Learning Analytics for Bridging the Skills Gap: A Data-Driven Study of Undergraduate Aspirations and Skills Awareness for Career Preparedness

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors The topic addressed by the article is very interesting.   Nowadays, not only knowledge is necessary for professional development, but also having other skills.   The study analyses the perceptions of specific students, well specified, but it does not indicate their ages or in what period of time the survey was carried out. Likewise, only the objectives pursued are indicated in the abstract.   In the methodology section, both the general and specific goals should be specified. A starting hypothesis could also have been indicated.   On the other hand, it is not stated which computer program the data was processed with.   The work is well developed, the results are clearly shown and the conclusions can help future students and also employers.   It would be interesting to be able to replicate the analysis for other degrees and also, in future occasions, to know the detailed opinion of employers.                          

 

Author Response

Reviewer #1

The topic addressed by the article is very interesting. Nowadays, not only knowledge is necessary for professional development, but also having other skills.

Comment 1: The study analyses the perceptions of specific students, well specified, but it does not indicate their ages or in what period of time the survey was carried out.

Response 1: We thank the reviewer for the recommendation to include the age demographic of participants and period of survey in the manuscript. (See line 171)

 

Comment 2: Likewise, only the objectives pursued are indicated in the abstract. In the methodology section, both the general and specific goals should be specified. A starting hypothesis could also have been indicated.

Response 2: We thank the reviewer for this comment and suggestion. We have added a detailed aim for this work and a starting hypothesis that motivates it. (See lines 158-169)

 

Comment 3: On the other hand, it is not stated which computer program the data was processed with.

Response 3: We have since included the name and version of our statistical program in the manuscript. All statistical analyses were performed using a  Python (version 3.11.0) package SciPy v1.13.0, supported by NumPy 1.22.4. (See lines 253-254)

 

The work is well developed, the results are clearly shown and the conclusions can help future students and also employers. It would be interesting to be able to replicate the analysis for other degrees and also, in future occasions, to know the detailed opinion of employers.

Thank you very much for your kind and insightful comment. We completely agree that professional development today requires not only knowledge but also a diverse set of skills. We are pleased to share that we have already initiated a follow-up study specifically focused on the alignment of students’ employment or internship experiences with their future career aspirations. We believe this will provide deeper insights into how these roles influence skill development and perceptions of career readiness. Your observation reinforces the importance of addressing this topic, and we are glad that you found the subject of our study to be relevant and interesting. We hope our work contributes meaningfully to this important conversation.

Reviewer 2 Report

Comments and Suggestions for Authors

This paper provides good insight into what role higher education should play in improving students’ career readiness. In particular, it provides a timely, methodologically rigorous, and practically relevant perspective on an important issue. Learning analytics and data-driven methods are well suited to address gaps in college students’ career readiness and competency perceptions in a more scientific manner. However, the clarity of specific technical descriptions (e.g., the application of machine learning methods) could be improved for a broader social science audience that is not familiar with technical concepts.

Author Response

Reviewer #2

This paper provides good insight into what role higher education should play in improving students’ career readiness. In particular, it provides a timely, methodologically rigorous, and practically relevant perspective on an important issue. Learning analytics and data-driven methods are well suited to address gaps in college students’ career readiness and competency perceptions in a more scientific manner.

Comment 1: However, the clarity of specific technical descriptions (e.g., the application of machine learning methods) could be improved for a broader social science audience that is not familiar with technical concepts.

Response 1: Thank you for your valuable feedback regarding the clarity of the technical descriptions. We appreciate your observation about making the application of machine learning methods more accessible to a broader social science audience. We have aimed to provide a concise explanation of the techniques used, such as hierarchical clustering and k-nearest neighbor for clustering terms, we recognize that a deeper elaboration might be beneficial.

To maintain the focus and flow of the manuscript, we have kept the technical descriptions concise and to the point. For readers who may wish to explore these methods in greater depth, we have included references to foundational works on these techniques in the manuscript. We hope this approach strikes a balance between technical detail and accessibility. The following citations are included in the manuscript, targeting a broader social science audience.

1) Murtagh, F.; Contreras, P. Algorithms for hierarchical clustering: an overview. WIREs Data Mining and Knowledge Discovery 2011, 2, 86–97. https://doi.org/10.1002/widm.53.

2) Fonseca, J.R. Clustering in the field of social sciences: that is your choice. International Journal of Social Research Methodology 2012, 16, 403–428. https://doi.org/10.1080/13645579.2012.716973. 

