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Purpose: The language load within mathematics test items can lead to inaccuracy in assessing "Orang Asli" pupils' mathematical ability due to their struggle in comprehending the academic language. This study aims to determine the validity of using test accommodations in the form of oral academic language and oral native language when administering mathematics computation and word problem test items among the "Orang Asli" pupils. Methodology: By employing a quantitative approach, this study utilised random equivalent group design to examine the validity of using oral test accommodations among 334 Grade Four "Orang Asli" pupils. Three testlets were constructed and used in this study: written test in the academic language (WAL), oral test in the academic language (OAL), and oral test in the native language (ONL). The three testlets comprised Paper 1 and Paper 2, each consisting of 20 mathematics computation and 20-word problem test items, respectively. One-way ANOVA was used to analyse and examine if there were differences in the mathematics performance between the three groups of tests administered to the "Orang Asli" pupils. Findings: The results indicated that the test scores of the pupils on the oral tests were higher compared to the written test administered to the pupils for both mathematics computation and word problem items. For computation items, the pupils performed better in the OAL testlet, followed by the ONL testlet. On the other hand, the pupils obtained the highest test scores in the ONL testlet compared to the OAL testlet for word problem items. Furthermore, the results illustrate that there is a difference in the mathematics performance of the "Orang Asli" pupils between the oral tests in both languages and written tests in the academic language. Significant: This study clearly shows that language plays a prominent role in affecting the "Orang Asli" pupils' mathematics performance. By utilising the oral test as a promising test accommodation, this measure could help address the language barrier faced by the "Orang Asli" pupils in mathematics testing.

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 investigates the contribution of plugged and un-plugged activities to primary school students' development of computational thinking skills. The plugged and unplugged activities were used together in this study. In the implementation, in addition to the un-plugged activities prepared by the "Ministry of National Education," activities prepared by the researcher were also used. Plugged activities were also determined and implemented on the code.org website according to the age of the students and subjects. A quasi-experimental design was used with a single group to determine the changes before and after learning and to investigate the research questions. The measurements were performed with the Bebras tasks both before and after the implementation. Bebras consists of internationally valid tasks that measure computational thinking. The results showed that the combination of plugged and unplugged activities helped improve students' computational thinking skills. Our findings show that using a combination of unplugged and plugged activities is beneficial for primary school students. Further research is needed to evaluate these activities separately and their role in providing gains. Additionally, the effects of using different teaching methods in programming education can be examined. [Note: The issue number (2) shown in the citation on the PDF is incorrect. The correct issue number is 3.]

Information technology (IT) is transforming the world. Therefore, exposing students to computing at an early age is important. And, although computing is being introduced into schools, students from a low socio-economic status background still do not have such an opportunity. Furthermore, existing computing programs may need to be adjusted in accordance to the specific characteristics of these students in order to help them to achieve the learning goals. Aiming at bringing computing education to all middle and high-school students, we performed a systematic literature review, in order to analyze the content, pedagogy, technology, as well as the main findings of instructional units that teach computing in this context. First results show that these students are able to learn computing, including concepts ranging from algorithms and programming languages to artificial intelligence. Difficulties are mainly linked to the lack of infrastructure and the lack of pre-existing knowledge in using IT as well as creating computing artifacts. Solutions include centralized teaching in assistive centers as well as a stronger emphasis on unplugged strategies. However, there seems to be a lack of more research on teaching computing to students from a low socio-economic status background, unlocking their potential as well to foster their participation in an increasing IT market.

The study focuses on the identification of relationships and/or rules between computational thinking (CT) concepts among the undergraduate students of Applied Informatics due to their attitudes towards mathematics. We analyze three CT concepts -- decomposition, pattern recognition, and algorithmic thinking. We assume that students who have a closer relationship with mathematics, a positive attitude towards mathematics, have better developed CT. We conducted the experiment during the 2022/2023 academic year on the Information Coding and Displaying subject. The results indicate that those students who have no relationship to mathematics, negative attitude towards mathematics, have no problem with decomposition and pattern recognition, but without significant algorithmic thinking. On the other hand, students who have a close relationship with mathematics are also able to decompose or recognize patterns, but moreover they have shown algorithmic thinking. The contribution of the study comprises the identification of relationships and/or patterns of computational thinking concepts among students who have a relationship to mathematics, who cannot assess their relationship to mathematics, as well as among students who have no relationship to mathematics. Our results indicate a different occurrence of computational thinking concepts as well as links and/or relationships between them.

In this new era, computational thinking is becoming increasingly intriguing for indepth study. The 2022 PISA framework illustrates that computational thinking can play a significant role in solving real-world mathematical problems, both in formulating problems and in mathematical reasoning processes. Many countries have integrated computational thinking into their curricula, starting as early as elementary education. The 2022 PISA framework marks the first time that substantial attention has been given to the intersection between computational thinking and mathematical thinking. Consequently, research was conducted to analyze the validation process, practicality, and effectiveness of using integrated computational thinking and mathematical practical design thinking to enhance students' computational thinking skills. The research results demonstrate that the integrated computational thinking and mathematical thinking learning design are highly suitable for implementation. According to expert judgment, its validity rate is 95.5%, with a practicality score of 93.75%. The instructional design applied in this study has proven to be effective in enhancing the computational thinking skills of elementary school students. It's important to note that this research is limited to elementary school students, and further studies are needed to explore this topic at higher education levels.

