Models to Measure Students’ Learning in Computer Science

As computer science becomes integrated into K-12 education systems worldwide, educators and researchers continuously search for effective methods to measure and understand students’ learning levels in this field. The challenge lies in developing reliable and comprehensive assessment models that accurately and discreetly gauge student learning. Teachers must assess learning to support students’ educational needs better. Similarly, students and parents expect schools to document students’ proficiency in computing and their practical application. Unlike conventional subjects such as math and science, very few relevant assessments are available for K-12 CS education. This article explores specific models used to measure knowledge in various CS contexts and then examines several examples of student learning indicators in computer science.

Randomized Controlled Trials and Measurement Techniques

An innovative approach to measuring student performance in computer science education involves evaluating the effectiveness of teaching parallel programming concepts. Research by Daleiden et al. (2020) focuses on assessing students’ understanding and application of these concepts.

The Token Accuracy Map (TAM) technique supplements traditional empirical analysis methods, such as timings, error counting, or compiler errors, which often need more depth in analyzing the cause of errors or providing detailed insights into specific problem areas encountered by students. The study applied TAM to examine student performance across two parallel programming paradigms: threads and process-oriented programming based on Communicating Sequential Processes (CSP), measuring programming accuracy through an automated process.

The TAM approach analyzes the accuracy of student-submitted code by comparing it against a reference solution using a token-based comparison. Each element of the code, or “token,” is compared to determine its correctness, and the results are aggregated to provide an overall accuracy score ranging from 0% to 100%. This scoring system reflects the percentage of correctness, allowing for a detailed examination of which students intuitively understand specific elements of different programming paradigms or are more likely to implement them correctly.

This approach extends error counts, offering insights into students’ mistakes at a granular level. Such detailed analysis enables researchers and educators to identify specific programming concepts requiring further clarification or alternative teaching approaches. Additionally, TAM can highlight the strengths and weaknesses of different programming paradigms from a learning perspective, thereby guiding curriculum development and instructional design.

Competence Structure Models in Informatics

Torsten et al. (2015) introduced a new model in their discussion aimed at developing a competence structure model for informatics with a focus on system comprehension and object-oriented modelling. This model, part of the MoKoM project (Modeling and Measurement of Competences in Computer Science Education), seeks to create a competence structure model that is both theoretically sound and empirically validated. The project’s goals include identifying essential competencies in the field, organizing them into a coherent framework, and devising assessments to measure them accurately. The study employed the Item Response Theory (IRT) evaluation methodology to construct the test instrument and analyze survey data.

The initial foundation of the competence model was based on theoretical concepts from international syllabi and curricula, such as the ACM’s “Model Curriculum for K-12 Computer Science” and expert papers on software development. This framework encompasses cognitive and non-cognitive skills pertinent to computer science, especially emphasizing system comprehension and object-oriented modelling.

The study further included conducting expert interviews using the Critical Incident Technique to validate the model’s applicability to real-world scenarios and its empirical accuracy. This method was instrumental in pinpointing and defining the critical competencies needed to apply and understand informatics systems. It also provided a detailed insight into student learning in informatics, identifying specific strengths and areas for improvement.

Limitations

The limitation of this approach is its specificity, which may hinder scalability to broader contexts or different courses. Nonetheless, the findings indicate that detailed, granular measurements can offer valuable insights into the nature and types of students’ errors and uncover learning gaps. The resources mentioned subsequently propose a more general strategy for assessing learning in computer science.

Evidence-centred Design for High School Introductory CS Courses

Another method for evaluating student learning in computer science involves using Evidence-Centered Design (ECD). Newton et al. (2021) demonstrate the application of ECD to develop assessments that align with the curriculum of introductory high school computer science courses. ECD focuses on beginning with a clear definition of the knowledge, skills, and abilities students are expected to gain from their coursework, followed by creating assessments that directly evaluate these outcomes.

