Parent–child interaction is the scaffold for future emotional and cognitive development and future well-being. As this interaction includes several domains, such as motor, speech, emotional expression, and more, characterizing the quality of parent–child interaction is often performed by a qualified person decoding it by observation. The development of computational tools allows relating to the interaction between the child and parent as synchronized data representing the correspondence between the two, focusing on brain-to-brain, voice/speech, eye contact, motor, and heart-rate signals. In this perspective, we will convey a new approach aiming to gather different sources of synchronization into one domain to reflect the quality of parent–child interaction.

The interaction between the parent and child is essential for the child’s cognitive and emotional development and sets the path for future well-being. These interactions, starting from birth, are necessary for providing the sensory stimulation the child needs in the critical time window of brain development. The characterization of parent–child interactions is traditionally performed by human decoding. This approach is considered the leading and most accurate way of characterizing the quality of these interactions. However, the development of computational tools and especially the concept of parent–child synchronization opened up an additional source of data characterizing these interactions in an objective, less human-labor manner. Such sources include brain-to-brain, voice/speech, eye contact, motor, and heart-rate synchronization. However, can a single source synchronization dataset accurately represent parent–child interaction? Will attending to the same stimulation, often resulting in a higher brain-to-brain synchronization, be considered an interactive condition? In this perspective, we will try to convey a new concept of the child–parent interaction synchronization (CHIPS) matrix, which includes the different sources of signals generated during an interaction. Such a model may assist in explaining the source of interaction alterations in the case of child/parent developmental/emotional or sensory deficits and may open up new ways of assessing interventions and changes in parent–child interactions along development. We will discuss this interaction during one of the parent–child joint activities providing opportunities for interaction, i.e., storytelling.

We present the analysis of an episode of mathematical problem solving in a group, where data came from multiple advanced recorders, including multiple video cameras, Smartpen recorders, and mobile eye tracking glasses. Analysis focused on a particular group that was ineffective in their problem-solving process. Relying on the commognitive theory of learning on the one hand, and on quantitative descriptors of eye-tracking data on the other hand, we ask how do the interpretations of the discourse analysis and gaze data complement each other in understanding the obstacles to problem-solving in this episode. The setting included four Finnish 9th grade students solving a geometrical problem in the students’ authentic mathematics classroom. The commognitive analysis revealed intensive social communication (subjectifying) along with the mathematical one (mathematizing), which seemed to interfere with the problem-solving process. Specifically, it masked the differences in students’ interpretation of the tasks, and did not allow explication of meta-rules according to which students endorsed mathematical claims. Diagrams of quantified gaze data enabled a more macro-level picture of the full 15 min interaction, revealing differential loci of attention of the group members and thus triangulating the micro-analysis.

Science, technology, engineering, and mathematics (STEM) education aims to develop creative, broadminded, and inquisitive graduates who think critically. Critical thinking (CT) is conceptualized as purposeful, self-regulatory judgment involving thinking skills such as analysis, evaluation, and inference. This research, designed as an exploratory study, presents a theoretical digital curation (DC) model enabling a comprehensive pedagogical framework for developing and utilizing CT in a digital environment in higher education.

The database was composed of digital collections created by undergraduate and graduate students (n = 94) from three Israeli universities. The research questions were: (1) whether and in what ways the engagement of students in the DC activity required CT, (2) whether a significant difference in the level of CT would be found between undergraduate and graduate students in the DC activity, (3) whether a significant difference in the level of CT would be found between students provided with instruction on DC before the DC activity and a control group that received no instruction. Factor analysis of the students’ strategies produced 3 factors corresponding to evaluation, analysis, and inference that accounted for 75.34% of the total explained variance. These results suggest that as a pedagogical approach, DC can tap the key features of CT. Further statistical analyses showed that the graduate students had higher scores on evaluation than the undergraduates and that academic level and previous instruction in DC improved the quality of the DC. The factor that most influenced the quality of the digital curation was the analysis variable. Thus, DC can create a rich, active environment that promotes CT.

The Covid-19 pandemic escalated demand for scientific explanations and guidance, creating opportunities for scientists to become publicly visible. In this study, we compared characteristics of visible scientists during the first year of the Covid-19 pandemic (January to December 2020) across 16 countries. We find that the scientists who became visible largely matched socio-cultural criteria that have characterised visible scientists in the past (e.g., age, gender, credibility, public image, involvement in controversies). However, there were limited tendencies that scientists commented outside their areas of expertise. We conclude that the unusual circumstances created by Covid-19 did not change the phenomenon of visible scientists in significant ways.

