Pirate plunder: game-based computational thinking using scratch blocks

Policy makers worldwide argue that children should be taught how technology works, and that the ‘computational thinking’ skills developed through programming are useful in a wider context. This is causing an increased focus on computer science in primary and secondary education. Block-based programming tools, like Scratch, have become ubiquitous in primary education (5 to 11-years-old) throughout the UK. However, Scratch users often struggle to detect and correct ‘code smells’ (bad programming practices) such as duplicated blocks and large scripts, which can lead to programs that are difficult to understand. These ‘smells’ are caused by a lack of abstraction and decomposition in programs; skills that play a key role in computational thinking. In Scratch, repeats (loops), custom blocks (procedures) and clones (instances) can be used to correct these smells. Yet, custom blocks and clones are rarely taught to children under 11-years-old. We describe the design of a novel educational block-based programming game, Pirate Plunder, which aims to teach these skills to children aged 9-11. Players use Scratch blocks to navigate around a grid, collect items and interact with obstacles. Blocks are explained in ‘tutorials’; the player then completes a series of ‘challenges’ before attempting the next tutorial. A set of Scratch blocks, including repeats, custom blocks and clones, are introduced in a linear difficulty progression. There are two versions of Pirate Plunder; one that uses a debugging-first approach, where the player is given a program that is incomplete or incorrect, and one where each level begins with an empty program. The game design has been developed through iterative playtesting. The observations made during this process have influenced key design decisions such as Scratch integration, difficulty progression and reward system. In future, we will evaluate Pirate Plunder against a traditional Scratch curriculum and compare the debugging-first and non-debugging versions in a series of studies

Using App Inventor to Explore Low-Achieving Students\u27 Understanding of Fractions

A student\u27s understanding of fraction magnitude impacts his/her understanding of algebra (e.g., Booth & Newton, 2012; Siegler et al., 2012), which then influences his/her likelihood of graduating high school (Orihuela, 2006) or succeeding in higher education (Adelman & United States., 2006; Trusty & Niles, 2004). Literature suggests that students gain this understanding when they create and work with various representations of fractions (e.g., Ainsworth, Bibby, & Wood, 2002; Panaoura et al., 2009; Siegler, Fazio, Bailey, & Zhou, 2013), which can occur when students engage in constructivist activities such as developing games (Kafai, 1996, Apr). This study examines an intervention where low-achieving eighth-grade students develop games about fraction magnitude using App Inventor, a novice programming environment, to determine what representations students create in their games, how their understanding of fraction magnitude develops when making their games, and what challenges they experience other than challenges concerning fractions. It uses a holistic case study with embedded units to understand the major themes for each research question while considering the influences of individual backgrounds and the various kinds of games each developed. Kolb\u27s (1984) experiential learning theory, which states that ideas are formed by experiences and which occurs when one programs or codes a computer (Robins, Rountree, & Rountree, 2003), grounds the data analysis. The findings of this study indicate that students primarily use numeric representations and area models to represent fraction magnitude, which are also the most common representations found in textbooks (Zhang, 2012). They developed their understanding by working with area models, talking about area models, or by developing code to compare two fractions. The way they constructed and critiqued these representations map to the experiential learning cycle, showing that they engaged in concrete experiences with fractions, reflected on the experience, conceptualized their new learning, and experimented with that learning to develop their understanding of fraction magnitude. The challenges they experienced ranged from coding difficulties, such as decomposing their designs into components to code, to non-coding challenges, such as collaborating. Limitations of this study are discussed and implications for practice and future research are delineated

The cognitive effects of computational thinking: A systematic review and meta-analytic study

In this paper, we review and meta-analyze the findings of experimental studies published between 2006 and 2022 that examined the effects of coding and programming interventions on children's core and higher order executive functions (response inhibition, working memory, cognitive flexibility, planning and problem solving). The systematic review and meta-analysis aimed to address three research questions: 1) Which executive functions are most impacted by the teaching of CT? 2) Which instructional modality (educational robotics/virtual coding/unplugged coding) is most effective in enhancing executive function skills in learners aged 4–16 years? and 3) Does the cognitive effectiveness of coding vary with children's age? A total of 19 studies with 1523 participants met the selection criteria for the systematic review. The meta-analysis included 11 of those studies. The results reveal beneficial effects of structured virtual and tangible coding (educational robotics) activities for preschoolers and first graders, and significant effects of more unstructured virtual coding activities (e.g., Scratch-based) for older students. A multivariate fixed-effects model meta-analysis shows that the teaching of coding significantly improves problem-solving with the highest effect (dppc2 = 0.89), but also planning (dppc2 = 0.36), and inhibition and working memory with lower effects (dppc2 = 0.17, dppc2 = 0.20)