The approach entails specifying the domain-specific tasks that students should be capable of performing, identifying the evidence that would indicate their proficiency, and designing assessment tasks that would generate such evidence. The model further includes an analysis of assessment items for each instructional unit, considering their difficulty, discrimination index, and item type (e.g., multiple-choice, open-ended, etc.). This analysis aids in refining the assessments to gauge student competencies and understanding more accurately.

This model offers a more precise measurement of student learning by ensuring that assessments are closely linked to curriculum objectives and learning outcomes.

Other General Student Indicators

The Exploring Computer Science website, a premier resource for research on indicators of student learning in computer science, identifies several key metrics for understanding concepts within the field:

Student-Reported Increase in Knowledge of CS Concepts: Students are asked to self-assess their knowledge in problem-solving techniques, design, programming, data analysis, and robotics, rating their understanding before and after instruction.
Persistent Motivation in Computer Problem Solving: This self-reported measure uses a 5-point Likert scale to evaluate students’ determination to tackle computer science problems. Questions include, “Once I start working on a computer science problem or assignment, I find it hard to stop,” and “When a computer science problem arises that I can’t solve immediately, I stick with it until I find a solution.”
Student Engagement: This metric again relies on self-reporting to gauge a student’s interest in further pursuing computer science in their studies. It assesses enthusiasm and inclination towards the subject.
Use of CS Vocabulary: Through pre- and post-course surveys, students respond to the prompt: “What might it mean to think like a Computer Scientist?”. Responses are analyzed for the use of computer science-related keywords such as “analyze,” “problem-solving,” and “programming.” A positive correlation was found between CS vocabulary use and self-reported CS knowledge levels.

Comparing the Models

Each model discussed provides distinct benefits but converges on a shared objective: to gauge precisely students’ understanding of computer science. The Evidence-Centered Design (ECD) model is notable for its methodical alignment assessments with educational objectives, guaranteeing that evaluations accurately reflect the intended learning outcomes. Conversely, the randomized controlled trial and innovative measurement technique present a solid approach for empirically assessing the impact of instructional strategies on student learning achievements. Finally, the competence structure model offers an exhaustive framework for identifying and evaluating specific competencies within a particular field, like informatics, ensuring a thorough understanding of student abilities. As the field continues to evolve, so will our methods for measuring student success.

References

Daleiden, P., Stefik, A., Uesbeck, P. M., & Pedersen, J. (2020). Analysis of a Randomized Controlled Trial of Student Performance in Parallel Programming using a New Measurement Technique. ACM Transactions on Computing Education, 20(3), 1–28. https://doi.org/10.1145/3401892

Magenheim, J., Schubert, S., & Schaper, N. (2015). Modelling and measurement of competencies in computer science education. KEYCIT 2014: key competencies in informatics and ICT, 7(1), 33-57.

Newton, S., Alemdar, M., Rutstein, D., Edwards, D., Helms, M., Hernandez, D., & Usselman, M. (2021). Utilizing Evidence-Centered Design to Develop Assessments: A High School Introductory Computer Science Course. Frontiers in Education, 6. https://doi.org/10.3389/feduc.2021.695376

October 15, 2023October 16, 2023

Teaching Computer Science with Minecraft

Introduction to Minecraft

Minecraft is currently one of the most popular games of 2023, boasting over 140 million monthly active users, according to searchlogistics.com. Despite this popularity, many players overlook that Minecraft offers an engaging and immersive environment for learning terminal commands, programming basics, computational thinking, and even artificial intelligence. ISTE standard 4.3a for coaches indicates that a successful coach should “Establish trusting and respectful coaching relationships that encourage educators to explore new instructional strategies.” So, in this blog post, I will delve into the educational benefits of Minecraft and explore the differences between the Java and Education editions.

While Minecraft is often regarded as merely a game, educators have recognized its potential as a valuable learning tool. At its core, Minecraft is built upon programming concepts. Players use blocks made of various materials to construct anything they can imagine, from simple houses to complex machines that require advanced knowledge of electronics, chemistry, and physics. This encourages computational thinking, creativity, and problem-solving as students work to bring their visions to life.

