FOSTERING GLOBAL COMPETENCIES AND DEEPER LEARNING WITH DIGITAL TECHNOLOGIES RESEARCH ... · 2019....

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FOSTERING GLOBAL COMPETENCIES

AND DEEPER LEARNING WITH DIGITAL TECHNOLOGIES RESEARCH SERIES

TEACHING AND LEARNING WITH CODING: DIFFERENTIATED EFFECTS ON TEACHERS’TECHNOLOGICAL

PEDAGOGICAL CONTENT KNOWLEDGE (TPACK) AND STUDENTS’LEARNING

Source: thinkstockphotos.ca

Research & Development Toronto District School Board

March 2017 Report No. 17/18-15

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About this Project: This report is the result of a collaborative project supported by the Council of Ontario Directors of Education (CODE), Technology and Learning Fund, TDSB Teaching and Learning Department-STEM K-12 and TDSB Research and Development led by Research Coordinator Erhan Sinay.

TITLE: Fostering Global Competencies and Deeper Learning with Digital Technologies Research Series: Teaching and Learning with Coding: Differentiated Effects on Teachers’ Technological Pedagogical Content Knowledge (TPACK) and Students’ Learning AUTHORS: Erhan Sinay, Thomas G. Ryan & Kamini Jaipal-Jamani Copyright © Toronto District School Board (March 2017) Cite as: Sinay, E., Ryan, T. G., & Jaipal-Jamani, K., (2018). Fostering global competencies and deeper learning with digital technologies research series: Teaching and learning with coding: differentiated Effects on Teachers’ Technological Pedagogical Content Knowledge (TPACK) and students’ learning. (Research Report No. 17/18-15). Toronto, Ontario, Canada: Toronto District School Board. Reproduction of this document for use in the schools of the Toronto District School Board is encouraged. For any other purpose, permission must be requested and obtained in writing from: Research & Development Toronto District School Board 1 Civic Centre Court, Lower Level Etobicoke, ON M9C 2B3 [email protected] Every reasonable precaution has been taken to trace the owners of copyrighted material and to make due acknowledgement. Any omission will gladly be rectified in future printings. R02(STEM\2016-17\Deep Learning and GC Reports\Research Series 3\GC Research Series)es.1485

mailto:[email protected]

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Acknowledgements We would like to thank and acknowledge the support of TDSB Leadership Team: Antonio Santos, Central Coordinating Principal, Toronto District School Board Roula Anastasakos, Executive Superintendent, Research, Organizational Design and Information Service, Educational Partnerships Beth Butcher, Executive Superintendent, LC 1 Leadership-School Effectiveness Manon Gardner, Executive Superintendent, Teaching and Learning, Alternative, International Education We would like to thank and acknowledge the support and contributions of the following research team members in this study: Kamini Jaipal-Jamani, Associate Professor, Science Education, Department of Teacher Education Brock University. Faculty of Education Margaret Douglin, Researcher Coordinator, Research & Information Services, Toronto District School Board Tammy Tse, Researcher, Research & Information Services, Toronto District School Board Cosmin Marmureanu, Researcher, Research & Information Services, Toronto District School Board Sarah Walter, Researcher, Research & Information Services, Toronto District School

Dimitris Graikinis, Researcher, Research & Information Services, Toronto District School Ashkan Safari, Researcher, Research & Information Services, Toronto District School We would like to thank and acknowledge the following academics granting us permissions to use all or part of their survey tool on teacher engagement:

Jason Ravitz, Education Outreach Evaluation Manager, Google

Robert M. Klassen, Department of Education, University of York, York, United Kingdom

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Sündüs Yerdelen, Middle East Technical University, Ankara, Turkey and Kafkas University, Kars,

Turkey

Tracy L. Durksen, Postdoctoral Fellow, The University of New South Wales, Sydney, Australia

Trevor P. Robinson, Utah State University

Stephen H. Whitehead, California University of Pennsylvania

Punya Mishra, Associate Dean of Scholarship & Innovation, Professor, Leadership & Innovation Mary Lou Fulton Teacher's College, Arizona State University

Peggy A. Ertmer, Professor of Learning Design and Technology, Purdue University, College of Education

Ahmad Khanlari, PhD Candidate, University of Toronto

Kaye Ebelt, The Greene School, Florida

Carol Goodenow, Tufts University

Margaret Blanchard, Associate Professor of Science Education, North Carolina State University, College of Education

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Table of Contents

Executive Summary ............................................................................................................... 9

Introduction ........................................................................................................................ 10

Background and Rationale ................................................................................................... 11 Advantages of Coding ............................................................................................................................. 11 Teaching Coding ...................................................................................................................................... 12 Coding: An International View ................................................................................................................ 14 Coding in Canada .................................................................................................................................... 17

Teaching and Learning with Coding at the TDSB ................................................................... 18 Education Technology and Educator Survey Results .............................................................................. 18 Research Questions ................................................................................................................................ 18 Method ................................................................................................................................................... 18 Theoretical knowledge Framework: TPACK Model ................................................................................ 19 Study Demographics ............................................................................................................................... 19

Gender Distribution ............................................................................................................................ 20 Age Range ........................................................................................................................................... 20 Teaching Experience ........................................................................................................................... 21 Grades Taught ..................................................................................................................................... 21

Pre and Post Professional Learning ...................................................................................... 22 Teacher Confidence about Technology and Coding ............................................................................... 22 Teacher Technology Integration ............................................................................................................. 23

Technological Knowledge ................................................................................................................... 23 Content Knowledge............................................................................................................................. 24 Pedagogical Technological Knowledge ............................................................................................... 24 Technological Content Knowledge ..................................................................................................... 25 Pedagogical Content Knowledge ........................................................................................................ 26 Technological Pedagogical Content Knowledge ................................................................................. 26

Aggregate TPACK Results ........................................................................................................................ 27 Teacher Technology Usage and Learning ............................................................................................... 28

SAMR Model ....................................................................................................................................... 28 Usage of Technology in Teaching ........................................................................................................ 28

Post Professional Learning ................................................................................................... 30 Teacher Engagement .............................................................................................................................. 30 Teacher Professional Learning Experience and STEM Coach ................................................................. 30

Number of Coding Sessions ................................................................................................................ 31 STEM Involvement and Coaching Support .......................................................................................... 31 STEM or STEAM? ................................................................................................................................. 32

Teachers' Perceptions of Students ......................................................................................................... 32 Student Skills Development ................................................................................................................ 32

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Student Skills Growth .......................................................................................................................... 33

Conclusions and Recommendations ..................................................................................... 34 Conclusions ............................................................................................................................................. 34 Key Recommendations ........................................................................................................................... 35

References .......................................................................................................................... 37

Appendix A .......................................................................................................................... 43

List of Figures

Figure 1: Advantages of Coding .................................................................................................................. 12

Figure 2: Teaching Coding/Programming Recommendation ...................................................................... 14

Figure 3: STEM in Australian Curriculum (Australian Curriculum, Assessment and Reporting Authority) . 15

Figure 4: International Coding Initiatives .................................................................................................... 16

Figure 5: TPACK Model ................................................................................................................................ 19

Figure 6: Gender Distribution of the Educators .......................................................................................... 20

Figure 7: Age Range of Educators ............................................................................................................... 20

Figure 8: Teaching Experience .................................................................................................................... 21

Figure 9: Grades Taught by Educators ........................................................................................................ 21

Figure 10: Educators’ Confidence with Coding ........................................................................................... 22

Figure 11: Percentage of Educators who Strongly Agreed or Agreed to Increased Technological Knowledge Pre and Post Coding PL .................................................................................................... 23

Figure 12: Pre and Post Coding PL: Percentage of Educators who Strongly Agreed or Agreed with Items Related to Coding and STEM Knowledge ............................................................................................ 24

Figure 13: Percentage of Educators who Strongly Agreed or Agreed with Items Related to Technology in Teaching and Learning Pre and Post Coding PL .................................................................................. 25

Figure 14: Pre and Post Coding PL levels of Agreement Related to technological Content Knowledge .... 25

