This study explores the pedagogical bases of ICT integration in pre-service chemistry teacher education within the “University-School Bridge” framework. The research aimed to examine how digital technologies and pedagogical strategies collectively influence the professional development of future chemistry teachers. Using a quantitative quasi-experimental longitudinal design with pre-, mid-, and post-tests and statistical analysis via ANOVA, the study evaluated changes in students’ chemistry knowledge, digital literacy, and pedagogical competencies. Participants included 34 pre-service teachers and 224 secondary school pupils. The finding revealed significant improvement across all variables, with large effect sizes for overall performance (η2 = 0.65), and pedagogical methods (η2 = 0.41). The integration of digital tools such as Moodle, Kundelik.kz, and Quizlet, combined with classroom-based mentoring, was associated with higher levels of reflective practice and professional readiness. The results also support the University-School Bridge model as being associated with improved alignment between theoretical preparation and school-based practice. These outcomes highlight the importance of continuous, data-driven, and practice-oriented, adaptive, and research-minded educators.
chemistry teacher educationdigital literacyICT integrationpre-service teacher educationuniversity-school partnershipThe author(s) declared that financial support was not received for this work and/or its publication.section-at-acceptanceDigital Learning InnovationsIntroductionTeacher training and professional competence in the 21st century
In the rapidly evolving education system of the 21st century, effective teacher training is essential for enhancing educational quality. Teachers should be equipped with subject knowledge and a wide range of skills, such as critical thinking, communication and information competence. In this context, developing professional competencies is considered the primary focus of training future teachers (Zamora and Zamora, 2022). The publication Educational Theory in the 21st Century (2022) states that teacher training in the modern education system is closely linked to new pedagogical approaches, innovative technologies, and the promotion of lifelong learning (Alpaydın and Demirli, 2022). Systematic reviews have currently shown that using assessment tools, including rubrication materials, greatly improves the quality of education by developing students’ critical thinking and improving their ability to self-assess, thereby enhancing learning outcomes (Nsabayezu et al., 2022). In the 21st century, training future chemistry teachers has become an urgent issue. In the context of rapid scientific and technological development, teachers must have not only subject knowledge, but also critical thinking, creative, digital and communication skills. When preparing to-day’s students for future professional activities, it is important to foster innovative thinking and creative abilities using methods such as blended learning and project-based learning (Sumarni et al., 2021). At the same time, modern research highlights the crucial role of integrated schemes for the use of digital technologies and the improvement of pedagogical skills in professional teacher training in the formation of their competence (Serrano-Ausejo and Mårell-Olsson, 2024).
The role of ICT in modern education
In the context of modern educational reforms that introduce information and communication technologies, information competence is crucial for future professional activity. Future chemistry teachers should possess the knowledge and skills to use information technology effectively in their work, including creating animations, giving presentations and conducting educational experiments (Alimova et al., 2021). Modern teachers need professional competencies, benevolence, computer literacy, creativity and adaptive thinking. The professional competence of a future chemistry teacher encompasses gnostic, constructive, organizational, communicative, informational, and research components (Mamajonov et al., 2021). In addition, the education of students involves the interrelationships between natural science disciplines, forming a scientific worldview alongside ICT. They can apply the knowledge gained at university in practice, i.e., as teachers in schools (Yusupova, 2022). Oralkul et al. (2025) found that integrated science education increases future science teachers’ interest in spatial thinking and the subject. Furthermore, integrating digital technologies into the educational process fosters environmental and critical thinking skills (Amangeldi et al., 2025).
In his study, Domenici (2022) demonstrates the effectiveness of informal learning spaces and teaching methods based on STEAM projects in science museums for training future chemistry teachers. According to the author, this approach increases student motivation and allows them to teach chemistry in the context of real-life situations.
Rodríguez-Becerra et al. (2020) have shown that using computational chemistry modules contributes to the technological and pedagogical development of future chemistry teachers. The authors note that combining computer tools with problem-oriented learning strengthens students’ motivation and promotes a deeper understanding of their professional training. Developing CIRFR and STEM/STEAM competencies is not enough in the training of future chemistry teachers; these skills must be systematically developed in university programs (Elías et al., 2022).
