This study delved into the factors affecting secondary school students’ interest to learn Mathematics. The aim was to gather insights that can inform strategies aimed at enhancing students' engagement, enthusiasm, and achievement in Mathematics education. Literature information was downloaded using databases such as Google Scholar, ERIC, Search 4 Life, Scopus, Web of Science, and Academia. Of the 129 studies obtained, 117 articles were retained after removing duplicates and studies that did not meet the themes of the study. Further filtering of studies by removing primary and higher learning school-related studies allowed the retention of 25 relevant pieces of research published between 2000 and 2024. The results from the systematic reviews analysis showed that instructional strategy, instructional materials, the importance of Mathematics, a future career in Mathematics, students’ attitudes towards Mathematics, students’ enjoyment of Mathematics lessons, teachers and parental support, and students’ perception towards Mathematics, are amongst the key factors affecting positively secondary school students’ interest to learn Mathematics.  

This study investigated the integration of artificial intelligence (AI) tools into secondary school chemistry education in Zimbabwe, assessing their impact on student engagement and academic performance. Grounded in Vygotsky’s Sociocultural Theory and Cognitive Load Theory, the research employed a mixed-methods approach within a pragmatic framework. Quantitative data were collected through pre-test and post-test assessments and structured surveys, comparing an experimental group using AI tools with a control group employing traditional methods. Qualitative data from student and teacher interviews and classroom observations were analysed thematically. ANCOVA analysis revealed a statistically significant difference in post-test scores between the experimental and control groups, F (1, 117) = 188.86, p < .005, η² = 0.617, demonstrating a large effect size of AI integration on academic performance. Students in the experimental group exhibited a mean improvement of 20%, controlling for pre-test differences. Additionally, interaction effects between AI use and gender (F (1,115) = 0.17, p = .684) as well as prior chemistry knowledge (F (1,115) = 0.05, p = .829) were not statistically significant. Furthermore, 85% of the experimental group reported higher engagement levels, confirming AI’s role in fostering motivation and conceptual understanding. AI tools facilitated personalized learning paths, interactive simulations, and real-time feedback, optimizing cognitive efficiency and deep learning. Despite these advantages, significant challenges emerged, including limited internet access, insufficient technological resources, lack of teacher training, and curriculum integration difficulties. These barriers highlight the need for strategic investments in digital infrastructure, professional development for educators, and curriculum revisions to fully integrate AI into chemistry education. The findings underscore AI’s transformative potential in STEM education within developing nations. Addressing infrastructural and pedagogical challenges is critical to maximizing AI's impact, ensuring equitable access, and fostering long-term sustainability in educational innovation.

The article investigates the impact of assessment data analysis on promoting deeper learning in Canadian high schools, specifically focusing on teachers’ flexibility in data-driven evaluation. The research contributes to the discourse on assessment practices by emphasizing the importance of authentic assessments, competency-based learning, and grading methodologies. Selected high school teachers drawn into this further study formed a fraction of the initial set of participants. Classroom practices of assessments concentrate on: (a) Freedom to facilitate deeper learning in instructing, assessing, and sustaining interest. The others are: (b) Teacher’s emphasis on competency-based (standard-based) learning to make learning appealing to students in educational spaces, and (c) Testing, collecting test score data, analyzing, and reporting students grades to present parents and school districts/boards with accurate progressive data reflective of diversity in learning. In this qualitative focus group case-study discussion, participants indicated time expended in performing critical analysis of data to grade students is burdensome, but the joy of such practice far outweighs the inherent difficulties, knowing that student success is founded on flexibility, freedom in decision-making, and being reflective as educators.

This study examines the validity of the Force Concept Inventory (FCI) in Ugandan secondary schools using Item Response Curves (IRCs) and provides a comparative evaluation of its effectiveness across different educational contexts. The survey focused on Senior Four students preparing for the Uganda Certificate of Education (UCE) examinations, with a representative sample of 941 students (aged 15–17) selected through a multi-stage sampling technique. The initial analysis employed Classical Test Theory (CTT) metrics before the detailed analysis of IRCs for the FCI items. The CTT evaluates item-level and whole test statistics like item difficulty level, discrimination index, and reliability. The CTT indices revealed that the FCI was highly challenging, with an average score of 5.76 out of 30 and a low-reliability coefficient (α = 0.15). Additionally, 73.3% of the items showed poor discrimination, and some distractors were ineffective. The detailed analysis of IRCs showed that several FCI items are inefficient in the context of the Ugandan education system. The IRCs also demonstrated a widespread choice of distractors for many items, with overall scores falling below the threshold indicative of a generally agreed-upon understanding of Newtonian physics. Comparative analysis from other global contexts studies suggests that language barriers, curriculum differences, and instructional methods influence student performance. These findings underscore the necessity of adapting the FCI tool to better fit local educational contexts and implementing additional instructional strategies to enhance conceptual understanding. A more culturally and contextually adapted diagnostic tool may improve physics education and better assess students’ conceptual comprehension of force and motion within the region.

This study explores how mentor teachers in specialized teaching areas, particularly chemistry, interact in an Online Professional Development (OPD) program. The Mentor Teacher Professional Development (MeT-PD) program was designed to improve mentoring practices by creating opportunities for collaborative learning through various online activities, such as Individual Response (IR), Interactive Individual Response (IIR), Small Group Discussions (SGD), and Large Group Discussions (LGD). Using a qualitative case study approach, the research analyzed data collected from Zoom recordings and Nearpod activity logs. The findings indicate that while LGDs were useful for interactions between facilitators and learners, they were not as effective in fostering interaction among learners themselves, mainly due to the cognitive demands and how these discussions were structured. On the other hand, SGDs seemed to foster stronger participant interaction, probably because the smaller group settings led to more valuable exchanges. These findings highlight the need of thoughtful planning of OPD activities, with particular focus on group size management and selection of suitable discussion formats to improve both interaction and learning outcomes.