Figure 15: Percentage of Teachers who Strongly Agreed or Agreed or with Items Related to Pedagogical Content Knowledge Pre and Post Coding PL....................................................................................... 26

Figure 16: Percentage of Educators who Strongly Agreed or Agreed with Items Related to Technological Pedagogical Content Knowledge Pre and Post Coding PL .................................................................. 27

Figure 17: Aggregate Percentages for Each TPACK Component ................................................................. 27

Figure 18: Student Application of SAMR Model ......................................................................................... 28

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Figure 19: Percentage of Educators who Indicated Frequently Using Various Technologies Pre and Post Coding PL ............................................................................................................................................. 29

Figure 20: Teacher Engagement at the End of the Year of Coding Professional Learning ......................... 30

Figure 21: Application of Learning during PL in the Classroom .................................................................. 31

Figure 22: Number of Coding Sessions Educators Conducted with their Students Post PL ....................... 31

Figure 23: Frequency of Educators Working with STEM Coaches in their School ...................................... 32

Figure 24: Degree of Improvement in Student Skill Development ............................................................. 33

Figure 25: Student Skill Growth as a Result of Coding ................................................................................ 33

Figure 26: Coding Professional Learning impacts ....................................................................................... 34

Figure 27: Overview of General and Specific Study Recommendations ..................................................... 35

List of Tables

Table 1: Teachers’ Skills and Knowledge and Student Achievement............................................................ 9

Table 2: Educators who want STEM vs. STEAM .......................................................................................... 32

Table 3: General and Specific Recommendations ...................................................................................... 36

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“In the years ahead, digital fluency will become a prerequisite for obtaining jobs, participating meaningfully in society, and learning throughout a lifetime” (Resnick, 2002, p. 33).

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Executive Summary

In today’s world, there is a growing demand for digitally fluent individuals and a need to develop both teachers’ and students’ technological abilities through education (European Commission, 2017). The TDSB has introduced coding to support students’ digital literacy and fluency development via deep learning and has initiated related professional learning (PL) for teachers.

This report details a quantitative study on teaching and learning coding in the TDSB. Researchers examined the effects of coding PL opportunities on teachers’ Technological, Pedagogical Content Knowledge and elementary school student outcomes across the TDSB. In total, 245 educators, most of whom did not have prior experience with coding, were surveyed before and after taking part in Professional Learning (PL) coding sessions. Results indicated a positive outcome across several variables within the area of teacher skills and knowledge as well ad student global competencies is detailed in Table 1.

Table 1: Teachers’ Skills and Knowledge and Student Achievement

Teacher’s Skills & Knowledge

• Confidence in teaching coding → improvement (39% to 63%) in teachers’ confidence in teaching coding

• Knowledge of coding and STEM → improvement (14% to 51%) in teachers having sufficient knowledge of coding and STEM

• Knowledge of coding and STEM pedagogy → improvement (52% to 64%) in teachers having sufficient knowledge of STEM pedagogy

• Knowledge and use of appropriate technologies to learn about and teach coding (TCK)

→ improvement (40% to 62%) in teachers’ ability to use appropriate technologies to represent and teach coding

• Knowledge of how to combine coding and inquiry teaching strategies across different subjects (TPCK)

→ improvement (28% to 53%) in teachers’ integration of coding, inquiry-based teaching and technology across different subject areas.

Student Achievement

• Improved Global Competencies → Teachers described growth in collaboration, communication and perseverance behaviours in their students in addition to authentic problem solving and social skills

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Introduction

There is currently a growing demand for digitally fluent individuals to fill positions across a broad range of industries, which has led to the need to develop students’ technological abilities through education (Resnick, 2017). As a result, the TDSB has introduced coding as a way of supporting students’ digital literacy and learning. Coding also supports the development of a range of other abilities such as collaboration, creativity, problem solving, mathematical skills, and computational and logical thinking (Sullivan, Strawhacker, & Bers, 2017).

It is believed that teachers must be given the appropriate professional learning (PL) and support in order to be able to teach digital technologies to students (European Commission, 2017). The current report explores the effects of coding PL opportunities on teachers’ Technological Pedagogical Content Knowledge (TPACK), teacher engagement as well as the effects of coding instruction on student outcomes. In total, 245 educators1, from the elementary panel, most of whom did not have prior experience coding, were surveyed before and after taking part in PL coding sessions using the Scratch-coding platform.

Before presenting these findings, a review of applicable literature connected to youth coding is presented. Beyond simply the Canadian perspective, this review explores the strategies that other countries are currently using to incorporate youth coding into their education systems. Barriers to coding are reviewed herein as recognition of the need for digitally literate and fluent students in Canada is constantly growing.

1 In total, 245 educators participated in the Scratch coding professional learning (PL). The majority (92%) did not have any prior PL on the topic of Scratch programming. 240 educators responded in the pre-survey and 150 of the 245 responded in the post-survey. Please see the methods and study demographics sections for details.

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Background and Rationale

Today coding can be accomplished remotely from anywhere using a laptop or mobile device to create images such as characters and/or blocks of words or phrases (Hayes & Stewart, 2016; Solomon, 2015a). Coding and programming can be identified and labelled as a form of problem solving, as code writing is a portion of the problem and a task to complete within a learning journey (Wikijunior, 2016). Age, race, ethnicity and income levels do not tend to present barriers today, as students as young as pre-school, have shown that they can learn how to code. As Figure 1 suggests, children can move from beginner to proficient via several programs that are developmentally friendly using graphics before moving into text (Horn, Crouser, & Bers, 2012; Resnick, 2017; Zuckerman & Gal-Oz, 2013).

Many employer surveys have raised the concerns regarding the fact that Canada faces “difficulty filling ICT positions due to the lack of suitable talent” (The Information and Communications Technology Council, 2015, p. 13). A report released earlier this year by Information and Communications Technology Council mentioned that “…in Canada, school curriculums aren’t developing fast enough to prepare the next generation for as many as 182,000 high-paying tech jobs available by 2019,” (Freeman, 2016, p. 1). Hence, there is currently a need for preparing the next-generation ICT workforce in Canada.

In response to the need of more ICT workforce, TDSB allows students to have an opportunity to better use and connect with technology using coding practices. Beyond developing a specific skill set, coding additionally has led to the increase in overall student academic performance, as well as developing their 21st century skills and global competencies (Resnick, 2017). In combination, these elements lead to better preparation of students to be productive citizens in an increasingly technology infused future (Sullivan, Strawhacker, & Bers, 2017).

Advantages of Coding

Digital literacy and fluency (Spencer, 2015) are essential skills and the value of teaching the coding is generally supported for one of two reasons: “enabling students to understand what programming is all about, and the general value of computational thinking (CT) which will be of use regardless of a student’s career” (Duncan et al., 2014). According to Wing (2010), “computational thinking is the thought processes involved in formulating problems and their solutions so that the solutions are represented in a form that can be effectively carried out by an information-processing agent” (p. 1). Wing (2010) argues that computational thinking is a basic skill that should be taught across the school curriculum to promote learning of abstract, algorithmic and logical thinking that are required to solve complex and open-ended problems. For example, coding can be used to cultivate mathematical knowledge at the early stages of

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educational journey as it is a process that requires reflective thinking to realize solutions while solving problems (Duncan, Bell, & Tanimoto, 2014; Kalelioğlu, 2015).

Coding supports deep learning, which is using learning in one area to learn in another (Pellegrino, 2015, p. xvi). Deep learning promotes the development of 21st century competencies, such as critical thinking, problem solving, innovation, creativity, and entrepreneurship (Rillero, 2016; Sullivan, Strawhacker, & Bers, 2017) that are acknowledged as global competencies. It is well accepted that students need to be aware of and be able to successfully develop global competencies and be engaged in the society (Ontario Ministry of Education, 2016).

Figure 1: Advantages of Coding

Teaching Coding

Teaching coding at schools is not a new phenomenon; computer science has been part of the high school curricula in many countries since the early 1970’s (Solomon, 2015a,b). What is recent is the addition of programming to formalized elementary education curricula around the world and the way programming is being conceptualized in the 21st century.

