Search Results (528)

Short-form video platforms increasingly shape students’ media practices, yet little is known about the pedagogical value of student-generated videos in university mathematics. This study examined an intervention in a first-year mathematics course at a European university in which students produced 1–2 min explanatory videos solving integration problems and subsequently engaged in peer evaluation of selected exemplars. A mixed-methods design was employed, combining coursework and final examination scores with interview data. No statistically significant performance gains were observed; however, strong positive correlations between coursework, final examination and overall grade indicated stable achievement patterns across assessment points. Qualitative analysis suggested that the process of producing short instructional videos encouraged students to reflect on explanatory clarity, peer perspectives, and the communication of mathematical reasoning, despite linguistic and technical challenges. Overall, the findings provide exploratory insights into how student-generated videos can be integrated into undergraduate mathematics courses as a low-stakes instructional activity supporting reflective engagement and peer-oriented explanation. This study contributes to the scholarship of teaching and learning (SoTL) in STEM education by offering an empirically grounded account of a media-based, peer-oriented learning activity in a university mathematics context. Full article

This article studies practical argumentation in the context of designing application problems and transforming them into modelling problems. To this end, the practical arguments developed by prospective primary education teachers were analysed, using a scheme for structuring and representing these arguments and a modelling cycle for representing the solution plans proposed to these problems. This is a case study with three groups of prospective teachers who were taking a course on mathematical reasoning and activity in primary education, where problem solving and mathematical modelling were the two most relevant topics. For data collection, a questionnaire was applied to and an interview was conducted with the study subjects, thus identifying nine episodes of practical argumentation based on the justification of their pedagogical decisions made on the design and transformation of problems. Also, the written reports prepared by the study subjects were reviewed to analyse their solution plans proposed to the problems. The results showed that the study subjects developed practical arguments to justify the design of motivating learning situations and problems for students in realistic contexts close to their environment and the transformation of application problems into modelling problems by eliminating data from their statements and formulating an open-ended question. Full article

The aim of this study is to present an integrated approach to ski personalization by combining a detailed material and geometric characterization of ski structure with real-time on-slope dynamic performance data. Longitudinal and torsional stiffness were selected as the basic material parameters for a wide range of skis with different application profiles. Acceleration graphs for turns were synchronized with video footage recorded by a drone, which provided real-time monitoring of the skiers’ actual course and facilitated the precise correlation between kinematic data and on-slope performance. To estimate style similarity between different skiers, the Pearson correlation coefficient was utilized due to its robust mathematical properties. Comprehensive analyses helped to finally formulate a multistage ski personalization algorithm. Full article

The most widespread lesson preparation resource used by mathematics teachers is the textbook. Initial teacher training programmes should therefore develop the skill of curricular noticing, i.e., the ability to critically analyse and make decisions concerning an activity sequence from a textbook. This mix-method study focused on the interpretations and decisions adopted by 85 Spanish pre-service primary school teachers (PTs) in relation to a three-dimensional (3D) geometry activity sequence from a textbook. The PTs were assigned two tasks: the first was identifying the limitations of the activity sequence for supporting students’ geometrical understanding regarding three aspects—attributes, geometrical processes, and modes of representation—and the second was completing the sequence. Most PTs interpreted a number of activity sequence limitations. In terms of their decision-making, the PTs prioritised certain characteristics over others, such as introducing further attributes rather than changing representation modes, or adding geometrical processes to their activity sequence designs. Moreover, the analysis allowed determining how PTs completed the activity sequence to address limitations, thereby revealing relationships between their interpretations and decisions. The study findings help teacher educators to design courses aimed at supporting the PTs’ ability to make more informed and effective teaching choices that enhance student learning. Full article

This paper addresses the axiom of balance in Flow Theory from a physiological-and-group-based approach by a quasi-experimental study using mixed methods across two action–research cycles, each divided into pre-test, intervention, and post-test phases. The study involved 56 students in two control and two experimental groups attending robotics and design STEAM courses in natural settings, wearing Polar H10 bands. Each group participated in nine one-hour sessions, with the same instructor. While flow in control groups was measured with intuition-based teacher actions, in experimental groups the teacher received support from a synchronous physiological flow advisory system. Data from these groups were analysed using nonlinear techniques, finding preliminary evidence that suggests (1) more quickly reaching of the Zone of Proximal Development when the teacher has physiological guidance, (2) mathematical physiologically-based support for the axiom of balance of Flow Theory, and (3) nonlinear analysis in group contexts offer quantification to the previously found contradictions in Flow Theory. Moreover, these findings propose new hypotheses and potential redefinitions in Flow Theory. Full article

