Esta tese descreve um estudo quase experimental que teve como objetivo: investigar a relação entre a construção do Pensamento Computacional e o desenvolvimento do raciocínio de estudantes dos últimos anos do Ensino Fundamental. A pesquisa foi realizada utilizando um curso de extensão em Desenvolvimento de Games, ofertado em 2 escolas particulares de Porto Alegre, tendo a participação de 50 alunos. A prática de ensino-aprendizagem proposta foi construída com base em pressupostos teóricos da aprendizagem significativa e aprendizagem experiencial. O Pensamento Computacional e o raciocínio dos alunos foram avaliados antes e após o término do curso, sendo utilizados para este fim o Teste de Pensamento Computacional e as provas que compõe a Bateria de Provas de Raciocínio – BPR-5. A análise estatística dos dados permitiu evidenciar um incremento do Pensamento Computacional, bem como do Raciocínio Verbal, Raciocínio Abstrato e Raciocínio Mecânico dos alunos que participaram do experimento. Comprovou-se também uma correlação positiva entre o Pensamento Computacional e os cinco tipos de raciocínio avaliados. Os resultados deste estudo demonstram como a construção do Pensamento Computacional contribuí no desenvolvimento cognitivo dos alunos e é apresentada uma proposta pedagógica que pode servir de referência para novos estudos na área. / This thesis describes a quasi-experimental study aimed to investigate a relationship between the construction of Computational Thinking and the development of students' reasoning in Middle School. A research was carried out during a course about Games Development, offered in two private schools in Porto Alegre, with 50 students. The proposed teaching-learning practice was built on the theoretical assumptions of meaningful learning and experiential learning. Both, Computational Thinking and reasoning, of the students were measured before and after the course, using a Computational Thinking Test and a set of reasoning evidence tests (BPR-5). The statistical analysis of the data showed an increase in Computational Thinking, as well as Verbal Reasoning, Abstract Reasoning and Mechanical Reasoning of the students participating in the experiment. There was also a positive observation between Computational Thinking and the five types of reasoning. The results of this study demonstrate how the construction of Computational Thinking contributes to the cognitive development of students and presents a pedagogical proposal that can serve as a reference for new studies in the area.

Scratch is one of the most popular ways to teach younger children to code in K–8 throughout the U.S. and Europe. Despite its popularity, Scratch lacks a formative feedback tool to inform students and teachers of a student's progress in coding ability. Dr. Scratch was built to fill this need. This study seeks to answer if using Dr. Scratch as a formative feedback tool accelerates the students' progress in coding ability and Computational Thinking (CT). Forty-one 4th-6th grade students participated in a 1-hour/week Scratch workshop for nine weeks. We measured pre- and posttest results of the Computational Thinking Test (CTt) between control (n = 18) and treatment groups (n = 23) using three methods: propensity score matching (treatment = .575; control = .607; p = .696), information maximum likelihood technique (treatment effect = -.09; p = .006), and multiple linear regression. Both groups demonstrated significant increased posttest scores over their pretest (treatment = +8.31%; control = +5.43%), though which group improved the most varied depending on which test was run. We discuss the implications of using Dr. Scratch as a formative feedback tool and recommend further research on the use of such tools in elementary coding experiences.

Programmering har introducerats som ett nytt delmoment i den svenska läroplanen sedan2018. Denna fallstudie har därför fokuserat på elevers förståelse för programmering iintroducerande undervisning. Detta för att hjälpa lärare att få en bättre förståelse för hur de kanimplementera programmering i sin undervisning. Teorin datalogiskt tänkande har använts somgrund för analys av datamaterialet, för att identifiera elevers inkrementella och iterativa processsamt testa och felsöka processen. Teorin användes också för att identifiera elevers förståelse förprogrammeringsbegrepp. Insamlingen av datamaterialet utfördes med inspelningar av eleversskärmar och samtal, medan de introducerades till programmering i Scratch. Resultatet visadepå att elever hade svårigheter med vissa programmeringsbegrepp. En slutsats var även attprogrammeringsprocesserna var beroende av varandra för att utvecklas. / Programming is since 2018, a new subject in the Swedish curriculum. This case studytherefore focuses on students’ understanding of introductory programming, to help teachers geta better understanding of how to teach and assess the subject. The theory computationalthinking was used as a foundation for identifying the students’ incremental and iterative processas well as their testing and debugging process. It was also used for identifying the students’understanding of programming concepts. The data for this study was collected throughrecordings of students’ screens and conversations, while introducing them to programming inScratch. The results showed that students had some difficulties with some programmingconcepts. Furthermore, the results showed that the processes were dependant on each other’sdevelopment.

Over the last few years, the importance of computer science education for children has been promoted more and more vigorously. In addition, the demand for technology occupations has increased rapidly, and there are many job opportunities in computer science. However, there are not many women working in this field. One of the reasons is young women’s lack of interest in computer science. This study investigates how to attract young women to computer programming and support computational thinking through design and develop learning activities. This study’s approach includes several related researches, theories, and methodologies. Interviews, workshops, and observations were used to determine design requirements. The results demonstrate that tangible and meaningful artifacts are effective educational tools for computer programming. Based on the results, this research developed a prototype, “TomatoBox,” a do-it-yourself kit that creates toys while providing an enjoyableactivity to learn programming.