Rather than coding exercises for learning about algorithms and data structures, children now learn programming to create specific applications, be they video games or interactive stories. They are engaged by the potential to create something real and tangible that can be shared with others, converting the learning of programming — at least initially — from the study of an abstract

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discipline to a way of making and being in the world digitally. (Kafai & Burke, 2013, p. 1)

While there are few empirical investigations about what strategies are effective for teaching coding to elementary students, teacher practitioners report that for early childhood education, robots that involve active, floor space learning teach young children some of the principles of computer programming (Jacobson, 2016). The learning of visual programming languages rather than traditional programming languages is recommended as it enables students to learn the computational concepts rather than be cognitively burdened with the mechanics of programming (Lye & Koh, 2014). Transdisciplinary strategies involving project-based learning with design and art-based learning concepts are also being advocated as effective approaches for motivating students to engage in programming (Peppler, 2014). Another strategy that has been shown to have potential for teaching students to code is learning to program by playing or designing games (Sullivan, Strawhacker, & Bers, 2017).

Often coding unfolds in a game-based learning environment that can be made up of students in a digital environment playing and/or designing a game while learning (Lodaya, 2013). Game-based learning is authentic and links directly to the belief of learning by doing in educational philosophy (Dewey, 1957). Games are highly immersive (Berns, Gonzalez-Bardo & Camacho, 2013); games require cooperation and competition (Shute, Ventura & Ke, 2015), and games encourage risk-taking and provide real-time feedback of failure or success (Hsieh, Lin, & Hou, 2015). Hiltunen’s (2016) study of how programming is being taught in Finnish elementary schools resulted in a proposed framework for integrating programming into elementary subjects without introducing coding as a discrete subject. This framework proposes “the use of programming as a tool for solving subject-specific problems” (p. 52). Pedagogically, programming models elements of logical thinking and need not be entirely computer based. Instead, basic ideas, concepts, theory and practices can be learned while playing in a traditional manner (Liukas & Mykkänen, 2016). This understanding is gained by game-based learning where the focus is on object-oriented programming as a natural, authentic enterprise since humans naturally see objects with properties and behaviours (Corral, Balcells, Estévez, Moreno, & Ramos, 2014).

In addition, a logic-first approach is an effective way of teaching students under 12, via graphical game-based activities. However, teaching materials often take the opposite approach using syntax, variables and logical operations first without providing context that could be realized through computational (logical) thinking (Hiltunen, 2016). The use of a drag & drop (primary age) or code writing (junior age) is dependent upon the developmental stage of students (Fessakis, Gouli & Mavroudi, 2013). Hsieh et al. (2015) claim that gaming in schools

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can steadily increase student engagement, which is a significant challenge in a room full of digital users.

To achieve any ICT goals there is a need to improve computer science in secondary schools and to provide more PL opportunities for all teachers (Salomon, 2015b; Resnick 2017). Children as young as age six can be exposed to coding via graphics before moving into text (Resnick, 2017). Lack of teacher’s PL and coding practices at elementary level as well as fewer computer science classes at the secondary level are all barriers to learning in ICT (Information and Communications Technology Council, 2015). The alternative is that students turn to self-education online and outside formal education which can be viewed as wanting (Sullivan, Strawhacker, & Bers, 2017). Figure 2 summarises some of the key research-based recommendations of teaching coding.

Figure 2: Teaching Coding/Programming Recommendation

Coding: An International View

White (2013), an Australian author, suggests that including a subject such as digital fluency in the K-12 school curricula “will help to address the issues of professional learning, teaching pedagogy as well as assist students to learn new skills in a structured way” (p. 9). Today, a variety of programming tools exist including Logo, EToys, Crunchzilla, Code Monster and Scratch, which are designed for grades one through eight. There are few coding programs for pre-school and kindergarten students, which may be influenced by views that children under

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age 7, are not developmentally ready to understand abstract concepts (Armoni, 2012). Current coding programs require text commands and spatial (fine motor) skills to click or touch to communicate instructions, which may be beyond the developmental level of many pre-school, and kindergarten children (Flannery et al., 2013).

In the United States, President Barack Obama launched a Computer Science For All initiative earlier this year, providing $4 billion (U.S.) in funding for states to teach students, from kindergarten to Grade 12, to learn coding and other computer skills” (Freeman, 2016, p. 1). Beyond government funding, other initiatives like code club exist which is a worldwide network (12 countries) of after-school volunteer run clubs (500+) inviting children, aged 9 to 11, to code via programs such as Scratch, HTML & CSS and Python by making games, animations, and websites (Raspberry Pi Foundation, 2016).

Australia has decided to replace History and Geography with Coding classes, certainly a jump into Information and Communications Technology (ICT). ICT has developed new models to communicate the importance of digital development and as shown in Figure 3 shows an example representation of the relationship between the two subject areas and the thinking skills that (Falkner & Vivian, 2015, p. 5; ACARA STEM connections report. (2016).). Australia’s Labour party committed to invest nine million dollars that will establish a “National Coding in Schools centre (NCIS) so that all teachers in Australia have the opportunity to develop their skills, and every student can have access to exciting ways to learn coding” (Australian Labour Party, 2016, p. 1).

Figure 3: STEM in Australian Curriculum (Australian Curriculum, Assessment and Reporting Authority)

Source: Falkner & Vivian (2015) & Australian Curriculum, Assessment and Reporting Authority)

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Similarly, 12 European countries have already introduced computer programming and coding into their curricula. An additional 7 European countries are in the process of similar transformations. New Zealand and Singapore are also in the midst of including coding in their respective curricula’s, whereas computer programming and coding are already part of the primary curriculum in 12 EU countries (Australian Labour Party, 2016).

South Korea has introduced its software education curricula incrementally before nationwide implementation in 2018 (Park, 2016). A pilot phase was first rolled out across 900 schools offering this new curriculum. Some schools are given extra funding annually for software tools and resources and a means to share progress via seminars and reports. Improved curriculum addresses student satisfaction and teacher feedback (Park, 2016).

Finland is leading the way with their decision to embed programming skills as a mandatory part of Finnish primary school curriculum in the autumn of 2016. “Teaching of these skills will start from . . . grade one. The decision of starting teaching programming skills for 6-12 year old students is creating a new situation to the field of basic education in Finland” (Hiltunen, 2016, p. 2). Within Europe there is currently a European Code Week to increase understanding and knowledge of programming which is a part of the European digital agenda aiming at teaching programming skills for pre-school and primary age students (European Commission, 2017). The digital world is altering economies and societies resulting in the need for digital skills (C21 Canada, 2012). Figure 4 gives an overview of international coding initiatives in education systems.

Figure 4: International Coding Initiatives

Source: Australian Labour Party, 2016, Park, 2016, European Commission, 2017, Raspberry Pi Foundation, 2016

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Coding in Canada

In Canada, some provinces have begun the process of formally integrating coding into the middle and high school curricula. The British Columbia Ministry of Education detailed in their newly revised curriculum, a course called Applied Design, Skills and Technologies, available to grades 6 to 9 students (British Columbia Ministry of Education, 2016). Students in grades 6 to 8 are required to learn text-based coding as well as robotics coding and work with programs such as Scratch. This move is in response to a current Canadian situation where there is currently a lack of ICT workforce. The British Columbia Premier, Chirsty Clark, announced that every student in British Columbia would code from K-12 (Dolski, 2016). In addition, the BC Education Minister said that the province has set millions aside to train teachers and buy technology that can make good use of the high-speed Internet in every school (Dolski, 2016).

The current popularity of coding is also reflected in attendance at summer coding camps. A program manager at Geering Up, a summer series of science and technology camps hosted at the University of British Columbia, said he’s surprised at how popular coding camps have become this summer compared to previous years, it is a life skill (Dolski, 2016). Programs such as Ruby, Scratch and Unity are helping children learn how to code. More advanced coding skills are introduced in grade 9 such as HTML, CSS, JavaScript, binary representation of various data (e.g., text, sound, pictures, video) and programs such as Arduino, Raspberry Pi, and LEGO Mindstorms (British Columbia Ministry of Education, 2016).

