A Staged Framework for Computer Vision Education: Integrating AI, Data Science, and Computational Thinking
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThis paper proposes an innovative staged teaching framework in the field of computer vision education, aiming to systematically integrate neural network AI, symbolic AI, data science, and computational thinking into teaching. The paper constructs the learners' abilities through four progressively deeper stages, which demonstrates its forward-looking and innovative design at the theoretical level. However, during the review process, I believe that the paper has deficiencies in the following aspects, especially the lack of support from actual teaching cases, which affects the persuasiveness and practicality of its conclusions. So my preliminary review opinion is to reject the manuscript, but after re-reviewing the article, my opinion is to accept the manuscript after a major revision. Here are some specific issues:
1.Lack of practical teaching cases:The paper mainly relies on expert evaluation to verify the effectiveness and practicality of the framework. Although this method provides theoretical support to a certain extent, it lacks specific teaching cases and student learning outcome data. To enhance the persuasiveness of the paper, it is recommended that the author add analysis of actual teaching cases, including specific teaching implementation processes, student learning feedback, and learning outcomes presentation. These cases should cover different learning stages to fully reflect the application effect of the framework in practical teaching.
2.The lack of empirical research:The paper mentions in the conclusion that future empirical research is needed to evaluate the impact of the framework in real educational environments, which indicates that the author is aware of this. However, in order to make the paper more complete and convincing, it is recommended that the author at least try to conduct some preliminary empirical research in the current study, such as small-scale teaching experiments or pilot projects, to collect preliminary data to support their conclusions.
3.Insufficient implementation details of the framework:Although the paper describes the four stages of the framework, it is slightly lacking in specific implementation details. For example, the teaching materials, teaching methods, and evaluation criteria for each stage are not specified in detail. To make the framework more operational, it is recommended that the author supplement these key information so that other educators can more easily understand and apply the framework.
In summary, I believe that this paper excels in terms of its innovation and theoretical value in the field of computer vision education, but it falls short in terms of practical teaching cases and empirical research. Therefore, I suggest that the author conduct a major revision to add practical teaching cases and preliminary empirical research data support to further improve the argumentation and conclusions of the paper.
Author Response
Dear Reviewer #1,
Thank you for your insightful comments on our manuscript. We appreciate you taking the time to provide such detailed and constructive feedback.
Post-Comment 1: Lack of practical teaching cases: The paper mainly relies on expert evaluation to verify the effectiveness and practicality of the framework. Although this method provides theoretical support to a certain extent, it lacks specific teaching cases and student learning outcome data. To enhance the persuasiveness of the paper, it is recommended that the author add analysis of actual teaching cases, including specific teaching implementation processes, student learning feedback, and learning outcomes presentation. These cases should cover different learning stages to fully reflect the application effect of the framework in practical teaching.
Post-comments 2: 2.The lack of empirical research:The paper mentions in the conclusion that future empirical research is needed to evaluate the impact of the framework in real educational environments, which indicates that the author is aware of this. However, in order to make the paper more complete and convincing, it is recommended that the author at least try to conduct some preliminary empirical research in the current study, such as small-scale teaching experiments or pilot projects, to collect preliminary data to support their conclusions.
Regarding the lack of practical teaching cases, we agree that this was a significant limitation in our original manuscript. Previously, we had conducted a pilot test with 40 upper secondary students, but due to the statistical analysis not being completed, we did not include these results in the original paper. Thanks to the review comments, we have now completed the statistical analysis and included the pilot test results. The activities in the second session correspond to the image retrieval and captioning focus of Stage 2. And the third and fourth sessions align with the more advanced concepts introduced in Stage 3 of staged framework. This pilot test focused on integrating computer vision concepts with scientific inquiry. We have added a new section to the paper detailing this implementation.
|
1 |
Location |
Page 11, line 330. (Based on the revised manuscript) |
|
From |
ã…¡ |
|
|
To |
To validate the developed framework and teaching-learning process, a pilot program was conducted with 40 upper secondary school students. The entire program was delivered over four class hours as shown in Table 8.