Integrating information and communication technologies into the educational process is an effective way to enhance educational quality. Modern research has shown that ICT can open up new opportunities in chemistry teaching, increase students’ interest in the subject, and improve learning outcomes (Sagynbayeva et al., 2024). For instance, visualization tools help future teachers to interpret complex concepts and understand abstract phenomena (Semenikhina et al., 2021). At the same time, digital simulations and animations enhance students’ scientific and practical abilities, such as planning experiments, forecasting and summarizing (Kafanabo, 2022). Using ICT not only improves subject knowledge, but is also an effective way to develop professional competencies. This is because it enables future teachers to experiment with new methods in their pedagogical practice (Nechypurenko et al., 2021). Virtual laboratories and digital platforms support students’ independent work and contribute to the systematic development of their professional competencies (Karmanova, 2023). These approaches demonstrate the vital role of digital technologies in enhancing the professional development of prospective chemistry teachers.
Bridging the gap between university and school in pre-service teacher education
Theoretical knowledge acquired at university alone cannot replicate the professional experience required of future teachers in the real world. Many studies highlight the discrepancy between theory and practice as a key issue in teacher training (Jakhelln and Postholm, 2022). One solution to this problem is to emphasize the importance of students’ pedagogical practice. This issue is particularly relevant in Kazakhstan, where integrating theoretical training with classroom-based teaching remains a significant challenge in teacher education.
The Enhancing Rural Science Education through School District-University Partnership project found that partnerships between schools and universities positively impact the professional development of teachers and the quality of student education (Ferrell and Tharpe, 2024). This approach strengthens the confidence of student teachers and enhances the overall quality of science education by encouraging active collaboration between schools and universities.
Constant support for pedagogical practice, such as mentoring and training, strengthens the professional competence of future teachers (Gandolfi et al., 2024). It enables them to improve their observation, analysis, forecasting, and scientific reasoning skills. Using a scientific language called “Chemish” increases students’ scientific literacy and improves future teachers’ ability to interpret complex tasks using scientific language (Kahangwa, 2025; Mönch and Markic, 2022).
International practices state that university and school partners contribute to the development of subject training, social responsibility, and cultural values (Mellyzar et al., 2023). For example, Hidayah et al. (2022) consider the collaboration between the university and school to be a platform for creating an educational community, in which students, teachers, and school staff jointly develop new forms of knowledge exchange.
Research has consistently demonstrated that the integration of digital tools into practical learning environments significantly enhances the connection between theoretical knowledge and practical application (Wahyudiati, 2022). It has also been found that STEM and interdisciplinary projects encourage students to recognize the connections between different subjects and apply their learning to new situations (Li, 2025). Building bridges between universities and schools is crucial in developing the professional competencies of future chemistry teachers. However, as many authors have noted, such integration is not yet systematic, so developing effective models for combining theoretical knowledge and school practice in university programmes remains an urgent issue (Copriady et al., 2021).
Problem statement and research gap
Contradictions persist in the training of future chemistry teachers: the theoretical knowledge they gain at university often fails to translate into effective classroom practice. Despite the growing use of digital technologies, many students still find it difficult to use them during their school placements. This discrepancy between digital competence and pedagogical application impedes the professional development of future teachers.
Although literature often mentions ways to strengthen the link between university and school and the benefits of integrating ICT, the transition from theory to practice in training real future chemistry teachers has not been sufficiently studied through systematic, three-step observation. Thus, the key issue is the lack of comprehensive consideration of digital competence and practical training in chemistry teacher training programs.
The study formulated the following research questions (RQ) and hypotheses (H) based on the identified research gap:
RQ1: How will the digital and pedagogical competence of future chemistry teachers change across three stages: before the course, after the course, and after pedagogical practice?
RQ2: Are there significant differences between university training and school practice?
RQ3: How does the integration of ICT contribute to improving students’ professional competence in a school environment?
RQ4: Which components (theoretical knowledge or practical skills) are changing the most?
In accordance with these questions, the following hypotheses are proposed:
H1: The average scores of students after the course (T2: Appendix B) will be statistically higher than before the course (T1: Appendix A).
H2: The level of competence after teaching practice (T3: Appendix C) will be higher than the post-course level, as school experience will contribute to knowledge assimilation.
H3: Integrating ICT significantly increases students’ willingness to use digital technologies in school settings.
H4: An increase in practical skills (e.g., using digital tools) will be more closely linked to an increase in theoretical knowledge.
