Geometry word problems require students to integrate geometric concepts, algebraic reasoning, and procedural knowledge, yet many university students experience difficulties when solving such problems. This study examined the challenges students encounter, their perceptions of geometry word problems, and whether students’ performance differed significantly by gender. The study employed an explanatory sequential design. The quantitative phase involved 80 Level 100 Mathematics Education students, comprising 40 males and 40 females, selected through stratified random sampling from a target population of 240 students. Five students were purposively selected from the quantitative sample for the qualitative phase. Data were collected using a researcher-developed Geometry Word Problem Test consisting of three open-ended problems, with responses analysed across the five NEA stages: reading, comprehension, transformation, process skills, and encoding. Quantitative data were analysed using descriptive statistics while interview data were analysed thematically. The findings showed that process skill errors were the most frequent, followed by transformation errors, comprehension errors, encoding errors, and reading errors. These results indicate that students experienced greater difficulties with translating problem information into mathematical representations and executing appropriate mathematical procedures than with reading the problems. Qualitative findings revealed difficulties in understanding problem statements, translating verbal information into mathematical representations, performing algebraic manipulations, and constructing appropriate diagrams. The study concludes that instruction should place greater emphasis on strengthening students’ algebraic reasoning and comprehension through guided and integrated approaches to geometry problem solving.
BackgroundOccupational therapy (OT) remains uncommon in Japanese educational institutions. This study examined a school-based OT program that integrated Cognitive Orientation to daily Occupational Performance (CO-OP) within Partnering for Change (P4C).MethodsA convergent mixed-methods study was conducted across three public elementary schools and included 936 students across 39 classes. Tier 1 Universal Services reached all students; Tier 2 Targeted Services reached 19 unique classes (an estimated 490 students); and 15 students received Tier 3 Individualized Services. Canadian Occupational Performance Measure (COPM) ratings were averaged within students for the participant-level analysis (N = 15). Interviews with five educators were analyzed using reflexive thematic analysis and then integrated with quantitative findings.ResultsMean COPM Performance scores increased from 4.8 (SD 1.8) to 7.8 (SD 2.0) (p < .001, r = 0.85), and Mean COPM Satisfaction scores increased from 5.1 (SD 2.4) to 7.7 (SD 2.5) (p = .001, r = 0.82). Five themes addressed strategy-based reframing, perceived school-wide agency, specialist consultation, collaboration with parents around children's goals, and contextual limits on strategy use. Integration showed convergence at Tier 3; broader perceived effects varied with leadership, goal ownership, emotional regulation, and group size.ConclusionsThe program was associated with improved individual outcomes and perceived benefits extending beyond individual support. The uncontrolled design and absence of standardized Tier 1 and Tier 2 outcomes limit conclusions about the program's effectiveness and school-wide impact.
Satisfaction and willingness to recommend are related evaluations of university experience, but their associations with belonging-related experiences may differ. We examined cross-sectional questionnaire responses from 198 Chinese students and graduates of UK master's programmes, including 150 graduates (75.8%), comparing seven support-related content scores with a three-item satisfaction score and a single recommendation item. Relative to the mean of the remaining support items, the six-item belonging-related score had a positive conditional association with recommendation and little additional association with satisfaction; a paired comparison of the two increments was positive, although its confidence interval was wide and approached zero. Exploratory analyses undertaken during revision also found additional information about recommendation after conditioning on reported satisfaction, including when the remaining support items were included in the baseline. All results concern observed item scores: neither the continuous-item nor the ordinal factor analyses yielded an admissible seven-factor solution, the estimated increments were sensitive to influential responses, and the convenience sample consisted mainly of business and management students and of graduates reporting retrospectively. These findings motivate further assessment of belonging-related experiences alongside satisfaction and recommendation, with improved measurement and longitudinal sampling; they do not identify a validated belonging construct or an intervention that would increase recommendation.
As generative artificial intelligence (AI) increasingly automates technical execution, the labour-market value of narrowly defined technical skills is widely argued to be shifting toward human capacities that remain difficult to replicate, including perspective-taking, critical judgment, ethical discernment, and meaning-making. This conceptual analysis, informed by the author's reflective teaching practice, challenges the marginal positioning of literary study within innovation education and argues that literary thinking can contribute a substantive foundation for human-centred innovation. Its primary purpose is theory-building: it develops and specifies a pedagogical framework rather than reporting an empirical evaluation. It is guided by the question: how can literary and philosophical practices be integrated into innovation education as a structured pedagogical sequence that complements design thinking and responds to the risks of uncritical reliance on generative AI? Drawing on the contested experimental and meta-analytic literature on fiction reading and social cognition, on Aristotle's and Heidegger's accounts of technē (craft), poiesis (making or bringing-forth), and phronēsis (practical wisdom), and on research on design thinking, design fiction, responsible innovation, and AI literacy, the article develops the Narrative-Humanistic Innovation Pedagogy (NHIP). NHIP integrates four interrelated pillars: Empathic Immersion, Critical Interrogation, Ethical Grey-Zone Reasoning, and Narrative Prototyping, each with a specified form of engagement with generative AI. Rather than displacing established design-thinking, design-fiction, and responsible-innovation approaches, the framework complements them by sequencing literary reading, Socratic deliberation, and narrative construction as practices proposed to support cognitive, ethical, and imaginative capacities relevant to innovation. NHIP was developed through the author's teaching of innovation classes at the University of Kalba, United Arab Emirates, in which its activities have been implemented as part of routine teaching but have not been formally evaluated. A ten-week illustrative module, with a generative-AI strand running through every pillar, shows how the framework could be operationalised in classroom practice. The article advances, as a hypothesis for empirical testing, the proposition that humanistic pedagogy may help safeguard against cognitive offloading arising from uncritical technology adoption. Testable propositions and implications for curriculum design, faculty development, and empirical validation are discussed.
IntroductionThe rapid advancement of AI technologies is reshaping educational practices, offering new ways to support learning activities. This study examines the interrelationships among self-efficacy, engagement, attitudes, behavioral intentions, and perceived learning in secondary STEM education.MethodsThe study was based on real-world classroom teaching involving 710 students, who engaged with an AI learning companion that provided personalized guidance and support during STEM laboratory courses. Using a single-group, post-test-only design and partial least squares structural equation modeling (PLS-SEM), the study examined concurrent associations among students' self-reported perceptions.ResultsAttitude had the largest estimated direct association with perceived learning (β = .306), followed by engagement (β = .275), intention (β = .176), and self-efficacy (β = .119).DiscussionAmong students who used the AI learning companion, higher reported engagement was associated with higher perceived learning. These findings describe concurrent associations and do not establish that changes in learning are attributable to the AI learning companion.