In the first session, students learned the scientific concepts related to acid-base properties and titration using indicators. During the second session, students engaged in activities where they used BTB indicator to distinguish between acids and bases. They observed color changes and took images of BTB solutions with pH values measurement. In the third and fourth session, students analyzed the RGB values of these images using a Python environment. They implemented a linear regression machine learning to generate a regression model that could predict pH values based on the average RGB values of the images. The pilot implementation aligns closely with the Staged Framework for Computer Vision Education, particularly incorporating elements from the second and third stages. The activities in the second session, where students captured images of BTB solution color changes according to pH changes, correspond to the image retrieval and captioning focus of Stage 2. The third and fourth sessions, involving RGB value analysis and the implementation of a linear regression model, align with the more advanced concepts introduced in Stage 3. This includes feature extraction (RGB values) and model training (linear regression), which are central to Stage 3's learning objectives. By structuring the pilot implementation in this way, we were able to provide students with a practical, hands-on experience that progressed from utilizing image recognition to more complex data analysis and model development, mirroring the progressive nature of our staged framework. Students completed a survey both before and after the program. The survey included questions related to their perception of computer vision education, subject knowledge, and the application of artificial intelligence. The survey questions are outlined in Table 9. |
|
|
2 |
|
4.3 Pilot Implementation Results
The results of the pre- and post-program surveys were analyzed using a 5-point Likert scale. Statistical analysis revealed significant improvements across all questions, as outlined in Table 12.
For Question 1, which assessed students' perception of the interest level of computer vision education, the mean score increased from 3.500 (SD = 0.877) before the program to 4.575 (SD = 0.636) after the program. The t-value of 6.345 and the p-value of less than 0.001 indicate a statistically significant improvement in students’ interest in the subject. In Question 2, which examined the perceived usefulness of computer vision education, the mean score improved from 3.600 (SD = 0.900) before the program to 4.625 (SD = 0.628) after the program. A t-value of 6.176 and a p-value of less than 0.001 suggest that students found the program significantly more beneficial following participation. Question 3 focused on students’ knowledge of the color changes of acid-base indicators. The pre-program mean score of 2.300 (SD = 1.091) increased significantly to 4.175 (SD = 0.813) after the program, with a t-value of 9.531 and a p-value of less than 0.001, demonstrating a notable gain in scientific understanding. Question 4, which assessed students' ability to predict pH values using a computer vision machine learning model, showed a marked improvement. The mean score rose from 2.300 (SD = 0.939) before the program to 4.075 (SD = 0.917) after the program, with a t-value of 8.834 and a p-value of less than 0.001, reflecting a significant enhancement in students’ practical application of AI. Question 5, which evaluated students' understanding of how computer vision and AI are applied in inquiry-based activities, demonstrated an increase from a pre-program mean score of 1.900 (SD = 1.109) to 3.275 (SD = 1.213) post-program. The t-value of 5.561 and the p-value of less than 0.001 indicate a statistically significant improvement in students' comprehension of the integration of AI and computer vision in scientific exploration. |
Post-Comment 3: 3.Insufficient implementation details of the framework:Although the paper describes the four stages of the framework, it is slightly lacking in specific implementation details. For example, the teaching materials, teaching methods, and evaluation criteria for each stage are not specified in detail. To make the framework more operational, it is recommended that the author supplement these key information so that other educators can more easily understand and apply the framework.