In a typical citizen science scenario different groups of people take on various roles in a research process that is often coupled with educational, social or personal objectives. A widely accepted viewpoint asserts that such an endeavor should bring benefits to all involved parties and that no participating individuals should act in service of others or of the end goal. However, the large variety of implementation models, of participating individuals, and of desired impacts, leaves room for inconsistencies regarding what outcomes count towards mutual benefits. In this article we examine the ambiguity embedded in the definition of mutual benefits in citizen science and take a stand towards its resolution. We use school-based citizen science as a model for a multi-stakeholder, multi-objective citizen science. Focusing on teachers and scientists that work together to facilitate student participation in citizen science, nine teacher-scientist pairs that collaborated on nine different school-based projects were included as study participants. We examined participants’ motivations for school-based citizen science and perceived costs and benefits using a questionnaire that they filled while verbally explaining their answers. Our findings reveal multiple ways in which teachers and scientists tapped into their professional, social and personal identities to create multilayered sets of motivations and perceptions of benefits. Thus, we argue that a mutualistic perspective of citizen science should take this complexity into account and be prepared to answer multi-faceted expectations, which may reside not just among but also within participating individuals.

This study demonstrates a positive link between participation in an online project-based learning course, designed as a pedagogy of practice, and preservice teachers' professional-identity development process. The linguistic and content analyses revealed that the construction of professional identity was manifested through six motifs: Self-confidence, self-agency, sense of belonging, self-awareness, autonomy, and envisioning a future teaching self. Each of the motifs was portrayed with its characteristic set of time-oriented types of discourse (i.e., using past, present, and future tenses while describing feelings and thoughts regarding one's teaching self.) Based on the findings, we introduce a multidimensional model that explains identity construction as occurring at the intersection between the dimensions of identity and the temporal dimensions found in reflective discourse.

Executive Functions (EF) is an umbrella term for a set of mental processes geared towards goal-directed behavior supporting academic skills such as reading abilities. One of the brain’s functional networks implicated in EF is the Default Mode Network (DMN). The current study uses measures of inhibitory control, a main sub-function of EF, to create cognitive and neurobiological "inhibitory control profiles" and relate them to reading abilities in a large sample (N = 5055) of adolescents aged 9–10 from the Adolescent Brain Cognitive Development (ABCD) study. Using a Latent Profile Analysis (LPA) approach, data related to inhibitory control was divided into four inhibition classes. For each class, functional connectivity within the DMN was calculated from resting-state data, using a non-parametric algorithm for detecting group similarities. These inhibitory control profiles were then related to reading abilities. The four inhibitory control groups showed significantly different reading abilities, with neurobiologically different DMN segregation profiles for each class versus controls. The current study demonstrates that a community sample of children is not entirely homogeneous and is composed of different subgroups that can be differentiated both behaviorally/cognitively and neurobiologically, by focusing on inhibitory control and the DMN. Educational implications relating these results to reading abilities are noted.

This study explored a professional development (PD) program preparing mathematics coordinators to lead effective PD in their schools. Due to the COVID-19 pandemic emerging in the midst of this study, the PD program and school team PD were performed online instead of face-to-face. We explored the PD program and school team PD design before and during COVID-19 period, and the coordinators' expertise-based PD leadership professional identity (LPI). Findings revealed structural stability of the PD program design, and support of coordinators’ LPI. Coordinators enacted a structural transition of effective PD design into the school team PD, demonstrating their LPI in practice.

One type of thinking needed by engineers is abstract thinking, i.e., higher-order thinking that permits one to solve problems while maneuvering between several levels of complexity (levels of abstraction). In light of the above, the Faculty of Electrical and Computer Engineering (Technion – Israel Institute of Technology) decided to combine two introductory courses, focusing on different levels of abstraction, into a single undergraduate course ‘‘Digital Systems and Computer Structure’’ integrating multiple levels of abstraction, i.e., logical, micro-architecture and architecture levels. The study presented here characterized the attitudes of students and course faculty toward learning and instruction that combine several levels of abstraction. The research, which used quantitative and qualitative tools, involved 103 students and eight teaching staff members. According to the findings, students hold positive attitudes toward learning that incorporates multiple levels of abstraction, both cognitively and affectively, and the correlation between the components is positive, moderate, and significant. Students argue that this type of learning is interesting, provides a complete picture of computer systems, promotes higher-order thinking, and is relevant to industry work, but is also characterized by a high cognitive load. Course faculty claim that teaching that incorporates multiple levels of abstraction is enjoyable, imparts higher-order thinking among students but is very demanding. As for the behavioral aspect, the vast majority of students and instructors prefer learning and teaching that integrate multiple levels of abstraction over those that focus on a few levels.