Review Reports

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The central objective of this paper is to propose a course maturity model designed to enhance and align academic programs with the evolving demands of the labor market. The proposed framework, referred to as Ten Tools for Improving Course (TTIC), is a continuous, cyclical, and multi-component system intended to evaluate and systematically improve the quality and effectiveness of courses.

The authors present in Table 1 the Possibilities of Using the Course Maturity Model, based on three previous studies. This consolidation is particularly valuable; however, it is recommended to establish a clearer connection with the definitions of maturity model types and their respective requirements, as outlined in Bruin et al. (2005). Nevertheless, the fundamental notion of “cause and effect” may be misleading, as the processes under evaluation often involve complex social dynamics that do not adhere to straightforward causal patterns (Pfleeger, Fenton, & Page, 1994). Therefore, a careful and critical analysis is necessary to determine whether the observed relationships between processes and outcomes are indeed reliable.

The study highlights the role of predicting student performance as an integral component of the proposed course maturity model, emphasizing that evaluating future academic outcomes based on factors such as current grades, attendance, and group work participation can help instructors and students better anticipate learning prospects and develop targeted strategies for improvement. This predictive approach offers the clear advantage of enabling early identification of at-risk learners, thereby facilitating timely interventions and tailored support to enhance educational results. However, the analysis also acknowledges a potential drawback: excessive reliance on such indicators may introduce bias, as students could be prematurely labeled based on incomplete or context-insensitive data, potentially undermining motivation and equity in the learning environment.

While the use of contingency tables with Yates-corrected Chi-square provides an appropriate approach for evaluating associations between categorical variables, the study could be strengthened by incorporating an additional statistical validation method to confirm the robustness of the results. In particular, applying logistic regression analysis would allow for the simultaneous assessment of multiple explanatory variables while controlling for potential confounding effects. This approach could not only validate the associations observed in the Chi-square tests but also provide adjusted odds ratios, offering deeper insights into the strength and direction of the relationships under investigation.

The concluding remarks underscore two critical challenges (sustaining student performance to ensure the development of competent professionals, and aligning the course program with the evolving demands of education, industry, and the labor market). These points are of such fundamental importance to the overall rationale and relevance of the study that they would have benefited from being clearly articulated at the outset of the paper. Presenting these challenges early on would have provided readers with a stronger conceptual framework, allowing them to better contextualize the research objectives, methodology, and findings within the broader imperative of adapting academic programs to dynamic external conditions.

Given the central role of Figure 3 in supporting the core arguments of the study, it is recommended that it be further developed to fully convey its importance. While the current version is clear and accurate, its simplicity may limit the depth of insight it can provide to the reader. Enhancing the figure with additional layers of information—such as more detailed annotations, segmented data breakdowns, or visual elements that emphasize key patterns—could significantly strengthen its contribution to the overall understanding of the findings.

 

De Bruin, T.; Rosemann, M.; Freeze, R.; Kulkarni, U. Understanding the main phases of developing a maturity assessment model. In Proceedings of the 16th Australasian Conference on Information Systems, Sydney, Australia, 29 November–2 December 2005.

Pfleeger, S. L., Fenton, N., & Page, S. (1994). Evaluating software engineering standards. Computer, 27(9), 71–79. 

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Thank you for the precious opportunity to review this manuscript first. This manuscript presents a maturity model for academic courses for a programming course called Algorithmization and Programming of Solutions at Riga Technical University. The authors introduce the Ten Tools for Improving Course (TTIC) model, designed as a continuous, cyclic, multi-component framework to improve course quality, align curricula with labor market needs, and reduce student dropout rates. This manuscript has good content with a timely and relevant topic about student retention and success in a programming course. Also, this manuscript provides a thoughtful overview of existing maturity models in the area. Therefore, the TTIC framework is well-articulated and could serve as a helpful guide for educators. This manuscript will be better revised by addressing three things. First, the consistency of the terms used in the paper. The terms “Algorithmization” or “Algorithmization” and the term “academic debts” or “academic deficiencies.” Second, you may try consolidating the contents to avoid repeating them multiple times in a short paragraph to improve readability. Third, you may clarify why stage 2 is statistically essential when interpreting the results. Overall, this is a well-written manuscript with exemplary contributions to the field in higher education. 

