Although the absence of a control group represents a limitation, it is understandable within the context of educational research, where students are typically organized into existing classroom groups

The gap

Consensus across the literature

Supporting evidence — 3 representative gaps

• The Influence of Technology-enhanced Brain-based Learning on the Science Understanding and Performance of First-year Undergraduate Engineering Students (2026) · doi

  The following recommendations may be considered for future research: Expanding the study to include multiple universities, increasing the sample size, extending the intervention duration, and incorporating longitudinal measures would yield more robust evidence and enhance external validity. Additional studies at the tertiary level and in other subject areas may also be considered. Given the gender imbalance observed in this study, future trials should aim for a more balanced gender representation across groups or explicitly include gender as a variable in the analysis. Including interviews, focus groups, or classroom observations would provide deeper insights into students’ experiences, learning methods, and diverse responses across subgroups. Collecting feedback from control groups would support more meaningful comparisons of perceptions and engagement. In addition, future studies could adopt a comparative or factorial design to disentangle the individual and combined effects of instructional approaches. For example, separate groups could receive technology-only instruction, BBL- only instruction, and a combined technology-enhanced BBL intervention, allowing researchers to examine the relative contribution of each component to learning outcomes. Such designs would provide clearer insight into whether observed effects are attributable primarily to technology use, brain-based pedagogical principles, or their interaction. REFERENCES Achor, E.E., & Gbadamosi, O. (2020). Raising the achievement and retention levels of secondary school students in physics through a brain- based learning strategy in Taraba State, Nigeria. BSU Journal of Science Mathematics and Computer Education, 1(2), 87-97. Ansari, D., De Smedt, B., & Grabner, R.H. (2012). Neuroeducation-a critical overview of an emerging field. Neuroethics, 5(2), 105-117. Bada, A.A., & Jita, L.C. (2022). Integrating brain-based learning in the science classroom: A systematic review. International Journal of Pedagogy and Teacher Education, 6(1), 24-36. Bhattacherjee, A. (2012). Social Science Research: Principles, Methods, and Practices. 2nd ed. Global Text Project. Available from: https:// scholarcommons.usf.edu/oa_textbooks/3 [Last accessed on 2026 Mar 06]. Caine, R.N., Caine, G., McClintic, C., & Klimek, K. (2005). 12 Brain/Mind Learning Principles in Action: The Field Book for Making Connections, Teaching, and the Human Brain. United States: Corwin Press. Cheung, A.C.K., & Slavin, R.E. (2013). The effectiveness of educational technology applications for enhancing mathematics achievement in K-12 classrooms: A meta-analysis. Educational Research Review, 9, 88-113. Council for Higher Education. (2015). VitalStats: Public Higher Education, 2015. Council for Higher Education. Available from: https://www.che. ac.za/publications/vital-stats/vital

  Keywords: learning brain education groups technology future gender based principles science higher considered include intervention observed
• Enhancing pre-service teachers’ psychological well-being through service-learning in physical education (2026) · doi

  Although the absence of a control group represents a limitation, it is understandable within the context of educational research, where students are typically organized into existing classroom groups and random assignment is often not feasible. Moreover, assuming equivalence between groups may compromise ecological validity. authentic Consequently, pre–post designs educational settings are widely accepted for examining changes associated with pedagogical interventions (Bransford et al., 2000; Shadish et al., 2002). controlled designs, conducted in more even in and reflecting In the context of SL, pre-experimental pre–post designs without control groups are frequently used to assess both academic the socio-emotional outcomes, complexity of the intervention and the authenticity of the learning environment (Billig and Waterman, 2003). In the present study, a single-group pretest–posttest design was employed, and several strategies were implemented to mitigate key threats to internal validity. Classic methodological work identifies this design as particularly vulnerable to threats such as history, maturation, and testing effects in the absence of a control group (Shadish et al., 2002). To address these concerns, post-intervention outcomes were systematically compared with pretest measures, providing a baseline for evaluating change. Additionally, the relatively short duration of the intervention reduced the likelihood that observed improvements could be attributed to natural maturation rather than to the program itself. Collectively, these measures strengthen the interpretability and credibility of the findings. a context of Special Education Center, Nevertheless, given the relatively small sample size and the specific the generalizability of the results is limited. While the findings point to the potential benefits of SL for psychological well-being, they should be understood as context-dependent. Even so, they offer valuable insights and suggest that similar SL experiences may contribute to promoting psychological well-being in other pre- service teacher populations and educational settings.

  Keywords: context control group educational groups post designs intervention absence validity settings shadish even outcomes pretest
• Enhancing students’ understanding of numerical sequences through real-life contexts and python programming (2026) · doi

  This study has several limitations that should be considered when interpreting the findings. First, the sample size was relatively small, consisting of 48 students from two intact classes. Although the results provide valuable insights into classroom practice, the limited sample may affect the generalizability of the findings to broader educational contexts. Second, the study employed a quasi-experimental design based on existing classroom groups without random assignment. As a result, potential pre-existing differences between the experimental and control groups cannot be fully excluded, despite the use of statistical controls. Third, the research was conducted in a single school context, which may limit the transferability of the findings to other educational settings with different student populations, institutional structures, or teaching practices. Fourth, the duration of the intervention was relatively short (two weeks), which may not fully capture the long-term effects of contextualized instruction and the integration of digital tools on students’ learning outcomes. In addition, the observation checklist was completed by the teacher of the experimental group, which may introduce a degree of observer bias. Although structured criteria were used, subjective interpretation cannot be entirely ruled out. © 2026 by Author/s 21 / 25 Mahmuti et al. / Real-Life Contexts and Python for Sequence Finally, AI-assisted tools were introduced only as supportive resources during the post-test phase for the experimental group and were not examined as an independent treatment variable. Importantly, these tools were not used to generate complete mathematical solutions. Their primary role was to assist students in generating Python code and verifying computational results, while students remained responsible for identifying patterns, formulating mathematical models, deriving general terms, and interpreting the obtained results. Therefore, AI functioned mainly as a coding and verification aid rather than as a mathematical problem-solving tool. Although the availability of AI-assisted support may have contributed to students’ efficiency in checking and validating their work, its specific contribution cannot be separated from the broader instructional approach adopted in this study. Future research should investigate the effects of AI-assisted support independently in order to better understand its role within technology-enhanced mathematics learning.

  Keywords: students experimental cannot tools assisted mathematical interpreting sample relatively classroom broader educational contexts existing groups