Education Research Design Project: Quantitative and Program Evaluation Analysis Matrix Assignment

Article Type

QUANtitative Basic (QUAN-B)

Article Reference

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Bayık, R. N., & Ceylan, M. (2026). The relationship between pattern skills and computational thinking skills in young children aged 5–6. International Journal of Science and Mathematics Education, 24, Article 33. https://doi.org/10.1007/s10763-026-10662-2

Sampling Procedure

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The authors did not specify the sampling process; however, it combined purposive, criterion-based selection of schools with random selection of eligible children. The researchers asked the Ministry of National Education (MoNE) to include schools with 5-6-year-old children and diverse socioeconomic backgrounds, and MoNE agreed and selected ten schools as eligible to participate in the study (Bayık & Ceylan, 2026, p. 5). The authors then randomly selected children from those schools whose parents agreed to the study, who did not have special education needs, and who had Turkish as their first language (Bayık & Ceylan, 2026, p. 5). The authors said the children participated in five schools (Bayık & Ceylan, 2026, p. 5). Later, the authors recognized that the sample size was too small and geographically restricted, and a larger, randomly selected sample from other areas would provide more generalizable information (Bayık & Ceylan, 2026, p. 10).

Participants (who, how many, demographics)

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The final sample comprised 183 preschool children aged 61 to 72 months (M = 66.66, SD = 3.63) from five schools in a metropolitan city in south-east Türkiye (Bayık & Ceylan, 2026, p. 5). Of these children, 78 (43%) were boys, and 105 (57%) were girls (Bayık & Ceylan, 2026, p. 6, Table 1). Mothers' education was primary school for 26 children (14%), high school for 77 (42%), and university for 80 (44%), while fathers' education was primary school for 31 (17%), high school for 52 (28%), and university for 100 (55%) (Bayık & Ceylan, 2026, p. 6, Table 1). Only 8% of the children (15 children) had prior coding education, while 60% (111 children) had attended preschool before the study (Bayık & Ceylan, 2026, p. 6, Table 1). The regression analyses used some demographic characteristics as control variables, as mentioned in Bayık & Ceylan (2026, p. 5).

Intervention Description (if applicable; if not, write NA)

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NA. This study adopted a correlational, cross-sectional design because it did not manipulate variables or apply an intervention (Bayık & Ceylan, 2026, p. 5).

Measurements or Instruments Used

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The authors used two assessments of children, along with demographic factors. The Computational Thinking Test (CTT), developed by Relkin et al. (2020), was administered to young children following Bers's seven powerful ideas. The researchers’ Turkish-adapted the test, validated it with 288 children, and reported a Cronbach's alpha of .68 (Bayık & Ceylan, 2026, p. 5–6). The paper version is unplugged, contains 15 questions, and in each question, the child selects the appropriate picture from three pictures (Bayık & Ceylan, 2026, p. 6). This test, the Preschool Pattern Skills Test (PPST), was developed by Güven et al. (2019) to measure the pattern skills of children aged 61–76 months. The short form of this test, which has 17 items and has a reported reliability of .84, was used to reduce the burden of the test (Bayık & Ceylan, 2026, p. 6). The authors also noted participants' personal information, such as age, gender, parental education, and previous preschool and coding education, as control variables (Bayık & Ceylan, 2026, pp. 5–6).

Data Collection (who, how, when)

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All data were obtained during one-on-one assessment sessions with each child, in a quiet room at his/her school (Bayık & Ceylan, 2026, p. 7). The CTT took about 8-10 minutes, and the PPST about 20 minutes; they were administered one after the other on the same day, with breaks when children got tired (Bayık & Ceylan, 2026, p. 7). The scores were assigned as 1 for a correct answer and 0 for an incorrect one, and the total scores reflected each child's computational thinking and pattern skills (Bayık & Ceylan, 2026, p. 7). The data collection method was cross-sectional, as data were collected at one point in time (Bayık & Ceylan, 2026, p. 5). The authors obtained informed consent from parents, and children also gave informed consent before participating (Bayık & Ceylan, 2026, p. 7). The article does not mention the dates of data collection.

Data Analysis (which statistical analyses were used to determine what?)