This research study aims to evaluate the process of developing the professional competence of future chemistry teachers through integrating digital technologies at university. The research aims to reduce the gap between students’ theoretical training and teaching practice in schools. Based on this goal, the research sets out to achieve the following objectives:
To determine the level of educational and pedagogical competencies of students after theoretical training at university and pedagogical practice at school.
To assess changes in students’ professional skills based on test results and a check-list.
To analyze the impact of ICT tools on practical skills.
To propose an effective model of university and school integration.
Materials and methodsResearch design
This study adopted a quasi-experimental longitudinal design to evaluate the effectiveness of computerization in the professional training of future chemistry teachers. A one-factor repeated-measures ANOVA was used to compare mean scores across different stages of training. This approach is widely used in educational research to examine changes over time within naturally formed groups (Balyer, 2017; Sagala et al., 2019; Guillén-Gámez et al., 2021).
Data were collected at three stages:
before theoretical training (initial test and checklist);
before moving from theory to practice; and.
at the end of the school workshop.
The repeated-measures ANOVA allowed for an objective comparison of results across time points within the same cohort of participants and is considered a reliable statistical approach for analyzing longitudinal educational data.
Participants
The study included two groups of participants: thirty-four pre-service chemistry teachers enrolled in years two and three of the “6B01510–Chemistry” programme at Abai Kazakh National Pedagogical University in Almaty, Kazakhstan, and 224 secondary school pupils in years seven to nine at Gymnasium No. 81 in the Bostandyk district of Almaty. The inclusion of Years 7–9 was justified by the fact that chemistry is taught as a school subject in Kazakhstan from Year 7 onwards, providing an appropriate level for ICT-based instructional implementation during teaching internships.
As shown in Table 1, the total sample consisted of 258 participants (34 pre-service teachers and 224 secondary school pupils), of whom 124 were female and 100 were male. The distribution across grades and university years was balanced, enabling comparative analysis between groups. Participants’ ages ranged from 13 to 21 years, representing secondary and tertiary education levels within the ‘University–School Bridge’ model.
Characteristics of the research participants.
Group
Grade/year
Total
Female
Male
Age
Students
2nd year
20
15
5
19–20
3rd year
14
11
3
20–21
Total
34
26
8
Pupils
Grade 7
77
43
34
13–14
Grade 8
73
43
30
14–15
Grade 9
74
38
36
15–16
Total
224
124
100
Source: own compilation.
The study included pupils in years 7–9 because chemistry is officially introduced as a school subject in Year 7 in Kazakhstan. Therefore, this age group was the most appropriate one in which to observe the initial stages of chemistry learning. Additionally, conducting the pedagogical practicum in these grades allowed pre-service teachers to apply ICT-based teaching methods during school lessons.
As shown in Table 1, the total sample comprised 34 pre-service chemistry teachers (26 female, 8 male) and 224 pupils (124 female, 100 male). The distribution across grades and university courses was relatively balanced, enabling comparative analysis between groups. Participants’ ages ranged from 13 to 21 years, representing secondary and tertiary education levels within the ‘University-School Bridge’ model.
Figure 1 shows the demographic breakdown of all participants (including pre-service teachers and secondary school pupils) by gender and academic level. This balanced representation ensures the subsequent analysis is reliable and comparable.
Demographic breakdown of participants (students and pupils). Source: own compilation.
Horizontal bar chart showing demographic breakdown of participants by education level, with categories for Grade 7, 8, 9, 2nd year, and 3rd year students. Bars indicate counts of males in green, females in blue, and totals in purple, with higher participation in lower grades and fewer participants in higher education levels.
As shown in Table 1 and Figure 1, 34 students and 224 pupils participated in the study. The age and gender composition of the participants were relatively balanced. These data allow us to summarize the results of the study.
Instruments
The tools used in this study were analyzed based on previous work in the field of training future chemistry teachers. Scientists note that the professional competence of future teachers is developing not only due to subject knowledge, but also due to the effective integration of pedagogical methods and digital technologies (Mıhladız and Timur, 2011). Although digital tools have great potential in chemistry teaching, it has also been proven that their effective use depends on the perception, training, and infrastructural support of teachers (Wohlfart et al., 2023). In addition, critical thinking is considered a key skill for future chemistry teachers, but many studies show that their performance in this area re-mains low or moderate. Therefore, the need to introduce special assessment tools is specific (Irwanto et al., 2018). In order to reduce the gap between university and school, it is recommended to use TSET, checklists and laboratory tasks combining theory with practice (Mıhladız and Timur, 2011; Saribas and Ceyhan, 2015).