This research aims to develop a combining game-based learning with design thinking using block-based programming to enhance computational thinking and creative games for primary students and will be referred to as game-based learning from now on. The purpose of this research is to 1) develop a model for game-based learning, 2) develop the system for game-based learning, 3) evaluate students' computational thinking after implementing game-based learning, and 4) evaluate the creative games created by students after game-based learning is implemented. The research tools included 1) the model for the game-based learning, 2) the model-appropriate evaluation form, 3) the learning system evaluation form 4) the computational thinking evaluation form, and 5) the creative game evaluation form. The research results showed that 1) the evaluation results of the model are appropriate for teaching at the highest level ([X-bar] = 4.82, SD = 0.42) and could be used for experimental teaching, 2) The results of game-based learning system quality are at the highest level ([X-bar] = 4. 57, S.D. = 0. 50). The researcher implements a game-based learning model and system to teach a sample group of 24 students in grade 4, using a purposive sampling method. The results of the implementation could be summarized as follows: 1) the results of students' computational thinking evaluation after implementing the model and system are significantly higher than before at the .05 level. 2) evaluation results of creative games that students developed after implementing the model and system are at a high level ([X-bar] = 4.29, S.D. = 0.52).

Facilitating Computer Science Unplugged (CS-unplugged) activities with reflective thinking activities lead students to in-depth reviews of their decisions and think of ways to solve the CS-unplugged problems. This study aims to evaluate the instruction enriched with reflective thinking activities to develop computational thinking skills. The study was carried out as a case study in a Computer Science course. The participants were 24, 5th-grade secondary school students. Students used two-column reflection notes and daily diaries to reflect their thoughts. Data were collected through Computational thinking skills evaluation form (CTEF), interviews, and observations to examine the role of reflective thinking activities during the instruction. Descriptive analysis, the thematic analysis was carried out through interviews was used to analyze data. The use of the two-column reflection notes and diaries positively contributed to the development of students' computational thinking skills. The students who engaged in reflective thinking activities outperformed in exhibiting their computational thinking skills than those who did not engage in. We hope that the findings of this study will assist in the future design and implementation of CS-unplugged activities to improve CT skills.

This research aims to develop ECARsites, an online site designed to support data-related activities in science learning and to facilitate the implementation of data, computational thinking (CT), and self-directed learning (SRL) practices in a more contextualized and relevant way for students. The approach used design-based research (DBR) methods, focusing on developing high-quality learning products or systems that meet user needs. The study was conducted at MA X in Magetan, Indonesia, involving four middle school science teachers and eighteen middle school students. The research findings showed that DBR can be an effective method of professional development for science teachers, focusing on developing relevant and contextual innovations for students. Integrating data practices, CT, and SRL frameworks into the DBR process facilitated the development of students' scientific thinking skills, leading to a more immersive and appropriate science education for today's digital age. The study highlights the importance of using real data in teaching, involving students in data processing, and supporting teachers in designing and implementing data-driven learning. The study also emphasizes the importance of effective professional development in computational thinking and self-directed learning and the need for investment in long-term professional development programs to prepare teachers for an increasingly technology-dependent world of education.

From personalized advertising to economic forecasting, artificial intelligence (AI) is becoming an increasingly important element of our daily lives. These advancements raise concerns regarding the transhumanist perspective and associated discussions in the context of technology-human interaction, as well as the influence of artificial intelligence (AI) on education and critical pedagogy. In this regard, the purpose of this research paper was to investigate the intersection of AI and critical pedagogy by critically assessing the potential of AI to promote or hamper critical pedagogical practices in the context of transhumanism. The article provides an overview of the concepts of transhumanism, artificial intelligence, and critical pedagogy. In order to seek answers to research questions, qualitative research design was adopted, and GPT-3 was used as a data collection resource. Noteworthy findings include the similarity of the dialogue with the GPT-3 davinci model to a conversation between two human beings, as well as its difficulty in understanding some of the questions presented from a critical pedagogy perspective. GPT-3 draws attention to the importance of the relationship between humans in education and emphasizes that AI applications can be an opportunity to ensure equality in education. The research provides suggestions indicating the relationship between AI applications and critical pedagogy.

Computational Thinking (CT) is considered a core 21st century digital skill. The aspect of assessment is crucial and knowing what, who, when, how, and where to assess is important for assessment design. In this study, we conducted an umbrella review to gain insights regarding CT assessment in higher education. In total, we analyzed 11 reviews, focusing on: (1) bibliographical and methodological characteristics of the reviews; (2) aspects relevant of assessment design, including a) assessed constructs, b) applied assessment methodologies, and c) assessment contexts. Our findings suggest an increased attention on this topic. However, hardly any reviews reasoned the selection of their review methodology, and most of the reviews did not thoroughly examine existing reviews. Regarding assessment design aspects, most reviews did not confine their scope to higher education; however, findings on interventions and educational settings show commonalities. We identified 120 unique assessed constructs and around 10 types of assessment methods. Though a combined use of distinct assessment methods is suggested in reviews, guidelines for appropriate assessment design are yet to be constructed. Based on the findings, we argue that it is necessary to explore different combinations of assessment design in various contexts to construct assessment guidelines.

Even though working with data is as important as coding for understanding and dealing with complex problems across multiple fields, it has received very little attention in the context of Computational Thinking. This paper discusses an approach for bridging the gap between Computational Thinking with Data Science by employing and studying classification as a higher-order thinking process that connects the two. To achieve that, we designed and developed an online constructionist gaming tool called SorBET which integrates coding and database design enabling students to interpret, organize, and analyze data through game play and game design. The paper presents and discusses the results of a pilot study that aimed to investigate the data practices secondary students develop through playing and modifying SorBET games, and to determine the impact of game modding on student critical engagement with CT. According to the results, students developed and used certain data practices such as data interpretation and data model design to become better players or to design an interesting classification game. Moreover, game modding process motivated students to question the original games' content, leading them to develop a critical stance towards the game data model and representations.

