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Within the field of K-2 computer science (CS) education, unplugged computational thinking (CT) activities have been suggested as beneficial for younger students and shown to impact young students' skills and motivation to learn about CS. This study sought to examine how children demonstrate CT competencies in unplugged sequencing tasks and how children use manipulatives to solve unplugged sequencing tasks. This case study approach examined two unplugged sequencing tasks for six children ranging from ages five to eight (pre-kindergarten to 2nd grade). Children showed evidence of several CT competencies during the sequencing tasks: (1) pattern recognition, (2) algorithms and procedures, (3) problem decomposition, and (4) debugging. The strategies and use of manipulatives to showcase CT competencies seemed to evolve in complexity based on age and developmental levels. Taking into account children's abilities to demonstrate CT competencies, this study suggests that sequencing is a developmentally appropriate entry point for young children to begin engaging in other CT competencies. In addition, these unplugged sequencing tasks can also be easily integrated into other activities commonly experienced in early childhood classrooms.

This study explores how pre-service mathematics teachers' spatial visualisation skills evolved during a Cognitive Load Theory (CLT) based education. The study used the qualitative theory-testing case study method, which guided the identification of participants, the design of technology-supported education, and the data collection and analysis process. The four participants meeting specific criteria were selected as the study sample. A CLT-based education equipped with technology was provided to help participants overcome difficulties in spatial visualisation problems, improve their existing schemas, and build higher-order schemas. Various teaching approaches (e.g., worked examples) were applied to optimise participants' learning in CLT-based education. The study data (e.g., transcripts of interviews) were analysed using the pattern-matching technique, in which the observed patterns were compared with the derived hypotheses from the theoretic models regarding the problem-solving process and novice-expert schemas. The study achieved remarkable results: In CLT-based education, where teaching approaches have an important role, the improvement in their spatial visualisation skills happened as the participants overcame their challenges in problem-solving steps throughout their cyclic problem-solving processes and gained more knowledge and skills. The participants' acquisition of expertise in spatial visualisation skills went through various developmental stages. They strengthened their initial spatial problem-solving schemas by completing the deficiencies in their prior knowledge. They gained practicality in same category tasks and constructed higher-order problem-solving schemas when dealing with high-category tasks by activating their assimilation and adaptation processes.

Educational data mining is widely deployed to extract valuable information and patterns from academic data. This research explores new features that can help predict the future performance of undergraduate students and identify at-risk students early on. It answers some crucial and intuitive questions that are not addressed by previous studies. Most of the existing research is conducted on data from 2-3 years in an absolute grading scheme. We examined the effects of historical academic data of 15 years on predictive modelling. Additionally, we explore the performance of undergraduate students in a relative grading scheme and examine the effects of grades in core courses and initial semesters on future performances. As a pilot study, we analyzed the academic performance of Computer Science university students. Many exciting discoveries were made; the duration and size of the historical data play a significant role in predicting future performance, mainly due to changes in curriculum, faculty, society, and evolving trends. Furthermore, predicting grades in advanced courses based on initial pre-requisite courses is challenging in a relative grading scheme, as students' performance depends not only on their efforts but also on their peers. In short, educational data mining can come to the rescue by uncovering valuable insights from academic data to predict future performances and identify the critical areas that need significant improvement.

Background: Investigating emotion sequence patterns in the posts of discussion forums in massive open online courses (MOOCs) holds a vital role in shaping online interactions and impacting learning achievement. While the majority of research focuses on the relationship between emotions and interactions in MOOC forum discussions, research on identifying the crucial difference in emotion sequence patterns among different interaction groups remains in its infancy. Objectives: This research utilizes deep learning and sequential pattern mining to investigate whether there are differences in emotion sequence patterns across different groups of learners who exhibit various types of interactions in online discussion forums. Methods: Data from a comprehensive array of sources, including log files, discussion texts and scores from 498 learners in online discussion forums, were collected for this study. The agglomerative hierarchical algorithm is used to classify learners into groups with different levels of interactions. Additionally, we implement and evaluate multiple deep learning models for detecting different emotions from online discussions. Relevant emotion sequence patterns were identified using sequence pattern analysis and the identified emotion sequence patterns were compared across different groups with different levels of interactions. Results and Conclusions: Using an agglomerative hierarchical algorithm, we classified learners into three distinct groups characterized by different levels of interactions: high, average and low level. Leveraging the bi-directional long short-term memory model for emotion detection yielded the highest predictive performance, with an impressive F-measure of 94.01%, a recall rate of 93.83% and an accuracy score of 95.01%. The results also revealed that learners in the low-level interaction group experienced more emotion transition from boredom to frustration than the other two groups. Therefore, the aggregation of students into groups and the utilization of their MOOC log data offer educators the capability to provide adaptive emotional feedback, customize assessments and offer more personalized attention as needed.

