The universal algorithm for designing educational programs based on the analysis of students' educational needs

Introduction

Modern pedagogical science has theoretically substantiated and successfully implemented a competency-oriented educational process in educational practice (Ignatiev, Varlamova, Daramaeva, 2022), the methodological basis of which is the principles of: individualization (Sazonov, 2012) and personalization of education (Zeer, Symanyuk, 2021), taking into account the individual abilities and educational needs of each student in the midst of professional development (Zeer, 2021); cognitive activity (Osipova, Agisheva, 2016), leading to the cognitive competence of the student (Shmigirilova, 2014), characterizing their learning engagement; practice-based approach, demonstrating the relevance of the acquired knowledge in real life (Bondarenko, Latkin, 2012); conceptual understanding (Frolov, Frolova, 2010), which involves understanding the essence of the information provided by the teacher, the ability to apply the acquired knowledge in the future to independently solve problem situations in various fields of activity, including in education (Margolis A.A., 2021).

The primary tool for the practical implementation of the theoretical principles of a competency-based educational process is modern educational technologies, which enable the effective development of a student's individual educational trajectory (IET) (Zeer et al., 2017). Creating an individual, personalized educational trajectory (IPET) for each student, based on an assessment of their personal cognitive interests and involving the development of an individualized educational program, is a pressing issue in modern education (Zeer, 2021). The complexity and diversity of the personalized approach and a lack of joint understanding of its components in the scientific community highlight the problem of identifying the main structural elements of personalization (Shemshack et al., 2021; Zhong, 2022), arrangement of effective strategies for personalized learning (Cheung, et al., 2021), understanding the need to take into account the cognitive preferences of students in higher education (Morozova et al., 2023; Hooshyar et al., 2024), as well as taking into account the technological capabilities of implementing personalized learning in the era of digital transformation of education (Kem, 2022; Komalawardhana, Panjaburee, 2023).

Problem. Despite the theoretical justification by pedagogical science (Zeer, Symanyuk, 2021) for a "shift in the educational paradigm: from the traditional teacher-centered approach to a student-centered approach" (Kasprzhak, Kalashnikov, 2014), modern university educational practices practically lack universal mechanisms for systematically considering students' educational needs when designing and updating the content of basic professional educational programs (BPEP) (Hooshyar et al., 2024). There is a clear gap between the learning outcomes declared by the developers of educational programs and the learning outcomes actually sought by students, which are professional competencies (PCs). This leads to the formal compliance of educational programs with the requirements of the federal state educational standards (FSES) without matching the needs of the target audience due to the lack of standardized procedures for the practical implementation of student feedback and the integration of the resulting assessment of educational needs into the FSES design process. Accordingly, the hypothesis of our study was formulated: if an educational organization, designing an educational program and independently determining the list of professional competencies included in it, is guided by the educational needs of students, then this approach will provide a universal mechanism for its continuous updating, facilitating the implementation of personalized university training.

The purpose of this study is to develop a universal algorithm for designing core professional educational programs based on an analysis of educational needs and student attitudes toward the proposed learning outcomes in the form of professional competencies. This approach will subsequently refine its structure and enable the development of individual, personalized educational trajectories.

This approach has been repeatedly applied to the design of both master's programs in psychological and pedagogical education (Rubtsov et al., 2014) and undergraduate teacher training programs (Smolyaninova, Korshunova, 2015). However, it remains relevant, as "the specific educational needs of students necessitate tailoring their learning experiences to their educational background, experience, and personal qualities" (Oslon, Semya, Zinchenko, 2019).

Materials and methods

To develop a universal algorithm for designing a program based on students' educational needs and to test the proposed hypothesis about systematic analysis as a personalization mechanism, a combination of methods was used: questionnaires to construct a competency profile, expert assessments to correlate competencies and modules, and graph modeling to visualize the program structure.

We agree with the opinion that "the purpose of obtaining a master's degree, related to professional development, may reflect the most 'mature' level of the student's motivational and semantic sphere" (Gorkova, Bakanova, 2015). Therefore, the master's program in pedagogical education at Southern Federal University (SFedU) was selected for consideration. Its profile, "Tutoring and Mentoring in Education" (TME), presupposes the development of teachers' readiness to implement the individualized and personalized principles of education. First- and second-year master's students were administered the author questionnaire, dedicated to the reasons for choosing to study the SFedU TME program. The questionnaire consisted of three sections (General Information, Objectives, and Learning Outcomes), which used open-ended and closed-ended questions with single and multiple choice options. This provided a detailed qualitative analysis of the respondents' opinions and a verification of its validity (Appendix).