This study investigates the effect of programming courses on the computational thinking (CT) skills of elementary school students and the learning effectiveness of students from different backgrounds who are studying programming. We designed a OwlSpace programming course into an elementary school curriculum. Students in fourth and fifth grades were taught the fundamentals of programming. We measured and analyzed the effectiveness of their CT skills and self-efficacy in CT. The researchers analyzed the changes in the CT of different gender, different grade, and different past experience students in programming courses and then made specific recommendations for information technology teachers and related units. The results demonstrate that students learned and improved their CT skills by taking OwlSpace programming course. Additionally, gender, grade, and past experience are found to have no impact on the students' learning that means the course can improve students ability without limited any characteristics.

The incorporation of Computer Science teaching in educational systems has increased in recent years. Given international interest, Chile has promoted projects to promote the development of students' digital skills. Focusing on this new educational context, this research reports the results regarding the identification of computational concepts and practices that can be articulated with the contents and skills of the curriculum. of Chilean mathematics. for first grade of primary school based on the use of the Bee-Bot robot. For this, the study followed a qualitative approach, developing a case study of the Chilean study program with the content analysis technique and using, as analysis categories, computational concepts and practices from the field of educational computing. In total, 30 learning objectives of the study program were analyzed. The results indicate that, although there is little articulation between computational concepts and first grade content proposed in the curriculum, there is greater articulation between computational practices and mathematical skills suggested in the Chilean curriculum. It is concluded that Computational Thinking can be developed from the earliest school levels using the Bee-Bot robot (or similar), and this is demonstrated by the structure of the Chilean primary mathematics curricular program.

Some countries, including Indonesia, have a framework for understanding how students receive and process math concepts as new knowledge through learning styles. Learning style, particularly Kolb's model, is one of the learning styles that contribute to students' success in learning. Experts have explored the characteristics of Kolb's learning style and found many effects on student learning outcomes as a starting point in learning mathematical concepts. However, the research still focuses on exploring integer operation materials in specific math abilities. The researchers hardly found any discourse to study, such as exploring number patterns in computational thinking and semiotic mathematics. Therefore, this study aims to explore mathematical semiotics and computational thinking on number patterns in terms of Kolb's learning style model. This research uses hermeneutic phenomenology to explore through written tests and interviews. An explanation of the components, characteristics, and semiotic characteristics of mathematical and computational thinking seen in students who have the Kolb model of learning in solving problems of number patterns is part of the interpretation. These findings can be used as benchmarks in developing mathematics materials. Thus, this knowledge is a concrete foundation to guide future advances in curriculum, assessment methods, and learning approaches in mathematics education, particularly in algebra.

Current research indicates the presence of highly skilled and motivated students with robust computational thinking backgrounds seeking opportunities to leverage their expertise in driving innovation and success in this era. These studies also reveal that students' computational thinking skills vary widely depending on educational resources, curriculum emphasis, and individual aptitude. Nonetheless, there is a growing recognition of the importance of fostering these skills, with efforts underway to integrate them more comprehensively into education systems worldwide, including in Indonesia and Japan, as representatives of developing and developed countries. Therefore, assessing the competency of computational thinking in these two countries would be intriguing. The descriptive qualitative research method was employed to delineate the computational thinking competencies of students in Indonesia and Japan. Student worksheets, specifically designed for this purpose, were utilized to gauge the development of these competencies during the learning process using the Scratch application. The results revealed that students employed various strategies in solving the given geometry problems. On the other hand, geometry is one of the mathematics topics that can identify students' computational thinking using this application. These findings were utilized to categorize students' computational thinking skills in the two countries and to identify potential obstacles students experienced in their efforts to enhance these skills. Nevertheless, these constraints offer significant insights into potential areas for future investigation and enhancement. Subsequent endeavors could prioritize conducting experiments by implementing specific learning approaches or methods that have demonstrated effectiveness in improving students' computational thinking skills. This study not only underscores the potential for expanding research on students' computational thinking skills but also provides an overview of the learning process, learning culture, and students' competence in solving geometry problems with tiered difficulty levels using their computational thinking skills.

Solving systems of linear equations is a core concept in linear algebra and a wide variety of problems found in the sciences and engineering can be formulated as linear equations. This study sought to explore undergraduate students' development of the schema for solving systems of linear equations. The triad framework was used to describe the schema development in general and the system of linear equations was used as an example. A case study of an undergraduate class doing a linear algebra course in 2020 was considered in this study, where fifteen students participated in the study whereby they responded to a task with three question on solving a system of linear equations. The findings revealed that albeit minor manipulation errors, students were able to solve given systems of linear equations using the Cramer's rule and Gaussian elimination. However, students could not adequately go beyond the algorithmic computations to attain appropriate mathematical reasoning and establish underlying relations required to solve systems of linear equations, as no-one attained the trans-stage of conceptualization. It follows from this study that the identifications of the challenges that students encounter when solving systems of linear equations empowers course instructors on how to overcome the challenges.

This study investigated the effects of 3D model building activities with block codes on students' spatial thinking and computational thinking skills. The study group consists of 5th grade students in a secondary school in the Central Anatolia region of Turkey. For the study, a pretestposttest control group was utilized within the experimental design. A total of 66 students participated, 23 in the experimental group and 43 in the control group. While the activities prepared on the Tinkercad platform were applied in the experimental group, the courses were taught using the traditional teaching method in the control group. The study covers a period of three-weeks in the course information technologies and software. The study used the computational thinking levels scale and spatial thinking test scales as data collection instruments. The data was analyzed using both descriptive statistics and independent samples t-tests. Based on the study findings, there were no significant differences observed in the levels of computational thinking skills levels and spatial thinking test scores between the experimental and control groups.