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The researchers checked for missing values and outliers and found none, then tested the assumptions of hierarchical regression (Bayık & Ceylan, 2026, p. 7). Skewness and kurtosis ranged between −1 and +1, scatterplots suggested linearity, and variance inflation factors ranged from 1.00 to 1.47, indicating no serious multicollinearity issues. A Durbin-Watson value of 2.06 showed that the residuals were independent (Bayık & Ceylan, 2026, p. 7). Descriptive statistics were reported for both tests (CTT M = 8.20, SD = 2.51; PPST M = 12.7, SD = 2.60) (Bayık & Ceylan, 2026, p. 7, Table 2). The authors conducted a two-step hierarchical regression with CTT scores as the dependent variable to test whether pattern skills predicted CTT after controlling for background characteristics. PPST scores were significant predictors of CTT scores, accounting for 22% of the variance (β = .472, p < .001) in Step 1. For Step 2, the control variables were included, which resulted in a small but non-significant contribution to the variance (ΔR² = .01); none of the control variables were significant predictors, and PPST scores continued to be highly predictive (β = .463, p < .001) (Bayık & Ceylan, 2026, p. 8).

Article Type

QUANtitative Applied (QUAN-A)

Article Reference

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Lindström-Sandahl, H., Samuelsson, J., Danielsson, H., Samuelsson, S., & Elwér, Å. (2024). A randomized controlled study of a second grade numeracy intervention with Swedish students at-risk of mathematics difficulties. British Journal of Educational Psychology, 94(4), 1052–1071. https://doi.org/10.1111/bjep.12705

Sampling Procedure

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The authors did not identify the sampling procedure, although they used a screening-based (criterion) selection of at-risk students and then randomly assigned individuals to groups. The researchers selected two groups of first-graders from 21 public schools in six rural and urban municipalities in central Sweden, with 753 students (around 60% of all the first-grade students) consenting to participate via written informed consent (Lindström-Sandahl et al., 2024, p. 1056). The number of students who were identified as at risk for mathematics difficulties was 9.7% of the students who consented to be screened (Lindström-Sandahl et al., 2024, p. 1056) in the first grade (February and May screening). The students were randomly selected from a larger sample of 73, where 69 students were individually assigned to either the intervention or the control group, and four students were excluded due to teacher-rated behavioral difficulties and/or short attention span or fewer than two years of schooling in Swedish (Lindström-Sandahl et al., 2024, p. 1056). The authors set a recruitment target of approximately 30 students per group, assuming they could detect a medium effect size with at least 80% power (Lindström-Sandahl et al., 2024, p. 1056).

Participants (who, how many, demographics)

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The sample included 7–8-year-old children (32 in the intervention group and 30 in the control group) at the beginning of second grade (Lindström-Sandahl et al., 2024, p. 1056). They included 21 public schools in 6 central Swedish rural and urban municipalities, with 1 to 7 students per school participating (Lindström-Sandahl et al., 2024, p. 1056). Mean age was 97.62 months (SD = 3.25) in the intervention group and 97.57 months (SD = 3.43) in the control group (Lindström-Sandahl et al., 2024, p. 1057, Table 1). School records showed no statistically significant difference in the number of females in the intervention and control groups (58% vs 37%) (Lindström-Sandahl et al., 2024, p. 1056). The groups also did not differ significantly on WISC Block Design or Vocabulary scaled scores (Lindström-Sandahl et al., 2024, p. 1057, Table 1). Attrition was low: five students withdrew after randomization (one from the intervention group and four from the control group). One student's pretest data were lost (Lindström-Sandahl et al., 2024, p. 1056), and the CONSORT diagram indicates that 32 students in the intervention group and 30 in the control group participated in the final analysis (Lindström-Sandahl et al., 2024, p. 1057, Figure 1).