Collaboration has become an important way for organizations to leverage human resources to create shared organizational goals. Schools, as organizations, thrive on positive collegial partnerships among educators, with effective educator collaborations linked to improved school effectiveness and student outcomes. However, not all collaborations are successful. The current study draws heavily from Coleman's (1988) social capital framework to understand with whom educators choose to interact and how patterns of interaction facilitate educators' social capital development. Using an equal status, sequential mixed methods design, K-12 educators' interview and survey data were iteratively analyzed to explore how educators describe their school-based collaborations, how collaborations reveal distinct social network structures, and how school-based collaborations and network structures lead to educators' social capital. Findings illuminated four distinct profiles of collaboration that emerged as educators described their school-based collaboration, which were linked to different social network structures and qualities of social capital. These results can be used to inform professional development practices for school administrators and human resources teams through consideration of profiles that may inform responsive school-based collaborations. We discuss additional implications for educator hiring and retention as well as policy around PK-12 teacher standards.

Constructing a consensus problem space from extensive qualitative data for an ill-structured real-life problem and expressing the result to a broader audience is challenging. To effectively communicate a complex problem space, visualization of that problem space must elucidate inter-causal relationships among the problem variables. In this article, we demonstrate extraction of a problem space through text mining in R. Text mining, an artificial intelligence form of natural language processing, synthesizes and summarizes vast quantities of verbal data. Text mining provides visualization of large narrative datasets to illustrate the structure and connections within the problem space of an ill-structured problem. The Gates Open Research data set of 11,979 verbal autopsy responses (Flaxman et al., 2018) informs the ill-structured problem space of childhood death from infectious disease in developing nations. In this article we apply text mining to automate the process of identifying connections that efficiently illustrate this problem space.

Previous studies found an impact of language familiarity on face recognition in 9- and 12-month-olds. Own race faces are better recognized when associated with native language, whereas for other race faces, it is with non-native language. The aim of this study is to investigate if language familiarity can also influence abstract pattern recognition. We tested 9- to 12-month-old monolingual infants with a visual paired-comparison task. During a 30-s familiarization phase, infants were shown an image of abstract patterns associated with an auditory soundtrack of a speaker reciting a story either in their native (French) or in a non-native language (German). After the familiarization, the familiar and a new abstract pattern were displayed side by side for the recognition test. We found a significant preference for the novel object in the native language condition but not in the non-native condition. These results suggest that language familiarity effects on infant memory are not specific to faces but also influence, on a larger scale, how infants process their immediate visual environment.

This paper provides a longitudinal analysis of the understanding of repetition patterns by 24 Spanish children ages 3, 4 and 5, through representation and the type of justification. A mixed quantitative and qualitative study is conducted to establish bridges between algebraic thinking and computational thinking by teaching repetition patterns in technological contexts. The data are obtained using: (a) participant observations; (b) audio-visual and photographic records; and (c) written representations, in drawing format, from the students. The analysis involves, on the one hand, a statistical analysis of the representations of patterns, and on the other, an interpretive analysis to describe the type of justification that children use in technological contexts: "elaboration", "validation", "inference" and "prediction or decision-making". The results show that: (a) with respect to the representation of patterns, errors decreased by 27.3% in 3-to-5-year-olds, with understanding and correct representation of repetition patterns gaining prominence in more than 50% of the sample from the age of 4; (b) on the type of justification used, it is evident that in 3-and-4-year-olds, "elaboration" predominates, and at 5, progress is made towards "validation". We conclude that it is necessary to design learning sequences connected with theory and upheld through practice, and that foster the active role of the teacher as a promoter of teaching situations that help spur the beginning of computational and algebraic thinking.

The early detection of students' academic performance or final grades helps instructors prepare their online courses. In the Open University Learning Analytics Dataset, I found many online students clicked the course materials before the first day of class. This study aims to investigate how data mining models can use this student interaction data to predict their academic performance. In this study, this interaction information is called "week 0" data. The results suggest that "week 0" interaction data can be used to identify the academic success of online students and predict first assignment performance.

There is an increasing interest and growing practice in Citizen Science (CS) that goes along with the usage of websites for communication as well as for capturing and processing data and materials. From an educational perspective, it is expected that by integrating information about CS in a formal educational setting, it will inspire teachers to create learning activities. This is an interesting case for using bots to automate the process of data extraction from online CS platforms to better understand its use in educational contexts. Although this information is publicly available, it has to follow GDPR rules. This paper aims to explain (1) how CS communicates and is promoted on websites, (2) how web scraping methods and anonymization techniques have been designed, developed and applied to collect information from online sources and (3) how these data could be used for educational purposes. After the analysis of 72 websites, some of the results obtained show that only 24.8% includes detailed information about the CS project and 48.61% includes information about educational purposes or materials.