Thank you for your thoughtful and encouraging feedback. We are delighted that you found our study to provide valuable insights into the role of higher education in enhancing students' career readiness. Your recognition of the timeliness, methodological rigor, and practical relevance of our work is greatly appreciated. We fully agree that learning analytics and data-driven approaches are pivotal in addressing the gaps in students’ career readiness and competency perceptions, and we are pleased that our study aligns with this perspective.

Reviewer 3 Report

Comments and Suggestions for Authors

Please see the attached for my reviews. Thank you for the opportunity to review your work. I hope that my comments are useful to strengthen this manuscript. 

Comments for author File: Comments.pdf

Author Response

Reviewer #3

The authors conducted an observational survey study to examine the alignment between undergraduate students’ career aspirations, their perceived skill development, and the role of higher education institutions to bridge the skills gap. This research meaningfully contributes to the literature on career-readiness among undergraduate students and can be used to highlight the need for university leadership to consider ways to prepare students for their future careers.

Upon review, I have minor comments for the authors to address.

I would like to thank the authors for allowing me to review their work. Your paper was very well-written and informative. This work fills an important gap in understanding how to best support students in reaching their career goals.

I recommend accept with minor revisions and hope that my comments are useful to improve the strength of the paper.

Comment 1: After the words “a comprehensive survey was conducted among undergraduate students” provide the sample size: (n=143).

Response 1: We thank the reviewer for this comment. We have edited the manuscript to reflect this suggestion.

 

Comment 2: Page 2-3, Lines 91-93: When you mention the results of Cranmer are mixed, what do you really mean? Please elaborate with 1-2 sentences on what the actual findings from Cranmer et al. are.

Response 2: When we mention that Cranmer’s results are mixed, we are referring to findings that highlight the variability in the effectiveness of higher education interventions on employability outcomes. Specifically, Cranmer et al. found that while some employability-focused educational practices can positively influence graduates’ skills and readiness, others show limited or no significant impact, suggesting a need for a more nuanced understanding of what works and why. We thank the reviewer for this comment. We have edited the manuscript to reflect this suggestion. (See lines 93-99)

 

Comment 3: Page 3, lines 97: Include definitions of skills tagging and what you mean by “human-in-the-loop AI”

Response 3: Skills tagging refers to the process of identifying and categorizing specific skills within a dataset, such as resumes, job descriptions, or educational curricula, using predefined labels or frameworks. This technique allows for systematic analysis and alignment of skill sets with career requirements. Human-in-the-loop AI is an approach to artificial intelligence where human judgment is integrated into the AI system to guide, validate, or improve its outputs. This methodology ensures that automated processes are supplemented with human oversight to enhance accuracy and relevance, particularly in contexts requiring nuanced decision-making, such as skills mapping. We have included these definitions in the updated manuscript. (See lines 101-109)

 

Comment 4: Page 4 lines 108-117: I would like to see you add to this paragraph about some of the limitations and equity issues involving experiential learning. For example, unpaid internships may only be possible to students who come from higher affluence and may disadvantage students who lack financial support. While there is evidence to support experiential learning, some work shows that required internships may not benefit students. Furthermore, to approach experiential learning in a fair and equitable way, IHLs should consider how to compensate students who seek experiential learning opportunities.

Response 4: Thank you for your thoughtful comment regarding the limitations and equity issues surrounding experiential learning. We agree that these considerations are important, and we have included a brief acknowledgment of these concerns in the manuscript to highlight the need for equitable approaches without detracting from the focus of the literature review. (See lines 129-131)

The following citations were included:

• Castleman, B.; Meyer, K. Financial Constraints & Collegiate Student Learning: A Behavioral Economics Perspective. Daedalus 2019, 148, 195–216. https://doi.org/10.1162/daed_a_01767.
• Lee, Y.; Lee, G.; Kim, J.; Lee, M. Equity in Career Development of High School Students in South Korea: The Role of School Career Education. Education Sciences 2021, 11, 20. https://doi.org/10.3390/educsci11010020.
• Li, I.W.; Jackson, D. Influence of entry pathway and equity group status on retention and the student experience in higher education. Higher Education 2023, 87, 1411–1431. https://doi.org/10.1007/s10734-023-01070-4.
• Banerjee, M.; Bingen, K. A Case for Equity in Experiential Learning: Work-Based Learning as a Viable Alternative to Internships. Experiential Learning and Teaching in Higher Education 2024, 7.

