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

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.

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.

Skill sets such as understanding and applying computational concepts are essential prerequisites for success in the 21st century. One can learn computational concepts by taking a traditional course offered in a school or by self-guided learning through an online platform. Collaborative learning has emerged as an approach that researchers have found to be generally applicable and effective for teaching computational concepts. Rather than learning individually, collaboration can help reduce the anxiety level of learners, improve understanding and create a positive atmosphere to learning Computational Thinking (CT). There is, however, limited research focusing on how natural collaborative interactions among learners manifest during learning of computational concepts. Structured as a manuscript style dissertation, this doctoral study investigates three different but related aspects of novice learners collaboratively learning CT. The first manuscript (qualitative study) provides an overall understanding of the contextual factors and characterizes collaborative aspects of learning in a CT face-to-face classroom at a large Southeastern University. The second manuscript (qualitative study) investigates the social interaction occurring between group members of the same classroom. And the third manuscript (quantitative study) focuses on the relationship between different social interactions initiated by users and learning of CT in an online learning platform Scratch™. In the two diverse settings, Chi's (2009) Differentiated Overt Learning Activities (DOLA) has been used as a lens to better understand the significance of social interactions in terms of being active, constructive and interactive. Together, the findings of this dissertation study contribute to the limited body of CT research by providing insight on novice learner's attitude towards learning CT, collaborative moments of learning CT, and the differences in relationship between social interactions and learning CT. The identification of collaborative attributes of CT is expected to help educators in designing learning activities that facilitate such interactions within group of learners and look out for traits of such activities to assess CT in both classroom and online settings. / PHD

