Engineering Design and Evaluation of the Process Evaluation Method of Auto Repair Professional Training in Virtual Reality Environment
Round 1
Reviewer 1 Report
Manuscript Title: Engineering Design and Evaluation of the Process Evaluation Method of Auto Repair Professional Training in VR Environment
Ms. ID: applsci-1971327
The authors have attempted a Process Evaluation Method to investigated Auto Repair Professional Training in VR Environment, which is very technically sound but some of the observations that need to be incorporated for the quality improvement of the paper which are as follows:
Comment-1. I’d suggest about Consistency use of US or UK language throughout the paper.
Comment- 2. Overall checking for grammatical errors is required.
Comment-3. I’d suggest add some recent literatures in a tabular form for better understanding of the readers.
Comment-4: It has been observed that authors have mentioned several times “in this chapter”. I’d suggest please re-write paper instead of chapter.
Comment-5. Clearly mention equation (5) and equation (6). Its seems that these are same. Please modify.
Comment-6. In the title put full form of VR. Please modify it.
Minor revision will be needed for further processing.
Comments for author File: 
Comments.pdf
Author Response
Dear Editors and Reviewers,
Appreciate receiving your comments on our manuscript titled “Engineering design and evaluation of the process evaluation method of auto repair professional training in Virtual Reality environment”. Thanks to the reviewers for thoroughly reviewing our manuscript and providing very useful comments and suggestions to guide our revisions, and we are also glad that editors and reviewers approve of our works. All changes are described in the documentation below！
We are also pleased to submit the revised manuscript to you. To make it easier for you to find detailed responses to comments and suggestions, we provide a point-to-point response to the reviewer’s comments.
Response to Reviewer 1 Comments
Comment-1: I'd suggest about Consistency use of US or UK language throughout the paper.
Response 1: Thank you for your suggestion! As suggested by reviewer,we have expressed the whole article in US language.
Comment-2: Overall checking for grammatical errors is required.
Response 2: Thank your for your reminding! According to your suggestion, we corrected the grammatical errors and made an effort to correct the spelling and grammar errors and polish the whole manuscript.
Comment-3: I'd suggest add some recent literatures in a tabular form for better understanding of the readers.
Response 3: Thank you for your suggestion! We have checked the literature carefully and added the latest references of relevant research and presented it in a tabular form for better understanding of the readers.
Comment-4: It has been observed that authors have mentioned several times "in this chapter".
I'd suggest please re-write paper instead of chapter.
Response 4: Thanks for your carefully reviewing! We‘ve changed "in this chapter" to “"in this section”.
Comment-5: Clearly mention equation (5)and equation (6).Its seems that these are same.
Please modify.
Response 5: We deeply appreciate the reviewer's suggestion. According to the reviewer's
comment, we have added more detailed explanations to equation (5)and equation (6).
Comment-6: In the title put full form of VR.Please modify it.
Response 6: Thank you for your advice! I’ve complemented the full name.
Yours sincerely,
Author Response File: Author Response.pdf
Reviewer 2 Report
This paper is to investigate engineering design and evaluation of the process evaluation method of Auto Repair Professional Training in a virtual reality environment. The research results of this paper provide a new perspective and reference for learning evaluation of skill based training majors. However, I cannot recommend publishing in this version. I think that it should be improved. I suggest the comments as follows:
1. Line 450-451 : Why do the authors use this equation (1) ? Please explain the equation (1). Why do the authors think this equation is correct?
2. Line 454-455 : Why do the authors use this equation (2) ? Please explain the equation (2). Why do the authors think this equation is correct?
3. Similar to equation (1) and (2): Why do the authors use this equation (3) - (6) ? Please explain the equation (3) - (6). Why do the authors think these equations are correct ?