Contemporary society is characterized by diversity and intricacy, necessitating more meaningful learning experiences. To meet these evolving needs, the incorporation of computational systems into education must acknowledge the distinctive characteristics of learners. Therefore, we conducted a Systematic Mapping Study (SMS) to investigate technologies that support the Learner eXperience (LX) design in computational systems. LX refers to learners' perceptions, reactions, and achievements while engaging with learning resources, encompassing digital games, simulations, and multimedia. The SMS results uncovered distinct LX design technologies, with a notice able inclination towards learner-centric strategies. Interestingly, the results highlighted a scarcity of research targeting non-traditional learning environments (e.g., technical visits) and that facilitate interactions among learners beyond their own classmates (e.g., industry experts). In this way, the SMS contributes by revealing LX design technologies, LX design elements, relevant constructs/theories, computational systems, environments, contexts, and other related factors, thereby enhancing the understanding of optimal learning experiences within computational learning systems.

Previous studies have found positive effects of Game-Based Learning for mathematics. While most studies assume that this effect is explained by the presence of flow/immersion during games, this has not yet been established. The aim of the current study is to verify if immersion indeed is associated with mathematical skills improvement when using a Game-Based Learning intervention. This was tested among 59 Greek high school students, using authentic design. After having received a traditional education module, the students were tested and then engaged for four weeks in a desktop-based 3D Virtual Learning Environment where they could play mathematic minigames. They were subsequently re-tested to verify if they showed a significant increase in mathematical skills. The students showed an improvement in their mathematical skills (Cohen's d = 1.26), with significant results for functions, geometry, and thinking skills and methods. On the individual level, about half of the students showed a 10% increase in one of the domains (numbers & calculations, functions, geometry, thinking skills and methods, and algorithms and number theory). Immersion was found to be reflected by engagement and presence, but neither one of these aspects was associated with mathematical achievement after the intervention. It is concluded that Game-Based Learning is an effective approach to increasing mathematical skills, yet the underlying mechanisms are not yet understood. The authors discuss several alternative mechanisms based on the literature that can be verified in future studies.

We present an interactive spreadsheet that supports teaching essential concepts in classification using the logistic regression (LoR) model for binary classification. The interactive spreadsheet demonstrates the capabilities of LoR by integrating computation with visualization. Students will reinforce concepts like probabilities, maximum likelihood estimation (MLE), and the use of likelihoods to optimize parameters for the LoR. We then discuss using LoR for classifications while adjusting its decision boundary (DB), demonstrating how to convert assigned likelihoods into classification using the DB; impact classification outcome by varying DBs; designate predictions as true positive, true negative, false positive, or false negative; and determine the classification accuracy. We use a variety of performance measures, including sensitivity, specificity, precision, negative predictive value, F[subscript 1] and F[subscript 2] scores, the receiver operating characteristics curve, and lift/decile charts. These measures are dynamically adjusted when the DB changes. We also reiterate the usage of these measures in the context of crossvalidation and imbalanced data sets. We provide a case study that implements LoR and an option for teaching the details behind MLE. We discuss the pedagogical aspects of this spreadsheet based on a survey of the 2022 student cohort in the Master of Management Analytics Program at the Rotman School of Management.

This study employs meta-analysis to synthesize findings from 30 articles investigating gender differences in computational thinking (CT) among K-12 students. Encompassing 51 independent effect sizes, the meta-analysis involves a participant pool of 9181 individuals from various countries, comprising 4254 males and 4927 females. Results indicate statistically significant gender differences in CT (Hedges's g = 0.091, p < 0.05), albeit with a modest effect size, revealing higher CT scores among males compared to females. Further moderation analyses unveil the multifaceted nature of these gender differences. Specifically, while gender differences become significant during high school, recent studies suggest a gradual reduction in CT gender differences with societal progress among K-12 students. Moreover, findings illustrate variations in gender differences across geographical regions. Notably, while the overall gender disparity in CT is non-significant in the "East Asia and Pacific" region, it widens in "North America" and "Europe", with males scoring higher than females. Conversely, in "Europe and Central Asia", such gender differences present inconsistent outcomes, with females scoring higher than males. Importantly, assessment tool type does not significantly influence gender differences. Lastly, this study offers recommendations to address CT gender gaps, providing valuable insights for promoting gender equality in education.

The interest in Computational Thinking (CT) development among young learners increases with the number of studies located in literature. In this study, a meta-analysis was conducted to address two main objectives: (a) the effectiveness of empirical interventions on the development of CT in children aged of 3-8 years; and (b) the variables that influence the effectiveness of the interventions. Following PRISMA procedures, we identified 17 empirical studies with 34 effect sizes and 1665 participants meeting the inclusion criteria from Web of Science database. Overall, we found a statistically significant large effect size (d = 0.83 [95% CI: 730, 0.890]; p < 0.001) on the CT development of 3-8 years old children, which provides empirical support for having young children to engage in CT experiences. The effect size was significantly influenced by moderating variables including gender, scaffolding, and education level. Intervention length showed a marginally significant effect. Therefore, educators could refer to the significant moderators when designing tailored interventions for CT development in early childhood education while a call for more empirical studies of CT development in young children is proposed.

Addressing cognitive disparities has become a paramount concern in computational thinking (CT) education. The intricate and nuanced relationships between CT and cognitive variations emphasize the needs to accommodate diverse cognitive profiles when fostering CT skills, recognizing that these cognitive functions can manifest as either strengths or limitations in different students. Consequently, understanding the connections between students' cognitive functions and CT skills assumes pivotal importance in the design of personalized instructional strategies for CT. Despite a general consideration of learning variability in CT education, empirical insights exploring the correlation between cognitive skills and CT competencies remain notably scarce. This study endeavors to bridge this research gap by investigating the links between executive functions and CT skills, as well as the associations between their sub-dimensions. The results reveal a statistically significant correlation coefficient of 0.452 between these two domains, underscoring the notable connection between executive functions and CT abilities. Furthermore, the sub-dimensional analysis offers a comprehensive understanding of how specific executive functions uniquely contribute to certain CT skills. In light of these findings, this research offers a promising pathway for the development of tailored CT education programs that can cater to the unique needs of each individual, ultimately facilitating inclusive CT programs and making significant contributions to broaden STEM education and future workforce.