Test checklists consisting of three stages (pre-test, mid-test, and post-test) were used as the main research measurement tools. Each test consisted of four parts: (A) general questions (yes/no); (B) verification of chemical knowledge (number of molecules, reaction equa-tions, and chemical factors); (C) digital tools (Google Classroom, Moodle, Zoom, Kun-delik.kz, PhET, Labster, Kahoot, and Quizizz); and (D) pedagogical methods (project work, case studies, demonstrations, and lab work). The questions focused on assessing students’ knowledge, skills, and attitudes in three areas:
The level of theoretical knowledge in chemistry (e.g., the law of conservation of mass of matter, gas volume, pH value, reaction factors);
Competence in using digital tools for homework testing, virtual laboratory use, assessment and effective remote learning platforms;
Skills in applying pedagogical methods (group work and methods of interpreting experiments).
A key to the correct answers to all questions has been developed. While Part A focused on determining students’ subjective opinions, Parts B-D allowed for an objective assessment. The tests, which are performed in three different versions, enable the progress of students to be compared at each stage of the educational process (Figure 2).
Dynamics of “Yes” answers on General questions. Source: own compilation.
Bar chart comparing the percentage of affirmative responses to two general questions (Q1 in blue, Q2 in purple) at three intervals (pre, mid, post) in both second and third year groups, showing consistent increases over time, with third year percentages slightly higher overall.
Pre-test (Appendix 1): to determine the entry level before theoretical training.
Mid-test (Appendix 2): to assess knowledge and skills before practical exercises.
Post-test (Appendix 3): to evaluate results after practical training.
The test results were summarized and subjected to statistical analysis using ANOVA.
The research instruments were developed based on the national chemistry curriculum, teacher education program requirements, and relevant literature on professional competence development. The structure and content of the instruments were aligned with commonly used approaches in educational assessment. To ensure content clarity and relevance, the instruments were reviewed by experienced chemistry teachers and teacher educators, and minor wording adjustments were made based on their feedback prior to data collection. Detailed scoring criteria and examples of item evaluation are provided in Appendices A–C, where the assessment structure and point allocation for each task are described.
Procedure
The study was conducted over two semesters of the 2024/25 academic year. During the first semester (September–December), students attended theoretical lectures at the university. At the start of this period, a pre-test (see Appendix 1) was carried out to establish their initial level of knowledge. The aim of this test was to identify students’ knowledge of chemistry, digital literacy, and their views on the choice of pedagogical methods. Information about the participants is provided in Table 2.
Characteristics of pre-service chemistry teachers (students).
Year of study
Total
School placemark
2nd year
20
Grade 7 («А», «Ә», «Б»)
3rd year
14
Grade 8–9 («А», «Ә», «Б»)
Total
34
9
Source: own compilation.
In the second semester (January–May), students completed teaching internships in schools. According to the academic calendar in the Republic of Kazakhstan, this period corresponds to 3–4 quarters. Throughout the week, students observed schoolteachers’ lessons, provided mentoring, studied the use of digital tools and developed techniques for organizing laboratory work. In addition, each student taught a class independently at least once a month.
The mid-test (see Appendix 2) and post-test (see Appendix 3) were presented in a standardized form during the study. The answers were collected in paper and online formats depending on the capabilities of the university and school. Sections with correct answers were evaluated using a point system. The general questions allowed us to analyze the students’ subjective opinions.
From an ethical point of view, student participation was entirely voluntary. All data was processed anonymously and used only for scientific purposes.
These approaches are also consistent with previous educational research. Van Driel et al. (2002) emphasizes the need to develop the subject and methodological knowledge of future chemistry teachers through practical experience. Tenenbaum et al. (2017) demonstrated the role of mentoring and laboratory practices in increasing students’ interest and scientific literacy through near-peer mentoring. Achor and Duguryil (2014) also demonstrated that mentoring programs can significantly enhance the professional development of future chemistry teachers. Recent studies have shown that effectively using digital technologies in the transition from university to school is an important factor in developing students’ pedagogical and digital competence (Amangeldi et al., 2025; Tafrova-Grigorova et al., 2025) (Figure 3).
Overall performance of second- and third-year students (%). Source: own compilation.
