A Practice-Oriented Approach to Teaching Python Programming for University Students

1.1 Literature Review

Contemporary approaches to programming education in Kazakhstani universities are actively explored in the context of digitalization and the integration of IT into academic curricula (Baimukhamedov et al. 2022; Zheksembayeva 2021). International studies emphasize the significance of project-based learning and practice-oriented methods in enhancing the quality of IT specialist training (Dilafruz 2023). Swedish researchers point out that theoretical and practical components of academic programs are mutually reinforcing, and frequent transitions between them foster student engagement and improve knowledge retention (Eckerdal et al. 2024). Chinese scholars have demonstrated that the implementation of innovative learning tools substantially enhances practical skills, fosters innovative thinking, and improves Python programming competencies in the fields of artificial intelligence and big data analytics (Liu and Guo 2024).

Although computer literacy, ICT, educational technologies, and digital citizenship are integral elements of informatics, they differ from computer science education in that they focus primarily on the use of software rather than understanding its underlying mechanisms or developing new technologies (Connolly et al. 2022). In this regard, eight key variables have been identified – perceived enjoyment, friendliness, ease of learning, social influence, creative and programming self-efficacy, and attitudes toward collaboration – all of which positively affect students’ behavioral intentions toward learning programming (Milutinović 2024). Structured programming curricula have been introduced in the United Kingdom (2014), Germany (2016), and in South Korea, China, and Taiwan since 2016 (Belmar 2022). Thus, the topic under investigation remains relevant and warrants further in-depth examination.

Russian scholars concluded that practice-oriented learning and the development of interdisciplinary skills are key contemporary trends in preparing future specialists (Khaimina et al. 2023). Other researchers argue that programming education is becoming increasingly popular and that offering introductory MOOCs (Massive Open Online Courses) in programming could serve as a means to make this education accessible to a broader audience (Lepp et al. 2018). Turkish scholars highlight key themes in teachers’ responses, including the improvement of educational quality, adaptation to technological advancements, and the integration of technology into the learning process (Eğin et al. 2025).

The primary challenge in this field lies in the gap between theory and practice, outdated curricula, and limited instructional methods (Lin and Fang 2023). A notable research gap persists in the comprehensive assessment of practical programming skills, particularly regarding code quality and algorithmic thinking (Lajis et al. 2018). The implementation of project-based learning requires substantial resources, posing difficulties for certain universities (Othman et al. 2023; Padzil et al. 2021; Sun et al. 2023). Although practice-oriented teaching methods have demonstrated their effectiveness, further research is needed to elucidate the mechanisms underlying their success and to develop universal pedagogical guidelines. Project-based learning proves effective in small-group settings, yet its efficacy may diminish with larger student cohorts.

The novelty of this study lies in its comprehensive approach to the implementation of practical Python programming exercises within the context of Kazakhstani higher education. It proposes the integration of project-based methods and modern educational technologies to enhance professional training in the era of digital transformation. A review of the literature reveals the absence of unified standards for the integration of project-based learning and a lack of empirical evidence regarding its effectiveness, underscoring the need for further investigation. Consequently, this research addresses both academic and practical demands, contributing to the improvement of specialist training quality under contemporary educational conditions.

1.2 Problem Statement

The motivation for this study lies in the development and justification of a practice-oriented approach to Python programming education for university students in Kazakhstan. This approach aims to enhance students’ professional skills to meet the demands of the digital economy and align the academic curriculum with the needs of the IT industry. The objective of this article is to develop and demonstrate a practical approach to Python programming education for Kazakhstani students while integrating the curriculum with industry requirements.

The research hypothesis posits that a practice-oriented approach to Python programming education, based on project-based activities and active collaboration with industry, contributes significantly to enhancing students’ professional competence. This includes the development of analytical thinking, teamwork skills, and the ability to solve applied problems. The effectiveness of this approach is particularly evident in the context of the digital transformation of education in Kazakhstan.

Research Objectives:

1. To develop and implement a model of practice-oriented Python programming education within the academic curriculum.

2. To assess the effectiveness of the proposed educational model by analyzing students’ academic performance and satisfaction with the learning process.

3. To evaluate students’ comprehension of the curriculum and their interest in innovative teaching approaches.

4. To collect feedback from instructors involved in teaching students in the experimental group.

2.1 Study Design

In this study, the research design adopts a mixed-methods approach, incorporating both quantitative and qualitative methodologies to comprehensively assess the effectiveness of the proposed teaching method. The quantitative methods involve evaluating students’ theoretical and practical knowledge at the beginning and end of the learning process, allowing for an assessment of their readiness for different stages of the educational process and, consequently, identifying any changes that have occurred. This enables an objective measurement of students’ academic performance based on clear numerical data.