The role of computation in science is ever-expanding and is enabling scientists to investigate complex phenomena in more powerful ways and tackle previously intractable problems. The growing role of computation has prompted calls to integrate computational thinking (CT) into science instruction in order to more authentically mirror contemporary science practice and to support inclusive engagement in science pathways. In this multimethods study, we present evidence for the Computational Thinking for Science (CT+S) instructional model designed to support broader participation in science, technology, engineering, and mathematics (STEM) pathways by (1) providing opportunities for students to learn CT within the regular school day, in core science classrooms; and (2) by reframing coding as a tool for developing solutions to compelling real-world problems. We present core pedagogical strategies employed in the CT+S instructional model and describe its implementation into two 10-lesson instructional units for middle-school science classrooms. In the first unit, students create computational models of a coral reef ecosystem. In the second unit, students write code to create, analyze, and interpret data visualizations using a large air quality dataset from the United States Environmental Protection Agency to understand, communicate, and evaluate solutions for air quality concerns. In our investigation of the model's implementation through these two units, we found that participating students demonstrated statistically significant advancements in CT, competency beliefs for computation in STEM, and value assigned to computation in STEM. We also examine evidence for how the CT+S model's core pedagogical strategies may be contributing to observed outcomes. We discuss the implications of these findings and propose a testable theory of action for the model that can serve future researchers, evaluators, educators, and instructional designers.

As evidence from evaluation and experimental studies continue to influence decision and policymaking, applied researchers and practitioners require tools to derive valid and credible inferences. Over the past several decades, research in causal inference has progressed with the development and application of propensity scores. Since their inception, propensity scores have made an important contribution to the improvement in estimation of causal impacts, particularly in the absence of randomization. When certain core assumptions hold, propensity score-based methods allow for bias-reduced estimation of average treatment effects. In addition to their important role in causal studies, propensity scores have also been integral in improving generalizations from causal studies, specifically when study samples are not randomly selected from a target population of inference. The current study provides an overview of propensity scores, a discussion of the assumptions needed to ensure their validity, and an illustration of the methods both for causal inference and generalization. We highlight the importance of propensity score methods and discuss current applications and directions for ongoing and future research.

Causal inference involves determining whether a treatment (e.g., an education program) causes a change in outcomes (e.g., academic achievement). It is well-known that causal effects are more challenging to estimate than associations. Over the past 50 years, the potential outcomes framework has become one of the most widely used approaches for defining, identifying, and estimating causal effects. In this paper, we review the potential outcomes framework with a focus on potential outcomes notation to define individual and average causal effects. We then show how three canonical assumptions, Unconfoundedness, Positivity, and Consistency, may be used to identify average causal effects. The identification results motivate methods for estimating causal effects in practice, which include model-based estimators, such as regression, inverse probability weighting, and doubly robust estimation, and procedures that target covariate balance, such as matching and stratification. Examples and discussion are grounded in the context of a running example of a study aimed at assessing the causal effect of receipt of special education services on 5th grade mathematics achievement in school-aged children. Practical considerations for education research are discussed.

The randomized control trial (RCT) is the primary experimental design in education research due to its strong internal validity for causal inference. However, in situations where RCTs are not feasible or ethical, quasi-experiments are alternatives to establish causal inference. This paper serves as an introduction to several quasi-experimental designs: regression discontinuity design, difference-in-differences analysis, interrupted time series design, instrumental variable analysis, and propensity score analysis with examples in education research.

Background: Recent research showed that cross-notation magnitude knowledge of fractions and decimals was related to better performance in fraction arithmetic, but it remains unclear whether it made an independent contribution to fraction arithmetic longitudinally when other cognitive variables are considered. Aims: To examine the extent to which children's earlier knowledge of cross-notation magnitude predicted subsequent performance in fraction addition and subtraction as well as fraction multiplication and division longitudinally. Sample: Three hundred and fifty-four Chinese children (Mage = 112.1 months). Methods: During the first wave of assessment, a range of cognitive abilities of children were measured, including within-notation fraction and decimal magnitude comparisons, whole-number arithmetic fluency, non-verbal intelligence, attentive behaviours, counting recall, word-level reading, and phonological awareness. Twelve months later, the same children were assessed again with two tasks of fraction arithmetic: fraction addition and subtraction as well as fraction multiplication and division. Results and Conclusions: Multiple linear regressions showed that within-notation fraction and decimal magnitude knowledge predicted fraction addition and subtraction longitudinally, after the effects of working memory, nonverbal intelligence, language skills, attentive behaviour, and whole-number arithmetic were controlled. Cross-notation magnitude knowledge made independent contributions to fraction addition and subtraction longitudinally beyond the influence of within-notation fraction and decimal magnitude knowledge and other covariates. However, within-notation fraction and decimal magnitude knowledge were not associated with fraction multiplication and division, whereas cross-notation magnitude knowledge remained a unique predictor. These findings suggest that it may be useful to incorporate cross-notation knowledge in the assessments of children's mathematics abilities and teaching.

Though a substantial amount of research exists on imputing missing scores in educational assessments, there is little research on cases where responses or scores to an item are missing for all test takers. In this paper, we tackled the problem of imputing missing scores for tests for which the responses to an item are missing for all test takers. We considered three missing-data imputation methods--the median method, the item response theory (IRT) method, and the two-way method--for imputing scores. We compared the performance of these three imputation methods with respect to their accuracy in estimating scaled scores and test reliability for the aforementioned problem. Real data were used in the comparison. All three methods performed well in imputing scaled scores with negligible imputation error: The IRT method and the median method provided slightly more accurate scaled scores. The two-way method provided the most accurate reliability estimates. Recommendations for practice are provided.