When assessing the subjective significance of the competences included and potentially included in the Basic Educational Program, a rating scale was used that reduces the interpretation subjectivity of the responses and allows for a quantitative assessment of the results obtained even with a small sample of respondents (N = 16). The weighted arithmetic mean values ​​of the competence scores (PCSi) were used as a criterion for conducting a comparative analysis and ranking of the PCs, which also took into account the different point weighting for master's students. The indicators presented competences (PCSIij) where i is the competency number (PC1, PC2, etc.), and j is the number of its indicator (PC1.1, PC1.2, PC2.1, PC2.2, etc.). For example, when evaluating the weighted arithmetic mean of the PC1 score, the numerator is the product of the scores given by the master's student for competence PC1 and the sum of the scores of its indicators PCI1.1 and PCI1.2. The resulting scores were summed up for all survey participants and divided by the total scores for the indicators of a particular PC level assigned by all survey participants. Master's students' PC assessment listed in the current BPEP, as well as those not used but included in the general list of possible mastery for SFedU master's pedagogical programs, allowed us to create a competency profile for students in the BPEP TME.

To associate specific educational modules (M) with each PC, adjacency matrices were used. Their construction was preceded by an expert discussion among the academic teaching staff (TS) regarding the substantive significance of the M and their focus on developing specific PCs and their indicators.

For a qualitative analysis of the PC-M interrelation, a graph method was used, the structure of which is entirely determined by the adjacency matrix. This visualization allows for the clear identification of the structure, central elements, integration zones, and potential gaps in the BPEP.

Results

To test the proposed set of methods and verify the hypothesis, we will consider the design of a master's program in "Tutoring and Mentoring in Education" based on students' educational needs.

The sample of master's students was random, but was fairly balanced in terms of their teaching experience. The survey revealed that 56,25% of respondents had teaching experience, working in educational institutions of various levels. Notably, 25% of active teachers and 12,5% ​​of tutors chose the BPEP TME program, indicating the existence of certain professional deficiencies. About half of the master's students pursue careers outside of teaching, in positions that involve coaching in various fields.

Master's students were offered a variety of answers as their primary goals upon enrollment in the BPEP TME program, from which they were required to select no more than three ones. The overwhelming majority of respondents (68,75%) cited mastering computer skills for tutoring as their goal of study. 31,5% identified mastering computer skills for implementing a personalized approach and the principle of individualization in education as their priority educational goals, which can be viewed as a conceptual understanding of the need to implement these principles in their professional work. It should be noted that the expected learning outcomes of master's students align with these educational goals, including developing tutoring competencies for working with various categories of students (56,25%), acquiring skills in implementing the principle of individualization in education (50%), and implementing a personalized approach (37,5%). Different number of respondents who identified skills in implementing the principles of individualization and personalization may serve as an indicator of understanding of the essence of these phenomena and the differences between them, which is undoubtedly a significant substantive outcome of the master's program of BPEP TME. Master's students understand that individualization is associated with adapting the learning process to the objective characteristics of the student, but based on the data and goals of the entire class. Personalization implies adapting learning to the personal interests, goals, and motivation of a specific student, focused on choosing "the content and technology of educational and cognitive activity, developing an individual (personalized) learning trajectory, and assessing their achievements" (Zeer, 2021). Moreover, the greater number of respondents prefers acquiring skills in implementing the principle of individualization. This is likely due to the existence of current Federal State Educational Standards at different levels of education, according to which teachers must lead all their students to a common educational goal, but do so through different paths. Implementing personalization skills in a general school setting is a more complex pedagogical task and, therefore, less in demand by most master's students.

In response to the survey about the professional deficiencies they hope to address during their studies and how they plan to apply their acquired knowledge after completing the BPEP, more than half of the master's students (56,25%) identified a lack of skills in couching for individuals with special educational needs. More than a third of respondents (37,5%) noted an inability to address the diverse educational needs of students and a lack of knowledge about modern targeted support technologies. These findings lead to a contradictory conclusion, simultaneously indicating a positive trend toward inclusion in society and a lack of understanding of the need for personalized support not only for individuals with disabilities or special educational needs.

When assessing the substantive significance of the BPEP TME, 75% of respondents emphasized mastering the methodological foundations of tutoring and methods for managing students' IET. However, the methodological foundations of mentoring were not a focus for most master's students (31,25%). However, the same number of students (31,25%) noted the importance of gaining expert knowledge for coordinating mentoring programs, which may indicate the demand for socio-pedagogical design for future self-fulfillment. These quantitative indicators correspond to the number of the working master's students whose professional activities are not related to education, demonstrating the internal consistency and coherence of respondents' judgments in their responses to the questionnaire.