The qualitative research methods complement the quantitative data by providing a deeper understanding of how students perceive the program and its impact on their motivation and interest. For instance, surveys and interviews facilitate the identification of subjective aspects, such as instructors’ perspectives on the use of project-based learning methods, students’ attitudes toward new teaching approaches, and their overall satisfaction with the learning process. The integration of these two methods creates a comprehensive framework that captures both measurable outcomes (knowledge acquisition) and the personal experiences and observations of study participants.

2.2 Sampling

The study was conducted over four months at a university in Kazakhstan. The sample for this research comprised second- and third-year students from the technical faculties of the following universities in Kazakhstan: Ozbeksali Zhanibekov South Kazakhstan Pedagogical University, Khoja Akhmet Yassawi International Kazakh-Turkish University in Turkestan, and Korkyt Ata Kyzylorda University. The total sample size consisted of 178 participants, including 75 students from Ozbeksali Zhanibekov South Kazakhstan Pedagogical University, 60 students from Khoja Akhmet Yassawi International Kazakh-Turkish University, and 43 students from Korkyt Ata Kyzylorda University. The sample included 93 male and 85 female students, aged between 19 and 21 years.

The inclusion criteria for the study were as follows: second-and third-year students from technical faculties residing in Kazakhstan who had actively participated in full-time educational programs over the last two semesters. This cohort was selected as it represents an optimal level of knowledge and experience necessary for engaging with both traditional and innovative learning tasks. Such a selection facilitates a clear comparison between conventional and innovative teaching methodologies. Furthermore, these students were able to actively participate in solving applied problems and assess the effectiveness of the project-based learning approach, particularly in the context of application development and process automation.

The exclusion criteria included students not enrolled in technical specializations, senior-year students with a more advanced level of knowledge, and students with significant absenteeism. Additionally, 10 instructors who conducted the training participated in the study and were surveyed upon its completion.

2.3 Procedure

During the study, students were divided into groups. The control group (CG) consisted of 89 students who were taught Python programming using traditional methods, including lectures and standard laboratory sessions. This group followed a conventional curriculum without the introduction of project-based tasks. The experimental group (EG) also comprised 89 students, who participated in a Python programming curriculum focused on application development, data analysis, and task automation, incorporating project-oriented assignments. This approach enabled an assessment of the effectiveness of new educational technologies.

The instructional methods used for the experimental group emphasized active and innovative approaches aimed at developing practical skills in real-world scenarios while deepening students’ theoretical knowledge. In contrast to traditional methods, which primarily consist of standard lectures and laboratory sessions, the experimental group’s learning process also included team-based work, participation in hackathons, and engagement in programming competitions. The instructional approach used for the experimental group is described in detail in Table 1.

The objective of this experimental group-based learning method is to develop practical skills, creative thinking, and teamwork abilities, thereby enhancing student engagement in the learning process and preparing them for real-world professional challenges.

2.4 Research Tools

As part of the study, the following tests were developed to assess students’ theoretical knowledge and practical skills before and after the learning process. The theoretical and practical tests (Appendix 1), designed specifically for the training program, aimed to provide a comprehensive evaluation of students’ theoretical understanding and practical competencies in programming, data analysis, and software development. The assessment of theoretical and practical knowledge consisted of 10 tasks, each scored on a 10-point scale, with a maximum possible score of 100 for the test.

Subsequently, a survey was conducted among the experimental group (EG) students to gather data on their perceptions of the course and their interest in innovative teaching methods (Appendix 2). The survey was administered using Google Forms, facilitating efficient data collection and processing. The questionnaire included statements assessing motivation, interest, and awareness of the teaching methods, rated on a 5-point Likert scale (ranging from “strongly disagree” to “strongly agree”). The survey was conducted by instructors at the end of the training sessions, after students had completed all assignments and projects. The average time to complete the survey was approximately 15–20 min. To ensure fairness and transparency, student responses were anonymized.

Additionally, interviews were conducted with instructors teaching students in the experimental group to collect their insights and observations (Appendix 3). The interviews took place at the end of the study period, following the experiment. They were conducted in person, allowing for direct interaction and detailed discussions on individual issues. Each interview lasted approximately 30–40 min. The interview questions focused on instructors’ perceptions of the new teaching methods, challenges faced, and suggestions for improving the curriculum. The interviews were conducted in an informal setting, enabling instructors to express their thoughts and observations freely. Their responses were recorded and analyzed to identify key themes that could be used to enhance future courses.

The reliability of the research instruments (theoretical and practical tests) was evaluated using Cronbach’s alpha reliability coefficient. The coefficient yielded values of 0.73 for theoretical assessments and 0.78 for practical assessments, indicating high reliability of the instruments. Additionally, Cronbach’s alpha was used to assess the reliability of the survey and interview instruments, resulting in values of 0.79 and 0.81, respectively, further confirming the reliability of the assessment tools.