Computer scientists have been working towards a common definition of abstraction; however, the instruction and assessment of abstraction remain categorically underresearched. Because abstraction is often cited as a component of computational thinking, abstraction has been summarily likened to a higher order thinking skill. A broad conceptual framework including philosophy, psychology, constructionism, and computational thinking was aligned with the descriptive qualitative design and guided the literature review and data analysis. This qualitative examination of how teachers determine curriculum, deliver instruction, and design assessments in K-12 computer science education provides insight into best practices and variables for future quantitative study. The instructional strategies, objectives, and assessments of twelve K-12 computer science teachers from 3 states were examined in this descriptive qualitative examination of instruction using thematic coding analysis. The majority of teachers had little to no professional development regarding teaching abstraction. All teachers in the study were unsure what student abstraction abilities should be according to grade level. Teachers'€™ understanding of abstraction ranged from very little knowledge to very knowledgeable. The majority of teachers did not actively assess abstraction. Teachers described successfully teaching abstraction through multiple instructional practices and spiraling curriculum. Practical descriptive insights illuminate additional variables to research the instruction of abstraction qualitatively and quantitatively, as well as provide anecdotal instructional successes.

Computational thinking is a contemporary mathematical and engineering concept that has been introduced to US science classrooms due to its emphasis within the Next Generation Science Standards (NGSS Lead States, 2013), yet it stands with no clear definition nor explicit methods for inclusion. Because biological evolution, an essential theory within biology, spans across temporal and organizational scales (Aho, 2012), computational thinking may facilitate evolution learning (Wilensky & Reisman, 2006), specifically by overcoming misconceptions, reinforcing the nature of science (NOS), and allowing student embodiment (as students become emerged in their models, i.e., personification; Weinthrop et. al. 2016). The complex nature of both teaching computational thinking and biological evolution lends toward the need for a learning progression that identifies the instructional context, computational product and computational process and spans from simple to complex (as modified from Berland & McNeill, 2010). I developed and present an appropriate learning progression that outlines biological evolution learning coupled with computational thinking. The defined components of computational thinking (input, integration, output and feedback) are coupled with biology student roles. Two major themes of biological evolution, unity and diversity have each been paired with both computational thinking and specific corresponding NGSS standards at levels of increasing complexity. To investigate the effectiveness of the learning progression, I developed and conducted a quasi-experimental research design study. I designed two learning experiences (interventions) which merged computation and biological evolution content based on AP biology laboratory lessons (College Board, 2009). I also developed two instruments for use in the study, one to assess computational knowledge and the other to assess biological evolution knowledge across scales. I measured knowledge gains in both biological evolution and computational thinking quantitatively and explored participant use of biological levels of organization and computational complexity through qualitative analysis of participant artifacts. The quantitative and qualitative results of the study support the argument to include computational thinking into biological evolution knowledge instruction. Knowledge gains differed between the two interventions indicating that one intervention was significantly more successful in learning both biological evolution and computational thinking. Students who made biological level connections across scales (spanning from the micro to the macro levels) also had significantly greater gains in biological knowledge. Considering the results collectively, computational thinking deserves a much greater emphasis within biology classrooms. There are virtually no previous studies which relate computation and evolution across scales and the present study paved the way for questions of importance, support, benefits and overall student achievement in relation to the advancement of science in education. / Teaching & Learning

Learners' affective states correlate with learning outcomes. A key aspect of instructional design is the choice of modalities by which learners interact with instructional content. The existing literature focuses on quantifying learning outcomes without quantifying learners' affective states during instructional activities. An investigation of how learners feel during instructional activities will inform the instructional systems design methodology of a method for quantifying the effects of individually available modalities on learners' affect. The objective of this dissertation is to investigate the relationship between affective states and learning modalities of instructional computing. During an instructional activity, learners' enjoyment, excitement, and motivation are measured before and after a computing activity offered in three distinct modalities. The modalities concentrate on visual and musical computing for the practice of computational thinking. An affective model for the practice of computational thinking through musical expression was developed and validated. This dissertation begins with a literature review of relevant theories on embodied cognition, learning, and affective states. It continues with designing and fabricating a prototype instructional apparatus and its virtual simulation as a web service, both for the practice of computational thinking through musical expression, and concludes with a study investigating participants' affective states before and after four distinct online computing activities. This dissertation builds on and contributes to extant literature by validating an affective model for computational thinking practice through self-expression. It also proposes a nomological network for the construct of computational thinking for future exploration of the construct, and develops a method for the assessment of instructional activities based on predefined levels of skill and knowledge. / Doctor of Philosophy / This dissertation investigates the role of learners' affect during instructional activities of visual and musical computing. More specifically, learners' enjoyment, excitement, and motivation are measured before and after a computing activity offered in four distinct ways. The computing activities are based on a prototype instructional apparatus, which was designed and fabricated for the practice of computational thinking. A study was performed using a virtual simulation accessible via internet browser. The study suggests that maintaining enjoyment during instructional activities is a more direct path to academic motivation than excitement.