Concerning programming, Minecraft helps teach fundamental coding concepts, including commands, functions, variables, loops, and conditionals. Students can employ block-based coding or full-fledged programming languages such as Python and JavaScript to automate actions within the game. This hands-on approach to learning captivates students more effectively than traditional coding lessons, as Minecraft provides them with an imaginative space to immediately apply their newfound skills. Creating Minecraft modifications (mods) teaches students how to extend existing programs, a critical programming skill.

Minecraft Versions

Several versions of Minecraft are available for players to choose from, including Minecraft: Java Edition, Minecraft: Bedrock Edition, Minecraft: Education Edition, and Minecraft: Pocket Edition. However, for the specific purpose of our educational analysis, we will concentrate solely on the Java and Education editions. These two versions offer unique features and opportunities for learning that make them particularly relevant in an educational context.

Minecraft: Java Edition

The Java Edition is the original version of Minecraft developed in 2009 by Mojang Studios for Windows, macOS, and Linux, and maintains its popularity among long-time Minecraft players.

The Java Edition offers distinct advantages when teaching advanced computer science concepts due to its “mod-ability” and access to the source code of the game environment. The semi-open-source nature of the Java Edition allows for limitless customization through mods and plugins. Writing mods can illustrate a wide range of advanced programming concepts, including event handling, parallel programming, algorithms, data structures, debugging, and software design patterns. Developing mods not only imparts practical software development skills but also encourages students to show their creativity.

The Minecraft community has produced numerous mods that cater to various lesson plans. For instance, ComputerCraft introduces programmable turtle robots, while RedstonePlus enhances the game with advanced circuitry. The diversity of available mods supports a wide range of educational objectives, not only in CS but other disciplines.

Minecraft: Education/Bedrock Edition

Opening screen of Minecraft: Education Edition

Minecraft: Bedrock Edition was initially released in August 2011 and is particularly advantageous for classrooms with various devices. Bedrock Edition supports mobile devices such as iPads and Android tablets, which many schools already incorporate into their teaching environments. This enables students to start their Minecraft lessons on a classroom desktop computer during the day and seamlessly continue playing on their smartphones or game consoles at home.

However, Bedrock Edition offers less mod support and limited access to code customization. Minecraft Education Edition is a version of Bedrock specifically tailored for classroom use. According to Microsoft, it “typically runs about one full version behind the current Minecraft Bedrock production version” (FAQ: Game Features, 2023).

Advantages of Minecraft Education in the Classroom

One of the most significant advantages of Minecraft Education in a computer science course is its block-based CodeBuilder / MakeCode editor, similar to Scratch or Snap. This editor allows students to drag and drop commands to perform actions in the game. Younger students can learn coding logic and structure by creating houses, gardens, and machines using these visual blocks before transitioning to text-based programming languages like Python or JavaScript.

Another advantage of Education Edition is the teachers’ ability to implement special restrictions, such as limiting chat or preventing students from destroying blocks. These classroom controls create a safe environment for student exploration. Teachers can also switch to spectator mode to observe students and provide feedback; they also have the capability to build worlds and restrict access as needed. Here is a quick start guide for reference.

The Education Edition library offers hundreds of pre-made interactive worlds and lesson plans aligned with computer science curriculum standards (source: https://education.minecraft.net/en-us/resources/computer-science-subject-kit). Teachers can find lesson plans tailored to any grade level, making it much easier for educators to get started with Minecraft compared to building worlds from scratch.

According to research by Bile (2022), their study found that children aged 8 to 10 in a Minecraft education setting were able to solve abstract and complex scientific problems without prior prompting or theoretical knowledge. The game format also helped students retain knowledge better. Vostinar & Dobrota (2022) similarly found that in a primary school class, even though the majority of students had not programmed before in block or Python, they found the lesson enjoyable and easy. Furthermore, according to Nika Klimová et al. (2021), girls in grades 5-10 typically outperform boys in Minecraft education coding challenges, suggesting it may be a valuable tool for increasing diversity in computer science.