The project presented in this paper aims to formulate a recommendation framework that consolidates the higher education students' particulars such as their academic background, current study and student activity records, their attended higher education institution's expectations of graduate attributes and self-assessment of their own generic competencies. The gap between the higher education students' generic competence development and their current statuses such as their academic performance and their student activity involvement was incorporated into the framework to come up with a recommendation for the student activities that lead to their generic competence development. For the formulation of the recommendation framework, the data mining tool Orange with some programming in Python and machine learning models was applied on 14,556 students' activity and academic records in the case higher education institution to find out three major types of patterns between the students' participation of the student activities and (1) their academic performance change, (2) their programmes of studies, and (3) their English results in the public examination. These findings are also discussed in this paper.

As we move further into the digital age, the acquisition of digital literacy (DL) and computational thinking (CT) skills is emerging internationally as an essential goal for students in contemporary school curricula. As the world becomes more uncertain and volatile due to impacts of artificial intelligence (AI), international unrest, climate change, global economic instability and unforeseen catastrophes such as the Coronavirus (COVID-19) pandemic, it is necessary to review, revise and refine school education curricula and policies. The issue of what is essential for students to learn, and how they learn it, is of growing importance to international organisations such as the Organisation for Economic Co-operation and Development (OECD) and the United Nations Educational, Scientific and Cultural Organisation (UNESCO) and is emerging as a significant driver for educational reform across the globe. The growing prominence of CT and DL skills across many industry sectors has prompted recent changes to international assessment frameworks such as the Programme for International Student Assessment (PISA) and the Trends in International Mathematics and Science Study (TIMSS). This paper will briefly discuss specific examples of alternative approaches to addressing CT in national curricula for the compulsory years of schooling and explain how CT has been adopted as a driver for mathematics curriculum change in Australia.

This study investigated the influence of CThink4CS2 Module on computational thinking (CT) skills of form four chemistry students. The CThink4CS[superscript]2 Module integrated CT with the Engineering Design Process (EDP) in chemistry class. This study utilized quantitative research methods and quasi-experimental design. Quantitative data were collected using the Computational Thinking Skill Test (CTST) which consisted of algorithmic reasoning, abstraction, decomposition, and pattern recognition constructs. A total of 73 students were in the treatment group (n=39) and control group (n=34). Experimental data were described by means of descriptive analysis and inferential analysis employing two-way MANOVA analysis. The results of the analysis indicated significant differences in CT skills between groups; students in the treatment group demonstrated better results compared to those in the control group. The paper provides insight into the integration of CT and EDP as effective pedagogical strategies for inculcating CT skills.

Data is a powerful tool for the healthcare industry to use for managing, analyzing, and reporting on critical events in the field. The analysis of broad, salient data files aids healthcare businesses in uncovering hidden patterns, market trends, and customer preferences; these details may then be used to improve the quality and delivery of care to patients in an organization's community. In this case, students use simple data mining procedures to investigate issues a healthcare organization faces regarding regional and national population patterns, directions for facility and service expansion, and prospective staffing changes. The exercise highlights the use of data analysis as a planning tool for a mid-sized rural hospital with limited resources and may be used in an undergraduate or graduate level management information systems or healthcare information systems course to illustrate data analysis and visualization concepts, reporting, and data driven strategy development.

"Research Findings:" Pattern creation (PC) is creating a pattern unit based on children's own understanding of the concept of pattern, without being provided with a pattern unit as a reference, and the repetition of this pattern unit to create a complete structure. This study explored the performance and predictors of pattern creation (PC) among Chinese preschoolers at five to six years of age. Altogether 48 children (Mage = 5.44 years, SD = 0.38, 56% girls) from one public kindergarten in Shanghai participated in two experiments designed to test individual PC abilities and cognitive and mathematical skills. The results indicated that: (1) 5 to 6-year-old Chinese preschoolers could create basic patterns of different types (single linear patterns, multiple linear patterns, and circular patterns) and different levels (one-dimensional Pattern, multi-dimensional Pattern, and no Pattern) with various self-designed patterning strategies (e.g., unit extraction, one-to-one appearance matching, counting elements with different colors and shapes, and self-talking); (2) visual working memory and series relations in reasoning ability were significantly related to children's PC performance, among which the visual working memory served as a unique predictor of children's overall PC competence. "Practice or Policy:" These findings provided empirical evidence and a profound understanding of young children's PC competence and patterning strategies and highlighted the impact of visual working memory and series relations on PC in early learning.

The focus in this article is on the usefulness of spatial sequencing as a tactic for interpreting a map and organizing our memory of it. This skill is useful whenever some condition varies in a systematic way with distance. When students are asked whether they see a pattern on a map of a topic like wildfires or terrorist activity, some students can answer "yes" while using a different brain network and perceiving something very different from what the teacher thinks is "the" pattern. Unless a geographic pattern is quite simple and the map is well designed, human eyes may not perceive the pattern all at once. Map readers have to assemble a mental image of the pattern while their eyes jump around the page. This interaction of time and space can introduce distortions in perception. Part of becoming a good map reader, therefore, is learning how to guide your eyes to pick out patterns on maps.

