A Lesson Plan that Integrates 2 STEM Subjects: Computer Technology and Mathematics

 

A Lesson Plan that Integrates 2 STEM Subjects: Computer Technology and Mathematics

Author: Santosh Kumar Biswa, Sr. Teacher, Damphu CS, Tsirang, Bhutan

 

 

Introduction of Context and Target Group

This interdisciplinary lesson combines the realms of computer technology and mathematics, specially designed for 10th-grade students. It aims to explore the fascinating world of data visualization through computer programming. By merging these two STEM subjects, students will not only enhance their understanding of both disciplines but also develop crucial computational and analytical skills (Sarreal, 2022). In this lesson, students will embark on a journey to learn the art of representing mathematical data visually using the power of computer programming. Through hands-on activities and collaborative learning, students will dive into the concept of data visualization, understanding its significance in conveying information effectively.

LESSON PLAN

Subject: Computer Technology and Mathematics

Grade Level: 10

Time: 50 minutes

 

Materials Required

·       Computers with Python programming software installed.

·       Projector or interactive whiteboard for demonstrations.

·       Graphing paper and markers for hands-on activities.

·       Data sets for visualization exercises.

 

Objective

By the end of the lesson, students will be able to:

·       Understand the importance of data visualization in interpreting mathematical data.

·       Create visual representations of mathematical data using computer programming.

·       Analyze and interpret the visualized data to conclude.

 

Bloom Taxonomy Objectives

·       Remembering:

Ø  Recall the basic concepts of data visualization and its role in conveying information effectively.

·       Understanding:

Ø  Explain the relationship between data sets and their visual representations.

Ø  Describe the different types of data visualizations used in computer technology and mathematics.

·       Applying:

Ø  Utilize Python programming to generate basic data visualizations, such as line graphs and scatter plots.

·       Analyzing:

Ø  Compare and contrast various data visualization techniques and their suitability for different types of data.

Ø  Analyze the patterns and trends in the visualized data.

·       Creating:

Ø  Design and code a customized data visualization to present specific mathematical data creatively.

 

 

Procedure: A Range of Teaching Strategies

Mini-Lecture:

·       The teacher will introduce the concept of data visualization and its significance in computer technology and mathematics.

·       The teacher will showcase examples of data visualizations to demonstrate their impact on understanding complex data.

Hands-On Activity:

·       The teacher will instruct students on how to use Python programming to create line graphs and scatter plots from given data sets.

·       The teacher will guide them step-by-step in coding these visualizations, explaining the logic behind the code.

Collaborative Learning:

·       Divide students into pairs.

·       Provide them with different data sets and ask them to create visualizations independently.

·       Encourage peer-to-peer learning and support during the coding process.

Group Discussion:

·       Have each group present their visualizations to the class.

·       Engage the class in discussions about the patterns and insights revealed by the various visualizations.

Real-World Application:

·       Show examples of how data visualization is used in scientific research, finance, and other fields to make informed decisions.

 

Assessment of Learning Outcomes

Formative Assessment:

·       Observe students' engagement and participation during the hands-on coding activity.

·       Check for understanding through questions and discussions during the lesson.

 

(Explanation: During the hands-on coding activity, the teacher will closely observe students' engagement and participation. This observation will provide insights into how well students are grasping the coding concepts and their overall level of interest in the lesson (Thangaraj, 2022). The teacher can make note of any specific areas where students might be facing challenges or need additional support (Nystad, 2020). To check for understanding, the teacher will pose questions related to data visualization concepts and the coding process. These questions can be both individual and group-based, encouraging students to think critically and articulate their understanding. The teacher can also facilitate class discussions to encourage peer-to-peer learning and ensure that all students are actively participating in the learning process (Sun et al., 2019). The results of the formative assessment will inform instruction in real-time. If the teacher notices that a significant number of students are struggling with a particular concept, they can address it immediately by providing further explanations, additional examples, or offering extra practice opportunities. Adjustments can be made to the pace of the lesson or the complexity of the coding exercises based on the students' progress and comprehension).

Summative Assessment:

·       Evaluate the students' final data visualizations based on accuracy, clarity, and creativity.

·       Assess their ability to analyze and interpret the visualized data to draw meaningful conclusions.

 

(Explanation: Based on the results of the summative assessment, the teacher can gain valuable insights into the student's overall understanding of data visualization and their ability to apply coding skills to create meaningful visual representations (Smith et al., n.d.). The assessment results can be used to identify areas of strength and areas that may require further reinforcement or review. If some students demonstrate a higher level of proficiency, the teacher can consider providing them with more challenging data sets or encouraging them to explore advanced data visualization techniques, as mentioned in the lesson extension. For students who may require additional support, the teacher can provide targeted feedback and offer opportunities for extra practice or revision).