Disadvantages of Minecraft

As Vostinar & Dobrota (2022, p. 652) pointed out, there are significant disadvantages to using Minecraft in education. One such drawback is that Minecraft is not free and requires an additional cost per student, which, as mentioned in my previous post, raises ethical concerns about the practice of making students pay for educational software. Another disadvantage is that Minecraft may only appeal to a certain type of student, particularly those with a more creative inclination, potentially excluding students who do not have an affinity for the game.

Furthermore, teachers must become proficient in the game’s mechanics and capabilities to integrate it into the classroom effectively. Given the abundance of “cheats” in Minecraft, more experienced players may find trivial command-line solutions to problems if the teacher is unaware of their existence. Finally, as highlighted by Vostinar & Dobrota (2022), it’s essential to impose adequate constraints on the virtual world, especially when students collaborate, to prevent them from destroying the world with TNT blocks and other mining tools.

References:

Vostinar, P., & Dobrota, R. (2022). Minecraft as a Tool for Teaching Online Programming. 2022 45th Jubilee International Convention on Information, Communication and Electronic Technology (MIPRO). https://doi.org/10.23919/mipro55190.2022.9803384

Bile, A. (2022). Development of intellectual and scientific abilities through game-programming in Minecraft. Education and Information Technologies, 1–16. https://doi.org/10.1007/s10639-022-10894-z

Nika Klimová, Jakub Sajben, & Lovászová, G. (2021). Online Game-Based Learning through Minecraft: Education Edition Programming Contest. https://doi.org/10.1109/educon46332.2021.9453953

FAQ: Game Features. (2023, September 15). Minecraft Education. https://educommunity.minecraft.net/hc/en-us/articles/360047117692-FAQ-Game-Features

August 13, 2023

Reflecting on a Study of Competitive Programming and Cultural Inclusion

Length of Study

The study is designed to take place over two academic terms, which provides adequate time to collect meaningful data. The inclusion of an initial summer term without competitive programming establishes a baseline for comparison. The second summer term incorporates competitive programming using standardized questions, allowing assessment of this pedagogical approach. The fall term offering adds the dimension of culturally relevant questions, enabling analysis of their impact. Extending the study over multiple terms enables more robust data collection and analysis.

Promoting Active and Engaged Learning

The core content is delivered through weekly lectures focused on programming concepts. The competitive programming contests complement the lectures by providing opportunities to practice applying concepts. Weekly competitive programming contests foster active learning in several key ways. Students must apply conceptual knowledge to solve concrete programming problems. This process reinforces their understanding and helps identify knowledge gaps. The contest format adds an engaging gamification element through scoring, feedback, and peer comparison. Using standardized questions initially assesses whether baseline content needs are being met.

Introducing culturally relevant questions aims to promote better integration of concepts by relating them to students’ cultural knowledge and experiences. Having students co-create contest questions in the fall term further activates learning. They must think critically to develop culturally relevant problems that integrate with the content. This approach promotes deeper engagement with the material and encourages collaboration with classmates, allowing students to take ownership of their learning.

Addressing Teachers’ Needs

The study aims to provide teachers with insight into using competitive programming and culturally relevant pedagogy. The data collected will help determine the effectiveness of these approaches in an international educational setting. Instructors will gain an understanding of how competitive programming engages students versus standardized practice problems. They will also see whether student-created culturally relevant questions increase participation and motivation. The study addresses teachers’ needs for effective and inclusive instructional strategies. They will gain practical knowledge from the comparative data on different contest designs.

Promoting Collaborative Participation

Collaboration is encouraged through the group development of culturally relevant contest questions. Students can brainstorm and build on each other’s ideas, which fosters teamwork. Producing questions from diverse cultural perspectives requires working together. Students are also given the choice of problem-solving in teams. Students can motivate each other and strategize in groups for the competitions. Their scores are tracked on a collective leaderboard which reinforces the collaborative element. The shift from individual to team contest creation necessitates and enables productive collaboration.

The multi-term study design, interactive contest format, customized problems, and collaborative elements demonstrate an interesting pedagogical approach that promotes engaged and inclusive learning. The results should provide valuable insights for computer science educators.