To address your concern about insufficient implementation details, we have expanded our description of each stage in the framework. We now provide more specific information about teaching materials, methods, and evaluation criteria for each stage. For example, we have included sample lesson plans and assessment rubrics. Additionally, we have incorporated a detailed account of the pilot implementation process. These additions make the framework more operational and easier for other educators to understand and apply.
|
3 |
Location |
Page 9, line 290. (Based on the revised manuscript) |
|
From |
ã…¡ |
|
|
To |
And after the first expert validity assessment, we developed and provided an example of a lesson plan along with the first expert validity questionnaire as shown in Table 7. This was intended to offer concrete guidance and facilitate a more detailed evaluation of the proposed educational framework by the experts. The lesson plan example provided in Table 7, focusing on Stage 2 (Utilizing Image Recognition) of the staged framework, demonstrates how the approach can be practically implemented in classroom. For example, in this activity, students progress from understanding basic scientific concepts to applying advanced computer vision techniques. Students engage in hands-on experimentation, analyzing the captured images using Python, and implementing a linear regression model to predict pH values based on RGB data.
[Table 7. Example of Lesson Plan (Stage 2: Utilizing Image Recognition)] |
We believe these changes significantly strengthen our paper, addressing the main concerns you raised while maintaining the innovative aspects of our work. We are grateful for your constructive feedback, which has helped us produce a more robust and practically relevant manuscript.
We look forward to your thoughts on these revisions and are open to any further suggestions you may have.
Best Wishes,
Authors
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsDear Authors,
The broad topic of education needs innovation. That's for sure. The progress in easily available AI for every person on the planet is definitely a game-changer on how should we design and perform education, both in terms of methodologies as well as in terms of the sole sense and goal of education.
The idea of preparing frameworks and starting with areas that are easy to grasp is good and popular. For instance, teaching Machine Learning is much easier when you start explanations with Computer Vision (than any other area).
For this reason, I have found your paper title rather interesting - despite its very complicated structure.
And here is the first issue/problem: The paper's title includes multiple terms, and the topic requires a very careful and slow reading to understand what the paper is supposed to be about. The journal name does not help here, while I was expecting sth from the area of applications of CV not from the area of education (and within the education: application). In your future works please take time (or at least try) to find a simpler and more catchy title. ...unless your idea is to use the topic as a "clickbait for search engines" so that your paper will be listed in more searches. That might also be a good point, tbh.
Line 8 - email is underlined (and other emails are not)
Line 8 - email is from KNUE not from (2) ((maybe remove it?))
L.15 - "X is a pivotal field in Y" - please, be very careful on using generative AI in writing text, LLMs have their "favorite words" so it is really visible. (+1:) in the future, the use of genAI may be banned (in some publishers it already is) so you would have to defend yourself that this was written back then when it was legal. (+2:) however, IF your text is real human-written text, then welcome to the new reality, where some words or phrases become "cursed" for being genAI's favorite ones. Now you/we have to replace them. For me personally, it is complicated: I read so many student reports/projects/exams/labs that I became allergic to GPT. When I encounter "pivotal", "alongside", "plays a crucial role", bulletpoint-like structure, the structure "X is [explanation]. It plays a pivotal role in [elaborate]. It includes [whatever]: [a series of 3-8 bulletpoints]. In summary [whatever].", then my "allergy to GPT" stops me from understanding the text, while I am frustrated that the author did not bother to write what he thinks, but used a bot.
L.19 - GPT's bulletpoints written in the form of enum within the abstract. The same comment as above. Of course, it is possible it is not GPT but your own work. In that case, it is unfortunate that it sounds GPT-ish, and that it makes it a little bit harder to read.
Narrative : ok
Journal scope : not really
Interest to readers : Applied (computer) sciences - not really
Interest to readers : Applied (engineering/technical) sciences - not really
Interest to readers : Applied (education-related) sciences - yes
Topic coverage (title) : not really; the title includes 10 scientific terms (Neuro-Symbolic, Computer Vision, Education, Framework, Staged Framework, Neural AI, Symbolic AI, Data Science, Computational Thinking), while the paper seems to describe the staged teaching/lesson structure for introducing CV, and the staged approach to teaching CV in general. I do not understand why there is "Neuro-Symbolic" or "Neural AI" or "Symbolic AI" in the title if the concept does not seem to benefit/use the specificity of AI. ((There is nothing special in the approach: Teaching CV should be aligned with the group competencies, and there are 4 levels/stages. Teaching CV should go deeper with the gain of competences of the students. --- Isn't that true for all education topics? Why is the AI mentioned in the topic? What's with the neuro-symbolic part? Why is it in the title? Shouldn't the title be like "Computer Vision Education: A Staged Framework Approach" ?))
