In this theoretical essay, I build on complexity theory and commognition to theorize the ways in which failure to teach/learn mathematics (FTLM) emerges through the dynamic interactions among teacher, learner, and curriculum demands. I focus on two main concepts of complexity theory: emergence in dynamic systems and attractor states (stable, self-reinforcing patterns), and link them to the commognitive terms of ritualization (rigid focus on procedures and lack of agency) and identity. The complexity-commognitive view of FTLM is first contrasted against common, more linear explanations of FTLM. This view is then illustrated with two case studies—one exemplifying in micro-scale the short-term stabilization of ritualization in the interactions between the author and Dana, a 7th grade student identified as failing persistently in mathematics; the second exemplifying the temporal emergence of ritualization as an attractor state across 2 years (grades 7–9) in the case of a student, Idit, who was originally identified as a strong student and over the years developed an identity of failure. Finally, I discuss methodological, empirical, and practical implications of this complexity-commognitive view of FTLM.

This study explores the role of ChatGPT in science-related information retrieval, building on research conducted in 2023. Drawing on online survey data from seven countries—Australia, Denmark, Germany, Israel, South Korea, Taiwan, and the United States—and two data collection points (2023 and 2024), the study highlights ChatGPT’s growing role as an information intermediary, reflecting the rapid diffusion of generative AI (GenAI) in general. While GenAI adoption is a global phenomenon, distinct regional variations emerge in the use of ChatGPT for science-related searches. Additionally, the study finds that a specific subset of the population is more likely to use ChatGPT for science-related information retrieval. Across all countries surveyed, science-information seekers report higher levels of trust in GenAI compared to non-users. They also exhibit a stronger understanding of how (Gen)AI works and, with some notable exceptions, show greater awareness of its epistemic limitations.

There is wide agreement that regulations are an important part of students’ learning. However, there is lack of theorizing and methods that look at the intricate relations between regulations and the mathematical content of learning. In this study, our goal is to develop a theoretical and methodological lens for looking at regulations in students’ discourse, from a commognitive point of view. We develop the Discourse Mapping Diagram (DMD)—a tool for micro-analytical examination of regulative routines in relation to the structure of the mathematical task. To exemplify the potential productiveness of this tool, we apply it to the case of a 7th grade student as she attempted to solve an algebraic problem. We show, through the DMD, that the student’s regulative routines grew in the process of solving the problem hand in hand with the process of learning, which we operationalize, according to commognition, as a process of de-ritualization (moving from procedure-orientation to product-orientation). In that way, the DMD allows us to observe the inextricable nature of regulative routines and mathematical routines in students’ learning.

The integration of Generative Artificial Intelligence (GenAI) in education has introduced innovative approaches to assessment. One such approach is AI chatbot-based assessment, which utilizes large language models to provide students with timely and consistent feedback. However, the effectiveness of AI chatbots in generating assessments comparable to human evaluators in educational contexts remains underexplored. This study compared the grades and feedback provided by AI chatbots, peers, and the course instructor for student projects in a higher education course. The participants were 76 undergraduate students who engaged in a group project involving three phases: questionnaire development, peer assessment, and chatbot-based assessment. Employing a mixed-methods approach, this study quantitatively compared project grades and qualitatively analyzed feedback quality. Results indicated that AI chatbots consistently assigned higher grades than human assessors, while peer and instructor grades were notably lower and closely aligned. Content analysis revealed that chatbots generally provided higher-quality feedback compared to peers, offering detailed insights and specific guidance for improvement, though they occasionally included irrelevant or contradictory information requiring student intervention. Conversely, peer feedback was more personalized and context-sensitive. These findings highlight the importance of human judgment, suggesting that integrating chatbot-based assessments with traditional methods can leverage their complementary strengths to enrich student learning.

Important everyday decisions warrant the integration of statistical, scientific, and Nature of Science (NOS) reasoning. While each of these three types of reasoning is distinct and has been mostly examined separately, they have many interrelations. A deeper appreciation of the interrelations between the three can greatly inform how to concurrently nurture all three, thus empowering today’s young learners with the necessary skills in our data-driven society. In this article, we introduce a framework we developed to describe some of the interrelations between the three types of reasoning in relation to the central statistical and scientific practice of formulating data-based claims. To illustrate its affordances, we then offer a case study of a pair of middle school students’ engagement with a learning sequence that accompanied the pair’s participation in a Citizen Science project, whose design was inspired by the framework. Our findings introduce four versions of the practice that the students expressed and the typical aspects of statistical, scientific, and NOS reasoning associated with each. A detailed account of how these emerged and progressed provides additional insight on the interrelations between the three types of reasoning. Particularly, while the students’ initial one-sided NOS views inhibit the expression of additional and more complex scientific and statistical reasoning, extending them through the engagement of the learning sequence we developed allowed the expression of more mature, rich and interrelated scientific, statistical, and NOS considerations.