Efforts to improve undergraduate education in mathematics and other STEM fields often work with instructors to implement research-based instructional practices that emphasize active and collaborative learning. To measure the progress and outcomes of such initiatives, researchers need measurement tools that are versatile, meaningful, and inexpensive to use, to know what teaching practices are occurring. Because students spend a great deal of time observing class conditions, they are well positioned to report the teaching that they experienced. We report results from some 2400 student surveys on the use of active and collaborative learning (ACL) approaches in over 200 recitation sections of gateway courses in tertiary mathematics, physics, and computer science. We developed a set of survey items, TAMI-SS, and a compound measure based on the items, called S-ACL for Student-reported Active and Collaborative Learning, that reflects the extent of active and collaborative learning as reported by students. We find that S-ACL scores compare favorably with instructor surveys and observations, and with students’ reports of their classroom experience using established measures. Moreover, S-ACL reflected departments’ progress in implementing ACL in recitations. When focused on specific, observable classroom behaviors, student surveys of instructional practice can be used to measure the progress of instructional change initiatives in mathematics and similar fields. Full article

A global push for teaching science, technology, engineering and mathematics (STEM) as a multidisciplinary endeavor is becoming increasingly prevalent in primary education. To understand how teachers are prepared to meet this need, we examined the programmatic design of seven teacher education programs, identified from among seventeen Robert Noyce Teacher Scholarship projects that focused on integrated STEM teacher education at the primary level. Specifically, we asked how the programmatic features and structures of the teacher education programs presented opportunities for prospective and practicing teachers to build capacity for integrated STEM teaching. Using case study methodology and qualitative content analysis, this study explored how primary teacher education programs framed integrated STEM across and within courses. The findings suggest that current initiatives aimed at meeting critical needs in STEM education do not sufficiently foster a focus on integrated STEM components in teacher education, especially at the primary level. The findings highlight a need for more intentional development of integrated STEM programs targeting primary teachers and provide guidance for the development or redesign of programs that better meet the demand. Specifically, integrated STEM can be woven into programs through a varying number of courses, which are introduced either later or from the beginning of programs. Full article

Across the globe, as countries implement policies and programs to increase college enrollment of youth to increase their workforce outcomes, a recently implemented education policy in Texas instead centers the student in selecting career pathways right out of high school. This paper explores the relationship between career-focused graduation plans and workforce outcomes of the 40% of Texas public school youth who do not continue into higher education. Through access to a statewide, individual-level data repository, this research produces a thorough descriptive analysis of the workforce outcomes of high school graduates who do not continue into higher education and estimates relationships between workforce outcomes and career-focused high school graduation plans. Our findings indicate that early in their implementation, career-focused graduation plans demonstrate no relationship to workforce outcomes for high school graduates who do not continue into higher education. We further found a declining trend in workforce participation for youth with only a high school credential. In conclusion, we recommend revising current graduation pathways to reinstate the requirement for higher-level mathematics courses across all graduation plans, while also ensuring that every student has access to these advanced math opportunities during high school. Full article

Developing expertise in physics requires appropriate integration and assimilation of physics and mathematics. Instructors and students often describe physics courses in terms of their emphasis on conceptual and quantitative problem-solving. For example, they may argue that a course emphasizes primarily conceptual over quantitative problem-solving or may emphasize equally on both depending on instructional context and assessment design. In this study, we investigated how students and instructors across different levels of physics instruction perceive the roles and development of conceptual and quantitative problem-solving in student learning and expertise development. Using departmental surveys administered at the beginning and end of each semester, we collected both Likert-scale and open-ended responses from students enrolled in introductory, upper-level undergraduate and graduate physics courses. These surveys assessed students’ self-perceived skills, preferences, and perceptions of instructors and course emphasis. To complement student perspectives, we conducted interviews with instructors, using parallel questions adapted to reflect instructional goals and expectations. Our findings highlight patterns in how students and instructors prioritize conceptual and quantitative problem-solving across course levels, as well as alignment and misalignment between student and instructor perspectives. Also, although the questions were framed around conceptual versus quantitative problem-solving, we do not view them as mutually exclusive; rather we seek to understand perceived course emphasis and student expertise development from student and instructor points of view in a language commonly used in physics. These results can help shape teaching, course design, and assessment practices to better support the development of expert-like problem-solving skills in students in physics and related disciplines. Full article