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Teaching and Learning with Coding at the TDSB

Education Technology and Educator Survey Results

The results of pre- and post- surveys administered to educators who participated in a Scratch coding professional learning (PL) workshop are presented. The aim was to evaluate its impact on implementation and usage of Scratch coding technologies in the classroom and the development of 21st century skills in their students.

Research Questions

1) Does professional learning in coding, along with additional classroom experience employing coding afterwards, alter the pedagogical approaches teachers use in their classroom in order to teach coding?

2) Does professional learning in coding, along with additional classroom experience employing coding afterwards, encourage teachers to use technology more often in their classroom?

3) Does using coding in classrooms improve the sense of engagement teachers have?

4) Does increased exposure to coding in the classroom improve students’ global competencies?

Method

The PL took place at the University of Toronto in February of 2016 and served as an introduction to Scratch coding. Specifically, a major element of the training was providing ideas to teachers with regards to connecting coding to their curriculum elements.

Chi-square or One-Way ANOVAs were conducted to examine statistically significant differences in the participants’ perception between pre- and post-surveys. Statistical significance was denoted when p ≤ 0.05. All statistical analyses were conducted using IBM SPSS Statistics Version 19. Analysis of this study based on two independent samples and no paired analysis has been done due to limitations of the data to match pre and post teacher participants. This may be seen as a limitation of this study however both pre and post samples included only the teachers who participated to the professional learning and majority (92%) of the teachers participated to the professional learning did not have a prior PL on coding.

Throughout the report “pre- and post-survey” and “before and after professional learning” words used interchangeably when displaying and interpreting the findings. At the end of each construct such as Teacher Confidence, Teacher Engagement, TPACK and etc., “total response rates” used to calculate the aggregated results for that specific construct.

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Theoretical knowledge Framework: TPACK Model

This survey uses Technological Pedagogical Content Knowledge (TPACK) framework that focuses on technology usage and integration into the classroom. The TPACK model, developed by Mishra and Koehler (2006), inter-relates three types of knowledge (technological, content, and pedagogy) and proposes that the combination of theses three knowledge types is essential for successful integration of educational technology into teaching practice. These knowledge areas interact with each other to produce the Pedagogical Content Knowledge (PCK), Technological Content Knowledge (TCK), Technological Pedagogical Knowledge (TPK), and the Technological Pedagogical Content Knowledge (TPACK) as depicted in Figure 5 (Koehler and Mishra, 2009, p. 67; Reproduced by permission).

The following charts detail the opportunities This analysis makes use of the TPACK model, which is an effective instrument to assess the ability that teachers have to integrate robotics successfully into their instruction. Teachers’ answers identify whether they agree or disagree that they have the skills listed.

Figure 5: TPACK Model

Source: Koehler and Mishra, 2009 (p. 67); Reproduced by permission.

Study Demographics

Teacher demographics are presented in the following sections. In total, 245 educators participated in the Scratch coding professional learning (PL). The majority (92%) did not have any prior PL on the topic of Scratch programming. 240 and 150 educators responded in the pre- and post-survey respectively. The pre-survey was administered on paper while the post-survey

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was administered online. Based on these efforts the conclusions and recommendations in this report emerged from the resulting survey data. Participants were assured of anonymity before completing their survey and no potential incentives were provided. It should be noted that in the following graphs the total percentage may not equal to 100% due to the rounding.

Gender Distribution

As shown in Figure 6, 68% were female, 30% were male and 1% were identified as other. As can be seen in this figure, participants were similarly represented by gender in the post-survey.

Figure 6: Gender Distribution of the Educators

Age Range

A similar proportion of educators who completed the survey indicated that they were 30 to 39 years old (39%) or 40 to 49 years old (34%), while fewer educators were 50 to 59 (14%) and 20 to 29 (11%) years old respectively. Only 2% of participants were over 60 years of age (Figure 7).

Figure 7: Age Range of Educators2

2 This graph represents only pre-survey results. This question asked only before the professional learning.

26

95 82

34

6 11% 39% 34% 14% 2%

0

20

40

60

80

100

20-29 years 30-39 years 40-49 years 50-59 years 60+ years

Pre Survey

Post Survey

68%

30%

1%

Gender

Female

Male

Other 70%

29% 1%

Gender

Female

Male

Other

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Post surveyPre survey

25

66

163

73

8% 20% 50% 22%0

50

100

150

200

Kindergarten Grs. 1 to 3 Grs. 4 to 6 Grs. 7 to 8

Grades taught

70

150

214

68

14% 30% 43% 14%0

50

100

150

200

250

Kindergarten Grs. 1 to 3 Grs. 4 to 6 Grs. 7 to 8

Grades taught

Teaching Experience

More than half of the participants (54% for the pre-survey and 61% for post-survey) have been teaching for 11 or more years while only 5% have been teaching for 1-2 years (see Figure 8).

Figure 8: Teaching Experience

Grades Taught

Educators were also asked on both pre- and post- surveys to indicate the grades they instructed. In the pre-survey, half of the participants (50%) taught Grades 4 to 6, followed by a similar proportion who taught Grades 7 to 8 (22%) and Grades 1 to 3 (20%). Similarly, in the post-survey, nearly half of the participants (43%) taught Grades 4 to 6, while 30% taught Grades 1 to 3 and an equivalent number of respondents taught Kindergarten and Grades 7 to 8 as noted in Figure 9.

Figure 9: Grades Taught by Educators

Post SurveyPre survey

1233

68

131

5%14% 28% 54%

020406080

100120140

1-2 years 3-5 years 6-10 years 11 or moreyears

Teaching experience

8 14

36

92

5% 9% 24% 61%0

20

40

60

80

100

1-2 years 3-5 years 6-10 years 11 or moreyears

Teaching experience

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0%

20%

40%

60%

80%

100%

I feel confident thatI have the skills

necessary to usecoding forclassroom

instruction.

I feel confident thatI can engage my

students toparticipate in

technology-basedSTEM projects.

I feel confident thatI can help my

students when theyhave difficulty with

coding.

I feel confident thatI can teach students

to code usingScratch.

39%62%

36% 44%

63%

37%

77%

50%

BeforeAfter

Significant difference between groups (P < 0.001) (P < 0.05)

Pre and Post Professional Learning

In the following section results pertaining to questions that were asked on both pre-and post-surveys are presented.

Teacher Confidence about Technology and Coding

Teachers were asked about their confidence level regarding having coding skills, teaching and helping students with coding as well as their ability of engaging students in STEM projects. Teachers rated their confidence level on the continuum of 0 to 100, where above 60 is the benchmark for “high confidence level”. Figure 10 shows the high confidence level responses of teachers to the questions asked in post- and pre- surveys before and after the coding PL. As can be seen in Figure 10, teachers felt more confident in “having the skills to use coding in their classroom instruction” and “helping students when they have difficulty coding” as the “high confidence level” increased considerably by 24% (from 63% to 39%) and 41% (from 77% to 36%) respectively followed by a slight increase by 6% (from 50% to 44%) in “teaching students to code using Scratch” after participating in the coding PL workshop and subsequent opportunities for classroom implementation of coding.

Figure 10: Educators’ Confidence with Coding

These results reported in this section show that the coding PL and subsequent classroom practice with coding had a highly positive impact on the confidence of teachers in most cases. However, when educators were asked about their “confidence engaging students to participate in technology-based STEM projects”, an overall decrease by 25% (from 37% to 62%) was reported. This finding is found to be consistent with previous studies conducted on teachers’ self-efficacy and STEM (Avery & Meyer, 2012; Jaipal-Jamani & Angeli, 2017) that shows pre-

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0%

20%

40%

60%

80%

100%

I can learntechnology

easily.

I keep up withimportant newtechnologies.

I know how tosolve my own

technicalproblems.

I have thetechnical skills I

need to usetechnology.