July 9, 2023

Incorporating Competitive Programming into a Beginner Programming Course

Introduction

Driven by the increasing automation and digitalization of virtually every workflow, programming has become an indispensable part of our lives. As a result, introducing programming at the earliest stage of education has become a hot topic of discussion among educators and academics alike.

A particular area of interest is the concept of competitive programming (CP). Long viewed as a niche domain, a small group of enthusiasts often pursue CP to challenge their coding capabilities; many faculty have challenged the area as an unnecessary part of computer science. However, recent research underscores the potential of competitive programming as a useful pedagogical tool, especially in the context of introductory programming courses. This blog post will discuss the results of various studies that have been conducted on incorporating CP into a beginner’s programming course. I’ll review existing studies on integrating CP into intro-level programming courses, examining its effects on learning outcomes, student engagement, and skill acquisition. In addition, I will also propose some areas of CP that require further research.

Understanding Competitive Programming

Competitive programming is a mind sport, like chess and bridge, that involves participants competing to solve algorithmic problems as quickly and efficiently as possible. The ACM ICPC (Association for Computing Machinery – International Collegiate Programming Contest) is one of the world’s oldest, largest, and most prestigious programming contests, which started in the 1970s. Today, it has grown to involve tens of thousands of participants, attracting the world’s top Computer Science universities.

Several elements define each problem in the contest. First, there’s a problem statement describing the issue the team needs to solve. Next are the input and output specifications, which explain the type of data the team’s program should accept and produce. Thirdly, sample inputs and outputs are given to help the team understand the problem. Finally, constraints are provided to outline the maximum size or other limitations of the inputs and the required efficiency of the solution.

The contest is scored based on the number of problems solved and the time penalty. The number of problems solved is the most critical factor; the more problems a team solves, the higher their rank will be. Teams are primarily ranked by the number of problems they have solved. To break ties among teams who have solved the same number of problems, the ICPC uses a time penalty calculated from the beginning of the contest to the time of the first correct submission, with an additional penalty added for each incorrect submission. The team with the shortest total time is ranked highest.

The Impact of Competitive Programming on Beginners

Studies such as those conducted by Moreno et al. (2018) and Bandeira et al. (2019) employed this scoring system and contest setup to engage first-year students in programming classes. Both studies found that students introduced to competitive programming in their first year demonstrated a superior understanding of programming principles compared to those who did not. These students exhibited faster problem-solving abilities, improved code efficiency, and an increased capacity to work under pressure. Additionally, these students reported higher retention of material and reduced difficulty in grasping programming concepts.

However, not all studies concluded that CP led to improved performance. Coore and Fokum (2019), facing a lack of teaching assistants and quality feedback in first-year programming courses, employed a system of weekly competitive programming competitions to reinforce the week’s material. Their study found that while using competitive programming in assessments did increase student engagement and interest, it did not enhance the overall performance of the first-year students.

The Challenges

While CP introduces students to the rigours and excitement of coding under constraints, it’s important to recognize that CP cannot address every aspect of introductory programming. Also, certain facets of CP, such as its pace and competitive element, may only suit some learners.

Astrachan (2004) has pointed out that competitive programming only allows students to delve into key areas such as Object-Oriented Programming (OOP) design principles and enhancing code quality. CP emphasizes speed and efficiency, often overlooking the importance of well-structured, maintainable code, a crucial aspect in real-world development.

While competitive programming can inject a sense of competition into the classroom, it’s important to remember that it’s not a one-size-fits-all solution. The competitive aspect of CP may be intimidating for some students, leading to heightened anxiety and stress. This could, in turn, hinder learning and deter participation. Moreover, the pace of competitive programming, which requires swift comprehension of problem statements and speedy code implementation, may only cater to some learning styles. Some students may require more time to thoroughly grasp concepts and develop robust solutions, which could make the fast-paced environment of CP feel overwhelming.

Given these characteristics of CP, it’s clear that it should not be used as the sole determinant in course assessments. Relying too heavily on CP for grading could inadvertently favour students who possess abilities unrelated to computer science, such as high reading speed and fast typing. These intangibles can be advantageous in a competitive programming environment but have little relevance to a student’s understanding of computer science principles or their potential as a programmer.