Iteration, the repetition of computational steps, is a core concept in programming. Students usually learn about iteration in an entry-level Computer Science class. Virginia Tech's Computational Thinking (CT) course is designed to teach non-CS majors computing skills and new ways of thinking. The course covers iteration on Day 8 of the class. We conducted a pretest before, and three post-tests after, Day 8 of the Computational Thinking class in Spring 2018 on 137 students. The pre-test was intended to measure knowledge of iteration before the material was covered. We found from the post-tests that students' knowledge of iteration did not satisfy the course objectives in Spring 2018, because the knowledge gain shown between pre-test and post-tests was not significant. We developed interactive visualizations and exercises for Fall 2018 and Spring 2019. For three semesters we conducted tests and compared the data from Fall 2018 and Spring 2019 (the treatment) against Spring 2018 (the control). We found that Spring 2019 students had greater knowledge gains than Spring 2018 students. Also, we conducted surveys in Fall 2018 and Spring 2019 from students to learn more about their recall, helpfulness, and reuse of the interactive visualizations. Finally, we analyzed data from the interactive exercises and page use to investigate students' usage behavior. / Master of Science / Iteration is a process of repeating a set of instructions or structures. An iterative process repeats until a condition is met or a specified number of repetitions is completed. Students usually learn about iteration in an entry-level Computer Science class. Virginia Tech’s Computational Thinking (CT) course is designed to teach non-CS majors computing skills and new ways of thinking. The course covers iteration on Day 8 of the class. We conducted a pretest before, and three post-tests after, Day 8 of the Computational Thinking class in Spring 2018 on 137 students. The pre-test was intended to measure knowledge of iteration before the material was covered. We found from the post-tests that students’ knowledge of iteration did not satisfy the course objectives in Spring 2018. In particular, the knowledge gain shown between pre-test and post-tests was not significant. We developed interactive visualizations and exercises that were used during Fall 2018 and Spring 2019. We conducted tests and compared the data from Fall 2018 and Spring 2019 (the treatment) against Spring 2018 (the control). To see if there was a statistically significant difference between the absolute score means of three groups, we used independent sample t-tests. Also we used paired sample t-tests to see if there was a greater knowledge gain after using our invention. By analyzing the results of the t-tests, we found that Spring 2019 students had greater knowledge gains than Spring 2018 students. Also, we conducted student surveys in Fall 2018 and Spring 2019 to learn more about their opinions on recall, helpfulness, and reuse of the interactive visualizations. We analyzed data from the interactive exercises and page use to investigate students’ usage behavior.

As the focus on computer science in K-12 classrooms grows, the 'Hour of Code' program has also grown. As Hour of Code is one of the largest educational campaigns, it is worth evaluation to ensure effects are well understood so that implementation can be made most effective. This research sought to better understand the impact of Hour of Code. This thesis presents findings from a systematic review and from a quasi-experimental study. A large number of research articles have been published on Hour of Code. Systematic review identified 64 papers including reports from experiments testing the efficacy of Hour of Code, analysis of learner behavior, reports of participation and suggestions for facilitating. Analysis of these articles provided detail into the known impact of Hour of Code and available resources. However, many questions remain and are outlined in the review. One such remaining question includes questions about the impact specifically on K-12 students. The quasi-experimental study reports findings from computer science education research with over 1000 7th-grade students who engaged in HOC activities. Students' interest and perceptions of CS were collected before and after completing HOC activities. Statistical analysis provided mixed results with some positive and some negative shifts but overall limited effect size.

<p>  </p> <p>This research study aims to explore the understanding and implementation of CT and its core concepts by CS High School teachers. This research study examined CS teachers' understanding of CT’s core concepts; a) decomposition, b) pattern recognition, c) abstraction and d) algorithm design. Furthermore, the study also explores how these CS teachers applied these core concepts to their instructional practice. </p> <p>The qualitative case study utilized the Pedagogical Content Knowledge (PCK) framework as a lens to explore the teachers’ understanding. For this qualitative research study, purposeful sampling was employed to recruit participants with specific knowledge or experience about a topic of interest. In-depth semi-structured interviews were performed with five CS high school teachers for data collection. The researcher used coding and thematic analysis to analyze the data. Teachers shared their understanding of CT, its core concepts, and how they incorporate these into their instructional practice.</p> <p>The findings in the study present the different understandings of the teachers regarding CT’s core concepts and how each of them applied such concepts through different pedagogical approaches to their instructional practice. The findings in the study could provide an opportunity for high school teachers to explore different understandings from other high school teachers and potentially provide collaborative opportunities. </p> <p>The research study concludes with two significant findings and their implications for the field of CS education. It also recommends other researchers and provides collaborative opportunities with other high schools. Moreover, this research contributes to and enriches the current literature on CT in education. </p>