4. Please improve the part of Analysis of subsection 4.2. Experimental Result and Analysis.
5. Section 5. Conclusion and Future Work. The author's present is not interesting. Please improve this section.
Author Response
Dear Editors and Reviewers,
Appreciate receiving your comments on our manuscript titled “Engineering design and evaluation of the process evaluation method of auto repair professional training in Virtual Reality environment”. Thanks to the reviewers for thoroughly reviewing our manuscript and providing very useful comments and suggestions to guide our revisions, and we are also glad that editors and reviewers approve of our works. All changes are described in the documentation below！
We are also pleased to submit the revised manuscript to you. To make it easier for you to find detailed responses to comments and suggestions, we provide a point-to-point response to the reviewer’s comments.
Response to Reviewer 2 Comments
- Line 450-451 Why do the authors use this equation (1)? Please explain the equation (1). Why do the authors think this equation is correct?
Response 1: So kind of you for reminding me, We added some description: “Among them, according to the survey of experts, the number of mistakes in se-lecting tools and parts can reflect the students' knowledge mastery in practice. The number of mistakes in selecting tools and parts is 0 times, and students with good knowledge mastery can get full marks; the number of mistakes is 1 to 3 times, and the score is 5; if the number of mistakes is more than 5 times, the score is 0, it indicates that students' knowledge of relevant content is not good.”.
- Line 454-455: Why do the authors use this equation(2)? Please explain the equation (2). Why do the authors think this equation is correct?
Response 2: Thank you for your advice! We added some description: “Where the number of viewing resource times can reflect students' knowledge grasp, and students' scores will decrease with the increase of checking learning prompts.”.
- Similar to equation (1)and(2): Why do the authors use this equation (3)-(6)? Please explain the equation (3)-(6). Why do the authors think these equations are correct?
Response 3: Thank you again! We added some description:
Equation(3):
“Among them, when the ratio of learner's response duration plus the actual maintenance duration and the expected maintenance duration is less than or equal to 1, it indicates that the learner's operating proficiency is higher and the score is higher. A higher ratio indicates that learners need more time in the response process or in the maintenance process, indicating that the operation is not skilled enough.”.
Equation(4):
“Where, the parts operating position within 20% of the trigger range gets full marks, more than 30% is not scored. Full marks are given for correct tool use, but no marks are given for incorrect tool use. The success rate of maintenance is equal to the sum of the two scores.”
Equation(5):
“Among them, the return learning times indicates that they are uncertain about their own mastery. The more times they return to learning scenarios, the more uncertain they are about their own mastery and the lower their confidence level.”
Equation(6):
“Where, the positioning of tools and parts can reflect the operation specifications of learners after the training. When all the items meet the conditional standards, the cor-responding maintenance specifications will be scored 10 points. If any item is violated, 2 points will be deducted.”
- Please improve the part of Analysis of subsection 4.2. Experimental Result and Analysis.
Response 4: This is a very good advice, I’ve added in “Experimental Result and Analysis” section:
“For the practical training evaluation prediction model composed of five machine learning algorithms, linear regression, decision tree regression, support vector ma-chine regression (SVM regression), KNN regression and random forest regression, we seriously analyzed the value of R Squared(r2 score), RMSE (Root Mean Squared Error) and MAPE (Mean Absolute Percentage Error) of the prediction model based on these five algorithms.”
“The root mean square error is used to measure the deviation between the observed value and the true value, indicating that except the prediction model of SVM and deci-sion tree algorithm, the difference between the predicted value and the real result of other models is small, while the deviation between the predicted value and the real result of the prediction model of KNN algorithm is the smallest.”
“MAPE itself is often used as a statistical indicator to measure the prediction accuracy. MAPE can describe the accuracy. The value range of MAPE of the prediction model of the five algorithms is 0.13%-0.41%, and the average AVG is 0.24%, indicating that the prediction error of the evaluation model is very small.”
- Section 5.Conclusion and Future Work. The author's present is not interesting. Please improve this section.
Response 5: Thanks for your advice. We have deleted unecessary sentences which may confuse readers, and recombed the content to make the article more readable.
Yours sincerely,
Author Response File: Author Response.pdf
Round 2
Reviewer 2 Report
The authors have made all the required changes. They have made revisions to improve the quality of the paper.