Initiatives to implement computational thinking and science, technology, engineering & modelling into learning processes have been taken by many authorities worldwide, and in Sweden as well. The Swedish government made changes in the high school curriculum in order to develop student’s digital competence including programming which has been in force since June 2018 (Skolverket, 2018). The new curriculum, K9, sets challenges for both students and teachers that can be tackled with studies that point out the difficulties and the fields which require more time and work on learning materials used by schools. Digital simulations are fast becoming a key instrument in school education and several attempts to show and understand the outcome of the usage of simulations have been done recently. Previous studies report that the usage of Algodoo and other simulations programs in physics education has positive effects on students. A considerable amount of literature has been published on computational thinking and simulations. These studies provide insights into how students can benefit from simulations in Algodoo to improve their digital thinking and performance in physics. A study that examines this topic is "Algodoo for Interactive Learning: Effects on Students’ Achievement and Motivation Towards Science'' by Aslı Saylan Kirmizigül (2021). The study investigates how the Algodoo software affects students' attitudes, motivation and performance in physics using both qualitative and quantitative methods. The results show that the students had positive attitudes towards Algodoo and that the use of the software had a positive effect on the students' performance and their computational thinking. Another study on the subject by Elias Euler and Bor Gregorcic ”Never far from shore: productive patterns in physics students' use of the digital learning environment Algodoo” shows that simulations can improve students' understanding of physical concepts and help them develop a deeper understanding of the subject. (Euler & Gregorcic, 2020). This study examines high school students' computational thinking experience and perception, and their learning process and outcomes through the visual simulation program, Algodoo. The study was carried out in a physics class in Stockholm with one specific topic, optical density, and light propagation through different mediums. This is a case study with second- and third-year high school students and the data collected by researchers. Smart boards and personal computers were used to construct the demonstration. The participants' experiences and thoughts were documented in a questionnaire and observations, followed by an interview. The questionnaire consists of fifteen open-ended questions, and the verbal interviews were without any specific leading questions. The study used qualitative methods to collect and analyse primary data. / Initiativet för att implementera datalogiskt tänkande och vetenskap, teknik, ingenjörskonst & modellering i lärandeprocesser har tagits av många myndigheter över hela världen och även i Sverige. Den svenska regeringen gjorde ändringar i gymnasieskolans läroplan för att utveckla elevernas digitala kompetens inklusive programmering som trädde i kraft efter juni 2018 (Skolverket, 2018). Läroplanen för gymnasieskolan har som mål att eleven ska kunna ”använda såväl digitala som andra verktyg och medier för kunskapssökande, informationsbearbetning, problemlösning, skapande, kommunikation och lärande”, (Skolverket). Detta mål ställer både elever och lärare i utmaningar som kan hanteras med studier som pekar på svårigheterna och de områden som kräver mer tid och arbete med läromedel som används av skolor. Digitala simuleringar håller snabbt på att bli ett nyckelinstrument i skolundervisningen och flera försök att visa och förstå resultatet av användningen av simuleringar har gjorts i av många forskare.Tidigare studier rapporterar att användningen av Algodoo och andra simuleringsprogram i fysikundervisningen har positiva effekter på eleverna. Dessa studier ger insikter i hur elever kan dra nytta av simuleringar i Algodoo för att förbättra sitt datologisk tänkande och prestanda inom fysik. Enstudie som undersöker detta ämne är "Algodoo for Interactive Learning: Effects on Students Achievement and Motivation Towards Science'' av Aslı Saylan Kirmizigül (2021). Studien undersöker hur Algodoo-mjukvaran påverkar elevernas attityder, motivation och prestation i fysik med hjälp av både kvalitativa och kvantitativa metoder. Resultaten visar att eleverna hade positiva attityder till Algodoo och att användningen av mjukvaran hade en positiv effekt på elevernas prestationer och deras datalogiskt tänkande. En annan studie av Elias Euler och Bor Gregorcic ”Aldrig långt från land: produktiva mönster i fysikstudenters användning av den digitala lärmiljön Algodoo” visar att simuleringar kan förbättra elevernas förståelse för fysiska begrepp och hjälpa dem att utveckla en djupare förståelse av ämnet (Euler & Gregorcic, 2020). Den här studien undersöker gymnasieelevers erfarenhet och uppfattning av datalogiskt tänkande och deras inlärningsprocess och dess resultat genom ett visuellt simuleringsprogram, Algodoo. Studien hade ägt rum i en fysikklass med ett specifikt ämne, optisk densitet och ljusspridning genom olika medier. Detta är en fallstudie med andra och tredje års gymnasieelever och data som samlats in av skribenten. Smartboard och persondatorer hade använts för att konstruera demonstrationen. Deltagarnas erfarenheter och tankar hade dokumenterats i ett frågeformulär, observationer och intervju efteråt. Enkäten består av femton öppna frågor och de verbala intervjuerna är utan några specifika ledande frågor. Studien använde kvalitativa metoder för att samla in och analysera primärdata.

<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>

TITLE: Teaching approaches to pupils' concept development about computer programming in SCRATCH AUTHOR: Bc. Hana Šandová DEPARTMENT: Department of information technology and education SUPERVISOR: Doc. RNDr. Miroslava Černochová, CSc. ABSTRACT: This thesis deals with perceptions of pupils about how computers and computer programs work. The main objective is to inquire of pupils (ISCED-2) about how computer and computer programs work, and what ideas influence them. The theoretical part focuses on the history of the use of computers in teaching and an analysis of current approaches to teaching computer science and programming in the Czech Republic and in selected European countries. The practical part is devoted to mapping concepts of pupils aged 11-14 years involved in a teaching experiment organized at two schools in Prague, the aim of which was to examine the impact of teaching programming in "Scratch" over one semester in relation to pupil's ideas about how a computer works and in terms of the development of pupil's computational thinking. KEYWORDS: pupil's imagination, algorithm, programming, computational thinking, Scratch

We shall examine the Pedagogical Content Knowledge (PCK) of Computer Science (CS) teachers concerning students’ Computational Thinking (CT) problem solving skills within the context of a CS course in Dutch secondary education and thus obtain an operational definition of CT and ascertain appropriate teaching methodology. Next we shall develop an instrument to assess students’ CT and design a curriculum intervention geared toward teaching and improving students’ CT problem solving skills and competences. As a result, this research will yield an operational definition of CT, knowledge about CT PCK, a CT assessment instrument and teaching materials and accompanying teacher instructions. It shall contribute to CS teacher education, development of CT education and to education in other (STEM) subjects where CT plays a supporting role, both nationally and internationally.