Many K-12 computer science (CS) education initiatives at the local, state, and federal levels have recently started to focus on engaging the young children. Although most CS education research has focused on the secondary level, only minimal research has explored how computer science (CS) and computational thinking (CT) can be taught in elementary and especially at kindergarten. Understanding how CS and CT are taught at the youngest levels is critical to support the creation of progressive quality curricula and professional development. Therefore, in this study, we delved into the CT/CS curriculum design considerations of five teachers for kindergarteners. Our findings revealed that the primary learning objectives encompassed: (1) Social skill acquisition, (2) Fostering a growth mindset, and (3) Developing basic CT competencies. Moreover, when crafting their CT/CS curriculum, teachers weighed factors such as (1) Their approach to adapting the CT/CS curriculum--whether it be a personalized approach or relying on a pre-packaged curriculum--and (2) Adhering to Developmentally Appropriate Practice (DAP), which took into account individual abilities, interests, and socially relevant contexts. We conclude by discussing the ramifications of these findings in terms of professional development and the shaping of future CT/CS curricula for young learners.

Bebras tasks are considered to develop Computational Thinking (CT) and are currently used for this purpose in many studies. However, the relationship between Bebras tasks and CT is recent and, given the scarcity of validated instruments for assessing CT that are not associated with a specific learning environment, the expected development of CT through Bebras tasks has not been sufficiently evaluated, especially in Primary School. For this reason, we designed the ABC-Thinking program for the development of CT in which a set of Bebras tasks were selected and categorised according to the CT skills they were related to, specific lesson plans were designed to integrate these tasks in a 12-week curriculum, and teachers were trained to apply the program. Using the competent Computational Thinking test (cCTt) in a pre-post-test experimental design, we looked to determine whether Primary School students developed specific CT skills after the program, and, therefore, whether Bebras could be considered suitable for the development of this competence. The results show a significant development in specific CT skills, interesting findings in the gender differences in this development, and between high and low performers. Finally, qualitative data indicate the suitability of the ABC-Thinking program with respect to teachers' practice.

Computational thinking (CT) has received growing interest as a research subject in the last decade, with research contributions attempting to capitalize on the benefits that CT may provide. This study included a systematic analysis aimed at revealing current trends in the CT subject, identifying educational interventions, and emerging assessment instruments. It also gave an overview of how teachers learned CT skills and how they integrated the CT curriculum into classroom practices. We searched the data in the Web of Science database and identified 360 articles. Most importantly, it emphasized the following points: (a) the most popular subject areas in CT literature; (b) CT intervention tools; (c) CT assessment practices used so far within educational courses; and (d) effective CT approaches to influence pre-service teachers. Results from this review revealed that CT's promotion in education had achieved significant progress in recent years. Along with the growth in the number of CT studies, the number of subjects, research questions, and teaching approaches also increased in recent years. It was also found that CT was mostly used in science, mathematics, programming, and computer science tasks, with little work in artificial intelligence (AI) and non-STEM areas. The essence of this paper implicated the researchers in designing the curriculum based on different subject domains. Furthermore, we recommended integrating augmented reality-based games using CT methodologies into the curriculum.

In the context of our rapidly digitizing society, computational thinking stands out as an essential attribute for cultivating aptitude and expertise. Through the prism of computational thinking, learners are more adeptly positioned to dissect and navigate real-world challenges, poising them effectively to meet the exigencies of future societal landscapes. The paradigm of project-based learning, heralded as a potent educational methodology, significantly amplifies students' prowess in computational thinking. To delineate the nuanced interplay between project-based learning and its augmentation of computational thinking, we engaged in meta-analytical investigations, synthesizing outcomes from 31 distinct external experimental and quasi-experimental studies. Our analytical journey underscored that project-based learning markedly elevates students' competencies across five critical spectra: innovation, collaboration, critical analysis, algorithmic cognition, and problem resolution. Dissecting its impact across the K12 continuum reveals that primary students predominantly hone their collaborative acumen; middle-grade learners witness a pronounced ascent in creativity, critical, and algorithmic faculties, while the apex of high school education primarily sharpens critical analytical prowess. Thus, educators sculpting the contours of project-based learning should be calibrate their strategies, ensuring they resonate with the developmental and age-specific nuances of their audience, to fully unleash its latent potential in nurturing computational thinking.

Perceptual learning theory suggests that perceptual grouping in mathematical expressions can direct students' attention toward specific parts of problems, thus impacting their mathematical reasoning. Using in-lab eye tracking and a sample of 85 undergraduates from a STEM-focused university, we investigated how higher-order operator position (HOO; i.e., multiplication/division operators and the presence of superfluous brackets impacted students' time to first fixation to the HOO, response time, and percent of correct responses). Students solved order-of-operations problems presented in six ways (3 HOO positions × presence of brackets). We found that HOO position and presence of superfluous brackets had separate and combined impacts on calculating arithmetic expressions. Superfluous brackets most influenced undergraduates' performance when higher-order operators were located in the center of mathematical expressions. Implications for learning and future directions are discussed about observing eye movements and gaining insights into students' processes when solving arithmetic expressions.