It's also worth noting that SFedU is seeking research tracks that integrate master's and postgraduate programs into a comprehensive IET program. It's concerning, however, that only 18,75% of respondents identified the development of research skills as a significant learning outcome. However, this indicates that the BPEP TME is being chosen by practicing teachers who plan to continue their teaching careers at the same educational institution after completing it.

Turning to the assessment of the PCs formed by the BPEP by master's students, it should be noted that, according to SFedU's internal educational standard, there are mandatory PCs (PC-1, PC-2, PC-3) and recommended PCs for pedagogical master's programs. Variation in the indicator is permitted only for the recommended PCs. The indicator formulations for the mandatory PCs remain consistent across all pedagogical education programs at this level, regardless of the BPEP profile.

The names of the mandatory PCs and their indicators, as well as the obtained weighted arithmetic mean scores, based on the students’ results, are presented in Table 1. The obtained quantitative data demonstrate full compliance between the selection of the list of mandatory PCs for mastering by the developers of the BPEP for pedagogical master's degree and their ranking based on the opinions of master's students in the presented list (Safontseva, 2024).

Table 1

Mandatory professional competencies for pedagogical master's programs at SFedU

The ranking of the recommended PCs based on the weighted arithmetic mean scores arranged in descending order allowed us to identify the PC preference profile of the BPEP TME master's degree students (Table 2).

Table 2

Profile of preferences for professional competencies of master's students

It should be noted that BPEP TME for the student intake in 2024 and 2025 PC-4, PC-6, and PC-10 were included as learning outcomes. However, Table 2 shows that the inclusion of PC-10 in the BPEP is justified, as, according to quantitative assessments, this competency ranks second among master's degree students. Meanwhile, PC-4 and PC-6 divide up fifth and sixth place, significantly behind not only the most preferred one (PC-13), but also PC-8 and PC-5. It should also be noted that they have an equal chance of being included in the BPEP along with PC-7, which has the same points. The resulting quantitative data for the list of PCs, eligible for inclusion in master's degree pedagogical programs, reflect the educational needs of master's degree BPEP TME students. The resulting competency profile allows for the clarification of the goals and content of a specific educational program, as well as the improvement of the program's pedagogical design, which involves the creation of relevant educational materials for mastering the content and optimal achievement of the educational goals set by each student.

Each PC is associated with specific educational modules (M) in the structure of the professional unit (PU) of the BPEP, using adjacency matrices. The PU can then be visually represented as a directed graph, the vertices of which are the PC and M, and the arrows represent the connections between them (PC → M). Note that the PU structure also includes various types of practical training, but their association with the PC is generally comprehensive and straightforward.

The graph for 2024 student intake for the BPEP (Fig. 1) is incoherent, as the profile of six initially declared competencies is implemented by seven training modules (M1–M7), each of which is aimed at developing a specific competency. The exception is PC-3, which is developed by two modules, M3 and M4.

Fig. 1. Graph of the professional unit of the educational program, 2024 student intake

Matrix representation and graphical visualization of relations allowed the developers of the BPEP to refine its design for the 2025 intake. New modules (M8, M9, M10) were introduced into the BPEP, the pedagogical design of the majority of "old modules" was improved, and M5 was removed from implementation. This led to changes of the original PC profile and increased coherence in the BPEP PU graph (Fig. 2), to which, at SFedU request, PC-9 was added, aimed at developing the research competence of master's students.

A student survey allowed for updating the BPEP competencies profile, which must include, in addition to the mandatory PC-1 – PC-3, at least two more: PC-13 and PC-10. It was decided to retain PC-9, which is not a priority for master's students. This decision by the academic teaching staff is based on the fact that PC-13 is directly related to the educational and research activities of potential master's students, which only a teacher proficient in research competencies is capable of imparting.

The graph shown in Fig. 3 demonstrates the apparent increase in complexity of the PU; however, modules M11–M14 are not new to the overall structure of the BPEP 2025. Previously, their content in the BPEP 2024 was aimed at developing universal and/or general professional competencies in accordance with the Federal State Educational Standard. A survey of master's students allowed the developers to clarify the content of M and their interrelations with the relevant professional competencies. Understanding the adjacency matrix allowed us to differentiate the BPEP PU structure, identifying modules that are mandatory for all master's students (M1–M3, M6, M7, M10, M11, M13), elective (M8, M12), and optional (M4, M9, M14).