Tables: good. nice. Your paper needs to have a break from pure text. You have a lot of "the same melody" in your narrative, so adding a table or chart, or photo, or figure, or anything - is great.
Experts: Ah, the mysterious AI experts. 25 of them. How did you manage to get 25 AI experts, and what for? ((Today, anyone is an AI expert (?) )). I would prefer to see some education experts, while your paper seems to be on the topic of teaching, not AI. I would like to see comparisons of your approach and the old approach (whatever it could be) to see if you managed to grasp the interest of students. But this may be difficult now, and it may take a lot of time. So maybe in the next paper.
I do not see a strong relation between the title and the paper, and between the paper and the results. I am not able to fully appreciate the scientific value of your paper. You need to revise your goal and results, write it more clearly, go back to introduction, narrow it towards your goal, rewrite the abstract, to indicate the research value. Right now, the abstract (as the title) seems to be very "noisy" with unnecessary terms and promises, which try to make the text more noble, but they make the text less professional. Let me show you here:
"educational settings." - why settings? isnt the surroundings a part of the learning-teaching process already?
"real-world educational settings." - why "real?" are there any "fake" or "laboratory" settings for education?
"it recommends future empirical studies to assess its impact in real-world educational settings." - why future, isn't it already being applied in your teaching? why do you disconnect from the "future" "impact" it's like you do not believe in the successful implementation of your framework. //by the way, the journal is APPLIED Sciences, not "NOT APPLIED Sciences" so in my opinion you should be VERY inclusive when it comes to the "tactical thinking".
... and so, there are many individual words/phrases that may be a start of a discussion, but the real innovation that you want to show within the paper becomes less and less visible with each additional word.
Maybe I did not understand your intentions to the full extent. But this also may serve as an indicator, that some of the readers may have difficulties in grasping the innovation or motivation in your text.
I wish you a good career and good papers,
Best regards;
a reviewer.
English is grammatically good.
(English) narrative is somewhat "noisy" - it includes additional side-terms, which do not add value, but distract from the narration flow.
The text should be rewritten by authors, not by language editors.
Author Response
Dear Reviewer #2,
Thank you for giving us the opportunity to submit a revised draft of our manuscript titled "A Staged Framework for Computer Vision Education: Integrating AI, Data Science, and Computational Thinking" to Applied Sciences. We appreciate the time and effort that you and the reviewers have dedicated to providing your valuable feedback on my manuscript. We are grateful to the reviewers for their insightful comments on our paper. We have been able to incorporate changes to reflect most of the suggestions provided by the reviewers. We have highlighted the changes within the manuscript.
Post-Comment 1: The paper's title includes multiple terms, and the topic requires a very careful and slow reading to understand what the paper is supposed to be about. The journal name does not help here, while I was expecting sth from the area of applications of CV not from the area of education (and within the education: application). In your future works please take time (or at least try) to find a simpler and more catchy title. ...unless your idea is to use the topic as a "clickbait for search engines" so that your paper will be listed in more searches. That might also be a good point, tbh.
Regarding the complexity of the paper's title, we acknowledge your concern and have simplified it to make it more concise and engaging while still accurately reflecting the content of our research.
The new title is "A Staged Framework for Computer Vision Education: Integrating AI, Data Science, and Computational Thinking".