The Building Painted Cubes Task is a groupworthy algebraic task. Students build cubes using linking unit cubes, search for algebraic patterns, and report findings on posters. This task can create spaces for students to see themselves as doers of mathematics.

Our nervous system has the remarkable ability to adapt our gait to accommodate changes in our body or surroundings. However, our adapted walking patterns often generalize only partially (or not at all) between different contexts. Here, we sought to understand how the nervous system generalizes adapted gait patterns from one context to another. Through a series of split-belt treadmill walking experiments, we evaluated different mechanistic hypotheses to explain the partial generalization of adapted gait patterns from split-belt treadmill to overground walking. In support of the credit assignment hypothesis, our experiments revealed the central finding that adaptation involves recalibration of two distinct forward models. Recalibration of the first model generalizes to overground walking, suggesting that the model represents the general movement dynamics of our body. On the other hand, recalibration of the second model does not generalize to overground walking, suggesting the model represents dynamics specific to treadmill walking. These findings reveal that there is a predefined portion of forward model recalibration that generalizes across context, leading to overall partial generalization of walking adaptation.

An "analogy" is a statement of relationship. A "spatial analogy" is an analogy based on a spatial relationship that is observed in a location. Spatial analogies can be based on any spatial relationship, including distance, direction, elevation, proximity, enclosure, or position in a pattern, region, or sequence - in short, every topic for spatial reasoning that has been mentioned in all five of the previous articles in this series. The symbols on the model or map have the same spatial relationships as the real-world features they represent. As a result, geography teachers could learn how to make better map-reading lessons by studying what psychologists and neuroscientists have discovered about the mental processes involved in thinking about analogies.

Patterns are a fundamental component of mathematics, and the patterning ability of young children has been well researched; however, this research has largely been conducted with relatively small cohorts (±70) and in an interventionist way (in laboratory settings or with researchers directly intervening in educational contexts). The current study examines the patterning skills of approximately 3 200 children in a naturalistic setting. As part of a larger, early years' STEM engagement program, children were provided patterning opportunities across one school term. The data presented here provides information regarding the children's performance on the tablet-based activities embedded in the program. Findings indicate that 3.5- to 4.5-year-old children were able to copy, extend, insert missing elements, fix, and create patterns, using a range of two, three or four elements pattern structures. These findings, from a large-scale naturalistic setting, confirm some aspects of the existing laboratory-based research, whilst also indicating that some children are more capable in terms of creating patterns than the existing research suggests. These findings have implications for educators when they are planning play-based patterning activities with preschool children.

This paper describes how the notion of the strongly didactic contract can serve to characterize the teaching adopted to implement a task in probability. It is particularly focused on the reality of mathematical work performed by students and teachers. For this research, classroom sessions were developed in an in-service teacher training course designed (and adapted) according to the Japanese Lesson Study model. Through the combined use of the Theory of Didactical Situations (TDS) and the Theory of Mathematical Working Spaces (ThMWS), a coding of the sessions observed was developed. Based on this coding, different patterns emerged which gave each session a specific rhythm and identity from which it was possible to recognize and characterize different strongly didactic contracts. The study highlights the difference between the potential contracts intended by the teachers and those observed in practice. The tools, and especially the coding, developed for the study could be used for future research on instructional situations or in-service teacher training.

Purpose: This paper defines and assesses student learning patterns under the influence of problem-based learning (PBL) and their classification into a reasonable minimum number of classes. Study utilizes PBL implemented in an undergraduate Statistics and Operations Research course for techno-management students at a private university in India. Design/methodology/approach: Study employs an in situ experiment using a conceptual model based on learning theory. The participant's end-of-semester GPA is Performance Indicator. Integrating PBL with classroom teaching is unique instructional approach to this study. An unsupervised and supervised data mining approach to analyse PBL impact establishes research conclusions. Findings: The administration of PBL results in improved learning patterns (above-average) for students with medium attendance. PBL, Gender, Math background, Board and discipline are contributing factors to students' performance in the decision tree. PBL benefits a student of any gender with lower attendance. Research limitations/implications: This study is limited to course students from one institute and does not consider external factors. Practical implications: Researchers can apply learning patterns obtained in this paper highlighting PBL impact to study effect of every innovative pedagogical study. Classification of students based on learning behaviours can help facilitators plan remedial actions. Originality/value: 1. Clustering is used to extract student learning patterns considering dynamics of student performances over time. Then decision tree is utilized to elicit a simple process of classifying students. 2. Data mining approach overcomes limitations of statistical techniques to provide knowledge impact in presence of demographic characteristics and student attendance.

