Enhancing Knowledge-Concept Recommendations with Heterogeneous Graph-Contrastive Learning
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThis article presents an innovative approach to enhancing knowledge concept recommendation in online learning environments. It effectively addresses a significant challenge and provides empirical evidence to support its effectiveness. With some adjustments to improve clarity and expand on key points, the passage could more comprehensively convey the significance and implications of the proposed method.
The following suggestions will improve the quality of the article.
1. kindly avoid the word 'we' in the article.
2. The quality of Fig 1 needs to be enhanced.
3. The contributions of the article need to be revised.
4. How does the proposed approach perform better than the other approach?
5. Literature support can be given to evaluation Metrics.
6. Obtained results need to be discussed more to brief the essence of the research findings.
7. RQ4 was not answered properly.
8. The following articles can be referred to in the introduction section.
(i) Research on the Dynamic Evolution Law of Online Knowledge Sharing under Trust
(ii) Anxiety and Self-efficacy in STEM Education: A Scoping Review
9. Revise the conclusion with research findings.
Comments on the Quality of English Language
Moderate editing of the English language is required
Author Response
Please see the attachment.
Author Response File: 
Author Response.docx
Reviewer 2 Report
Comments and Suggestions for AuthorsAuthors proposed a multi-task strategy for knowledge concept recommendation to enhance the characteristic feature of learner Preferences.
overall the idea of the proposal are clear however, some points needs more clarifications :
Line 71 authors needs to add how that structure-aware contrastive learning method enhance learner representation ?
line 76 How performance can be recommended ?
In line 78 the statement "as the
training objective loss to more effectively distinguish differences in learners’ preferences between group structures" need more details and connection about the negative sampling technique .
its better to add objective for the work and give more details about the problem statement before summarizing the contribution.
In line 127 how transferable knowledge is extracted ?
How predict function f is generated ?( line 156)
in line 161 why |N | + |E| > 2 ? it needs more specification as well as in 163 V1
→ V2 → · · · → Vl+1. and what E is represented ?
in the proposal section its better to describe the whole method and then summarize it with the figure 2 to give more explanations about the proposal . i
its better to discuss every details in the figure .
equation 1 is totally not understandable and how it can use in the proposal .
similar in eq 3 why the auxiliary task objectives is used ?
overall all equations need some further details to link the usage with the proposal .
How the joint loss can affect the multi- task strategy ?
Line 259 the statement "OFirst, " is un suitable to be used .
figure three need more discussion related to the results.
its better at the end to discuss the research questions which addressed in line 274- 284.
Comments on the Quality of English Language
Some proofreading is required !
Author Response
Please see the attachment.
Author Response File: Author Response.docx
Reviewer 3 Report
Comments and Suggestions for AuthorsThe structure of the paper is acceptable, and it presents enough novelty. However, I believe the authors could improve the 'Related Work' section by including significant studies based on RS in heterogenous graph and Contrastive Learning (CL) that they did not mention.
Comments on the Quality of English LanguageQuality of English Language is acceptable.
Author Response
Please see the attachment.
Author Response File: Author Response.docx
Round 2
Reviewer 1 Report
Comments and Suggestions for AuthorsCongrats to the authors.
Comments on the Quality of English LanguageModerate editing of the English language is required
