Computational Thinking Skills in Senior High (PDF)

Computational Thinking Skills Among Senior High School Students of Sta Cruz National High School  BACKGROUND OF THE STUDY Computational thinking (CT) skills, including pattern recognition, decomposition, abstraction, and algorithms (Tsai et al., 2018), are crucial for students to...

Computational Thinking Skills Among Senior High School Students of Sta Cruz National High School  BACKGROUND OF THE STUDY Computational thinking (CT) skills, including pattern recognition, decomposition, abstraction, and algorithms (Tsai et al., 2018), are crucial for students to solve complex problems and succeed in technology-driven fields. However, CT is still underrepresented in the K-12 curriculum, with limited support for both students and teachers to connect existing knowledge to CT requirements (Winthrop et al., 2019). This study aims to evaluate the CT skills of senior high school students across four academic strands; STEM, HUMSS, ABM, and GAS using pattern recognition and decomposition assessments. This research aims to provide valuable insights into improving CT education and addressing gaps in the current curriculum. RESEARCH QUESTIONS HYPOTHESES This study aims to measure the level of computational thinking (CT) skills among senior high school students at Santa Cruz Ha: There is a significant National High School for the 2024-2025 school year. The difference between the level of research addresses the following questions: computational thinking skills of senior high school students in 1. What are the demographic factors of students in terms of: terms of their demographic 1.1 Sex, and; factors (sex and strand). 1.2 Strand? Ho: There is no significant 2. What is the level of CT skills among students in terms of: difference between the computational thinking skills of 2.1 Pattern recognition, and; senior high school students in terms of their demographic 2.2 Decomposition? factors (sex and strand). 3. Is there a significant difference in CT skills based on: 3.1 Sex, and; 3.2 Strand?  SCOPE AND DELIMITATION This study uses a descriptive-comparative methodology to investigate the computational thinking skills (decomposition and pattern recognition) of Grade 12 students in various academic tracks (ABM, GAS, HUMSS, STEM) at Sta. Cruz National High School for the School Year 2024-2025. The research employs questionnaires to gather data, with a limited number of respondents focused on the senior high school campus. It assesses computational thinking skills while excluding factors like abstraction, algorithmic thinking, prior computational training, socioeconomic background, or access to technology. The academic strand or field of study can affect familiarity and experience with computational thinking, influencing participants' performance. The researchers will use a descriptive-comparative design to examine the relationship between demographic factors and computational thinking skills, utilizing stratified sampling for respondent selection and conducting data collection through validated survey questionnaires.  CONCEPTUAL FRAMEWORK The study examines how senior high school students' demographic factors, such as sex and strand, influence their computational thinking skills, specifically pattern recognition and decomposition (Liu et al., 2024). It also explores the impact of teaching methods and curricular approaches on these skills (Acevedo-Borrega et al., 2022). By assessing these relationships, the research aims to identify effective strategies to improve computational thinking education in senior high schools. The findings will contribute to better educational practices and enhanced student outcomes in computational thinking.  THEORETICAL FRAMEWORK This study explores the computational thinking skills of senior high school students at Sta. Cruz National High School using The Computational Thinking Framework (Wing, 2006). It focuses on problem-solving, social context, and demographic factors such as sex and track as dependent variables, with Computational Thinking Skills (CTS) and prior computing experience as independent variables. The study specifically examines pattern recognition and decomposition (Wing, 2006) and aims to assess how demographic characteristics, experience, and personal attributes influence these skills (Acevedo-Borrega et al., 2022). Additionally, it looks at the impact of teaching methodologies and curricular approaches on computational thinking skills, seeking effective strategies for high school education (Liu et al., 2024).  RESEARCH DESIGN This study uses a descriptive-comparative approach to investigate the computational thinking skills (decomposition and pattern recognition) of Grade 12 students in various academic tracks (STEM, ABM, HUMSS, GAS) at Sta. Cruz National High School. It aims to identify differences based on demographic factors such as age, gender, socioeconomic status, and educational background (Nurse Key, 2017). By employing questionnaires, the study collects data to assess computational thinking proficiency while excluding other factors like abstraction and algorithmic thinking. The research analyzes how demographic characteristics influence these skills and highlights potential areas for improvement in computational thinking education.  SAMPLING TECHNIQUE This study employs a stratified random sampling technique to fairly select respondents from the Academic Track (STEM, ABM, HUMSS, GAS) at Sta. Cruz National High School (Etikan & Bala, 2017). The population is divided into strata based on shared characteristics, and a random sample is chosen from each stratum according to its size (Berndt, 2020). The population is divided into strands and sections within each strand, with respondents selected through systematic sampling. The researchers arrange students alphabetically in each section, number them, and use an interval to identify respondents. Using Slovin's formula, they determine the sample size by dividing the number of students in a track by the total population. They calculate the percentage of students within each academic strand and multiply this percentage by the sample size to identify the interval needed for respondent selection.  RESEARCH INSTRUMENT This study will use a structured test questionnaire, adapted and modified from Ocampo et al. (2024) and Munawarah et al. (2021), as the research instrument. The questionnaire consists of 30 items, with 15 items dedicated to Pattern Recognition and 15 items to Decomposition. The objective is to assess the computational thinking skills of Senior High School Students at Santa Cruz National High School. The test aims to evaluate students' abilities in these two key areas of computational thinking. Table 2 provides the scale for assessing the level of computational thinking skills. The research aims to analyze the results to determine the students' proficiency levels.  STATISTICAL TOOL Statistical Tool to Assess the Computational Thinking Skills among Senior High Students in Sta. Cruz National High School The statistical tools that were used in the study are the following: Frequency: The number of times the scores of the students belong in the particular strand appeared in the data. Mean: To get the average level of the student's awareness of computational thinking skills. Standard Deviation: Measures the number of variations in Computational Thinking Skills scores. T-test:Determines if there is a significant difference in the computational thinking skills scores in terms of sex and track.