This study analyzes the mathematical knowledge of 40 preservice Chilean Early Childhood and Primary Education teachers when designing mathematical tasks on patterns, in the context of teaching early algebra. Based on the domains of the Mathematical Knowledge for Teaching (MKT) model, we have adopted a descriptive qualitative methodological approach that relies on the content analysis technique. The results show that pre-service teachers exhibit little mathematical knowledge in the description of the mathematical tasks they pose, addressing partial aspects of the subdomains of specialized content knowledge and knowledge of content and teaching. We conclude that training experiences should be given that allow students to further their acquisition of mathematical knowledge in order to achieve effective learning of the contents that early algebra promotes, such as patterns, in Early Childhood and Primary Education classrooms.

Infants develop attachment to their caregivers very early on. The quality of attachment is considered to be crucial for the emotional development of humans and animals alike. Despite its importance, very little is known about how attachment develops between children with Autism Spectrum Disorder (ASD) and their caregivers. The purpose of the present study was to assess the attachment patterns of two young children with ASD with their parents and to identify the means for promoting parent, child, and parent-child relational characteristics that may contribute to the development of secure attachment. The results replicated prior findings pertaining to attachment quality of children with ASD and demonstrated the effectiveness of a naturalistic, behavior-analytic intervention in improving the quality of their attachment.

To test the suitability of an automatic system for emotional management in the classroom following the control-value theory of achievement emotions (CVT) framework, the performance of an emotional expression recognition software of our creation is evaluated in an online synchronous context. Sixty students from the Faculty of Education at the University of Alicante participated in 16 educational activities recording close-ups of their faces and completing the AEQ emotional self-report, as well as detailed reports from the subsequent review of their videos. In addition, they completed the VCQ-36 test to measure their volitional competencies and relate their influence on their emotional response. The results indicate a high coherence between the emotional expressions detected by the automatic system and the detailed emotional self-reports, but insufficient precision to meet the CVT requirements. On the other hand, both the AEQ test results and the emotion expression recognition software suggest students' preference for participative activities as opposed to passive ones. Meanwhile, statistical analysis results indicate that volitional competencies seem to influence the emotional response of students in the educational context, although the AI system does not show sufficient sensitivity in this field. Implications and limitations of this study for future work are discussed.

Pattern recognition is an important skill in computational thinking. In this study, an equation puzzle game was developed by combining pattern recognition with algebraic reasoning, and scaffolding was designed to support learners' learning. Sixty participants were enrolled in this study, divided into a control group and an experimental group to compare the results and differences in game achievement, flow, anxiety, and motivation of participants with and without algebraic reasoning scaffolding. The results of the study showed that the participants in both groups had positive flow and motivation during the game, did not feel over-anxious, and there was no significant difference in the game achievement of the two groups. In addition, the game with the scaffolding may have the potential to make a positive correlation between game achievement and psychological status. The results of this study indicated that the game did not cause too much anxiety to the participants. The scaffolding-based design achieves the intended effect on the participants' assistance and facilitates the participants' engagement in pattern recognition problem solving. And as learners became more focused and engaged, they could also perform better in the game. This game mechanism can be used as a reference for designing pattern recognition games.

Given the importance of modelling in mathematics classrooms, and despite the extensive body of research on teacher support for promoting the mathematical modelling cycle in the classroom, authors have overlooked how teacher support for argumentation can contribute to this cycle. This study is aimed at characterizing teacher support for argumentation in the mathematical modelling cycle in the classroom. We analyzed 10 class episodes taken from the cases of two teachers, Soledad and Ángeles. The episodes were analyzed considering teacher support for argumentative orchestration (communicative strategies and pattern recognition). In the two cases studied, we found that argumentative orchestration exhibited different types of overall presence and recurrence throughout the stages of the mathematical modelling cycle, with communicative strategies being more present across the board and more recurrent in the mathematical modelling cycle than pattern recognition strategies.

MOOCs can be considered as a powerful alternative in extraordinary situations where people cannot reach formal education. In recent years, the widespread use of the internet worldwide and especially the COVID-19 has increased the need of people for MOOCs. However, in order to increase the effectiveness of MOOCs, and to provide a better learning environment, the need to evaluate MOOCs has arisen. One of the indicators of quality in online learning is student satisfaction. Accordingly, this research aims to reveal learner satisfaction in MOOCs. The most important indicator for measuring this satisfaction in MOOCs is user comments. In this study, 39101 comments of the participants in 960 MOOCs were examined by using text mining techniques within the framework of satisfaction.

This study explored students' mathematics-related beliefs and the relationship between the beliefs and their strategies for solving non-routine mathematical problems. The study was guided by Daskalogianni and Simpson's 2001 belief systems categories and strategies for non-routine mathematical problems. The participants were 625 grade 11 students from five high schools in Tshwane North District, Gauteng province of South Africa. Data were collected using a mathematics beliefs questionnaire, a mathematics problem-solving test and interview. Quantitative and qualitative research techniques were used for data analysis. It was found that the students held all the three belief systems (utilitarian, systematic and exploratory) at different degrees of intensity and the belief systems and strategies for problem-solving had a weak positive linear relationship, and there were no statistically significant differences among mean scores of the students holding systematic, exploratory and utilitarian beliefs. They apply unsystematic guess, check and revise; systematic guess, check and revise; systematic listing; looking for patterns; consider a simple case; modelling; logical reasoning; no logical reasoning; trial-and-error and use a formula in solving non-routine mathematical problems. Furthermore, it was found that the systematic belief system could explain the students' behaviour in problem-solving more than the exploratory and utilitarian belief systems.