Educational robots have unrivaled advantages and value in developing students' computational thinking. Currently, there are fewer studies on the overall effects of educational robots on K12 students' computational thinking, especially at the instructional style and cross-grade level. In order to investigate the overall effect of educational robots on students' computational thinking, the study used a meta-analytic approach to analyze 27 experimental and quasi-experimental research papers from the last decade. It was found that: educational robots have a positive and significant effect on K12 students' computational thinking development (SMD = 0.558), and their application effects are influenced by moderating variables such as school level, teaching style, participation style, experimental period, and sample size. Specifically: educational robots have a positive and significant effect on K12 students, with the most significant effect at the early childhood level; there is a significant difference in the development of students' computational thinking when using educational robots in different teaching modes, with the best effect being achieved in the design-based teaching mode; (3) participating in educational robots in small groups is more likely to promote the development of computational thinking in K12 students; (4) Different experimental cycles have a positive effect on students' computational thinking, but a long experimental cycle does not mean a good effect, etc.; (5) There is a significant difference between different sample sizes of educational robots on K12 students' computational thinking, and arranging a medium-sized (50-100 students) educational robotics activity can achieve a better learning effect.

Completion of remedial mathematics has been identified as one of the keys to college success. However, completion rates in remedial mathematics have been low and are of much debate across America. This study leverages machine learning techniques in trying to predict and understand completion rates in remedial mathematics. The purpose of this study is to build machine learning models that can predict students that are least likely to complete remedial mathematics and identify which factors are most influential when computing those predictions. The study discovers random forest as the highest performing model. Furthermore, the study reveals that the remedial course a student begins with, credit completion rate, math placement score, and high school G.P.A as the most influential predictors of completion rates. The study also offers future research directions, especially in how to improve the performance of the machine learning models.

Model-eliciting activities (MEAs) challenge students to interpret a problem and collaboratively create solutions using the engineering design process. This Innovation to Practice article describes how science, technology, engineering, and mathematics (STEM) teacher educators can use MEAs to build elementary teachers' understanding of computational thinking (CT). Our Countdown Timer MEA is aligned with Grade 2 mathematics and computer science standards. We discuss how this MEA created opportunities for teachers in an online graduate STEM education course to engage as learners in unplugged and plugged CT activities. Teachers used algorithmic design and debugging to model a digital display and to create block-based code for a recess countdown timer. Teachers' reflections on how their learning experiences were different than those offered in step-by-step CT lessons allowed teachers to envision how MEAs can promote important CT dispositions of persistence in open-ended problem solving and tolerance for ambiguity. By aligning MEA design and facilitation with the phases of the engineering design process, teacher educators can use modeling to engage elementary teachers in facets of CT that are transferable to the elementary STEM classroom.

The last decade witnessed a spectacular rise in the volume of available textual data. With this new abundance came the question of how to analyze it. In the social sciences, scholars mostly resorted to two well-established approaches, human annotation on sampled data on the one hand (either performed by the researcher, or outsourced to microworkers), and quantitative methods on the other. Each approach has its own merits -- a potentially very fine-grained analysis for the former, a very scalable one for the latter -- but the combination of these two properties has not yielded highly accurate results so far. Leveraging recent advances in sequential transfer learning, we demonstrate via an experiment that an expert can train a precise, efficient automatic classifier in a very limited amount of time. We also show that, under certain conditions, expert-trained models produce better annotations than humans themselves. We demonstrate these points using a classic research question in the sociology of journalism, the rise of a "horse race" coverage of politics. We conclude that recent advances in transfer learning help us augment ourselves when analyzing unstructured data.

Creativity is highly valued in both education and the workforce, but assessing and developing creativity can be difficult without psychometrically robust and affordable tools. The open-ended nature of creativity assessments has made them difficult to score, expensive, often imprecise, and therefore impractical for school- or district-wide use. To address this challenge, we developed and validated the Measure of Original Thinking for Elementary School (MOTES) in five phases, including the development of the item pool and test instructions, expert validation, cognitive pilots, and validation of the automated scoring and latent test structure. MOTES consists of three game-like computerized activities (uses, examples, and sentences subscales), with eight items in each for a total of 24 items. Using large language modeling techniques, MOTES is scored for originality by our open-access artificial intelligence platform with a high level of agreement with independent subjective human ratings across all three subscales at the response level (rs = .79, .91, and .85 for uses, examples, and sentences, respectively). Confirmatory factor analyses showed a good fit with three factors corresponding to each game, subsumed under a higher-order originality factor. Internal consistency reliability was strong for both the subscales (H = 0.82, 0.85, and 0.88 for uses, examples, and sentences, respectively) and the higher-order originality factor (H = 0.89). MOTES scores showed moderate positive correlations with external creative performance indicators as well as academic achievement. The implications of these findings are discussed in relation to the challenges of assessing creativity in schools and research.

Contribution: This article presents a new look at teaching the Laplace transform for engineering students by emphasizing the obsolescence of the current method of finding the inverse Laplace transform when solving differential equations, and by recognizing the important role of a computer-assisted environment in helping the students understand the main idea behind the Laplace transform, instead of asking the students to repeat computational processes by hand. Background: The Laplace transform is a widely used integral transform that has important applications in many areas of engineering, and therefore, has a central place in the curricula for engineering education. However, according to several research articles, many students experience great difficulties understanding the Laplace transform. Research Question: Is the use of partial fractions and Laplace transform tables necessary for a proper conceptual understanding of the Laplace transform method? Methodology: Using the anthropological theory of the Didactic as an educational platform, the current teaching of the Laplace transform method, is analyzed. A parallel discussion of the teaching of logarithms at the upper secondary school level is drawn, where, previously, this also took place using the tables of logarithms, but now the reliance on calculators is overwhelming. The authors suggest a method of teaching the Laplace transform in a computer-assisted environment. Findings: In the light of the shift in computer hardware and software, the authors conclude by calling for innovation in and revision of engineering education through bridging the gap between procedures and understanding, by using computer software, where it is suitable.