Motivation plays a major role in the learning process. Therefore, many studies have explored the factors driving undergraduate students to study engineering. However, research on the motivation of two-year technical college students is scarce. In light of the above, the study investigated the factors driving two-year college students to study electronics and compared them to the factors motivating two-year college graduates to work as electronics technicians. The study took place in ten two-year technical colleges and various companies in the private and public sectors in Israel. Three hundred and twenty-nine electronics students in their second year of study and 125 electronics technicians (two-year college graduates) participated in the research. The participants filled out a Likert-type scale, inspired by self-determination theory. The study revealed that both students and graduates were driven primarily by intrinsic motivation and identified regulation. However, significant gaps were found in the relative autonomy index and intrinsic motivation between the two groups. These gaps, in favor of the graduates, indicate that studying electronics is perceived as much less interesting and enjoyable than working in electronics.

Students’ conceptual difficulties have been characterized in many fields of knowledge. However, the relevant research literature is limited in the case of analog and digital circuits. This gap should be addressed in light of the central role played by electronic circuits in electrical engineering and related disciplines. Therefore, the study aimed to identify undergraduate engineering students’ conceptual difficulties in analog and digital circuits. The study took place at two leading universities in Israel. Over 300 exams in electronic circuits (solved by undergraduate students) were analyzed. Based on this analysis, a list of core concepts in analog and digital circuits that caused conceptual difficulties was generated. In addition, a problem based on these concepts was created. Thirty electrical engineering students who completed introductory courses in electronic circuits were interviewed. In these think-aloud interviews, the students explained the above-mentioned concepts and solved the problem. The data underwent content analysis by two engineering education experts. According to the findings, students’ difficulties are mainly in the basic concepts of operating point, feedback and gain.

In this paper, we present findings from an exploratory qualitative study that investigated perceptions of science and scientists, which can shape how learners participate in narrative-based socioscientific digital media and hands-on activities. We examine how middle school learners conceptualize science and scientists’ roles through their possible selves and the way in which they express their interests. We present findings of interviews with middle school students who took part in digital media and hands-on science activities in a school science classroom. Our findings show that while there are variations in the ways that students perceive science and scientists, many students in our study are influenced by formal learning environments (e.g., school), and some students have difficulty making connections between informal and formal science activities. By examining learners’ heterogeneous perceptions of science and scientists, we identified students’ perceptions of the qualities of scientists, science responsibility, and science in daily life, along with ideas inspired by the activities we presented. In our discussion, we consider how the constructs of possible self and interest in science can enable students to engage in science and to think about science and scientists in more expansive ways. Lessons from this study can support middle school students in developing a positive scientist possible self and increase their interest in science.

This study explores the implementation of Digital Curation (DC) as an innovative technological approach that transforms traditional science education through new possibilities for personalized learning. Through a qualitative case study of 28 middle school science students, we examine how emerging digital curation tools enable students to become active architects of their learning experiences. The research reveals how students leverage modern curation platforms to develop personalized learning strategies, demonstrating novel applications of technology that extend beyond conventional digital content management. Data collected through lesson observations, semi-structured interviews, and analysis of student-curated collections indicate that DC enhances students' critical thinking and digital literacy while introducing new technological possibilities for collaborative and individualized learning. Students discovered innovative ways to utilize curation tools, often extending beyond their intended educational use. The findings suggest that DC represents a significant technological advancement in science education, offering new pathways for student-centered learning experiences. This research contributes to understanding how emerging technologies can transform traditional educational approaches while supporting personalized learning in science education.

This study aimed to determine the involvement of neural circuits associated with executive functions (EF) during Hebrew reading in its two forms, deep and shallow, on a word and contextual level in children. Functional MRI data was collected employing 49 8-12-year-old Hebrew-speaking children performing the n-back task. Functional connectivity within and between EF networks (i.e. the cingulo-opercular; CO and fronto-parietal; FP networks) was calculated and included in prediction models for single word as well as contextual reading measures on a deep and shallow orthographic level. Working memory was positively associated with single word reading in Hebrew-speaking children. Functional connectivity between CO-FP networks better predicted deep orthography reading in single word reading compared to shallow orthography. Results highlight the utilisation of EF networks during the deep form of Hebrew in single word reading.