This case study explores how 12-13-year-old students encounter proportional reasoning while working with geometric patterning tasks using concrete materials. The focus is on the students' use of spontaneous concepts when first dealing with such patterns in the context of collaborative work. Based on video recordings of a single lesson, a microgenetic analysis was performed to identify students' learning trajectories, starting with students familiarizing themselves with pattern structure, followed by engagement in proportional reasoning, and ending with students perceiving a new technique to handle a situation where proportional reasoning did not suffice. While some student groups were able to move along the whole trajectory, most groups, when facing challenges, regressed to simpler techniques. The results provide new insights into students' learning trajectories, which can be used to support students' progress in the context of student-teacher interaction.

The Inclusive Assessment of Computational Thinking (CT) designed for accessibility and learner variability was studied in over 50 classes in US schools (grades 3-8). The validation studies of IACT sampled thousands of students to establish IACT's construct and concurrent validity as well as test-retest reliability. IACT items for each CT practice were correlated to examine construct validity. The CT pre-measures were correlated with post-measures to examine test-retest reliability. The CT post-measures were correlated with external measures to examine concurrent validity. IACT studies showed moderate evidence of test-retest reliability and concurrent validity and low to moderate evidence of construct validity for an aggregated measure of CT, but weaker validity and reliability evidence for individual CT practices. These findings were similar for students with and without IEPs or 504s. IACT is the first CT tool for grades 3-8 that has been validated in a large-scale study among students with and without IEPs or 504s. While improvements are needed for stronger validity, it is a promising start.

There are two broad views of children's theory of mind. The mentalist view is that it emerges in infancy and is possibly innate. The minimalist view is that it emerges more gradually in childhood and is heavily dependent on learning. According to minimalism, children initially understand behaviors rather than mental states, and they are assisted in doing so by recognizing repeating patterns in behavior. The regularities in behavior allow them to predict future behaviors, succeed on theory-of-mind tasks, acquire mental state words, and eventually, understand the mental states underlying behavior. The present study provided the first clear evidence for the plausibility of this view by fitting head cameras to 54 infants aged 6 to 25 months, and recording their view of the world in their daily lives. At 6 and 12 months, infants viewed an average of 146.5 repeated behaviors per hour, a rate consistent with approximately 560,000 repetitions in their first year, and with repetitions correlating with children's acquisition of mental state words, even after controlling for their general vocabulary and a range of variables indexing social interaction. We also recorded infants' view of people searching or searching for and retrieving objects. These were 92 times less common and did not correlate with mental state vocabulary. Overall, the findings indicate that repeated behaviors provide a rich source of information for children that would readily allow them to recognize patterns in behavior and help them acquire mental state words, providing the first clear evidence for this claim of minimalism.

This study evaluates the effectiveness of a dialogic reading picture book intervention on preschoolers' repeating patterning ability. Ninety-four children age 4 years 1 month to 6 years 8 months (M[subscript age] = 5 years 0 months) were randomly assigned to intervention (n = 46) or active control (n = 48) conditions. Well-trained university and college students read two researcher-designed picture books five times each over 3 weeks (i.e., 10 reading sessions) to small groups of three to five children. In the intervention condition, repeating patterns were present in the text and the illustrations, and the dialogic reading questions elicited patterning interactions. In the control condition, there were no references to patterns and the dialogic reading questions focused on content other than patterns or mathematics. A repeating patterning measure (including extending, generalizing, identifying the unit) and a numerical measure (including counting, number recognition, number order) were administered at pretest and at posttest. At posttest, differences in repeating patterning between both conditions were not significant when controlling for pretest performance and receptive vocabulary, despite a small effect size. When further exploring the differences on each of the patterning tasks separately, the intervention group outperformed the control group on extending but not on generalizing or unit identification. There were no significant transfer effects on the numerical measure. Overall, this study shows that reading picture books specially designed around patterning skills can stimulate young children's ability to extend repeating patterns. Picture books are a promising tool to support children's patterning ability, because they are easy to implement in several early learning environments.