89%74%

63% 75%

89%83%

77% 87%

BeforeAfter


service and practicing teachers do not have robust confidence implementing technology, STEM projects with students (Sinay, Jaipal-Jamani, Nahornick, & Douglin, 2016; Zuger, 2012). The actual implementation of such projects in the classroom requires a set of skills and knowledge about STEM pedagogy (TPACK), which teachers do not anticipate (Stohlmann, Moore, & Roehrig, 2012). In addition, each technology-based project may require a different set of technical skills, which teachers believe they do not possess (Jaipal-Jamani & Figg, 2015).

Teacher Technology Integration

The following charts detail the opportunities that teachers need to weave technology and coding into their pedagogical practice before and after receiving PL associated with coding. This analysis makes use of the TPACK model to assess the ability that teachers need to integrate coding successfully into their instructions.

Technological Knowledge

Four questions were asked to illuminate educators’ technological knowledge. An equivalent proportion of educators agreed or strongly agreed before and after PL that “they could learn about technology easily”. A greater number of educators agreed or strongly agreed in “keeping up with new technologies” (9% increase), “knowing how to solve their own technical problems” (14% increase), and “having the technical skills needed to use technology” (9% increase) after PL. The latter three results suggest that participation in PL contributed to increases in educators’ technological knowledge as conveyed in Figure 11.

Figure 11: Percentage of Educators who Strongly Agreed or Agreed to Increased Technological Knowledge Pre and Post Coding PL

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0%

20%

40%

60%

80%

100%

I have sufficientknowledge aboutcoding for use in

teaching andlearning.

I have sufficientknowledge of

coding as itapplies to STEM.

I have variousstrategies for

developing myunderstanding of

coding.

I have sufficientknowledge of

STEM pedagogy.

24% 14% 32%52%

58%51%

68%64%

BeforeAfter


Content Knowledge

Following coding PL, significantly more educators agreed or strongly agreed that they “have sufficient knowledge about coding for use in teaching and learning” (see Figure 12). This change was expected, as 92% of participants had no prior PL on coding. As can be seen in Figure 12, there were also notable increases in educators’ level of agreement in “having sufficient knowledge of coding as it applies to STEM”, “having various strategies for developing my understanding of coding” and “having sufficient knowledge of STEM pedagogy” after the PL. These findings suggest that more educators indicated having more content knowledge of coding and STEM after the coding PL.

Figure 12: Pre and Post Coding PL: Percentage of Educators who Strongly Agreed or Agreed with Items Related to Coding and STEM Knowledge

Pedagogical Technological Knowledge

As shown in in Figure 13, many educators believed that they did have pedagogical technological knowledge as reflected in high pre-survey levels of agreement in their abilities in “choosing technologies that enhance the teaching approaches for a lesson” (75%), “choosing technologies that enhance students' learning for a lesson” (78%), “thinking critically about how to use technology in the classroom” (87%), and “varying tasks according to an individual student's technological knowledge” (71%). All items showed significant gains from pre-to-post survey as by shown in Figure 13.

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0%

20%

40%

60%

80%

100%

I can choosetechnologies that

enhance theteaching

approaches for alesson.

I can choosetechnologies that

enhancestudents' learning

for a lesson.

I am thinkingcritically about

how to usetechnology in my

classroom.

I can vary tasksaccording to an

individualstudents'

technologicalknowledge.

75% 78% 87%

71%

87% 89% 93%81%

BeforeAfter


0%

20%

40%

60%

80%

100%

I know abouttechnologies that I

can use forunderstanding and

doing coding.

I am able to useappropriate

technologies torepresent the

content of coding.

I am able to useappropriate

technologies forthe research of myprimary teaching

subjects.

I know abouttechnologies that I

can use forunderstanding andintegrating STEM.

32% 40%

77%

46%

70%62%

92%

73%

BeforeAfter


Figure 13: Percentage of Educators who Strongly Agreed or Agreed with Items Related to Technology in Teaching and Learning Pre and Post Coding PL

Technological Content Knowledge

Educators showed significant gains from pre-to-post survey in the level of agreement about “knowledge of technologies that can be used for understanding and doing coding” as it increased from 32% to 70% (See Figure 14). As also shown in Figure 14, after the coding PL, educators’ ability in “using appropriate technologies to represent the content of coding” (i.e. multimedia resources, simulations, and digital tools) was improved by 22% (from 40% to 62%) as was their ability in “using appropriate technologies for research of their primary teaching subject(s)” and “knowledge about technologies that can be used for understanding and integrating STEM” by 15% and 27% respectively.

Figure 14: Pre and Post Coding PL levels of Agreement Related to technological Content Knowledge

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0%

20%

40%

60%

80%

100%

I can use effectiveteaching approaches

to guide studentthinking and

learning usingcoding.

I am able to assessstudents' mastery of

coding and adaptmy teaching based

on the results.

I am able toefficiently locateresources for the

research of coding.

I can use effectiveteaching approaches

to guide studentthinking and

learning in differentsubject areas.

40%

24% 35%

83%

63%51%

64%85%

BeforeAfter


Pedagogical Content Knowledge

Pre-to-post survey data changes indicated gains in educators’ ability in “using effective teaching approaches to guide student thinking and learning using coding” (23% increase) and “assessing students' mastery of coding and adapt teaching based on results” (27% increase) after the coding PL as depicted in Figure 15. Also as can be seen in this figure, respondents indicated notable improvement (29%) in “efficiently locating resources for the research of coding” while ability in “effective teaching approaches to guide student thinking and learning in different subject areas” (e.g. math, science and technologies, social studies and language) only slightly improved by 2%. Results suggest that assessing students’ mastery of coding and adapting teaching based on the results is an area nearly half of the teachers still need support.

Figure 15: Percentage of Teachers who Strongly Agreed or Agreed or with Items Related to Pedagogical Content Knowledge Pre and Post Coding PL

Technological Pedagogical Content Knowledge

As illustrated in Figure 16, there were positive changes in TPACK items after coding PL. Namely, more educators indicated moderate improvements in their teaching ability in “appropriately combining coding, inquiry-based teaching approaches and technologies” (25%) as well as designing learning experiences that “develops students’ collaboration, creativity, and innovation skills” (24%) and “provides opportunities for authentic conversations” (22%) after PL. Results also showed slightly higher numbers of teachers (6%) believed “they were able to use strategies that combine content, technologies, and inquiry-based teaching approaches in their classroom” after PL. The modest gains observed concerning TPACK items suggest the need for continued PL in the all areas mentioned above.

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0%

20%

40%

60%

80%

100%

I can teach lessonsthat appropriatelycombine coding,

inquiry based teaching,and technologiesacross differentsubjects areas.

I am able to designlearning experiences

that develop students'collaboration,creativity, and

innovation skills byappropriately

combining coding,inquiry based teaching,and technologies in my

classroom.

I am able to designlearning experiences

that provideopportunities for

authenticconversations by

appropriatelyincorporating coding,

inquiry based teaching,and technologies in my

classroom.

I can use strategiesthat combine content,

technologies, andinquiry based teachingin my classroom that I

learned about inprofessional training.

28% 39%33%

72%

53% 63%55%

78%

Before

After


Figure 16: Percentage of Educators who Strongly Agreed or Agreed with Items Related to Technological Pedagogical Content Knowledge Pre and Post Coding PL

Aggregate TPACK Results

Figure 17 provides an overall view of changes of TPACK components after PL with coding, indicating most components rose within a moderate (9% in TK and TPK) to more notable change (30% in CK, 25% in TCK and 21% in PCK, 19% in TPACK) rates while no change was observed for PK. Based on these findings, it is suggested that professional learning is clearly essential in enhancing teacher’s coding/STEM knowledge for their teaching practices.

Figure 17: Aggregate Percentages for Each TPACK Component

75%

43% 78% 49%

30%

45%

98%

62% 87% 74%

60% +30

66%

98% 0

84% +9 +25 +9

+19

+21

Source: Koehler and Mishra, 2009 (p. 67); Reproduced by permission.