Horizontal bar chart comparing overall performance percentages across 2nd and 3rd year groups, showing three categories: Pre (light blue), Mid (dark blue), and Post (green). Post scores are highest, especially in the 3rd year.Data analysis
The collected data were processed using SPSS 26.0 and Excel. First, all the answers were encoded: the correct answer received 1 point, while the incorrect answer received 0 points. The general opinion questions (Part A, Yes/No) were analyzed as a percentage. Evaluation and scoring for each stage (pre-, mid-, post-).
Domains were evaluated within the following limits:
Part B (Chemistry knowledge): two tasks, minimum two points;
Part C (Digital tools literature): four tasks, a maximum of four points (although three tools were mentioned in the post-test versions, the correct answer was calculated as one point per tool);
Part D (pedagogical methods): two tasks, maximum two points.
The total score for each student was calculated as follows:
Overal=B+C+D8×100.
where B ∈ [0,2], C ∈ [0,4], D ∈ [0,2].
Data cleaning. Extreme values were compared using the ±3 SD rule. In the absence of missing values, analysis was performed using the listwise. If single missing values were found (less than 5%), the pairwise approach was used.
For scale variables, the assumption of normality was tested using the Kolmorov-Smirnov test (Sharipo-Wilk test for small samples), homogeneity of variance was checked by Levene’s test, and the asuumption of sphericity for repeated measures was examined using Mauchly’s test; when violated, the Greenhouse–Geisser correction was applied.
The basic model. A mixed variance analysis (mixed/repeated ANOVA) was per-formed for each domain and overall.
Within-subject factor: Time (Pre, Mid, Post);
Between-subject factor: Year (2nd vs. 3rd year).
Although significant baseline mutual effects were found, paired time comparisons were validated using a Bonferroni-adjusted paired t-test, and simple effects between groups were validated using an independent t-test.
Effect size: Partial eta squared (ηp2) was calculated for ANOVA, and Cohen’s d and 95% CI were shown for paired comparisons. The significance level is α = 0.05.
Attitudinal variables (Part A). The proportion of participants who answered “Yes” was compared using the Cochran Q test (three-period binary measurement from one participant) over time, and the X2 test (or Fisher’s exact test where necessary) was used to compare groups of two and three courses. Camer’s V was provided for the effect size.
Internal identity (reliability). The internal consistency of the dichotomous test items was assessed using the Kuder–Richardson Formula 20 (KR-20). The reliability coefficients were acceptable across measurement stages: KR-20 = 0.74 for the pre-test, 0.78 for the mid-test, and 0.81 for the post-test, indicating satisfactory internal consistency.
Visualization. Descriptive results (M ± SD) were presented in tables and line-bar charts, and the values of ηp2 for inferential results were shown on a dot chart.
Limitations
The study was conducted with a small sample size (34 pre-service chemistry teachers and 224 school pupils) from one specific region, which may affect how generalizable the findings are. In addition, the study relied primarily on self-reported data and academic test results, which may be subject to bias. As the research was limited to a single academic year and focused on a specific specialization (chemistry education), further studies are required to validate these results across different disciplines and contexts. In addition, the sample was drawn from a single pedagogical university and one partner gymnasium, which may reflect specific institutional, curricular, and organizational characteristics.
The pre-service teacher sample also showed an uneven gender distribution (26 females and 8 males), which may influence ICT self-efficacy and perceptions of digital competence; however, gender-specific analyses were beyond the scope of the present study. Therefore, the findings should be interpreted with caution and may not be directly generalized to other universities, schools, or educational contexts without further multi-site studies.
Data availability statement
The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. All data supporting the findings of this research are stored in institutional repositories and can be provided in anonymized form to ensure participant confidentiality.
ResultsDescriptive statisticsPart A. General questions
When creating test questions for students, the learning goals of modern, updated education in Kazakhstan were taken into account. According to the updated educational program, the Kudelik.kz platform creates lesson plans and study materials related to the lesson. Therefore, future chemistry teachers need to learn how to work with the “Kudelik.kz” website during their pedagogical practice at school.
The “Kudelik.kz” platform works in integration with digital platforms. This allows pupils to systematically monitor their progress and enables teachers to plan lesson con-tent effectively [41]. Such electronic systems personalize the learning process and provide quick feedback, offering valuable experience for future teachers, particularly during teaching practice.
Specific indicators of students’ responses to general questions are shown in Table 3 and Figure 4. The indicators show that there has been a significant increase in “Yes” answers over time among students of the 2nd and 3rd courses.