Future of Competitive Programming in Classrooms

Although much research has been done involving introducing competitive programming into the classroom, little work explores the impact of cultural relevance in problem-setting, the role of artificial intelligence (AI) in integrating CP, and how CP interacts with various cultural and social intersections in the academic sphere.

The classroom is often characterized by a variety of cultural and social intersections. Incorporating CP in such a setting prompts us to consider how it might affect the likeability, acceptability, and academic performance across these intersections. Is CP equally appealing and accessible to students of different cultures, genders, or social backgrounds? How might the competitive nature of CP impact the dynamics of these intersections? Delving into these questions would allow us to devise strategies to ensure a more equitable and inclusive learning environment.

A unique feature of competitive programming is its creative liberty in problem-setting. This opens the possibility of integrating culturally relevant problems. Introducing programming problems referencing students’ home countries or cultures could make the learning experience more relatable and be a powerful tool to increase engagement among international students. However, the impact of such an approach is yet to be fully understood. How might culturally sensitive problems influence students’ interest and engagement? Could they enhance learning outcomes, or could they unintentionally alienate students who do not share the same cultural background?

Artificial Intelligence offers exciting possibilities in CP. For instance, large language models such as ChatGPT can assist in problem setting, which is typically a significant demand on an instructor’s time. AI-based tools could also serve as programming partners for first-year students, providing personalized assistance such as debugging help or hints for specific problems during a contest. This could supplement the responses from auto-grading judges, which is currently limited to categorized feedback that can sometimes be vague. This approach increases access to individualized learning support and mitigates common challenges associated with competitive programming, such as anxiety and intimidation. However, areas that require further exploration include the effectiveness of such tools and the best strategies for integrating them into the learning experience.

References

Moreno, J., & Pineda, A. F. (2018). Competitive programming and gamification as strategy to engage students in computer science courses. Revista ESPACIOS, 39(35).

Bandeira, I. N., Machado, T. V., Dullens, V. F., & Canedo, E. D. (2019, October 1). Competitive programming: A teaching methodology analysis applied to first-year programming classes. IEEE Xplore. https://doi.org/10.1109/FIE43999.2019.9028518

Astrachan, O. (2004). Non-competitive programming contest problems as the basis for just-in-time teaching. https://doi.org/10.1109/fie.2004.1408553

Coore, D., & Fokum, D. (2019). Facilitating Course Assessment with a Competitive Programming Platform. Proceedings of the 50th ACM Technical Symposium on Computer Science Education. https://doi.org/10.1145/3287324.3287511

June 4, 2023June 5, 2023

Computer Science Curriculum Integration for K-9 Teachers

Introduction

With a rapidly advancing world and integrating more technology into life, it has increasingly become evident to parents, teachers, and students alike that technological literacy is essential to primary education. Parents are pushing for increased computer science instruction in elementary schools as they realize how vital this knowledge will be in preparing their children for success beyond grade school. According to code.org (Computer Science Education Stats, n.d.), 90% of parents want their children to study computer science, but only 53% of high schools offer it in their curriculum. Research has also shown that students who take computer science courses in high school are 17% more likely to pursue higher education, with even higher percentages observed among traditionally marginalized populations such as females and Black and Latino students (Brown & Brown, n.d.).

While computer science courses provide fundamental skills like coding and web design, their significance extends far beyond that. Understanding technology equips students with data analysis capabilities and problem-solving skills applicable across various fields, extending beyond digital work environments. Consequently, many believe it is crucial to prioritize teaching computer science concepts. This post outlines my efforts to understand the computer science curriculum’s current needs and promote computer science education among elementary school teachers in British Columbia.