2.5 Data Analysis and Processing

The test results were analyzed using statistical methods to determine differences between the control group (CG) and the experimental group (EG). For each theoretical and practical test question, the mean group score (ranging from 0 to 100) was calculated, reflecting overall trends in students’ responses to specific statements. The Student’s t-test was employed to evaluate differences in knowledge test results between the control and experimental groups. This test was selected as it compares the mean values of two independent samples, making it particularly suitable for assessing the impact of project-based learning methods on student learning outcomes.

In analyzing the survey results, descriptive statistical methods were applied to calculate the percentage of student responses to each survey question. This approach allows for a clear and accessible presentation of data, facilitating the identification of trends and patterns among respondents. Each survey question was analyzed using a Likert scale, where response options were scored as follows: 1 – “Strongly Disagree,” 2 – “Disagree,” 3 – “Neutral,” 4 – “Somewhat Agree,” and 5 – “Strongly Agree.” These percentages illustrate the degree to which students agree or disagree with various statements, enabling an assessment of their perceptions of the project-based learning approach in the curriculum.

The interview data were analyzed using thematic analysis to identify key themes and recurring ideas among different instructors. Responses were recorded and categorized into themes such as “positive observations,” “challenges,” and “recommendations for improvement.” This method provides a deeper understanding of instructors’ perceptions of the new teaching methods and facilitates the identification of areas for further curriculum enhancement.

2.6 Ethical Issues

All participants in the study, including both students and instructors, provided informed consent to participate in the experiment. They were informed about the purpose of the study, data collection methods, and potential implications of their participation. Participation was voluntary, and individuals had the right to withdraw from the study at any time without any consequences. All collected data, including survey responses and interview transcripts, were anonymized. Participants’ identities were not disclosed, and responses were processed solely for research purposes. Furthermore, all data were securely stored and used exclusively for academic research, ensuring confidentiality and compliance with ethical research standards. To minimize potential power imbalances between teachers and students, especially in cases where the teacher is a researcher, it was emphasized that participation did not affect grades or academic progress. Data collection was carried out after the completion of the learning module, and the processing of the results was carried out independently of the teaching process.

2.7 Research Limitations

The student sample was limited to technical programs from three universities in Kazakhstan, which may restrict the generalizability of the findings to students in other academic programs or countries. This limits the applicability of the study results to a broader population. Additionally, the study did not account for the influence of external factors, such as students’ personal circumstances or extrinsic motivation, which may have affected their participation and performance. These factors could have introduced bias into the data, particularly in students’ perceptions of the learning process. Despite employing a mixed-methods approach, this study lacked sufficient longitudinal measures to accurately assess long-term changes in students’ academic and professional skills. The effects of the project-based learning approach may take time to manifest, potentially limiting conclusions regarding its long-term effectiveness. Furthermore, the qualitative data obtained through interviews and surveys may reflect participants’ subjective opinions and preferences, which could affect the objectivity of the results – especially if participants already had a preference for a practice-based approach.

3.1 Testing Results

Table 2 presents a comparative analysis of the theoretical and practical knowledge assessment results for students in the control group (CG) and the experimental group (EG) before and after training, allowing for an evaluation of the effect of traditional and innovative teaching methods. This study utilized theoretical and practical tests, with each test scored on a scale from 1 to 10, yielding a total possible score of 100. The mean score and standard deviation (SD) for each test are provided in Table 2. Additionally, the T-values for independent samples and P-values are reported to assess the statistical significance of differences between the groups.

In the theoretical test, the control group (CG) demonstrated an increase in the mean score from 65.2 to 71.3, while the standard deviation remained nearly unchanged (5.4 and 5.5, respectively). This indicates a moderate improvement in knowledge, primarily attributed to the acquisition of theoretical concepts through traditional teaching methods. However, the t-value of 4.21 and p-value of 0.0002 suggest that this improvement is statistically significant.

In the experimental group (EG), the mean score increased from 67.8 to 82.1, while the standard deviation decreased from 6.2 to 5.7. This reflects a substantial increase in theoretical knowledge, accompanied by greater consistency in students’ performance within the group. The statistical significance of these changes was confirmed with a t-value of 4.47 and a p-value of 0.0001.

Similar results were observed in practical tasks. In the control group, the mean score increased from 70.1 to 77.3, and the standard deviation decreased from 7.8 to 6.7, indicating a notable improvement in practical skills. These changes were statistically significant, as evidenced by a t-value of 3.27 and a p-value of 0.0001.

In the experimental group, the mean score increased from 70.7 to 81.9, while the standard deviation decreased from 7.1 to 5.8, suggesting both a significant enhancement in students’ skills and more stable performance across the group. The statistical significance of these improvements was confirmed with a t-value of 3.87 and a p-value of 0.0003.