Intervention Description (if applicable; if not, write NA)

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The intervention consisted of a supplemental, one-to-one numeracy and arithmetic program comprising 36 scripted lessons for second-grade students at risk for mathematics difficulties (Lindström-Sandahl et al., 2024, p. 1058). The program ran for 10 weeks (September–November), with 35 minutes per lesson, four days each week, and was offered to compensate for lessons missed during the 12-week absence (Lindström-Sandahl et al., 2024, p. 1058). The warm-up of the oral counting took about 5 minutes, the conceptual and procedural instruction and practice took about 20 minutes, and about 10 minutes were spent on the story problems in each lesson (Lindström-Sandahl et al., 2024, p. 1058). Content included counting forward and backward to 220; identifying and sequencing numbers; number lines; place value and base-10 understanding; cardinality; subtilizing; and number relations, as well as number combinations within 20, with lots of practice in doubles and doubles +1 (Lindström-Sandahl et al., 2024, p. 1058). The instruction was scaffolded and inspired by direct instruction, including scripted lessons, clear and explicit teacher modelling, and sequential skill building, as summarized in Table 2 (Lindström-Sandahl et al., 2024, pp. 1058–1059). Lessons were delivered by 13 qualified special education teachers, three experienced elementary teachers, and a member of the research team at the students' schools following two 2-hour trainings on program content, delivery, and test administration (Lindström-Sandahl et al., 2024, p. 1058). The control groups received teaching as usual, which may include additional special education intervention provided by each school (Lindström-Sandahl et al., 2024, p. 1058). The research team met with the interventionists four times a day to track attendance and deviations, and teachers filled out a short questionnaire at the end of the intervention (Lindström-Sandahl et al., 2024, p. 1059).

Measurements or Instruments Used

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The study used screening tests and pre- and post-test measures. To assess procedural knowledge, the Arithmetic Fluency subtest of the Woodcock-Johnson III was used, with a test–retest reliability of .92 (3 minutes); for conceptual knowledge, the Number Series subtest was used, with a reliability of .90 (5 minutes) (Lindström-Sandahl et al., 2024, p. 1056). A number-line estimation task was also included but was not used as a selection measure because it had low sensitivity and floor effects (Lindström-Sandahl et al., 2024, p. 1056). The outcome measures included a 22-item conceptual knowledge test (McIntosh, 2008) on number relations, place value, and number line estimation, which was read aloud by the teachers and took 6 minutes (stopped after achieving 6 errors or 15 minutes); a 26-item test on arithmetic calculation (McIntosh, 2008) on single- and multi-digit addition and subtraction, read aloud by the teachers and took 6 minutes (stopped after achieving 6 errors or 15 minutes). A mathematical problem-solving subtest of the Swedish adapted WRA (Lindström-Sandahl et al., 2024), in which items were read aloud by the teachers and the test was stopped after 6 errors or 15 minutes (15 items). 1056–1058). The WISC Block Design and Vocabulary subtests were given before the intervention to measure general cognitive ability (Lindström-Sandahl et al., 2024, p. 1058).

Data Collection (who, how, when)

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Regular or special education teachers administered the screening tests to whole classes twice in first grade (February and May), as described in Lindström-Sandahl et al. (2024, p. 1056). The extended individual tests were conducted by the special education teachers and the first author (Lindström-Sandahl et al., 2024, p. 1056) before and after the intervention in September and November–December of second grade, respectively, and following random allocation. The intervention took place between September and November (Lindström-Sandahl et al., 2024, p. 1058). The study was a randomized controlled pretest–post-test design (Lindström-Sandahl et al., 2024, p. 1055). The authors also gathered fidelity information through a daily intervention protocol, meetings with interventionists, and a teacher questionnaire after each daily intervention (Lindström-Sandahl et al., 2024, p. 1059).

Data Analysis (which statistical analyses were used to determine what?)

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Seven data points were missing on the mathematics tests, which were imputed using predictive mean matching with the mice package in R (Lindström-Sandahl et al., 2024, p. 1059) to create five complete datasets. The authors compared age and cognitive ability for baseline equivalence using t-tests (Lindström-Sandahl et al., 2024, p. 1057, Table 1). To assess if the intervention had a greater impact than teaching as usual, the authors conducted one regression per mathematics test using the post-test as the dependent variable and the group and pretest score as independent variables, then compared the intervention and control groups' post-test scores via planned contrasts (Lindström-Sandahl et al., 2024, p. 1060). Because the problem-solving analysis did not meet the assumptions of normality, homoscedasticity, and homogeneity of variance, they ran all three analyses using robust regression to compare the effects (Lindström-Sandahl et al., 2024, p. 1060). They applied directional hypotheses, used the Benjamini–Hochberg procedure to control the false discovery rate across the three analyses, and reported effects as Hedges' g (Lindström-Sandahl et al., 2024, pp. 1059–1060). Arithmetic calculations, conceptual knowledge, and problem solving showed a medium effect (Lindström-Sandahl et al., 2024, p. 1060) for the intervention group compared to the control group, with the intervention group improving more than the control group in these three areas.