Assessing Current Needs

According to Dr. Shannon Thissen, Regional Administrator for Educational Technology and Computer Science Teaching and Learning in Capital Region ESD 113 in Washington, integrating computer science concepts into the curriculum poses significant challenges. Dr. Thissen mentions that many untrained teachers express concerns and seek guidance. The primary obstacle is teachers’ fear of the unknown, and overcoming this fear is crucial. Several programs, including those offered by code.org, provide free resources for teachers. However, the participation of schools in these programs in British Columbia remains limited. Dr. Thissen also highlights that teachers may hesitate to incorporate such programs into their curriculum if they are not mandated.

In a previous post, I discussed the inconsistency of computer science programs in grade schools across British Columbia, particularly emphasizing the needs of interior and rural areas. While I faced difficulties connecting with schools that desired my services, I recently connected with a Programming 11/12 teacher in Kamloops. I had the opportunity to speak to her class, providing valuable insights into the needs of both teachers and students. Students showed great interest in game development and 3D animation. Despite being a beginner-level class, students had diverse backgrounds in the subject, with some having significant experience with Unity while others struggled with the early stages of block programming. However, their shared enthusiasm for the subject validated studies showing that a majority of students enjoy computer science (Computer Science Education Stats, n.d.). This highlights the need for increased promotion of computer science in the earlier grades to nurture this enjoyment.

Currently, I am collaborating with John Knox Christian School, which is revamping its computer science and technology curriculum. Working with the Director of Curriculum and Computer Science, we are developing a series of workshops to assist K-9 teachers in integrating computer science into their classrooms. This collaboration is an excellent opportunity as John Knox controls its curriculum from K-12, enabling longer-term assessment of student success through the workshops with the same group of students.

Project Design

The central portion of the project is divided into four phases: Assessing Needs, Planning and Foundations, Integration, and Reflection.

Assessing Needs:

To ensure alignment with the school’s desired outcomes and address the specific needs of K-9 teachers, a survey will be created and distributed to all K-9 teachers. The survey aims to gather information on teachers’ objectives, knowledge levels, and areas of interest in computer science. The survey includes choices for dedicated topics such as programming, computational thinking, artificial intelligence, game development, and computer hardware. The project will tailor its objectives and content to meet the needs and interests of the teachers. The survey will be sent out during the first week of summer break.

Planning and Foundations:

This phase involves conducting a workshop covering computer science fundamentals, including vocabulary, problem-solving, and demystifying computer science. The workshop will also focus on integrating hands-on activities for teachers to experience and practice computer science concepts firsthand. Teachers will be guided in designing activities that promote problem-solving, collaboration, and critical thinking skills. Additionally, resources such as coding platforms, educational apps, and lesson plans will be provided to support teachers in implementing computer science activities beyond the workshop. By the end of the workshop, teachers should have a plan to incorporate computer science concepts into an existing lesson. This workshop is scheduled for the first professional development day in September 2023.

Integration:

Teachers will execute their plans to integrate computer science concepts into the classroom during the integration stage. One strategy for this stage is to have coaches co-teach a computer science lesson alongside the classroom teacher. Initially, the director and I will act as coaches, but the plan is to train coaches for future iterations. Co-teaching allows the teacher to observe and learn from the coach’s expertise and experience, helping teachers gain confidence and deepen their understanding. The coach can help guide students through hands-on activities and will provide positive reinforcement, celebrate teachers’ achievements, and acknowledge their efforts in integrating computer science concepts. In addition to co-teaching, coaches will observe regularly and provide teachers with constructive feedback. Classroom observations, either in person or through video recordings, will be conducted to assess the implementation of computer science lessons and the effectiveness of teaching strategies. Coaches will then provide feedback, highlighting strengths and offering suggestions for improvement. This feedback loop will enable teachers to reflect on their practice, refine their instructional techniques, and make continuous progress in integrating computer science effectively.

Reflection:

Integration is ongoing, and teachers require continued support beyond the initial stages. Follow-up meetings, workshops, or online forums will be organized to facilitate knowledge sharing, questions, and further guidance. These support mechanisms will help sustain the momentum and provide teachers with ongoing professional development and collaboration opportunities. Coaches will curate and share relevant resources, including lesson plans, coding activities, and best practices, to further support teachers’ integration efforts. This stage is scheduled for the third professional development day in November 2023.