Examining Trajectories of Elementary Students’ Computational Thinking Development Through Collaborative Problem-Solving Process in a STEM-Integrated Robotics Program

Developing K-12 students’ computational thinking (CT) skills is essential. Building on the existing literature that has emphasized programming skill development, this study expands the focus to examine students’ use of underlying CT cognitive skills during collaborative problem-solving processes. A case study approach was employed to examine video data of 5th graders engaging in an integrated-STEM robotics curriculum. The findings reveal that students applied algorithmic thinking most frequently and prediction the least. They recorded most debugging behaviors initially in the problem-solving process, but after accumulating more experiences their uses of other CT skills, including algorithmic thinking, pattern recognition, and prediction, increased. Implications for developing young learners’ CT skills to solve real-world problems are discussed

Exploring Trends in Middle School Students\u27 Computational Thinking in the Online Scratch Community: A Pilot Study

Teaching computational thinking has been a focus of recent efforts to broaden the reach of computer science (CS) education for today’s students who live and work in a world that is heavily influenced by computing principles. Computational thinking (CT) essentially means thinking like a computer scientist by using principles and concepts learned in CS as part of our daily lives. Not only is CT essential for the development of computer applications, but it can also be used to support problem solving across all disciplines. Computational thinking involves solving problems by drawing from skills fundamental to CS such as decomposition, pattern recognition, abstraction, and algorithm design. The present study examined how Dr. Scratch, a CT assessment tool, functions as an assessment for computational thinking. This study compared strengths and weaknesses of the CT skills of 360 seventh- and eighth-grade students who were engaged in a Scratch programming environment through the use of Dr. Scratch. The data were collected from a publicly available dataset available on the Scratch website. The Mann-Whitney U analysis revealed that there were specific similarities and differences between the seventh- and eighth-grade CT skills. The findings also highlight affordances and constraints of Dr. Scratch as a CT tool and address the challenges of analyzing Scratch projects from young Scratch learners. Recommendations are offered to researchers and educators about how they might use Scratch data to help improve students’ CT skills

Using Arts and Crafts to teach Computer Science at the YMCA After-School Program

Computer Science (CS) is a popular, flourishing field. To prepare young students, CS is now being taught in middle school. Our team created a curriculum that delivers CS education using arts and crafts. We crafted a multi-week program that taught CS concepts including pseudo code, debugging, functions, and algorithms using art activities involving navigating a maze and drawing pixel art. We piloted the curriculum with 6th/7th graders at a YMCA after-school program, where we observed the students’ perspective, interest, and knowledge of CS. After the pilot program, we analyzed the results to measure the curriculum’s effectiveness and found the students better understood CS. The project resulted in a curriculum and set of recommendations for future groups conducting similar projects

How can second-grade students learn algorithmic thinking and pattern recognition through collaborative learning?

In today’s technology-filled world, employers seek applicants with strong computational thinking (CT) skills and computer science (CS) backgrounds. The demand for CT education reaches all the way to the elementary level. Wing (2006) states “computational thinking is a fundamental skill for everyone, not just for computer scientists” (p. 33). Though researchers are continuing to define all aspects of CT, the major elements include: algorithmic thinking, pattern recognition, decomposition, and abstraction. The digital age has also caused an increase in screen time, time children spend in front of a device, which has prompted studies on the negative physical and psychological effects it can have on children. Scoggin (2018) explains that school students are demonstrating a lack of social skills due to increased screen time in the classroom. As a response to this research, this capstone builds on relevant studies and provides a unit of lessons to answer the question: How can second-grade students learn algorithmic thinking and pattern recognition through collaborative learning? The detailed project includes cooperative activities and assessments to teach CT skills without the use of devices