Article Type

QUALitative Applied (QUAL-A)

Article Reference

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Movahedazarhouligh, S., Kermani, H., & Aldemir, J. (2023). STEM integrated curriculums in early childhood education: An exploration of teachers’ pedagogical beliefs and practices. International Journal of Modern Education Studies, 7(1), 106–127. https://doi.org/10.51383/ijonmes.2022.266

Sampling Procedure

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The authors did not specify the sampling procedure, but this appears to be a convenience (volunteer) sample from two program sites. The researchers formally invited all lead teachers from a public Pre-K center and a Head Start program; three public Pre-K teachers and two Head Start teachers responded by expressing interest and volunteering to participate (Movahedazarhouligh et al., 2023, p.112). A qualitative method was selected due to the nature of the phenomenon and the limited number of participants; in addition, a phenomenological design was used to gain insight into the lived experience of the phenomenon (the STEM professional development) as experienced by the teachers (Movahedazarhouligh et al., 2023, p. 111).

Participants (who, how many, demographics)

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Three teachers worked in a public Pre-K center, and two worked in a Head Start center in a small town in an eastern state in the United States (Movahedazarhouligh et al., 2023, p. 111). Of the five, all were female; four were white, and one was Black/person of color (Movahedazarhouligh et al., 2023, p. 111, Table 1), and they ranged in age from 29 to 40. All had a B.A. and a Birth–Kindergarten license; two had a graduate program, and their experience as Pre-K lead teachers ranged from three to ten years (Movahedazarhouligh et al., 2023, p. 111). As lead teachers, they designed and implemented the curriculum and evaluated their students (Movahedazarhouligh et al., 2023, p.111). None of the five teacher assistants invited to the professional development could attend sufficiently, and they were not interviewed (Movahedazarhouligh et al., 2023, pp. 110, 112).

Intervention Description (if applicable; if not, write NA)

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The intervention consisted of a STEM-integration professional development (PD) series for teachers, and the study examined their perceptions of the intervention's impact. Teachers participated in three formal 90-minute PD sessions over 10 weeks (March to mid-May) in a study period (Movahedazarhouligh et al., 2023, p. 110). Two researchers and one former kindergarten teacher with backgrounds in science and engineering facilitated the sessions, which aimed to enhance teachers' content knowledge and pedagogical knowledge of the STEM curriculum (Movahedazarhouligh et al., 2023, p. 110). The teachers selected the unit topics (living and non-living things, ocean animals, the human body, weather and water, and motion). Then they covered them through project-based learning activities (Movahedazarhouligh et al., 2023, p. 110). The teachers' discussions and brainstorming of teaching strategies and practice of the activities during each session were similar to how they conducted them with young children, and the two researchers mentored the teachers and assisted them in planning and executing units when necessary (Movahedazarhouligh et al., 2023, p. 110). The teachers received STEM materials and technology such as laptops, iPads, and digital cameras, while researchers provided additional materials to support the teachers' curriculum plans (Movahedazarhouligh et al., 2023, p. 114).

Measurements or Instruments Used

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The main instrument was the semi-structured in-person interview protocol, which consisted of 10 open-ended questions, including the teachers' description of the project experience, the best aspects of implementing STEM, how children reacted, changes in children's learning, impacts on teachers' attitudes and teaching practice, desire to try more STEM activities, how they would use the STEM model in the upcoming years, and challenges they faced (Movahedazarhouligh et al., 2023, p. 112, Table 2). The interviews were digitally audio-recorded, and the interviewer took notes as a backup and used specific prompts when needed (Movahedazarhouligh et al., 2023, p. 113). The authors also verified data reliability using written field notes (Movahedazarhouligh et al., 2023:113). The teachers also agreed to share their project work and their documentation, but the analysis procedures described by the authors could also have used interview data (Movahedazarhouligh et al., 2023, p. 111, 113).