Generalizing, conjecturing, representing, justifying, and refuting are integral parts of algebraic thinking and mathematical thinking in general (Lannin et al., 2011). The activity described in this article makes a case for generalizing as an overall mindset for any introductory algebra or geometry class by illustrating how generalization problems can be used to develop algebraic reasoning and meaningful symbol use (Kinach, 2014). The activity is presented in the context of technology, three-dimensional geometry, and spatial visualization--things that help students build on the innate pattern-recognizing capability of the brain that allows us to see the general in the particular (Mason et al., 2010; Kinach, 2014). Technology can also play a powerful role in developing students' reasoning and sense-making skills, while also helping them communicate better. This activity can be used with middle grades and high school students but has also been implemented in mathematics classes designed for preservice teachers.

Our most moving experiences, the ones that "stick," are hardly ever static but are dynamic, like a conversation, a gesture, or a dance. Previous work has shown robust memory for simple actions (e.g., jumping or turning), but it remains an open question how we remember more dynamic sequences of complex and expressive actions. Separately, with static images, previous work has found remarkable consistency in which images are remembered or forgotten across people--that is, an intrinsic "memorability"--but it is unclear whether semantically ambiguous and expressive actions might similarly be consistently remembered, despite the varying interpretations of what they could mean. How do we go from static memories to more memorable dynamic experiences? Using the test case of a rich and abstract series of actions from dance, we discover memorability as an intrinsic attribute of movement. Across genres, some movements were consistently remembered, regardless of the perceiver, and even regardless of the dancer. Among a comprehensive set of memory, movement, and aesthetic attributes, consistency in which movements people remembered was most predicted by subjective memorability, and importantly by both subjective (observer ratings) and objective (optical flow analysis) measures of the scale of motion, such that the less overall motion in a dance segment, the more memorable the movements tended to be. Importantly, we discover that memorability of a sequence is additive, where the memorability of individual snapshots and constituent moments ultimately contribute to the memorability of longer sequences.

There are two main approaches to how statistical patterns are extracted from sequences: The transitional probability approach proposes that statistical learning occurs through the computation of probabilities between items in a sequence. The chunking approach, including models such as PARSER and TRACX, proposes that units are extracted as chunks. Importantly, the chunking approach suggests that the extraction of full units weakens the processing of subunits while the transitional probability approach suggests that both units and subunits should strengthen. Previous findings using sequentially organized, auditory stimuli or spatially organized, visual stimuli support the chunking approach. However, one limitation of prior studies is that most assessed learning with the two-alternative forced-choice task. In contrast, this pre-registered experiment examined the two theoretical approaches in sequentially organized, visual stimuli using an online self-paced task--arguably providing a more sensitive index of learning as it occurs--and a secondary offline familiarity judgment task. During the self-paced task, abstract shapes were covertly organized into eight triplets (ABC) where one in every eight was altered (BCA) from the canonical structure in a way that disrupted the full unit while preserving a subunit (BC). Results from the offline familiarity judgment task revealed that the altered triplets were perceived as highly familiar, suggesting the learned representations were relatively flexible. More importantly, results from the online self-paced task demonstrated that processing for subunits, but not unit-initial stimuli, was impeded in the altered triplet. The pattern of results is in line with the chunking approach to statistical learning and, more specifically, the TRACX model.

Generalizing patterns is an important feature of algebraic reasoning that is accessible to students across grade-levels because it connects their numerical reasoning to algebraic reasoning. In this article, the authors describe how teachers can use the game Crack the Code to introduce generalizing to their students or can extend students' generalizations. Engaging students in this game gives teachers myriad ways to differentiate their instruction by asking questions that increase the level of difficulty with which students generalize, by providing manipulatives that will support and challenge students' thinking, and by selecting codes that will support new forms of algebraic reasoning to emerge. This article describes how the authors differentiated gameplay to meet the needs of all students while playing this game with sixth-grade students.

Since speech is a continuous stream with no systematic boundaries between words, how do pre-verbal infants manage to discover words? A proposed solution is that they might use the transitional probability between adjacent syllables, which drops at word boundaries. Here, we tested the limits of this mechanism by increasing the size of the word-unit to four syllables, and its automaticity by testing asleep neonates. Using markers of statistical learning in neonates' EEG, compared to adult behavioral performances in the same task, we confirmed that statistical learning is automatic enough to be efficient even in sleeping neonates. We also revealed that: (1) Successfully tracking transition probabilities (TP) in a sequence is not sufficient to segment it. (2) Prosodic cues, as subtle as subliminal pauses, enable to recover words segmenting capacities. (3) Adults' and neonates' capacities to segment streams seem remarkably similar despite the difference of maturation and expertise. Finally, we observed that learning increased the overall similarity of neural responses across infants during exposure to the stream, providing a novel neural marker to monitor learning. Thus, from birth, infants are equipped with adult-like tools, allowing them to extract small coherent word-like units from auditory streams, based on the combination of statistical analyses and auditory parsing cues.