Part A—General questions (%/Yes)
Questions
2nd year
3rd year
Pre
Mid
Post
Pre
Mid
Post
Q1
68%
82%
95%
71%
85%
97%
Q2
54%
70%
88%
60%
76%
92%
Source: own compilation.
Effect sizes (ηp2) across domains: Chemistry knowledge, Digital tools literacy, Pedagogical methods, and Overall performance. Source: own compilation.
Dot plot illustrating effect sizes (eta squared) from an ANOVA summary across four domains: Chemistry knowledge, Digital tools literacy, Pedagogical methods, and Overall performance, with effect sizes ranging from 0.04 to 0.65.
Figure 4 shows the dynamics of student responses to general questions such as “Do I need digital tools?” and “Are online assignments effective?” As can be seen from the indicators, the proportion of “yes” responses increased slightly from the pre-test to the post-test in both groups. Second-year students scored 68% on the initial test and 95% on the post-test. In the third year, growth was even higher, rising from 71 to 97%. This suggests that future teachers’ views have changed significantly during their training.
Part B. Chemistry knowledge
Future chemistry teachers at university should have chemical knowledge, the ability to master pedagogical methods, and digital skills. An integral part of the skill required to engage modern schoolchildren and teach them chemistry is digital proficiency. The results of the assessment of students’ chemical knowledge are shown in Table 4. There was a clear increase in both groups from the pre-test to the post-test.
Part B—Chemistry knowledge (M ± SD)
Students
Pre
Mid
Post
2nd year
0.8 ± 0.5
1.2 ± 0.6
1.6 ± 0.5
3rd year
1.0 ± 0.5
1.5 ± 0.5
1.8 ± 0.4
Source: own compilation.
The data in Table 4 show that students’ level of chemical education has steadily in-creased in three stages. Although the results of both groups were similar at the beginning, the difference was obvious in the middle and final tests. The average grade of third-year students was higher than that of second-year students throughout the period. This is because they had more opportunities to apply their chemical knowledge during their school teaching practice. These results demonstrate that combining theoretical and practical knowledge has a positive impact on the professional training of future teachers.
Part C. Digital tools literacy
Table 5 shows competence in using digital tools. Third-year students achieved particularly high scores in the post-test. These indicators characterize the ability of future teachers to use ICT effectively in the educational process. The study also found that students’ use of digital tools in class and assessment became more frequent and reliable with each period.
Part C—Digital tools literacy (M ± SD).
Students
Pre
Mid
Post
2nd year
1.2 ± 0.7
2.1 ± 0.6
3.0 ± 0.6
3rd year
1.5 ± 0.8
2.5 ± 0.7
3.3 ± 0.5
Source: own compilation.
As illustrated in Table 5, the digital literacy rates of both groups increased significantly between the pre-test and the post-test. Second-year students achieved above-average results, while third-year students achieved even higher results. The difference between the mid-test and post-test is especially noticeable for third-year students. This suggests that they improved their practical skills during their school teaching practice by using various digital platforms and tools, such as Kahoot, Quizizz, Phet, Moodle, and Kundelik.kz. Overall, these results demonstrate that the systematic use of digital tools enhances the quality of teacher training.
Part D. Pedagogical methods
The results for students based on the choice of pedagogical methods are presented in Table 6. Both groups improved gradually, but the academic performance of the third-year students was slightly higher.
Part D—Pedagogical methods (M ± SD).
Students
Pre
Mid
Post
2nd year
0.9 ± 0.5
1.3 ± 0.6
1.7 ± 0.4
3rd year
1.1 ± 0.6
1.6 ± 0.5
1.9 ± 0.3
Source: own compilation.
The data in Table 6 show how student literacy grows when different pedagogical methods are chosen. Although the performance of second- and third-year students is gradually improving, third-year students are slightly more successful. This is because they have the opportunity to conduct lessons in a school environment and try out teaching methods in practice. These results also prove the effectiveness of combining digital and traditional pedagogical approaches. Overall, students’ ability to select appropriate pedagogical methods indicates an increase in their professional competence.
Overall performance
To integrate the outcomes of the different assessment dimensions, an overall performance index was calculated by averaging the chemistry knowledge, digital literacy, and pedagogical methods scores. This index reflects students’ cumulative improvement in professional competencies throughout the pre-, mid-, and post-test stages. Table 7 presents the summarised results. Table 7 presents the overall scale of the success rates of the study participants.