Data Collection (who, how, when)

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The researchers then met with interested teachers, explained the study procedures, and obtained their verbal and written consent, as highlighted by Movahedazarhouligh et al. (2023, p. 112). One week after the PD, each teacher was interviewed in person for 90-120 minutes (Movahedazarhouligh et al., 2023, p. 112). One researcher conducted all interviews, had no prior contact with the teachers, and was not a mentor or support person (Movahedazarhouligh et al., 2023, p. 112). The interviews were audio-recorded with notes, and the study lasted 10 weeks, from March to mid-May (Movahedazarhouligh et al., 2023, pp. 110, 113). To help ensure accuracy, the main points of each interview were summarized at the end of each interview, and teachers' comments were rephrased during the interview; a copy of the transcription was sent to each teacher, but none of them requested to read it (Movahedazarhouligh et al., 2023, p. 113).

Data Analysis (how was data analyzed?)

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Movahedazarhouligh et al. (2023, p. 113) applied an inductive qualitative analysis based on the procedures proposed by Braun and Clarke (2006) and also the quality indicators proposed by Brantlinger et al. (2005). The interviews were first transcribed and read and reread, with initial ideas noted. Second, the representative topics were organized into categories by the units of meaning. Third, the researchers determined specific topic categories within each overall category, and finally selected the most representative verbatim statements for each emerging topic to illustrate it (Movahedazarhouligh et al., 2023, p. 113). To ensure credibility, one researcher developed the initial codes and met with a second researcher to discuss the coding frame; the two researchers met regularly to discuss codes that emerged, and each researcher coded two sets of interviews independently, achieving 78% agreement and resolving disagreements by consensus (Movahedazarhouligh et al., 2023, p. 113). The field notes also helped assess trustworthiness (Movahedazarhouligh et al., 2023, p. 113). This paper presents the five themes that emerged from the analysis (Movahedazarhouligh et al., 2023, p. 114).

Article Type

Mixed Methods Applied (MM-A)

Article Reference

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Bağçeli Kahraman, P., & Arabacıoğlu, B. (2026). The effect of STEAM activities on the development of problem-solving skills in preschool children: A mixed-methods study. Early Childhood Education Journal, 54(5), 4249–4261. https://doi.org/10.1007/s10643-026-02172-1

Sampling Procedure

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The authors used one public preschool through convenience sampling (Bağçeli Kahraman & Arabacıoğlu, 2026, p.4251), which follows the national preschool curriculum. During the quantitative phase, the children in the participating classes constituted the sample, and the experimental group consisted of the class whose teacher volunteered to implement the STEAM activities, with classes matched on pre-test results (Bağçeli Kahraman & Arabacıoğlu, 2026, p. 4253). The authors used another procedure for the qualitative phase: a random subsample of the experimental group consisting of 10 children, their parents, and two experimental teachers (Bağçeli Kahraman & Arabacıoğlu, 2026, p. 4252).

Participants (who, how many, demographics; if different participants were in quantitative and qualitative portions, please indicate the differences)

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The quantitative part included 97 children from one public preschool: 48 in the experimental group and 49 in the control group (Bağçeli Kahraman & Arabacıoğlu, 2026, p. 4251). There were no differences between the two groups in terms of gender ratio, previous preschool experience, or the number of children in the family (52.1% male and 51.0% female in the experimental group, 43.8% and 44.9% in the control group, respectively, and 60.4% and 53.1% for two children, respectively) (Bağçeli Kahraman & Arabacıoğlu, 2026, p. 4252). Most mothers were aged 31 to 35, over 60% were not working, and most fathers had at least a high school education (Bağçeli Kahraman & Arabacıoğlu, 2026, p. 4252). The five teachers who were aged 30 or more with at least 8 years of experience, two of whom were in the experimental group and three were in the control group, were all female. The research was carried out in one location in the town of Gönen, Balıkesir (Bağçeli Kahraman & Arabacıoğlu, 2026, p. 4259). Because the qualitative participants were drawn only from the experimental group, the authors randomly selected 10 children and their parents from the experimental group, as well as the two experimental teachers (Bağçeli Kahraman & Arabacıoğlu, 2026, p. 4252). The article does not state how many parents were interviewed.