Post-Comment 2: "X is a pivotal field in Y" - please, be very careful on using generative AI in writing text, LLMs have their "favorite words" so it is really visible. (+1:) in the future, the use of genAI may be banned (in some publishers it already is) so you would have to defend yourself that this was written back then when it was legal. (+2:) however, IF your text is real human-written text, then welcome to the new reality, where some words or phrases become "cursed" for being genAI's favorite ones. Now you/we have to replace them. For me personally, it is complicated: I read so many student reports/projects/exams/labs that I became allergic to GPT. When I encounter "pivotal", "alongside", "plays a crucial role", bulletpoint-like structure, the structure "X is [explanation]. It plays a pivotal role in [elaborate]. It includes [whatever]: [a series of 3-8 bulletpoints]. In summary [whatever].", then my "allergy to GPT" stops me from understanding the text, while I am frustrated that the author did not bother to write what he thinks, but used a bot.
We greatly appreciate your cautionary note regarding the use of AI-generated text. We would like to provide additional clarification on our writing process and the steps we are taking to address your concerns.
Our manuscript was initially drafted in Korean, our native language, to ensure the most accurate expression of our ideas and research findings. Subsequently, we utilized translation tools to convert the text into English. We now recognize that this process may have inadvertently introduced phrases and structures that resemble AI-generated content, particularly the overuse of terms like "pivotal" and "plays a crucial role". We have conducted a thorough review of the entire manuscript. And, we have revised and rephrased sections of the text. Furthermore, we want to assure you that following the final review process, we plan to utilize MDPI's professional language editing service.
|
1 |
Location |
Page 1, line 13. (Based on the revised manuscript) |
|
From |
ã…¡ |
|
|
To |
Computer vision education is increasingly important in modern technology curricula, yet it often lacks a systematic approach integrating both theoretical concepts and practical applications. This study proposes a staged framework for computer vision education designed to progressively build learners' competencies across four levels. This study proposes a four-staged framework for computer vision education, progressively introducing concepts from basic image recognition to advanced video analysis. Validity assessments were conducted twice with 25 experts in the field of AI education and curriculum. Results indicated high validity of the staged framework. Additionally, a pilot program, applying computer vision to acid-base titration activities, was implemented with 40 upper secondary school students to evaluate the effectiveness of the staged framework. The pilot program showed significant improvements in students' understanding and interest in both computer vision and scientific inquiry. This research contributes to the AI educational field by offering a structured, adaptable approach to computer vision education, integrating AI, data science, and computational thinking. It provides educators with a structured guide for implementing progressive, hands-on learning experiences in computer vision, while also highlighting areas for future research and improvement in educational methodologies. |
Post-Comment 3: Experts: Ah, the mysterious AI experts. 25 of them. How did you manage to get 25 AI experts, and what for? ((Today, anyone is an AI expert (?) )). I would prefer to see some education experts, while your paper seems to be on the topic of teaching, not AI. I would like to see comparisons of your approach and the old approach (whatever it could be) to see if you managed to grasp the interest of students. But this may be difficult now, and it may take a lot of time. So maybe in the next paper.
We sincerely appreciate your skepticism regarding the "25 AI experts" mentioned in our manuscript. Your comment has prompted us to provide a more comprehensive and accurate description of our expert evaluation process. We acknowledge that our initial description was too vague and potentially misleading, and we have thoroughly revised this section to offer a clearer picture of our methodology.
Our validity assessment was conducted specifically with AI education experts in South Korea. This choice was deliberate and reflects the unique context of AI education in the country. It's worth noting that South Korea has implemented AI education as a national policy, and many teachers are now specializing in this field through dedicated programs in graduate schools of education.
The experts who participated in our evaluation are all highly qualified professionals with extensive experience in both education and AI. Each evaluator is a teacher with at least 10 years of experience in teaching students and designing curricula in schools. They all hold a minimum of a master's degree specializing in AI education, ensuring a deep understanding of both pedagogical principles and AI technologies. Furthermore, these experts have practical experience in implementing AI education programs in school settings, which adds a valuable practical perspective to their evaluations.
We selected these experts based on their comprehensive understanding of educational practices and AI technologies, ensuring a thorough and well-rounded evaluation of our framework. Their dual expertise in education and AI makes them uniquely qualified to assess the practicality and effectiveness of our proposed approach.
