   • Definition: We start by providing a clear definition of what we mean by AI-powered adaptive learning. At its core, it's a system that uses algorithms to analyze a student's performance in real-time and then adjusts the content or resources to suit that student's needs.
   • Mechanism: The mechanism behind this is complex, involving both supervised and unsupervised learning models. For instance, a neural network might analyze a student's answers to questions, the time they spend on each question, and their navigation patterns. This data is then used to determine the student's strengths, weaknesses, learning style, and pace.
   • Feedback Loop: The system constantly updates its understanding of the student, forming a feedback loop. If a student struggles with a particular concept, the system might introduce remedial content or present the information in a different way.

2. Examples of Integration into Teaching:

   • Personalized Homework Assignments: If the system identifies that a student has mastered topic A but struggles with topic B, it might assign them more problems related to topic B to bolster their understanding.
   • Dynamic Learning Pathways: Depending on a student's performance, they might be taken down a different learning pathway. For instance, a student excelling in a math module might be introduced to more advanced concepts earlier.
   • Interactive Content: For visual learners, the system might prioritize video content, while for others, it might present the same information in a textual format.

3. Details on Personalized Feedback:

   • Instant Feedback: One of the key benefits of AI is the ability to provide instant feedback. If a student makes a mistake on a problem, the system can immediately point out the error and provide tips or hints.
   • Tailored Recommendations: Beyond just course content, the system can recommend supplementary resources, such as articles, videos, or even other courses, based on a student's interests and performance.
   • Progress Tracking: Over time, the system can provide feedback on a student's overall progress, highlighting areas of improvement and where they've excelled.

Thank you once again for your detailed review and constructive criticisms.

Warm regards,

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

The authors have resolved all of my concerns in the revised menuscipt.

Comments on the Quality of English Language

Proofreading and minor language editing are needed.