Intervention Description (if applicable; if not, write NA)

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The intervention consisted of a series of 32 structured STEAM activities with 40-50 minutes of intervention per session for four sessions a week for 8 weeks (from March to May) (Bağçeli Kahraman & Arabacıoğlu, 2026, p. 4253). The activities were based on inquiry-based learning, design thinking, and collaborative learning, and teachers facilitated the children by asking questions to help them problematize and develop solutions (Bağçeli Kahraman & Arabacıoğlu, 2026, p. 4253). To encourage alternative approaches, the challenges were presented, and the children shared their products at the end of each activity (Bağçeli Kahraman & Arabacıoğlu, 2026, p. 4253). The activities differed from the classroom curriculum mainly because they were based on a problem the children would solve in a STEAM way, and the disciplines were integrated systematically (Bağçeli Kahraman & Arabacıoğlu, 2026, p. 4253). The study used bird observations as an example to create a problem; a baby bird with air propulsion was designed, and a family-involved painting activity was conducted (Bağçeli Kahraman & Arabacıoğlu, 2026, pp. 4253–4254, Figs. 3–5). Teachers implemented the activities in the children's regular classroom without specific training in the targeted skills; the activities were communicated in advance, and teachers implemented them in collaboration with the researcher (Bağçeli Kahraman & Arabacıoğlu, 2026, p. 4253). The control group did not receive the activities and followed the regular preschool curriculum, and the control teachers were not informed about the activities (Bağçeli Kahraman & Arabacıoğlu, 2026, p. 4253).

Measurements or Instruments Used (what was used for quantitative portion? Qualitative portion?)

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The researchers used a researcher-made Personal Information Form to collect parents' age, level of education, the child's birth order, the number of siblings, and previous preschool experience (Bağçeli Kahraman & Arabacıoğlu, 2026, p. 4252). main outcome measure was the Problem-Solving Skills Scale (PSSS) by Oğuz and Akyol (2015), which consisted of 18 problem scenarios presented with a five-point Likert scale for children aged 60-72 months (Bağçeli Kahraman & Arabacıoğlu, 2026, p. 4252). The scale has good content validity indices (CVIs) (.99 for item relevance, .96 for alignment with the illustrations) and showed a unidimensional structure; Cronbach's alpha values of .86 and .60 in test–retest correlation, respectively, in this study, .85 on the pre-test and .98 on the post-test (Bağçeli Kahraman & Arabacıoğlu, 2026, p. 4252). The researchers prepared a semi-structured interview form, modified based on experts' feedback, in which they asked 4 open-ended, age-appropriate questions of children, 4 questions of parents, and 6 questions of teachers (Bağçeli Kahraman & Arabacıoğlu, 2026, pp. 4252–4253). A preschool teacher–researcher (Bağçeli Kahraman & Arabacıoğlu, 2026, p. 4253) also observed the experimental sessions.

Data Collection (who, how, when for quantitative and qualitative portions)

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All children completed the PSSS as a pre-test before the activities and as a post-test at the end of the eight-week program to establish baseline equivalence between the groups (Bağçeli Kahraman & Arabacıoğlu, 2026, pp. 4252–4253). The scale was administered to each child in a short introductory talk, with only minimal non-directive prompts when attention waned, and each child completed the task at his or her own pace (Bağçeli Kahraman & Arabacıoğlu, 2026, p. 4252). Parents provided the demographic information on the Personal Information Form (Bağçeli Kahraman & Arabacıoğlu, 2026, p. 4252). The article does not specify who gave the PSSS. Qualitative portion: After the activities, follow-up interviews were held one month later (Bağçeli Kahraman & Arabacıoğlu, 2026, p. 4251). The authors used drawings, games, or materials as needed during interviews with children, and they interviewed parents and teachers using their own forms (Bağçeli Kahraman & Arabacıoğlu, 2026, p. 4252). The interviews conducted were audio-recorded, transcribed, and then securely deleted, and the article does not mention the interviewer (Bağçeli Kahraman & Arabacıoğlu, 2026, p. 4252).