Despite growing global interest in improving mathematics outcomes, there has been limited empirical research in Nigeria that has rigorously evaluated the impact of structured after-school intervention programs on low-achieving students, particularly through a gender-based lens. This study addresses this gap by examining the effectiveness of after-school mathematics instruction on the performance of senior secondary school students in Oyo State, Nigeria. The researchers adopted a quasi-experimental pretest–posttest control group design with a 2 × 2 factorial structure. The sample consisted of 92 purposively selected low-achieving students (47 males and 45 females) from eight public, co-educational secondary schools, who were randomly assigned to experimental and control groups. Over the course of six weeks, the experimental group received structured after-school mathematics lessons that targeted foundational skills, while the control group continued with conventional classroom instruction. Data was collected using a researcher-developed Mathematics Achievement Test (MAT), which was validated by mathematics education experts and yielded a Cronbach’s alpha of 0.82. Analysis of Covariance (ANCOVA) revealed a statistically significant improvement in the mathematics achievement of students in the intervention group (F(1, 87) = 114.88, p < 0.05), with a large effect size (Partial η² = 0.569). Although no significant interaction effect between gender and treatment was observed (F(1, 87) = 0.208, p > 0.05). This study contributes to the limited literature on gender-responsive after-school interventions in sub-Saharan African contexts. Findings support the implementation of targeted support programs to enhance mathematics outcomes for struggling learners, regardless of gender. Full article

The increasing adoption of video-based instruction and digital assessment in higher education has reshaped how students interact with learning materials. However, it also introduces cognitive and behavioral biases that challenge the accuracy of self-perceived learning. This study aims to bridge the gap between perceived and actual learning by investigating how illusion learning—an overestimation of understanding driven by the fluency of instructional media and autonomous study behaviors—affects cognitive performance in university mathematics. Specifically, it examines how students’ performance evolves across Bloom’s cognitive domains (Understanding, Application, and Analysis) from midterm to final assessments. This paper presents a data-driven investigation that combines the theoretical framework of illusion learning, the tendency to overestimate understanding based on the fluency of instructional media, with empirical evidence drawn from a structured and anonymized dataset of 294 undergraduate students enrolled in a Linear Algebra course. The dataset records midterm and final exam scores across three cognitive domains (Understanding, Application, and Analysis) aligned with Bloom’s taxonomy. Through paired-sample testing, descriptive analytics, and visual inspection, the study identifies significant improvement in analytical reasoning, moderate progress in application, and persistent overconfidence in self-assessment. These results suggest that while students develop higher-order problem-solving skills, a cognitive gap remains between perceived and actual mastery. Beyond contributing to the theoretical understanding of metacognitive illusion, this paper provides a reproducible dataset and analysis framework that can inform future work in learning analytics, educational psychology, and behavioral modeling in higher education. Full article

The paper presents methods of applying AI to generate mathematical and programming exercises for the purpose of creating courses on an educational platform. Various challenges and advantages are highlighted and discussed in the context of a new interactive platform—Compass. The proposed learning methods based on user–platform interaction are described, along with the results of evaluations conducted among university students who learned with Compass. Full article

This study aims to examine the thematic coherence among preservice mathematics teachers’ noticing components when analysing students’ thinking about classification in geometry, as well as the actions they propose to respond to those students. The research was conducted within an instructional module on the teaching of geometry, embedded in a mathematics methods course of a master’s programme. The module was designed to foster preservice secondary mathematics teachers’ pedagogical content knowledge alongside their noticing skills. Considering the mathematics education literature about the process of classification in geometry and the components of noticing, an analytical framework was developed to identify the thematic coherence of preservice mathematics teachers’ noticing of students’ thinking from two fictitious classroom episodes. Data came from individual written responses of 12 preservice mathematics teachers to an instructional task. The results overall patterns reveal strong thematic coherence in attending and interpreting, with responding also showing substantial, though comparatively lower, coherence. The findings also indicate that preservice teachers frequently proposed coherent responses that were both specific and may foster students’ conceptual understanding. This study highlights that promoting coherence in professional noticing, particularly within the responding component, is vital for cultivating teaching practices that are both responsive and conceptually grounded. Full article