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Teacher Technology Usage and Learning

SAMR Model3

As depicted in Figure 18, pre- and post-survey results revealed a fairly consistent pattern of “technology use with students”. Post-survey results show that approximately one-third of the teachers frequently or very frequently had students “using technology as a substitution tool to complete tasks”. Results also show that some educators had students who frequently or very frequently “use technologies to augment learning” and “modify learning and tasks using technology”. However, fewer educators had students who frequently or very frequently “use technology to completely redefine learning and task completion”.

Figure 18: Student Application of SAMR Model

Usage of Technology in Teaching

As shown in Figure 19, results revealed an increase in the frequency of “use of collaboration tools” (17%) such as Google apps and the “use of computers/laptops” (32%) after teachers took part in coding PL. Additionally, results showed slight increases in the use of “interactive whiteboards” (4%) and “video-conferencing” (1%) between pre- and post-survey, while a

3 For details about the STEM Professional Learning and technology integration models including TPACK and SAMR please see: Sinay, E., Jaipal-Jamani, K., Nahornick, A., & Douglin, M. (2016). STEM teaching and learning in the Toronto District School Board: Towards a strong theoretical foundation and scaling up from initial implementation of the K-12 STEM strategy. Research Series I. (Research Report No. 15/16-16 Toronto, Ontario, Canada: Toronto District School Board. Sinay, E., Ryan, T. G., Nahornick, A., & Sauriol, D. (2018). Fostering global competencies and deeper learning with digital technologies research series: Improving global competencies and deeper learning through integration of robotics and technology into the classroom. (Research Report No. 17/18-15). Toronto, Ontario, Canada: Toronto District School Board.

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decline (or no change) was observed in the use of “mobile devices” (0%) and “social media” (-1%) and video-conferencing”. These findings may reflect on educators’ limited access to technology-based resources as addressed in other studies (Levin & Wadmany, 2008; Ryan & Bagley, 2016).

Figure 19: Percentage of Educators who Indicated Frequently Using Various Technologies Pre and Post Coding PL

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In class, I care about the problems of my students.

In class, I am empathetic towards my students.

Teacher Engagement

I am excited about teaching.

At school, I am committed to helping my colleagues.

While teaching, I really "throw" myself into my work.

At school, I value the relationships I build with my colleagues.

I love teaching.

While teaching I pay a lot of attention to my work.

11%

7%

12%

11%

8%

7%

5%

9%

9%

41%

31%

36%

29%

36%

36%

18%

23%

31%

47%

61%

49%

57%

53%

57%

76%

67%

58%

150

150

148

150

148

149

147

149

1191

Occasionally Sometimes Often Frequently AlwaysResponse Count

Post Professional Learning

Although the previous section did look at pre- and post-survey results, there were some unique questions that were asked only in the post-survey. As noted earlier in the method section, 150 of the 245 responded in the post-survey and exist as standalone items here exclusively. In the following graphs total percentage may not equal to 100% due to rounding.

Teacher Engagement

As illustrated in Figure 20, results from a post-survey administered to teachers after they completed the coding professional learning revealed that the majority of teachers were frequently or always “committed to helping colleagues” (92%), “paid a lot of attention to their work” (93%), “cared about the problems of their students” (94%) and “were empathetic towards students” (90%). Many teachers reported that they were frequently or always “excited about teaching” (88%), “loved teaching” (89%), and “valued the relationships built with colleagues” (86%). Many frequently or always really ”threw themselves into their work” (85%) .

Figure 20: Teacher Engagement at the End of the Year of Coding Professional Learning

Teacher Professional Learning Experience and STEM Coach

Almost all respondents in the post-survey attended the two-day coding professional learning sessions conducted at the University of Toronto. When asked to what degree they applied what they learned to their teaching practice, more than half stated they had applied some, most or all of it in their practice as noted in Figure 21. This suggests that more teachers could use support and encouragement to utilize coding and technology learning in their teaching practice;

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since teaching practices are often a consequence of resources and motivation (Strawhacker, Lee, & Bers, 2017).

Figure 21: Application of Learning during PL in the Classroom

Number of Coding Sessions

As shown in Figure 22, almost a quarter of teachers had carried out 10 or more coding sessions in their classes since February 2016, and nearly one-third had conducted 5 or more sessions in their classes. It was found that the other half of the teachers offered 4 or less coding sessions to their students. Again, the data indicate that more teachers need to be encouraged to implement coding in their classrooms.

Figure 22: Number of Coding Sessions Educators Conducted with their Students Post PL

STEM Involvement and Coaching Support

Another interesting outcome of the coding surveys was that few teachers regularly worked with STEM coaches. This clearly demonstrates that educators need to be encouraged to work more closely with STEM coaches at their schools and suggests that STEM coaches need to be more visible and accessible to teachers (see Figure 23).

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23.9% (34) 73.2% (104) 2.8% (4)

STEM or STEAM?

Would you like to see the TDSB STEM strategy continued as STEM education or as STEAM education?

STEM STEAM Other

Figure 23: Frequency of Educators Working with STEM Coaches in their School

STEM or STEAM?

Nearly three quarters of educators would like to see a STEAM strategy implemented explicitly to incorporate arts as well as science, technology, engineering and mathematics (Table 2).

Table 2: Educators who want STEM vs. STEAM

Teachers' Perceptions of Students

Student Skills Development

Teachers were asked about the extent to which students’ skills had improved over the course of the year during which coding may have been implemented in their classes. Results showed that most teachers indicated moderate to high improvement in students’ skills such as “sharing ideas with others” (79%), “helping other students needing assistance” (71%) and “working well with others to accomplish a task” (71%) as depicted in Figure 24. Additionally, majority of educators believed students demonstrated moderate to high improvement in “following to instructions from other students” (62%), “asking questions to better understand the problem” (63%), “trying out different possibilities to solve the problem” (66%), and “coming up with their own ideas about how to solve the problem” (65%).

About half of the teachers stated their students exhibited moderate to high improvement of confidence in their “ability to solve problems” (52%), “giving feedback to others” (57%), “working on a problem until it was solved” (57%), and “listening to feedback from others” (56%). These results are quite positive suggesting that teachers need to focus on supporting students and encouraging them to feel confident about their problem-solving abilities.

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They work on the problem until it is solved.

They feel confident about their ability to solve the problem.

They discover new ways of doing things.

They work well with others to accomplish the task.

They are able to follow instructions given by other students. They often ask questions to better understand the problem.

Student Skill Development

They share ideas with others.

They give feedback to others.

They listen to feedback from others.

They help students who need assistance.

They try out different possibilties to solve the problem and answer the question. They come up with their own ideas about how to solve the problem and answer the question. They often test out different ideas and work to improve them.

11%

10%

9%

6%

6%

5%

7%

14%

14%

5%

11%

11%

9%

9%

18%

28%

28%

28%

30%

33%

14%

29%

30%

24%

32%

37%

31%

28%

52%

43%

38%

45%

43%

39%

45%

43%

41%

42%

43%

40%

38%

42%

19%

19%

25%

21%

22%

23%

34%

14%

15%

29%

14%

12%

22%

21%

104

103

103

104

101

103

102

100

100

100

100

100

100

1320

No change Some importance Moderate Importance High importanceResponse Count

15%12%10%12%11%10%7%

13%11%

529 100%

Please select the areas in which your students have shown growth as a result of coding:

Perseverance 67Problem based learning 56

Total

Creativity 59Innovatitive thinking 55Inquiry-based learning 37

Communication 64Computational skills 53Confidence 61

Collaboration 77

Figure 24: Degree of Improvement in Student Skill Development

Student Skills Growth There was also evidence from the survey that learning coding may have contributed to the development of several student skills and competencies as shown in Figure 25. Many teachers perceived an improvement in collaboration (15%), communication (12%) and perseverance (13%) behaviours in their students and one-third of educators indicated increased student confidence (12%), creativity (11%), innovative thinking (10%), problem-based learning (11%) and computational thinking (57%). Very few educators also noted growth in inquiry-based learning skills (10%). In order to increase development in the latter skill area, PL for teachers should include concrete examples of how coding can be used to promote inquiry-based learning.