Background: Artificial intelligence-generated content (AIGC) has stepped into the spotlight with the emergence of ChatGPT, making effective use of AIGC for education a hot topic. Objectives: This study seeks to explore the effectiveness of integrating AIGC into programming learning through debugging. First, the study presents three levels of AIGC integration based on varying levels of abstraction. Then, drawing on extended effective use theory, the study proposes the underlying mechanism of how AIGC integration impacts programming learning performance and computational thinking. Methods: Three debugging interfaces integrated with AIGC by ChatGPT were developed for this study according to three levels of AIGC integration design. The study conducts a between-subject experiment with one control group and three experimental groups. Analysis of covariance and a structural equation model are employed to examine the effects. Results and Conclusions: The results show that the second and third levels of abstraction in AIGC integration yield better learning performance and computational thinking, but the first level shows no difference compared to traditional debugging. The underlying mechanism suggests that the second and third levels of abstraction promote transparent interaction, which enhances representational fidelity and consequently impacts learning performance and computational thinking, as evidenced in test of the mechanism. Moreover, the study finds that learning fidelity weakens the effect of transparent interaction on representational fidelity. Our research offers valuable theoretical and practical insights.

Background: A resurgence in teaching coding in primary school classrooms has led to a pedagogical swing towards using physical computing and coding to develop students' use of algorithms, computational thinking, and problem-solving skills. Two obstacles impede the optimal development of these objectives: the availability of a suitable pedagogy and an instructional sequencing model for primary school teachers to effectively present coding and computational thinking concepts and skills to students in alignment with their developmental stage. Objective: This study aims to address both obstacles by introducing the 3C Model, a newly developed instructional sequence grounded in established pedagogies and designed to effectively teach coding and computational thinking skills to primary school students based on their developmental stage. Methods: The qualitative study employed two data sources to triangulate findings, using: (1) semi-structured interviews and thematic analysis to investigate 11 primary school students' perceptions of their learning experiences with the 3C Model, and (2) researcher observations along with reflections of the students' developed and demonstrated learning through the method of knowing-in-action, reflection-in-action, and reflection-on-action. Results and Conclusions: The findings of this study fill a gap in the existing literature by demonstrating that the pedagogical and sequential approach embedded in the 3C Model not only enhanced students' engagement levels but also resulted in improved curriculum learning outcomes. The 3C Model provides teachers with a coherent and age-appropriate instructional structure. It uses physical computing devices and digital coding platforms to introduce coding concepts, furthering the development of computational thinking skills in primary school students beyond mere procedural and rote learning. Implications: The study holds important implications for practical applications, as it addresses an absence in the literature of an established pedagogy and instructional sequencing model for effectively teaching coding and computational thinking concepts and skills to primary school students. Drawing on established pedagogical and developmental learning theories, the 3C Model provides primary school teachers with an engaging, age-appropriate instructional method that avoids decontextualised teaching and surface-based learning. Instead, it encourages collaborative student work and contextualised learning, steering away from isolated and generic approaches.

Background: Science teachers' understanding of the roles of computing practices in science frame how they enact scientific computational practices in their teaching and how their students perceive the relationship between computational practices and scientific endeavours. Objectives: This critical, integrative review synthesizes teacher learning literature about the role of computational literacy and computing practices in K-12 science teaching. Methods: We examined 54 peer-reviewed articles and analysed the ways the researchers and teacher participants describe the affordances of integrating computational thinking (CT) and other computational practices in science. We characterize how CT and computational practices are framed in relation to scientific learning goals. We identify six primary affordances for integrating computational practices with science that are conveyed to teachers and by teachers, as represented in these studies of teacher learning. Results and Conclusions: These six perspectives include (1) learning computer science principles, (2) developing CT dispositions, (3) engagement and inclusion in science, (4) taking ownership of science, (5) supporting learning science content, and (6) participating in computational practice as a form of scientific epistemic practice. Our analysis indicates that computational thinking and computational practices are often integrated in science in order to teach something about computing (e.g., Perspective 1), rather than to support learners' scientific work. Only the 29 articles coded for the sixth perspective--that is, in service of epistemic aims in science--demonstrate commitment to students' uses of computational ideas and practices as epistemic tools to participate in the sensemaking work of science. Takeaways: Comparison of Perspectives 5 and 6 illustrates the nuance between computational practices in science that reify something students have already "figured out," rather than those that serve epistemic goals. Perspective 6 encapsulates the deep synergy among (1) the reflexive nature of computing with scientific ideas and (2) computing as a central practice in science and engineering. We contend that a more focused message of computational practices in service of scientific sensemaking goals is necessary if we expect teachers to enact CT and related computational practices in their classrooms.

The Precipitating Change Project was a 5-year development, implementation, and research study of an innovative 4-week middle school curricular unit in computational weather forecasting that integrates students' learning and use of meteorology and computational thinking (CT) concepts and practices. The project produced a list of CT skills and definitions that students use to predict the weather, CT assessment instruments, and a CT classroom observation protocol. Data was collected from 306 eighth grade (ages 13-14) students in rural indigenous communities in the Arctic and urban and suburban Northeast communities in the USA. The project met its goal of producing an intentional instructional sequence that integrates disciplinary science and CT practices to increase students' science knowledge and their ability to use CT skills and processes. The results indicate that teachers were able to use the curriculum to embed CT practices into the classroom. Students, in turn, had the opportunity to practice using these skills in class discussion as evidenced by the classroom observation data, and students' science knowledge of CT content and practices significantly increased as evidenced by their performance on the weather content and CT skills pre- and post-assessments. While statistically significant gains in science knowledge and CT skills and practices were evident in all settings (urban, suburban, and rural indigenous communities), there were noticeable differences in gains in students' CT skills and practices between the three settings and additional research is needed in a diversity of settings to understand this difference.