As the transformative influence of novel technologies sweeps across industries, organisations are called upon to position their staff in the equally dynamic operational environment, which includes embedding technical and legal communication skills in their training programs. For many organisations, internal and external communication of data modelling and related concepts, reporting, and monitoring still pose major challenges. The aim of this research is to develop an effective data training framework for learners with or without mathematical or computational maturity. It also addresses subtle aspects such as the legal and ethical implications of dealing with organisational data. Data was collected from a training course in Python, delivered to government employees in different departments in the United Arab Emirates (UAE). A structured questionnaire was designed to measure the effectiveness of the training program using Python, from the employees’ perspective, based on three key attributes: their personal characteristics, professional characteristics, and technical knowledge. A descriptive analysis of aggregations, deviations, and proportions was used to describe the data attributes gathered for the study. The main findings revealed a huge knowledge gap across disciplines regarding the core skills of big data analytics. In addition, the findings highlighted that previous knowledge about statistical methods of data analysis along with prior programming knowledge made it easier for employees to gain skills in data analytics. While the results of this study showed that their training program was beneficial for the vast majority of participants, responses from the survey indicate that providing a solid knowledge of technical communication, legal and ethical aspects would offer significant insights into the big data analytics field. Based on the findings, we make recommendations for adapting conventional data analytics approaches to align with the complexity or the attainment of the non-orthogonal United Nations Sustainable Development Goals (SDG). Associations of selected responses from the survey with some of the key data attributes indicate that the research highlights vital roles that technology and data-driven skills will play in ensuring a more prosperous and sustainable future for all. Full article

Background/Objectives: Machine learning is an extremely important issue, considering the potential to prevent the onset of long-term complications from coronavirus disease or to ensure timely detection and effective treatment. The aim of our study was to develop an algorithm and mathematical model to predict the risk of developing long COVID in children who have had acute SARS-CoV-2 viral infection, taking into account a wide range of demographic, clinical, and laboratory parameters. Methods: We conducted a cross-sectional study involving 305 pediatric patients aged from 1 month to 18 years who had recovered from acute SARS-CoV-2 infection. To perform a detailed analysis of the factors influencing the development of long-term consequences of coronavirus disease in children, two models were created. The first model included basic demographic and clinical characteristics of the acute SARS-CoV-2 infection, as well as serum levels of vitamin D and zinc for all patients from both groups. The second model, in addition to the aforementioned parameters, also incorporated laboratory test results and included only hospitalized patients. Results: Among 265 children, 138 patients (52.0%) developed long COVID, and the remaining 127 (48.0%) fully recovered. We included 36 risk factors of developing long COVID in children (DLCC) in model 1, including non-hospitalized patients, and 58 predictors in model 2, excluding them. These included demographic characteristics of the children, major comorbid conditions, main symptoms and course of acute SARS-CoV-2 infection, and main parameters of complete blood count and coagulation profile. In the first model, which accounted for non-hospitalized patients, multivariate regression analysis identified obesity, a history of allergic disorders, and serum vitamin D deficiency as significant predictors of long COVID development. In the second model, limited to hospitalized patients, significant risk factors for long-term sequelae of acute SARS-CoV-2 infection included fever and the presence of ≥3 symptoms during the acute phase, a history of allergic conditions, thrombocytosis, neutrophilia, and altered prothrombin time, as determined by multivariate regression analysis. To assess the acceptability of the model as a whole, an ANOVA analysis was performed. Based on this method, it can be concluded that the model for predicting the risk of developing long COVID in children is highly acceptable, since the significance level is p < 0.001, and the model itself will perform better than a simple prediction using average values. Conclusions: The results of multivariate regression analysis demonstrated that the presence of a burdened comorbid background—specifically obesity and allergic pathology—fever during the acute phase of the disease or the presence of three or more symptoms, as well as laboratory abnormalities including thrombocytosis, neutrophilia, alterations in prothrombin time (either shortened or prolonged), and reduced serum vitamin D levels, are predictors of long COVID development among pediatric patients. Full article