Overall performance (M ± SD, %).
Students
Pre
Mid
Post
2nd year
45.3 ± 8.4
62.1 ± 9.0
81.5 ± 7.2
3rd year
50.5 ± 9.2
71.4 ± 8.7
88.7 ± 6.5
Source: own compilation.
As shown in Table 7, students’ overall performance improved consistently from the pre-test to the post-test stages. These data suggest that both groups showed positive development in professional competencies. Figure 4 further visualises the comparative progress of the second- and third-year students, clearly illustrating the overall upward trend across all stages.
Table 7 shows the percentage of students who were successful overall. There was clear progress in both groups from the pre-test to the post-test. The proportion of second-year students increased from 45.3 to 81.5%, and the proportion of third-year students in-creased from 50.5 to 88.7%. These figures suggest a gradual improvement in students’ theoretical and practical skills. Although there was no significant difference between the two groups at the mid-test stage, the third-year students achieved slightly higher results in the final test. This difference can be explained by the varying influence of pedagogical experience and experience of using digital tools in a real learning environment. The overall results confirm students’ growing orientation towards and competence in using ICT. At the same time, these results demonstrate the effectiveness of the collaboration between the university and the school.
Inferential statistics
Repeated Measures ANOVA (time: pre, mid, post; year: 2nd and 3rd course) was conducted to analyze the changes in each variable depending on time and group factors. The results showed that the indicators are influenced by the stage of the course and the students’ level of experience. During the analysis, the conditions of normality and homogeneity of variance were checked, and no significant deviations were detected.
As can be seen from Table 8, the time factor had a statistically significant effect on all regions (p < 0.001). This indicates an improvement in students’ knowledge, digital literacy, and pedagogical skills between the pre-test and post-test. Additionally, the results of third-year students were found to exceed those of second-year students in all variables, indicating an improvement in professional qualifications through school experience. Although the interaction effect was significant in some areas (especially digital tools), the overall trend showed positive development in both groups. Thus, the ANOVA results fully confirmed the study’s hypothesis.
ANOVA summary (main effects and interactions).
Domain
Effect
F (df)
p-value
ηp2
Interpretation
Chemistry knowledge
TimeYearTime × Year
19.42 (2.64)4.11 (1.32)1.72 (2.64)
<0.0010.050.19
0.380.110.05
Significant growth over time3rd > 2nd yearn.s.
Digital tools literacy
TimeYearTime × Year
45.76 (2.64)5.83 (1.32)3.52 (2.64)
<0.0010.020.04
0.590.150.10
Strong time effect3rd > 2nd yearIntegration significant
Pedagogical methods
TimeYearTime × Year
22.31 (2.64)2.21 (1.32)1.18 (2.64)
<0.0010.150.31
0.410.060.04
Significant growthn.s.n.s.
Overall performance
TimeYearTime × Year
58.12 (2.64)6.94 (1.32)2.11 (2.64)
<0.0010.010.13
0.650.180.06
Very strong time effect3rd > 2nd yearn.s.
Source: own compilation; n.s., not significant.
To fully visualize the statistical analysis results presented in Table 8, Figure 4 shows the effect sizes (ηp2) for each domain. These indicators enable us to accurately compare the degree to which the factors used in the study influenced the results.
Figure 4’s dot diagram shows the distribution of ηp2 values across all domains. The greatest effects were observed in the areas of overall performance and digital tools literacy, with respective values of 0.65 and 0.59. These results suggest that students play a crucial role in professional development. It was noted that indicators of chemical education (0.38) and pedagogical methods (0.41) affect the average level. This result demonstrates that theoretical training and practical skills contribute jointly to students’ professional maturity. Overall, the high values of the effect criteria demonstrate the effectiveness of the research model and the positive impact of the applied pedagogical and innovative approaches.
Discussion
This study aimed to assess the effectiveness of integrating ICT-based pedagogical methods into pre-service chemistry teacher education though a “University-School Bridge” model. The overall findings indicated a significant improvement across all measured dimensions, particularly in digital literacy (η2 = 0.48), pedagogical methods (η2 = 0.41), and overall performance (η2 = 0.65). These results demonstrate that systematic exposure to digital environments (e.g., Moodle, Kundelik.kz, Quizlet) and practice-based tasks strengthened the professional competencies of student teachers. The higher post-test achievements of third-year students compared with second-year peers further suggest that continuous integration tools enhance reflective and pedagogical maturity.