The purpose of this study was to explicate one preservice middle grades mathematics teacher's Knowledge of Content and Students (KCS) in the context of multiple solution strategies. This study's purpose is to underline the importance of preservice teachers' KCS and provide possible investigative methods for evaluating preservice teachers' KCS. Specifically, the research inquiry guiding this study focused on how a middle school preservice mathematics teacher displays KCS when engaging with tasks about pattern recognition and linear functions in the context of multiple solution strategies. The data consisted of three videotaped semistructured interviews with the preservice mathematics teacher as well as the written work she produced during the interviews. This study explicated one preservice mathematic teacher's performance regarding two important themes of KCS: generating multiple possible solution strategies of middle school students and explaining multiple student solution strategies. In terms of generating multiple solution strategies of middle school students, the study found that the preservice mathematics teacher provided the same solution strategies that she employed when she solved the problems by herself. Regarding explaining multiple student solution strategies, this study revealed that the preservice teacher did not explicate how typical middle school students reason. The preservice teacher had limitations when explaining the possible procedures that students might have used to solve problems when given the final student solutions. With regard to the teacher's abilities to recognise and understand students' typical understandings and misunderstandings, the study demonstrated that the preservice teacher was capable of explaining some solution strategies but not all of them.

This paper studies how four primary-school in-service teachers develop the mathematical practices of conjecturing and proving. From the consideration of professional development as the legitimate peripheral participation in communities of practice, these teachers' mathematical practices have been characterised by using a theoretical framework (consisting of categories of activities) that describes and explains how a research mathematician develops these two mathematical practices. This research has adopted a qualitative methodology and, in particular, a case study methodological approach. Data was collected in a working session on professional development while the four participants discussed two questions that invoked the development of the mathematical practices of conjecturing and proving. The results of this study show the significant presence of informal activities when the four participants conjecture, while few informal activities have been observed when they strive to prove a result. In addition, the use of examples (an informal activity) differs in the two practices, since examples support the conjecturing process but constitute obstacles for the proving process. Finally, the findings are contrasted with other related studies and several suggestions are presented that may be derived from this work to enhance professional development.

Most studies on the subject have investigated relations between home math activities and child math skills, without paying much attention to the specific skills that such activities foster and their alignment with children's math assessments. The present study examined specific relations between subdomains of home math activities and children's corresponding math skills (e.g., home counting/cardinality activities related to children's counting/cardinality skills). Participants were 78 mostly middle-income, White parents and their four-year-old children (M age = 53.19 months; 45% girls). Parents completed a 24-item survey about the frequency of home activities supporting five subdomains of math: counting/cardinality, set comparison, number identification, adding/subtracting, and patterning. Children's skills in these same five subdomains were assessed using the Preschool Early Numeracy Scale (PENS) and the Early Patterning Assessment. Specific relations were observed in set comparison, adding/subtracting, and patterning, such that higher frequency of home activities in these subdomains related to advanced child math skills in the corresponding subdomains. No specific relations were found in counting/cardinality and number identification. Overall home math activities averaged across the five math subdomains positively related to children's overall math skills. Findings highlight the importance of engagement in specific math activities in the home environment and their significance for corresponding child math development.

We examined the effects of a morphological awareness (MA) training program on the enhancement of word and pseudo-word reading and phonological awareness in Arabic-speaking children with dyslexia. We compared two groups of children with dyslexia from Grade 3, an experimental group (n = 12; mean age = 112.4 months) with a control group (n = 13; mean age = 111.61 months). The training program focused on morphological analysis, derivational morphology and inflexional morphology. Results revealed that the experimental group outperformed controls on all post-training measures for MA, reading words and pseudo-words as well as phonological awareness. Also, the post-training measures were better achieved in the experimental group than pre-training ones, which confirm the efficacy of the morphological training program. We discuss these findings in light of the relationship between morphological awareness and word reading and phonological awareness, and the Arabic orthographic features as a morphological based language.

This study aims to develop a computer program for the identification key to insect orders (Arthropoda: Hexapoda) and to investigate its effectiveness as teaching material. Secondly, this study is aiming at whether this program improves students' computational thinking skills or not longitudinal quasi-experimental design. Firstly, the study is based on a longitudinal design involving repeated observations of the same variables over long periods and it is a repeated cross-sectional study also. Secondly, this study is based on a quasi-experimental design. According to this, there are two main groups selected for the study as the ones who took the course and the ones who didn't. There are two subgroups in those groups also the ones who identify the insects by dual diagnostic key and the ones who use the computer program developed by the researcher based on the dual diagnostic key. The population of the study consists of 621 students who took the courses related to entomology and 470 students who didn't take the course in the education period of 2010-2020 summing up 1091 students in total. The first indication of this study is that the computer program developed for the identification of insects is very effective for the students who took the course comparing the one using a classical method for the identification of insects. The second indication of this study is that the computer program developed for the identification of insects is very effective to improve the computational thinking skills of the students in terms of abstraction, automation, data analysis, decomposition, pattern generalization, pattern recognition skills. Finally, it can be concluded that computer program used for the identification of insects is very effective irrespective of whether the instruction is taken or not.