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

 

Abstract

'According to systematic reviews', line 8, is not clear which systematic reviewer you are referring to.

 

Introduction

 

'According to systematic reviews', line 28, is not clear which systematic reviewer you are referring to.

Explain about maturity models and their importance.

Line 50,76,113: citation arrangement, Pls check antrier document I didn’t note here all

 

Include limitataions and future research in separate headings before the conclution

 

Referancing – arrange referancing list according to APA 7th edition

 

Comments on the Quality of English Language

need  proof reading 

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

I find that the authors have been able to demonstrate a research gap in previous studies on maturity models, particularly regarding assessment instruments for continuous improvement.

In addition, this article also presents novelty by positioning the model not only as an assessment framework within a specific course to align with educational quality standards, but also as a tool to bridge curriculum development with the evolving demands of the market.

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 5 Report

Comments and Suggestions for Authors

The paper "Learning Course Improvement Tools: Search Has Led to the Development of a Maturity Model" is very interesting and relevant for university teaching practice. The literature study is very useful. It is great that the authors apply the maturity model in a case, a teaching course in a curriculum and that they provide the data about it. I like it very much that the model includes the importance of course design for the maturity in a proper way. The article has a good structure, it is clearly written and it is a pleasure to read it. My compliments for this good work! 
A small correction is necessary:
In the row 113 there is a mistake in a reference: it should be: Rabelo, A. M., & Zárate, L. E. (2025). In the references list in the row 425 it is necessary to correct the information in brackets.  

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 6 Report

Comments and Suggestions for Authors

The paper presents a commendable multi-year research effort introducing the development of a course maturity model (Ten Tools for Improving Course – TTIC), aimed at improving course quality and aligning it with labor market demands. I consider the paper valuable both for the academic and professional community and see it as the result of a systematic and long-term research endeavor directed toward the advancement of higher education. Particularly praiseworthy is the emphasis on the predictive elements of the model, which can help reduce student dropout and strengthen professional competencies. Below are several elements that should be revised in order to make the paper clearer and more strongly argued.

In the introduction, it is necessary to describe existing maturity models in more detail, so that the reader has a clearer overview of the literature. The sentences in lines 62–65 convey the same meaning and should be merged or rephrased. The passage in lines 139–150 would fit better in the introduction, as it contains general statements about the problem and its context. In lines 170–171, it is necessary to specify which statistical test was used to establish the significance of the results. In the captions of Figures 1 and 2, it should be stated whether they represent average scores. Before the first figure, the text should explain what the acronym NMT stands for and why it was included in the analysis. In line 253, you state that continuous, cyclic models with a multi-component structure are more suitable as dynamic tools – while this seems intuitively convincing, supporting evidence or references are needed. Statements in lines 272–275 should likewise be substantiated with appropriate references. It is not clear how the third and fourth subsections are related – this needs to be made explicit. Specifically, the analysis of the predictive function (identifying students likely to underperform, subsection 3) and the description of the proposed TTIC model (subsection 4) come across as two separate parts. It would be useful to show more clearly how this analysis is integrated into the model itself. At present, the reader may gain the impression that these are two different papers – one empirical and one conceptual. References are also not consistently formatted: individual brackets are used for each author (e.g., line 50), which should be aligned with the journal’s citation style.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

I have reviewed the paper. The authors address adequately for my concerns. I would like to suggest a small change in the structure of the paper as below.

The conclusion is the last part of the paper; therefore, please arrange the limitations and future research sections before the conclusion.

Author Response

Thank you for pointing this out. Your comment is very important and allowed us to improve the content and presentation of our work. 

Reviewer 6 Report

Comments and Suggestions for Authors

Thank you for carefully revising the manuscript and addressing all the points raised in my initial review. The revisions have substantially improved the clarity and overall quality of the work. I have no further comments or suggestions.

Author Response

Thank you very much for taking the time to read this manuscript. We appreciate your comments. They are invaluable and have helped us improve the content and presentation of our work.