The increase in mean scores in the control group (CG) was the result of a natural learning process, in which students assimilated material through traditional teaching methods. However, the improvement in knowledge and skills remained modest due to the limitations of the traditional approach, which is often less effective in enhancing student motivation and engagement.

In contrast, the significant improvement observed in the experimental group (EG) was attributed to the use of innovative teaching methods, such as active student participation, project-based learning, and the integration of modern technologies. These factors contributed to a deeper understanding of theoretical concepts and the development of practical skills while also reducing performance variability among participants. Thus, the study results demonstrate the advantages of innovative teaching approaches, which facilitate greater learning progress and a more uniform distribution of knowledge among students.

Table 3 presents the performance analysis of students in both the control and experimental groups, along with an additional statistical analysis of post-training changes. This analysis focuses on two aspects: the increase in mean scores and changes in the coefficient of variation, which allows for an assessment of both the overall learning progress and the degree of knowledge uniformity within each group. In the control group, the mean score on the theoretical test increased by 9.4 %, indicating a moderate improvement in academic performance. The coefficient of variation decreased from 8.3 % to 7.7 %, reflecting a slight improvement in the homogeneity of students’ knowledge. In the practical exam, the mean score increased by 10.3 %, while the coefficient of variation decreased from 11.1 % to 8.7 %, suggesting a greater improvement in the uniformity of knowledge and skills compared to the theoretical exam.

3.2 The Results of the Survey

Figure 1 illustrates the distribution of responses from students in the experimental group (EG) regarding their perception of project-based learning in Python programming. The responses were recorded using a Likert scale, ranging from “strongly disagree” to “strongly agree”. The first question reveals that the majority of students perceive the project-based approach as effective in deepening their understanding of Python programming. 83 % of students responded with “agree” or “strongly agree,” while only 7 % disagreed, confirming the positive perception of this approach and its educational effectiveness.

Figure 1:

Survey results. Source: Developed by the author.

In response to the second question, 83 % of students indicated that project-based work enables them to acquire practical skills applicable to real-life scenarios. This highlights the importance of practical tasks in professional development. The third question shows that learning Python through teamwork was perceived as more engaging, with 82 % of students agreeing with this statement. Only 9 % responded with “neutral”, indicating an overall positive perception of collaborative learning.

The fourth question indicated that the majority of students (84 %) were interested in solving tasks related to real-world examples. This suggests that such assignments effectively stimulate interest in learning Python.

For the fifth question, 80 % of students responded positively, indicating that project-based learning helps prepare them for their future professional careers. However, 13 % of students were uncertain in their assessment, which may indicate the need for further analysis.

The sixth question revealed that 79 % of respondents found working on a project challenging yet engaging. This suggests that the level of difficulty was appropriately set to foster interest and engagement.

The seventh question demonstrated that 75 % of students preferred learning through real-world projects over traditional lectures and practical exercises. However, 14 % were undecided, which may reflect individual learning preferences that should be considered in curriculum development.

The eighth question showed that 82 % of students reported that project-based learning increased their motivation to study Python programming, confirming the motivational role of this approach in the educational process.

For the ninth question, 82 % of students stated that the process of application development and data analysis was both useful and interesting, indicating the successful integration of practical elements into the curriculum.

Regarding the tenth question, 86 % of students stated that they would recommend project-based learning to others, suggesting a high overall level of satisfaction with this approach.

Thus, as shown in Figure 1, the majority of students expressed a positive attitude toward project-based learning in Python programming. This approach supports the development of practical skills and professional preparation while also enhancing student engagement and motivation through hands-on tasks and teamwork.

3.3 Results of Interviews with Teachers

During the interviews, instructors shared their observations and various aspects related to the implementation of project-based learning (Figure 2). Many instructors reported that students demonstrated increased interest in the learning process, actively engaged in project activities, and developed self-directed learning skills. Additionally, 80 % of instructors noted an increase in student engagement and an improvement in practical skills. However, instructors also encountered challenges, such as the need to allocate more time for preparing materials and assessing projects, as well as the requirement for professional development to effectively implement the new teaching approach.

Figure 2:

Interview results. Source: Developed by the author.

Furthermore, 60 % of instructors indicated that assessment materials need to be adapted to align with this teaching method. Additionally, 40 % noted insufficient preparation time, while 20 % reported challenges related to assessment and the need for additional time for preparation. Overall, educators believe that project-based learning enhances students’ critical thinking and creative abilities; however, its implementation requires significant effort and resources. 70 % of instructors suggested increasing the time allocated for practical work, while 30 % proposed allocating resources for course preparation. Although the positive feedback from instructors confirms the effectiveness of project-based learning, the identified challenges suggest that successful implementation of this approach requires additional training and resources.