Analyzing the Design

Using Adria Steinberg’s (1998) six A’s of evaluating project design, my proposed project aligns with these principles, ensuring the successful integration of CS into K-9 classrooms.

Academic Rigor: The project emphasizes challenging and intellectually stimulating CS content. It provides opportunities for students to engage in practical problem-solving, critical thinking, and analytical reasoning. The project also encourages teachers to adopt a different mindset when teaching CS.

Authenticity: The project provides real-world contexts and experiences that connect CS concepts to students’ lives and interests. It includes relating CS to authentic scenarios, which enhances student motivation and engagement in other subjects. By establishing these connections, the project makes CS more relevant and meaningful to students.

Applied Learning: The project emphasizes the hands-on application of CS knowledge and skills. It encourages teachers to apply CS-related skills to different activities in the classroom. This approach allows students to experiment, problem-solve, and apply CS principles in real-world contexts.

Active Exploration: The project encourages students to explore CS concepts through inquiry-based and self-reflective learning. It creates an environment that nurtures teachers’ and students’ curiosity, exploration, and independent thinking; this fosters a sense of ownership in their CS learning journeys.

Adult Relationships: The project recognizes the importance of fostering supportive and meaningful connections between students and adult mentors or educators. By incorporating coaches or mentors into the framework, the project provides students (and teachers) opportunities to interact with CS professionals, experts, or mentors. These interactions offer guidance, share real-world experiences, and serve as role models for students.

Assessment: The project includes an assessment plan to evaluate student learning and program effectiveness. It incorporates post-integration surveys and other assessment strategies to measure the impact of CS integration on student learning outcomes. Gathering feedback from stakeholders informs program improvements and ensures ongoing effectiveness.

Next Steps:

To effectively integrate computer science into K-9 classrooms, conducting one or two workshop iterations is needed. This allows for refinement and improvement of the workshop content and delivery based on feedback and insights from the initial sessions. During each iteration, it is important to collect feedback from participating teachers. Assessing the teachers’ comfort levels with integrating computer science concepts and their perceived efficacy in implementing the learned strategies in their classrooms will help gauge the workshop’s effectiveness. Additionally, student learning outcomes can be evaluated through pre- and post-workshop assessments to measure students’ knowledge growth, skills development, and attitude toward computer science. Comparing these assessments will provide evidence of the workshop’s impact on student learning.

Once the initial iterations of the workshop have been conducted and evaluated, the next step is to engage rural area schools with a more concrete plan. Building on the lessons learned and feedback from the initial workshops, it is essential to develop a targeted approach specifically tailored to the needs and challenges of rural schools. Further changes to the workshop may include solutions to overcome unique barriers such as limited resources, infrastructure challenges, or teacher training opportunities. Designing the workshop to accommodate these conditions will increase its relevance and effectiveness in rural settings.

If the workshop successfully enhances teacher comfort levels and promotes student learning, developing a Professional Development Program (PDP) at the university is an excellent opportunity. A PDP would offer a more structured and comprehensive training program for teachers seeking to integrate computer science into their K-9 classrooms. The Education Department can collaborate with the School of Computing Science and instructional design experts to design a robust PDP curriculum. This curriculum should address the specific needs of teachers, providing them with theoretical knowledge, practical skills, and ongoing support to integrate computer science concepts into their classrooms effectively.

References

Computer Science Education Stats. (n.d.). Code.org. https://code.org/promote/stats

Brown, E., & Brown, R. (n.d.). The Effect of Advanced Placement Computer Science Course Taking on College Enrollment. http://www.westcoastanalytics.com/uploads/6/9/6/7/69675515/longitudinal_study_-_combined_report_final_3_10_20__jgq_.pdf

CS Journeys Resources: Mentorship and community. (n.d.). Code.org. Retrieved June 3, 2023, from https://code.org/beyond/mentors-and-community

Steinberg, A. (1998). Real learning, real work : school-to-work as high school reform. Routledge.

K–12 Computer Science Framework. (n.d.). K12cs.org. https://k12cs.org/