Data Analysis (how was data analyzed quantitatively and qualitatively)

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The authors ran skewness and kurtosis tests ranging from −0.833 to 1.539, which supported using parametric tests, and the Shapiro–Wilk and Levene tests confirmed the assumptions (Bağçeli Kahraman & Arabacıoğlu, 2026, pp. 4253–4254). To check baseline equivalence, they compared the two groups using an independent-samples t-test on pre-test scores, which showed t = −0.010 (p > .05) (Bağçeli Kahraman & Arabacıoğlu, 2026, p. 4254). They used a two-way repeated-measures ANOVA (group × time) to assess changes in problem-solving scores over time and between-group differences, and they computed effect sizes as partial eta squared (Bağçeli Kahraman & Arabacıoğlu, 2026, p. 4253–4254). The mean of the experimental group increased from 23.40 to 59.69 and the mean of the control group increased from 23.41 to 28.27; also the group main effect and the time main effect and the group-by-time interaction were significant (interaction F(1, 95) = 376.230, p < .001, partial η² = .798) (Bağçeli Kahraman & Arabacıoğlu, 2026, p. 4254). The qualitative data were analyzed using content analysis, and another researcher checked codes and themes for high conformity (Bağçeli Kahraman & Arabacıoğlu, 2026, p. 4254); disagreements were resolved through discussion. Themes were inductively identified from the units, and the article supported the participants' statements and findings with direct quotations (Bağçeli Kahraman & Arabacıoğlu, 2026, p. 4254). Integration: The quantitative phase yielded the main findings, and the qualitative phase deepened the interpretation and provided a comprehensive understanding of the findings, following an explanatory sequential design (Bağçeli Kahraman & Arabacıoğlu, 2026, p. 4251).

Article Type

Action Research (AR-QUAL)

Article Reference

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Xing, G. Y., Cady, A. G., & Wang, X. C. (2025). Playful computational thinking learning in and beyond early childhood classrooms: Insights from collaborative action research of two teacher-researchers. Education Sciences, 15(7), Article 840. https://doi.org/10.3390/educsci15070840

Sampling Procedure

The authors did not specify a sampling technique. The authors selected participants based on their description. The first teacher-researcher conducted her program at a community center serving a low-socioeconomic-status community. She collaborated with families who participated in her program (Xing et al., 2025, Section 3.1, para. 1). The second teacher-researcher was a teacher at the lab preschool and identified students based on their summer session enrollment and which days they attended (Section 3.1, para. 2; Section 3.2, para. 2). No information is provided in the article on how the community-center families were recruited.

Participants (who, how many, demographics)

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Note: If using MM and different participants were in quantitative and qualitative portions, please indicate the differences.

Two groups of young children took part in two separate programs (Xing et al., 2025, Section 3.2, paras. 1–2). The community-center program included two parent–child pairs. One family was biracial, consisting of a White mother with two adopted African American siblings, ages 5 and 6. The other family was Asian, comprising a father and his 9-year-old daughter. None of them had any coding experience. In the lab preschool program, five children aged 4 and 5 (2 boys and 3 girls) were all White. None of them were acquainted with coding. This totals 8 children. The two authors of the programs were doctoral students and teacher-researchers with 2 and 10 years of preschool teaching experience (Section 3.1, paras. 1–2).

Intervention Description (if applicable; if not, write NA)

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All teachers-researchers created and implemented their own computational thinking curriculum involving physical coding toys (Xing et al., 2025, Section 3.1, paragraphs 27–29). 1–2; Section 4.1). In 2023, the community-center program ran for six weeks in nine sessions and was titled Learn CT Together. This co-learning program served children (5 to 10 years) and parents. Stop and Look was a four-week preschool course in 2023, with two sessions per week of small-group instruction. Both programs employed the following types of playful, embodied activities (Sections 4.2–4.3): making a sandwich to teach sequencing; floor-grid games where children played the role of the robot; naming and personalizing robots. They also drew on storytelling, in this case by having children create their own coded stories for scaffolding and assessment (Section 4.4). Teacher support was progressively faded across sessions (Section 4.4, para. 1).

Measurements or Instruments Used

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Note: If using MM, indicate which measurements or instruments were used for the quantitative and qualitative portions.

The study used no standardized tests or surveys. The authors deemed pre- and post-assessment of children's computational thinking outside the scope of the project. The authors agree with this (Xing et al., 2025, Section 5, para. 3). The data sources included video recordings of the sessions, children's artifacts (coding sequences, drawings, written steps, etc.), weekly lesson plans and program documentation, and the teacher-researchers' anecdotal reflection notes (Section 3.1, paras. 1 and 3).