Figure 25: Student Skill Growth as a Result of Coding

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Conclusions and Recommendations

Conclusions

The professional learning opportunity implemented by the TDSB, to develop educators’ knowledge of coding and how to teach coding to students, yielded promising results. As detailed below via Figure 26, there was growth in educators’ skills and knowledge in the following areas: 1) confidence to teach coding, 2) knowledge of coding and STEM, 3) knowledge of coding and STEM pedagogy, 4) knowledge and use of appropriate technologies to learn about and teach coding (TCK), 5) changes in teacher engagement, and 6) knowledge of how to combine coding and inquiry teaching strategies across different subjects (TPACK).

Figure 26: Coding Professional Learning impacts

The results also suggest that coding professional learning had an impact on student global competencies, as stated by Ontario Ministry of Education:

“Implementing coding in schools also promoted growth in 21st century global competencies and skills among students, with the most growth in areas such as collaboration, perseverance, and confidence and communication. The coding professional learning by the TDSB therefore contributes to the current vision for education in Ontario to “transform teaching and learning [to] ensure that students develop the knowledge, skills, and characteristics to become personally successful, economically productive, and actively engaged citizens.” (Ontario Ministry of Education, 2016, p. 55)

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The recommendation of this report is to continue expanding coding programs to more classrooms within schools already incorporating it and expanding it to schools that have no coding programs. With a variety of positive effects for students and educators, coding can influence engagement and the development of coding and programming skills are necessary for the new generation of students to learn as they eventually will be inheriting a world which will demand digitally fluent citizens.

Key Recommendations

Based on the impact of the coding professional learning on teachers’ Technological Pedagogical Content Knowledge (TPACK) and students’ learning responses, key study recommendations include: 1) continuation of coding professional learning, 2) increase in teacher and student technology use, 3) more encouragement to work with STEM coaches, and 4) scaling-up the coding initiative. Figure 27 gives an overview of General and Specific Study Recommendations.

Figure 27: Overview of General and Specific Study Recommendations

For each of the four recommendations, there are specific recommendations. Each specific recommendation is an actionable task to achieve general recommendations. The key general and specific recommendations are summarized in Table 3. Our recommendation is to use the table 3 as a checklist and guide for future implementation of teaching and learning of coding.

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Table 3: General and Specific Recommendations

Recommendation Specific Recommendations

• Continue Coding PL Focus on specific teaching strategies for coding

Technology-based STEM Projects Information on coding & STEM Assessment for coding Combining coding with other areas

• Increase Technology Use ncourage increased use of technology Provide concrete examples of technology

use for learning Coding & Inquiry-based learning

• Provide Follow-up Support

Follow-up support after PL Follow-up coding in the classroom

• Work with STEM Coaches

Encourage working with STEM coaches STEM coaches need to be more visible STEM coaches need to be more

accessible

• Scale-up Coding Promote coding throughout the TDSB Scale-up up coding in all schools

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Appendix A

*DT= Digital Technologies, D&T = Design and Technologies - Adapted from (Falkner & Vivian, 2015, pp. 44-48) Resource Resource URL/ Location Subject Competence

Required Technology Required? Main Topic(s) Audience

CS Field Guide http://www.csfieldguide.org.nz

DT Flexible None Data & Visualisation, Digital Data (incl. binary), Algorithms, Programming

Student (Teacher materials by request)

CS Unplugged http://csunplugged.org DT Beginner General purpose programming, Internet

Digital Data (incl. binary), Hardware & Software (incl. interface design), Algorithms, Compression and Encryption

Teacher (lesson/unit plans)

Google Computational Thinking

https://www.google.com/edu/resources/programs/exploring-computational-thinking/

DT Varied. 9-12 level assume competence and ability in Python.

Visual Programming, General purpose programming, Robotics, Electronics , Internet

Data & Visualisation, Algorithms, Programming, Computational Thinking


BBC http://www.bbc.co.uk/schools/0/computing/

DT Flexible None, Internet only required for the interactive tests

Data & Visualisation, Digital Data (incl. binary), Algorithms, Programming, Computational Thinking

Teacher (instructions/lesson plan) & Student (interaction with tutorial)

Common Sense Media

https://www.commonsensemedia.org/educators/lesson

DT Flexible Internet Creating and Interacting Online


Cyber Smart Cybersmart.gov.au DT Beginner Visual Programming Creating and Interacting Online


Think You Know? http://www.thinkuknow.co.uk

DT Beginner Internet Creating and Interacting Online


Web Maker https://webmaker.org/en-US/make-your-own

DT Intermediate Visual Programming, Internet

Programming, General resources built using web-based technology as examples


ABC Splash http://splash.abc.net.au/home#!/home

Both Beginner Visual Programming, Internet

Programming

Science Buddies http://www.sciencebuddies.org/

Both Intermediate Visual Programming Programming Student

Try Computing http://www.trycomputing.org/inspire

DT Intermediate Internet Digital Data (incl. binary), Networks, Algorithms


Thinking Yourself http://games.thinkingmyself.com/

DT Beginner General purpose programming, Internet

Computational Thinking Student

http://www.csfieldguide.org.nz/

http://www.csfieldguide.org.nz/

http://csunplugged.org/





http://www.bbc.co.uk/schools/0/computing/

http://www.bbc.co.uk/schools/0/computing/




http://cybersmart.gov.au/

http://www.thinkuknow.co.uk/

http://www.thinkuknow.co.uk/

https://webmaker.org/en-US/make-your-own

https://webmaker.org/en-US/make-your-own

http://splash.abc.net.au/home#!/home

http://splash.abc.net.au/home#!/home

http://www.sciencebuddies.org/

http://www.sciencebuddies.org/

http://www.trycomputing.org/inspire

http://www.trycomputing.org/inspire

http://games.thinkingmyself.com/

http://games.thinkingmyself.com/

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Resource Resource URL/ Location Subject Competence Required

Technology Required? Main Topic(s) Audience

EduTopia - Coding

http://www.edutopia.org/topic/coding-classroom

DT Intermediate None, Visual Programming, Internet

Algorithms, Programming

Teacher (professional learning)

Australian Curriculum Lessons - Digital Technologies

http://www.australiancurriculumlessons.com.au/category/technology-lesson-plans/digital-technologies-lesson-plans/

DT Beginner None Data & Visualisation, Algorithms, Programming


Teaching Ideas - Computing

http://www.teachingideas.co.uk/ict/contents.htm

DT Intermediate General purpose programming, Internet

Hardware & Software (incl. interface design), Creating and Interacting Online, ICT Literacy


Greenfoot http://www.greenfoot.org/doc

DT Intermediate Visual Programming, Electronics



Educade - Makey Makey

http://educade.org/lesson_plans/use-makey-makey-to-design-a-videogame-controller

Both Intermediate General purpose programming, Internet

Hardware & Software (incl. interface design)


ScratchED http://scratched.gse.harvard.edu


Data & Visualisation, Algorithms, Programming


Coding Bat http://codingbat.com DT Intermediate General purpose programming

Programming Student

Code Club http://projects.codeclubworld.org/en-GB/

DT Beginner Presentation Software (& any other showcase tech)

Programming Student

CS FIRST http://www.cs-first.com/materials

DT Beginner Internet Programming Teacher (instructions/lesson plan) & Student (interaction with tutorial)

Mobilizing CS http://www.mobilizingcs.org/for-teachers

DT Beginner Visual Programming, Internet

Data & Visualisation Teacher (lesson/unit plans)

CSTA - Computational Thinking

http://csta.acm.org/Curriculum/sub/CompThinking.html

DT Beginner Visual Programming, General purpose programming, Electronics , Hardware (e.g. Arduino), Internet

Computational Thinking Teacher (professional learning)

CSIRO CREST http://www.csiro.au/Portals/Education/Teachers/Classroom-activities/CREST/Overview/How-it-works.aspx

D&T Beginner Internet Depends on projects - science & technology


Bebras http://www.bebras.edu.au/ DT Beginner Internet Computational Thinking Student