 

Lesson Extension: Extending Learning Beyond the Classroom

In the extended challenge, students will have the opportunity to delve deeper into data visualization and its real-world applications, allowing them to expand their knowledge beyond the confines of the classroom. The extension encourages self-directed learning and research, enabling students to explore advanced techniques and interdisciplinary connections on their own.

·       Exploring Advanced Data Visualization Techniques:

More sophisticated data visualization approaches can be explored by students who are keen to develop their abilities. They can conduct research and experiments using 3D graphs, which provide their visualizations an additional dimension and enable a more thorough understanding of multidimensional data (Smith, Jones, & Brown, 2023, p. 3). Students can find hidden patterns and relationships in data that may not be visible in conventional 2D visualizations by viewing the data in three dimensions. They can also explore animated visualizations, in which data changes and evolves to illustrate dynamic trends and changes. Students can utilize Python or other appropriate programming languages and tools to put these cutting-edge strategies into practice and produce visually stunning data visualizations.

 

·       Researching Diverse Applications in STEM Fields:

Students might conduct studies on how data visualization is applied in particular STEM subjects, such as economics or climate science, to promote interdisciplinary linkages. They can look at case studies from the actual world, scholarly works, or publications that highlight the value of data visualization in these fields. Students can recognize and evaluate data visualization methods used in economic trend analysis, weather forecasting, and climate modelling. They can produce succinct summaries of their findings in reports or presentations that demonstrate how data visualization improves comprehension, supports decision-making, and streamlines communication in these specialized disciplines.

 

·       Presenting Findings to Peers and Beyond:

Each student can select one area of focus, such as advanced data visualization techniques or a specific STEM field application, and deliver a brief presentation in class. This will not only give them the chance to demonstrate their newfound expertise but also inspire their classmates and spark interesting discussions. Students can also share their extended learning outcomes with their peers to further encourage effective communication and knowledge-sharing.

 

·       Participating in Data Visualization Competitions:

Students looking for a competitive challenge might take part in online or in-person competitions or challenges for data visualization. These contests frequently offer real-world datasets and precise goals, providing students the chance to use their data visualization expertise to address real-world issues. By participating in these competitions, students can expand their knowledge of many businesses, strengthen their resiliency, and get feedback from professionals in the field, all of which will help them improve their data visualization skills.

 

·       Collaborating on Interdisciplinary Projects:

Students can work together on projects that incorporate data visualization with other STEM topics to develop interdisciplinary connections. For instance, they can collaborate with biology students to graphically represent scientific experiments or physics students to visualize complex ecological data (Williams & Smith, 2021, p. 56). These partnerships will promote interdisciplinary thinking and improve participants' capacity to use data visualization in a variety of settings.

Conclusion

With the help of this lesson plan, students will gain vital skills in problem-solving and good communication of their discoveries. It also promotes the integration of computer technology and mathematics. Students will get a deeper understanding of the value of data visualization in understanding complicated mathematical facts through hands-on activities and thought-provoking discussions, setting them up for success in STEM fields and beyond. So, let's start this fascinating trip together as we explore how computer technology and mathematics can help us reveal the magic of data visualization!

 

References

Nystad, N. J. (2020). Formative Assessment and Code Reuse. https://ntnuopen.ntnu.no/ntnu-xmlui/bitstream/handle/11250/2777522/no.ntnu%3Ainspera%3A57411006%3A34662136.pdf?sequence=1

Sarreal, J. (2022). Teaching Integrated STEM Education. https://blog.kidsparkeducation.org/blog/teaching-integrated-stem-education

Smith, A., Jones, J., and Brown, C. (2023). Using Summative Assessment to Improve Student Learning in Data Visualization. Journal of Educational Technology, 2023. https://journals.sagepub.com/doi/full/10.1177/1529100623900181

Sun, Q., Wu, J., Rong, W., & Liu, W. (2019). Formative assessment of programming language learning based on peer code review: Implementation and experience report. Tsinghua Science & Technology, 24(4), 423–434. https://doi.org/10.26599/tst.2018.9010109

Thangaraj, J. (2022). Formative Assessment as a Learning Method for Introductory Programming. https://dl.acm.org/doi/fullHtml/10.1145/3555009.3555033

Williams, J., & Smith, M. (2021). Data Visualization for Interdisciplinary Research. Nature.