Although the present study did not directly measure TPACK constructs, the observed outcomes can be interpreted in light of the TPACK framework. The findings are consistent with the theoretical alignment between technological, pedagogical, and content knowledge in pre-service chemistry teacher education, rather than providing direct empirical confirmation of the framework. Similar to Engida (2014), who conceptualized ICT-Enhanced Teacher Development (ICTeTD) as a multi-level process of competence growth, our results affirm that structured technological immersion fosters transformation from “emerging” to “transforming” stages of ICT competence. Likewise, Savec (2020) observed that pre-service science teacher who applied digital tools for conceptual exploration showed improved understanding when technology served pedagogical, not mechanical, purposes. These tendencies are also echoed in Krause and Eilks (2019), who used action research to demonstrate that digitally mediated chemistry instruction increases both confidence and teaching efficiency. Thorsteinsson’s (2012) virtual learning model and Krause et al. (2017) further reinforce the conclusion that technology-driven collaboration significantly enriches teachers’ self-efficacy and instruction performance.
The integration of ICT into university-school collaboration has proven particularly effective in bridging the persistent gap between theoretical preparation and classroom reality. The present finding revealed notable growth in inclusive and practice-oriented competencies among participants who experienced joint projects with schools. This pattern is consistent with Şoitu et al. (2014), who highlighted that sustained university-school partnerships enhance both professional readiness and reflective capacity. Similarly, Arnaiz-Sánchez et al. (2023) found that embedding inclusive, ICT-based practices during initial mitigates the disconnect between academic coursework and real teaching contexts. Furthermore, Tang and Putra (2025) showed that AI-supported dialogic learning promotes higher cognitive engagement and argumentation skills an out-come mirrored in this study, as trainees demonstrated increased autonomy and University-School Bridge model effectively operationalizes experiential learning and inclusive pedagogy through digital collaboration.
The statistical verification via ANOVA reinforced these qualitative observations. Significant F-values across all constructs (p < 0.001) confirmed the robustness of the digital intervention. Comparable results were reported by Qamariyah et al. (2021), who applied ANOVA and effect size analysis to show that inquiry-based chemistry instruction with socio-scientific contexts substantially enhances higher-order thinking skills. Finally, Amangeldi et al. (2025) demonstrated, through a four-stage pedagogical experiment using GIS and digital mapping, that technology-assisted instruction significantly elevates ecological and critical competencies in teacher education. Our results, aligned with these studies, confirm that data-driven, technology-integrated approaches foster measurable and meaningful pedagogical transformation.
In summary, the findings substantiate that integrating ICT-based methods within the University-School Bridge framework yields statistically and pedagogically significant progress in pre-service chemistry teacher preparation. Beyond measurable outcomes, the study contributes to strengthening the reflective, inclusive, and research-oriented mindset of future educators, ensuring that digital pedagogy functions not as an external tool but as o core element of professional growth.
Conclusion
The study confirmed that integration of ICT-based pedagogical methods within the University-School Bridge framework significantly enhances the professional competencies of pre-service chemistry teachers. The use of digital platforms, guided mentoring, and reflective practice led to measurable gains in chemistry knowledge, pedagogical performance, and ICT literacy. ANOVA results verified statistically significant progress across all domains, emphasizing that technology-supported learning environments promote both cognitive and methodical growths. The model effectively connects university-level theory with real school practice, ensuring that future teachers acquire balanced technological, pedagogical, and content knowledge. These finding advocate for the broader implementation of ICT-integrated bridge models in teacher education to strengthen innovation, adaptability, and evidence-based instruction in modern science classrooms.
Data availability statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.
Ethics statement
The studies involving humans were approved by Abai Kazakh National Pedagogical University. The studies were conducted in accordance with the local legislation and institutional requirements. The ethics committee/institutional review board waived the requirement of written informed consent for participation from the participants or the participants’ legal guardians/next of kin.
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that Generative AI was used in the creation of this manuscript. Portions of this text were edited for clearly using GPT-5 (OpenAI). The authors reviewed and verified all generated content for accuracy and originality. No generative AI tools were used to generate research data, analyses, or conclusions.
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Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feduc.2026.1729019/full#supplementary-material
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Edited by: Durgesh Verma, University of Delhi, India
Reviewed by: Luciane Chaquime, Federal Institute of São Paulo, Brazil