The BPEP design for the 2026 student intake has begun, developers turn from individualization to personalization of education. While agreeing with the notion that "the theoretical basis for personalized learning activity is the subjectivity of learners" (Zeer, 2021), we note that the development of the BPEP TME is directly linked to the identified demand among master's students for in-depth mastery of coaching technologies in inclusive education. Therefore, the PU will include elective M15 and optional M16, which will enable the implementation of four personalized educational tracks in the BPEP 2026.

IPET 1.1: "Tutoring and Mentoring in Inclusive Education," in which personalization is achieved by allowing master's students to choose from elective modules M8, M15, and optional M16 (Fig. 4).

IPOT 2.1: “Tutoring and Mentoring in Social and Pedagogical Design” (Fig. 5), aimed at master’s students whose professional interests lie outside the inclusive environment.

As well as IPET 1.2 and IPET 2.2, which represent research tracks within the above mentioned IPETs, the implementation of which may include the optional M9, aimed at students' detailed understanding of scientific research methodology and developing PC-9 in-depth. The graphs of these IPETs should differ from those shown in Fig. 4 and Fig. 5 by the presence of an additional interrelation: PC-9 → M9.

Visualization of the structure of PB BPEP 2024, 2025, and 2026 projects demonstrate the significant work of the administrative department to develop the TME program based on the educational needs of master's students, which is confirmed by the integration zones with a high concentration of PC → M interrelations in the graphs. An exception is PC-1, which is formed by a single module and is associated with the use of digital tools. However, this may serve as a reason for further updating the program in the context of the digital transformation of education.

Discussion

It is clear that a single educational program cannot develop all possible or desired competencies in students. However, it is possible to propose a universal algorithm for designing a basic educational program for any level and profile based on an analysis of students' educational needs, which can be represented by the following sequential stages:

• Diagnostic assessment of educational needs, which requires developing a comprehensive questionnaire to identify the respondents' social and age profile; analyzing professional deficiencies and students' educational expectations; and assessing the significance of the developed competencies for them. We emphasize the use of a standardized scale in the questionnaire, which ensures data comparability even with a small sample.
• Quantitative analysis of the PC significance based on the calculation of weighted arithmetic mean scores, taking into account the varying weights of the indicators for students.
• Development of a competency profile reflecting the current needs and priorities of the target audience of the BPEP, based on the PC ranking and on the obtained quantitative assessments.

4) Designing the BPEP content based on establishing a correspondence between the PC and M after expert discussion by the academic staff and the construction of adjacency matrices.

5) Visualizing the BPEP structure to display the PC → M interrelations, which facilitates updating its content, ensuring a match between students' educational needs and the proposed educational content.

The proposed universal algorithm can be considered as a system approach to designing a BPEP. By conducting a similar systematic study of student preferences, as well as potential applicants to any educational program, it is possible to identify real subjective educational needs. A quantitative assessment of the significance of the PC using weighted average data creates an objective basis for management decisions when updating the BPEP, allowing for the ranking of learning outcomes based on relevance and practical significance. Integrating the resulting educational demand assessment into the design process ensures the flexibility and adaptability of the BPEP substantive significance, enabling the creation of personalized educational trajectories relevant to the needs of any target audience, including small one.

Focusing on the shift of the educational paradigm in modern conditions from the traditional expert-centered model, in which the student is viewed as an object of pedagogical influence, to a student-centered one, we emphasize the need for serious efforts by the expert teaching community to bridge the existing gap between the learning outcomes declared by developers of educational programs and the learning outcomes actually sought by students. Recognizing students as subjects and co-creators of their education, we note that feedback does not replace, but rather enhances and legitimizes the work of experts, making it more effective and substantiated. Indeed, expert discussion by the academic teaching staff determines the content and pedagogical design of the educational program, but only the student can provide feedback on how these align not only with the current labor market but also with their subjective needs. This information helps to precisely adjust the educational program, preserving its fundamental nature and enhancing its practical application.

Another important consequence of providing feedback is a reduction in passive, consumptive attitudes and the emergence of co-responsibility among students for the process and outcome of their learning. Developing a mindset of conscious learning increases engagement and helps maintain attention and interest, which directly impacts academic results, which serve as a quality of education indicator.

Conclusion

To achieve the objective of this study and develop a universal algorithm for designing an educational program based on an analysis of students' educational needs, we hypothesized that systematic analysis of such needs would provide a mechanism for continuous program updating and personalized training. To test this hypothesis and achieve this objective, we used a combination of complementary methods: a questionnaire method for quantitative assessment and ranking competencies and constructing a competency profile; expert discussions to establish correspondence between competencies and educational modules through adjacency matrices; and a graph method for the final visualization and analysis of the program structure. Together, these methods allowed us to translate the educational needs into a specific project for the updated BPEP.

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