NCWiT - IT Outreach

http://www.ncwit.org/resources/outreach-box-discovering-it

Both Beginner Visual Programming, General purpose programming, Robotics, Electronics , Hardware (e.g. Arduino)

Overview of CS & Careers

Outreach Materials










http://www.greenfoot.org/doc

http://www.greenfoot.org/doc





http://scratched.gse.harvard.edu/

http://scratched.gse.harvard.edu/

http://codingbat.com/

http://projects.codeclubworld.org/en-GB/

http://projects.codeclubworld.org/en-GB/

http://www.cs-first.com/materials

http://www.cs-first.com/materials

http://www.mobilizingcs.org/for-teachers

http://www.mobilizingcs.org/for-teachers




http://www.csiro.au/Portals/Education/Teachers/Classroom-activities/CREST/Overview/How-it-works.aspx





http://www.bebras.edu.au/




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CS4FN http://www.cs4fn.org/teachers/

DT Intermediate Visual Programming, General purpose programming, Internet

Programming, Computational Thinking


CSTA - Curriculum Resources

http://csta.acm.org/Curriculum/sub/CurrResources.html

DT Beginner Digital Data (incl. binary), Networks, Algorithms, Programming, Project Management


DLTV https://dltv.vic.edu.au/search?query=Digital%20Technologies

DT Beginner Internet Algorithms, Computational Thinking, Digital Technologies learning area overview.


NCWiT - Agent Cubes

http://www.ncwit.org/resources/agentcubes-box-introduce-computing-through-game-design

Both Beginner Visual Programming Programming Teacher (lesson/unit plans)

Made With Code https://www.madewithcode.com/resources

Both Beginner Internet Hardware & Software (incl. interface design), Programming

Student

Exploring CS (Google)

https://sites.google.com/site/tasteofcs/ http://www.exploringcs.org/about/mission (First site leads to second)

DT Intermediate General purpose programming, Internet

Digital Data (incl. binary), Programming, Computational Thinking


Coursera https://www.coursera.org/course/ictinprimary

Both Beginner Depends on projects Hardware & Software (incl. interface design), Programming


Tickering Fundamentals (Coursera)

https://www.coursera.org/course/tinkering

Both Beginner Visual Programming Hardware & Software (incl. interface design)


Code Yourself! An Introduction to Programming (Coursera)

https://www.coursera.org/course/codeyourself

DT Beginner Visual Programming Algorithms, Programming

Anyone

Introduction to Programming with Scratch in Education MOOC

https://uni-cs4hs-scratch.appspot.com/preview

DT Beginner Internet Programming Teacher (professional learning)

Google Making Sense of Data MOOC

https://datasense.withgoogle.com/course

DT Beginner General purpose programming, Hardware (e.g. Arduino), Internet

Data & Visualisation Anyone

Teach Mobile CS https://ram8647.appspot.com/teach_mobileCSP/preview

DT Beginner Internet Hardware & Software (incl. interface design), Programming


Google Internet 101 Course

https://educourses.withgoogle.com/101

DT Beginner Internet Creating and Interacting Online, Internet

Student

IV MOOC http://ivmooc.cns.iu.edu/ DT Intermediate Internet Data & Visualisation Anyone

Teach IT MOOC https://css-cs4hs.appspot.com

D&T Beginner Internet Internet Teacher (professional learning)

CSER F-6 Digital Technologies MOOC

https://csdigitaltech.appspot.com/cser_foundations6/preview


Data & Visualisation, Digital Data (incl. binary), Hardware & Software (incl. interface design), Algorithms, Creating and Interacting


http://www.cs4fn.org/teachers/

http://www.cs4fn.org/teachers/




https://dltv.vic.edu.au/search?query=Digital%20Technologies







https://www.madewithcode.com/resources

https://www.madewithcode.com/resources

https://www.coursera.org/course/ictinprimary

https://www.coursera.org/course/ictinprimary










https://ram8647.appspot.com/teach_mobileCSP/preview





http://ivmooc.cns.iu.edu/

https://css-cs4hs.appspot.com/

https://css-cs4hs.appspot.com/




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Online, Computational Thinking

Creative Computing MOOC

https://creative-computing.appspot.com

DT Beginner None, Visual Programming, Internet

Algorithms, Programming, Computational Thinking


Scratch MIT - Makey Makey (Games)

http://scratch.mit.edu/studios/223260/

DT Intermediate Visual Programming, Internet

Hardware & Software (incl. interface design), Algorithms, Programming


Scratch - Makey Makey (Music)


DT Intermediate Visual Programming, Algorithms, Programming


Wonder Workshop - Dash and Dot

https://www.makewonder.com/play/ideas/

DT Intermediate Visual Programming, General purpose programming, Electronics, Dash and Dot



Little Bits http://littlebits.cc/browse-lessons

Both Intermediate Electronics , Internet Hardware & Software (incl. interface design), Algorithms, Programming


Kids Ruby http://kidsruby.com/ DT Intermediate General purpose programming, Internet



Ruby for Kids http://ruby4kids.com/ruby4kids

DT Intermediate Visual Programming, Tablet


Anyone

ScratchJR http://www.scratchjr.org/ DT Beginner Visual Programming Algorithms, Programming


Scratch MIT http://scratch.mit.edu/ DT Beginner Visual Programming, Internet

Data & Visualisation, Algorithms, Programming

Student

Hackety Hack http://www.hackety.com/lessons

DT Beginner Visual Programming, Hardware (e.g. Arduino)

Programming Anyone

MIT App Inventor http://appinventor.mit.edu/

Both Beginner General purpose programming, Internet



Kodu http://www.kodugamelab.com/resources/


Programming Teacher (instructions/lesson plan) & Student (interaction with tutorial)

Blockly Games https://blockly-games.appspot.com

DT Beginner Internet, Web based programming


Student

Bootstrap http://www.bootstrapworld.org/



Teacher (instructions/l

https://creative-computing.appspot.com/

https://creative-computing.appspot.com/







http://littlebits.cc/browse-lessons

http://littlebits.cc/browse-lessons

http://kidsruby.com/

http://ruby4kids.com/ruby4kids

http://ruby4kids.com/ruby4kids

http://www.scratchjr.org/

http://scratch.mit.edu/

http://www.hackety.com/lessons

http://www.hackety.com/lessons

http://appinventor.mit.edu/

http://appinventor.mit.edu/

http://www.kodugamelab.com/resources/

http://www.kodugamelab.com/resources/

https://blockly-games.appspot.com/

https://blockly-games.appspot.com/

http://www.bootstrapworld.org/

http://www.bootstrapworld.org/

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esson plan) & Student (interaction with tutorial)

Alice 3D http://www.alice.org/index.php#

Both Beginner/Varied Internet Programming, Creating and Interacting Online


Code Academy http://www.codecademy.com/learn

DT Can be beginner, but recommended some understanding of Algorithms, CS.

Visual Programming Hardware & Software (incl. interface design), Programming

Student

Code Academy http://www.codecademy.com/schools/curriculum

DT Intermediate Internet, Visual Programming

Digital Data (incl. binary), Algorithms, Programming

Teacher (professional learning), Student

Khan Academy - Computer Science

https://www.khanacademy.org/computing/computer-science


Digital Data (incl. binary), Algorithms, Programming

Student

Khan Academy - Computer Programming

https://www.khanacademy.org/computing/computer-programming

DT Beginner Internet Programming Student

Khan Academy - Computing Professional

https://www.khanacademy.org/computing/computer-programming/meet-the-computing-professional

Both Beginner Internet Careers and applications of CS

Student

Code.org code.org DT Beginner Visual Programming, General purpose programming, Internet

Digital Data (incl. binary), Algorithms, Programming, Computational Thinking


http://www.alice.org/index.php

http://www.alice.org/index.php

http://www.codecademy.com/learn

http://www.codecademy.com/learn

http://www.codecademy.com/schools/curriculum

http://www.codecademy.com/schools/curriculum











http://code.org/

http://code.org/

FOSTERING GLOBAL COMPETENCIES AND DEEPER LEARNING WITH DIGITAL TECHNOLOGIES RESEARCH ... · 2019....

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