Data Collection (who, how, when)

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In the teaching programs, the two teacher-researchers gathered the data with their own instructional processes (Xing et al., 2025, Section 3.1, paras. 1–3). They video-recorded each session, collected children's artifacts, and wrote reflection notes. The community-center sessions were 45 to 90 minutes long and resulted in approximately 16 hours of video (Section 3.1, paragraph. 1). The programs delivered in 2023, with the preschool program delivered in the summer session (Section 3.1, para. 2; Section 3.2, para. 2). The authors went through four phases (planning, acting, observing, and reflecting) and adapted their teaching and learning as they progressed (Section 3, para. 1; Section 3.1, para. 3). At the end of the programs they met to exchange experiences and obtained common themes, being supervised by the third author (Section 3.1, para. 3).

Data Analysis (what analyses were used?)

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The authors used a deductive thematic analysis, following Braun and Clarke's (2006) approach (Xing et al., 2025, Section 3.3). They began with codes from the literature, including six characteristics of playful learning, teacher planning, and scaffolding strategies. They then compared their transcripts, field notes, and artifacts to these codes and classified and modified them. The outcome was three coding schemas: playful elements (interest, ownership, and practices); embodied elements (design, illustration, and thinking); and storytelling (engaging, scaffolding, and assessment). These were arranged into two strands: teacher planning and teacher practice.

Article Type

Program Evaluation (PE-QUAL)

Article Reference

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Doganca Kucuk, Z., Yabas, D., Bozoglu, H. S., & Corlu, M. S. (2021). The impact of the earlySTEM program on teacher and student outcomes: The role of teachers' involvement in the program development. International Journal of Education in Mathematics, Science, and Technology, 9(3), 371–405. https://doi.org/10.46328/ijemst.1279

Sampling Procedure

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The authors did not specify a sampling technique. Their description suggests a voluntary-response sample. All 130 teachers who implemented the program were invited to complete the online questionnaire, and 66 teachers completed it (approximately 50%). School administrators recommended the program-developer teachers participate in the program development process (p. 376).

Participants (who, how many, demographics)

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The 66 participants were primary and preschool teachers across several campuses of a private school in Turkish cities (Doganca Kucuk et al., 2021, p. 376). Of these, 26 were program-developer teachers who also participated in program design; 40 were practitioner teachers who only implemented the program. Developers had an average of 18.7 years of teaching experience, and practitioners had 13 (p. 376–377). The sample included 50 females and 16 males. The majority had bachelor's degrees, and some had master's degrees. They provided services for preschool through grade 4, including 13 preschool students (p. 377, Table 1).

Intervention Description (if applicable; if not, write NA)

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The evaluated program was the earlySTEM program, implemented for kindergarten to grade 4 students and involving one hour of earlySTEM each week throughout the school year (Doganca Kucuk et al. 2021, p. 377). It included 4 themes, each focused on one STEM field, and students presented group projects at the end of each theme. It was developed around an integrated teaching approach in which students work on an open-ended, real-life problem (pp. 377–378). The authors provided student workbooks, storybooks, teacher booklets, teacher workshops, and teacher meetings to support teachers (p. 379). Program-developer teachers met twice a year for two days with academics to learn the themes and to create lesson plans (p. 378).

Measurements or Instruments Used

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The authors applied one instrument: an open-ended online survey with three parts Doganca Kucuk et al., 2021, p. 379–380). The first one gathered demographic information. The second part asked teachers about student outcomes in mathematics, science, and life skills. The third one inquired about teachers' personal professional results.

Data Collection (who, how, when)

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The authors sent the survey to teachers via their website, and teachers completed it voluntarily (Doganca Kucuk et al., 2021, p. 376). It was given at the end of the 3rd Quarter following the technology unit, 2016-2017 (p. 379). It is not stated who gave it to the author.

Data Analysis (what analyses were used?)

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The authors conducted a qualitative analysis of the survey answers, following Doganca Kucuk et al. (2021, p. 380). They encoded each unique concept in the answers, created categories, sorted the data by these categories, named them, and refined them. They used both codability and current codes, drawing on existing codes of teacher knowledge, the program’s teaching framework, and research on professional development (p. 380), as well as national mathematics codes of mathematics outcomes (p. 381). They discussed each coding decision until the two groups reached consensus, and they presented the results separately for developer teachers and practitioner teachers (p. 380) in tables (p. 382, Table 2). To ensure trustworthiness, they used lengthy engagement, peer debriefing, member checking, and a reflective journal (pp. 380–381).