Post-Comment 4: I do not see a strong relation between the title and the paper, and between the paper and the results. I am not able to fully appreciate the scientific value of your paper. You need to revise your goal and results, write it more clearly, go back to introduction, narrow it towards your goal, rewrite the abstract, to indicate the research value. Right now, the abstract (as the title) seems to be very "noisy" with unnecessary terms and promises, which try to make the text more noble, but they make the text less professional.
We sincerely appreciate your thorough review and insightful comments on our manuscript. Your feedback has been invaluable in helping us improve the clarity, consistency, and scientific value of our paper. We have carefully addressed each of your concerns and made substantial revisions to enhance the overall quality of our work.
We have conducted a comprehensive review of our manuscript to ensure consistency between the title and content. This process involved refining our focus and aligning all sections of the paper with our core research objectives. We have streamlined the content to more accurately reflect the essence of our study, removing extraneous information that may have obscured our main findings.
In the Introduction section, we have articulated our research goals more explicitly. We have provided a clearer rationale for our study, emphasizing its significance in the field of computer vision education. This revision helps readers understand the purpose and value of our research from the outset.
|
2 |
Location |
Page 2, line 71. (Based on the revised manuscript) |
|
From |
ã…¡ |
|
|
To |
This study aims to develop a staged framework for computer vision education by developing a staged framework, integrating neural network-based AI and symbolic AI at each stage. This framework incorporates AI, data science, and computational thinking to enhance problem-solving abilities. By emphasizing a staged framework, we design each stage to allow learners to simultaneously experience theory and practice through this integration. |
We acknowledge the confusion caused by terms like "Neuro-Symbolic AI" and have addressed this issue. We have removed ambiguous terminology and instead provided a more concrete explanation of our approach. We now clearly describe how we integrate the strengths of Symbolic AI and Neural network-based AI in our educational framework. This explanation offers readers a better understanding of the innovative aspects of our methodology.
|
3 |
Location |
Page 16, line 522. (Based on the revised manuscript) |
|
From |
ã…¡ |
|
|
To |
The integration of neural network-based AI and symbolic AI in this staged framework is exemplified in the acid-base titration activity. In this context, neural network-based AI is represented by the linear regression model that predicts pH values based on RGB data from images of BTB indicator solutions. This model learns patterns from data without explicit programming of rules, characteristic of neural network approaches. Concurrently, symbolic AI is incorporated through the process of analyzing RGB values from BTB indicator images using OpenCV and critically examining the reasons behind the machine learning model's pH predictions. For instance, students use OpenCV to extract RGB values from the indicator images, applying predefined algorithms and rules to process the visual data. This step represents a rule-based, symbolic approach to image analysis. Subsequently, students engage in a logical analysis of the machine learning model's predictions, utilizing their understanding of acid-base chemistry and color theory to interpret and validate the results. This analytical process involves creating explicit rules and heuristics to explain the relationship between RGB values and pH levels. The integration occurs as students use the symbolically derived RGB data to train the neural network model, and then apply symbolic reasoning to interpret the model's outputs. |
To address your concerns about the practical applicability of our research, we had conducted a pilot test and included its results in the paper. This pilot test demonstrates the effectiveness of our framework in a real educational setting. By presenting empirical evidence, we aim to show the feasibility and potential impact of our approach. This addition strengthens the applied nature of our research, aligning it more closely with the scope of Applied Sciences.
|
4 |
Location |
Page 11, line 330. (Based on the revised manuscript) |
|
From |
ã…¡ |
|
|
To |
To validate the developed framework and teaching-learning process, a pilot program was conducted with 40 upper secondary school students. The entire program was delivered over four class hours as shown in Table 8.