 

Comment 5: 1. Page 4, Line 145: Watch the tense shift from past to present (“target participants are”)

Response 5: We thank the reviewer for this comment. We have edited the manuscript to reflect the change. The manuscript was also checked to ensure grammatical consistency.

 

Comment 6: Page 5, Line 216: If this was approved by your IRB, please include that information somewhere in the methodology section where it best makes sense.

Response 6: We will include the IRB approval in the unblinded version of the manuscript. This information will be sent to the editors for verification.

 

Comment 7: Page 6, Figure 1: This was difficult to read. I had to zoom way in because the font is so small on the factors. Can you enlarge the font size to improve readability? Also, replace “Question 2.1” label with the actual question OR with an abbreviated version of the question so that it better communicates the figure. Please increase the size of the two labels as well as the key. The font size is too small and looks gray instead of black.

Response 7: We thank the reviewer for this comment. We have edited the figures so that they are clearer.

 

Comment 8: Page 6, Line 256: After you state analyzing differences between the two disciplines, can you include parentheses to remind readers that this is (SHAPE vs.STEM). I think this will improve clarity.

Response 8: We thank the reviewer for this comment. We have edited the manuscript to reflect this suggestion.

 

Comment 9: Page 7, Line 280: Can you define what you mean by “members”? I think this corresponds to the statements A-N represented in figure 2, but it’s not 100% clear when reading this section. You previously called them factors.

Response 9: We thank the reviewer for this comment. We have edited the manuscript to clearly reflect what we intended.

 

Comment 10: Page 8, Line 326: “and” is repeated

Response 10: We thank the reviewer for this comment. We have edited the manuscript to remove this typographical error.

 

Comment 11: Page 8, Line 328-329: This is a pet peeve (and I recognize it is minor) but “the work” didn’t use four classifications of skill… the authors did. I recommend revising this sentence: This work uses four classifications of skill types to conduct and analyze the survey responses. To: We used four classifications of skill types to conduct and analyze the survey responses.

Response 11: We thank the reviewer for this comment. We have edited the manuscript accrordingly.

 

Comment 12: Page 8, Lines 331-349: Provide citations for the definitions of each of the four skills presented.

Response 12: We thank the reviewer for this comment. We do not have a citation here as this is defined internally by the institution's career office for alignment.

 

Comment 13: Page 9, Table 3: Is this showing just the total number of students who endorsed each skill type? Or, the total number of times each skill type appeared in the free response? I’m having trouble identifying what the numbers in the table represent.

Response 13: We thank the reviewer for this comment. We have edited the manuscript, in particular the caption to Table 3,  to clearly reflect what we intended. The numbers in the table are the skill types identified by survey participants as important for their careers and identified as a gap. While the participants responded with skills, we used machine lerning to categorize these skills into the four categories. These skills were categorized using k-NN classification.

 

Comment 14: Page 11, Figure 5: Similar to before, change the label from questions 3.3 & 3.6 to something more descriptive. Improve the readability of this table by choosing a darker colored font and increasing the font size of the labels and the key.

Response 14: We thank the reviewer for this comment. We have edited the figures so that they are clearer.

 

Comment 15: I don’t see a limitations section. Specifically, it would be useful to know if students: 1) had jobs or internships that were aligned with their future career goals to account for the influence of those positions on their survey responses and 2) Examination of students who are first-generation students would also be very insightful to determine if the needs of these students differ from those who are not first generation, and 3) SES or affluence differences across students. One paragraph detailing limitations of this study would be sufficient.

Response 15: Thank you for your insightful feedback and for highlighting important areas for further consideration. We agree that exploring whether students' jobs or internships align with their career goals, examining the experiences of first-generation students, and investigating socioeconomic differences are all valuable directions for future research. Regarding your first suggestion, we are pleased to share that we have already initiated a follow-up study specifically focused on the alignment of students’ employment or internship experiences with their future career aspirations. We believe this will provide deeper insights into how these roles influence skill development and perceptions of career readiness. We will also highlight the other points you raised as critical areas for exploration in future studies and have included them in the revised manuscript.

 

Thank you very much for your thoughtful and encouraging feedback. We greatly appreciate your recognition of our work’s contribution to the literature on career readiness among undergraduate students and its practical implications for university leadership. We are grateful for your detailed comments and suggestions, and we have carefully address them to further strengthen the paper. Thank you again for taking the time to review our work and for your valuable insights.