Submitted by Lucienne Costa (lucienneferreira@ufcg.edu.br) on 2018-08-29T18:05:26Z No. of bitstreams: 1 ERICK JOHN FIDELIS COSTA – DISSERTAÇÃO (PPGCC) 2017.pdf: 3969742 bytes, checksum: 640b82ab0868dc91523e02df93ab3a6e (MD5) / Made available in DSpace on 2018-08-29T18:05:26Z (GMT). No. of bitstreams: 1 ERICK JOHN FIDELIS COSTA – DISSERTAÇÃO (PPGCC) 2017.pdf: 3969742 bytes, checksum: 640b82ab0868dc91523e02df93ab3a6e (MD5) Previous issue date: 2017-03-05 / O desenvolvimento da capacidade de resolução de problemas deve ser estimulado desde as séries iniciais. Diante dessa necessidade, o ensino de Computação passou a ser considerado com o objetivo de estimular e aprimorar competências essências para resolução de problemas. Desenvolvendo nos estudantes uma maneira de interagir com a Ciência da Computação por meio de um pensamento interdisciplinar, o Pensamento Computacional. O Pensamento Computacional se baseia nas competências adquiridas através da Ciência da Computação, não apenas como ferramenta, mas como uma forma de pensar de maneira organizada e capaz de explorar as potencialidades provenientes das tecnologias da informação e comunicação. As principais abordagens para estimular o Pensamento Computacional, são: através de disciplinas específicas da Ciência da Computação (programação, algoritmos, etc) e, através da aplicação conjunta do Pensamento Computacional em paralelo ao ensino de disciplinas do ensino básico (matemática, ciência e leitura) sem a necessidade de disciplinas específicas da Ciência da Computação. Levando em consideração a segunda abordagem, é um problema realizar aplicações práticas pela falta de subsídio na literatura (poucos estudos práticos), inviabilizando sua aplicação. Pensando nisso, uma abordagem foi concebida para estimular as competências do Pensamento Computacional em conjunto à disciplina de matemática do ensino básico. Tal abordagem teve como objetivo estimular a capacidade de resolução de problemas nos alunos, por meio da própria disciplina de matemática, estimulando as competências essenciais através de atividades práticas utilizando questões em maior conformidade com o Pensamento Computacional. A abordagem proposta evidenciou fatores que possivelmente contribuíram para melhorar a capacidade de resolução de problemas nos alunos envolvidos. Isso foi identificado através da aplicação de um quasi-experimento onde foi possível identificar o impacto das atividades práticas propostas, no que diz respeito ao estímulo à capacidade de resolução de problemas nos alunos. / The development of the capacity to solve problems should be stimulated from the initial grades. Facing this necessity, the teaching of Computing began to be considered with the objective of stimulating and improving essential competences to solve problems. Aiming that students can develop a way of interacting with the Computer Science through an interdisciplinary thinking, the Computational Thinking. The Computational Thinking is based on the competences acquired through the Computer Science, not only as a tool, but as a way of thinking in an organized way and being able to explore the potentiality from information and communication technologies. The main approaches to stimulate Computational Thinking are: through specific Computer Science disciplines(programming, algorithms, etc.) and through the joint application of Computational Thinking in parallel with the teaching of subjects in the basic education(mathematics, science and reading) without the need of specific disciplines from the Computer Science. Considering the second approach, it is a problem to perform practical applications with the lack of subsidy in the literature (few practical studies), preventing its implementation. In this sense, an approach was designed to stimulate the competences of Computational Thinking together with the discipline of mathematics in the basic education. Such approach had the objective to stimulate students’ capacity to solve problems through the discipline of mathematics, stimulating the essential competences through practical activities using exercises in greater conformity with the Computational Thinking. The proposed approach evidenced factors that may have contributed to improve the capacity to solve problems in the students involved. This was identified through the application of a quasi-experiment where it was possible to identify the impact of the practical activities proposed, regarding the stimulation of the students’ capacity to solve problems.