In the first session, students learned the scientific concepts related to acid-base properties and titration using indicators. During the second session, students engaged in activities where they used BTB indicator to distinguish between acids and bases. They observed color changes and took images of BTB solutions with pH values measurement. In the third and fourth session, students analyzed the RGB values of these images using a Python environment. They implemented a linear regression machine learning to generate a regression model that could predict pH values based on the average RGB values of the images. The pilot implementation aligns closely with the Staged Framework for Computer Vision Education, particularly incorporating elements from the second and third stages. The activities in the second session, where students captured images of BTB solution color changes according to pH changes, correspond to the image retrieval and captioning focus of Stage 2. The third and fourth sessions, involving RGB value analysis and the implementation of a linear regression model, align with the more advanced concepts introduced in Stage 3. This includes feature extraction (RGB values) and model training (linear regression), which are central to Stage 3's learning objectives. By structuring the pilot implementation in this way, we were able to provide students with a practical, hands-on experience that progressed from utilizing image recognition to more complex data analysis and model development, mirroring the progressive nature of our staged framework. Students completed a survey both before and after the program. The survey included questions related to their perception of computer vision education, subject knowledge, and the application of artificial intelligence. The survey questions are outlined in Table 9. |
|
|
5 |
|
4.3 Pilot Implementation Results
The results of the pre- and post-program surveys were analyzed using a 5-point Likert scale. Statistical analysis revealed significant improvements across all questions, as outlined in Table 12.
For Question 1, which assessed students' perception of the interest level of computer vision education, the mean score increased from 3.500 (SD = 0.877) before the program to 4.575 (SD = 0.636) after the program. The t-value of 6.345 and the p-value of less than 0.001 indicate a statistically significant improvement in students’ interest in the subject. In Question 2, which examined the perceived usefulness of computer vision education, the mean score improved from 3.600 (SD = 0.900) before the program to 4.625 (SD = 0.628) after the program. A t-value of 6.176 and a p-value of less than 0.001 suggest that students found the program significantly more beneficial following participation. Question 3 focused on students’ knowledge of the color changes of acid-base indicators. The pre-program mean score of 2.300 (SD = 1.091) increased significantly to 4.175 (SD = 0.813) after the program, with a t-value of 9.531 and a p-value of less than 0.001, demonstrating a notable gain in scientific understanding. Question 4, which assessed students' ability to predict pH values using a computer vision machine learning model, showed a marked improvement. The mean score rose from 2.300 (SD = 0.939) before the program to 4.075 (SD = 0.917) after the program, with a t-value of 8.834 and a p-value of less than 0.001, reflecting a significant enhancement in students’ practical application of AI. Question 5, which evaluated students' understanding of how computer vision and AI are applied in inquiry-based activities, demonstrated an increase from a pre-program mean score of 1.900 (SD = 1.109) to 3.275 (SD = 1.213) post-program. The t-value of 5.561 and the p-value of less than 0.001 indicate a statistically significant improvement in students' comprehension of the integration of AI and computer vision in scientific exploration. |
We believe these revisions address the key issues raised and strengthen the overall contribution of the paper. The changes are highlighted in bold text in the revised manuscript. Please let me know if you have any further questions or require additional modifications.
Thank you again for the opportunity to improve this work. We look forward to your favorable response.
Best Wishes,
Authors
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
Comments and Suggestions for AuthorsAfter reviewing it, I think the article is ready for publication. The computer vision education framework proposed by the authors has certain practicability. The author gave reasonable and comprehensive answers to the questions raised in the first review, and carefully adjusted the content of the paper. The description of the framework is more detailed and accurate, and an actual teaching case analysis is added, so I think the article is ready for publication.
Reviewer 2 Report
Comments and Suggestions for AuthorsDear Authors,
I have to tell you that you have really surprised me with the level of attention and effort that you have put into addressing my comments.
I am impressed, and very happy to see that you have taken seriously my point of view and decided to push the narrative in the suggested direction.
The title is MUCH better now, the abstract also, everything starts to make sense.
The way and the tone of your answers, the arguments that you present, the explanations on the possible AI-sounding sentences AND your intervention despite it -
- very kind and diplomatic.
I wish you a great career, and I do not see any reason to stop your paper from being published.
PS. Please remember to remind the Editor to change the title and abstract in the MDPI system for you !
Best Regards,
a reviewer.
