Understanding by Design: A Framework for Transformative Data Analysis Learning
The goal of my innovation plan is to ingrain transformational learning into our DNA at Valdymas College. From the school leadership to teachers in classrooms or online platforms to build a culture that is strongly advocating Transformational Learning. My goal will be to drive the minds of both teachers and students “from learning to leadership”. This I do hope to achieve by leveraging the advancement of digital platforms to ensure a superior learning environment is created with freedom to choose, ownership of content, where learners voice and visibility is prioritized, and knowledge is authentic. The learning should be applicable to solving real world or industry related challenges for the leaner.
Wiggins and McTighe’s Understanding by Design (UbD) model (2005) provides a compelling framework for designing an introductory data analysis course with Python programming. Its strength lies in its “backward design” approach, which prioritizes desired results at the outset ([Wiggins & McTighe, 2005]). This aligns perfectly with my innovation plan for Valdymas College students which is focused on the goal of fostering transformational learning,
The Fink’s 3 Column Table and the Understanding by Design (UbD) Template are both instructional design frameworks used to plan and develop effective learning experiences. While they share some similarities, they also have distinct features and purposes.
This is the analysis of the Fink’s 3 Column Table based on purpose, structure, and effectiveness.
Purpose:
Fink’s approach focuses on designing courses that foster and integrate significant learning experiences (SLEs) by aligning course goals, activities, and assessments (Fink, 2003). It emphasizes the importance of designing courses that promote deep learning and long-term retention of knowledge.
Structure:
- Column 1: Situational Factors and Goals: This column outlines situational factors influencing the course design, such as learner characteristics, institutional context, and course goals.
- Column 2: Learning Activities: This column describes specific activities and experiences designed to achieve the course goals and engage learners in meaningful learning experiences.
- Column 3: Feedback and Assessment: This column details assessment methods and feedback mechanisms used to evaluate student learning and provide opportunities for reflection and improvement (Fink, 2013).
Effectiveness:
Fink’s 3 Column Table is effective for designing courses that prioritize active learning, critical thinking, and application of knowledge in authentic contexts (Fink, 2003).
It encourages instructors to consider the broader context and goals of the course, including the needs and characteristics of learners and the institutional environment.
Understanding by Design (UbD) Template on the other hand can be viewed as well based on purpose, structure, and effectiveness:
Purpose:
The UbD Template, based on the Understanding by Design framework by Wiggins and McTighe (2005), emphasizes backward design, starting with desired learning outcomes and working backward to plan instructional activities and assessments. It focuses on aligning curriculum, instruction, and assessment to ensure meaningful learning.
Structure:
- Stage 1: Identify Desired Results: Clarify learning goals and objectives, including what students should know, understand, and be able to do by the end of the course.
- Stage 2: Determine Acceptable Evidence: Design assessments and performance tasks that provide evidence of student learning and achievement of desired results.
- Stage 3: Plan Learning Experiences and Instruction: Design instructional activities and experiences that support the achievement of desired results and prepare students for assessments (Wiggins & McTighe, 2005).
Effectiveness:
The UbD Template is effective for designing courses with clearly defined learning outcomes and assessments aligned with those outcomes (Wiggins & McTighe, 2005).
It encourages instructors to focus on understanding the “big ideas” and essential questions of the course, as well as the transferable skills and enduring understandings students should develop.
The approach of the Fink’s 3 Column Table emphasizes integrating significant learning experiences and active engagement, while UbD focuses on backward design and alignment of curriculum, instruction, and assessment. While Fink’s framework focuses on the holistic design of the course, considering situational factors, goals, and learning activities, the UbD focuses more narrowly on alignment of learning outcomes, assessments, and instruction.
And in terms of flexibility, the Fink’s framework allows more flexibility in course design, accommodating a wider range of learning experiences and activities, while UbD provides a more structured approach to ensure alignment and coherence in course design (Fink, 2003; Wiggins & McTighe, 2005).
Based on contextual effectiveness, Fink’s 3 Column Table may be more effective in courses emphasizing deep learning, critical thinking, and application of knowledge in real-world contexts. The UbD Template may be more effective in courses where the focus is on clearly defined learning outcomes, alignment of assessments, and ensuring coherence in curriculum design (Fink, 2003; Wiggins & McTighe, 2005).
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My Big Harry Audacious Goal (BHAG) for the course is: to ensure students who undergo the Advanced Level program in statistics with data analysis using python programing would be able to analyze all business sector data and provide appropriate meaningful executive decisions and use data analysis to solve industry problems.
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Stage 1 – Desired Results |
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Established Goals for the students, is that they can Develop proficiency in using Python programming for data analysis. Understand the key concepts and techniques of data manipulation and analysis. Apply data analysis skills to real-world datasets for industry-based critical thinking, decision making and problem solving. |
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Understandings: Completion of coding assignments and projects demonstrating proficiency in Python programming and data analysis. Automate data cleaning, manipulation, and analysis tasks. Write scripts to improve efficiency and avoid repetitive manual work. Leverage libraries like NumPy, Pandas, and Matplotlib for data manipulation and visualization. Performance on quizzes and exams evaluating understanding of key concepts and techniques. Analysis of real-world datasets and presentation of findings Completion of project works in analyzing real world data and making deductions |
Essential Questions: What are the fundamental concepts underlying data analysis (data types, variables, data collection methods)? How can data analysis be used to solve real-world problems and answer meaningful questions? How does data visualization effectively communicate insights and information extracted from data? How can Python programming be leveraged to automate data analysis tasks and enhance efficiency? How can students demonstrate their understanding of data analysis concepts through written explanations or code comments? How effectively can students manipulate and visualize data using Python libraries to communicate key findings? How well can students frame research questions, analyze data, and draw conclusions from a chosen dataset? |
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Students will know… Different data types (numerical, categorical, text) and their properties. The concept of variables and their role in storing and manipulating data. Various data collection methods (surveys, experiments, web scraping) and their strengths and weaknesses. Various communication of complex data in a clear and concise manner. How to Identify patterns, trends, and relationships within datasets. Effectively presenting data-driven findings to various audiences. Data privacy and responsible data collection practices. Recognizing and mitigating potential biases in data analysis. Ethical considerations in data visualization and presentation of findings.
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Students will be able to Solve real-world problems across different disciplines (science, social studies, business).Extract meaningful insights and answer questions from data.Inform decision-making processes based on evidence and analysis. Automate data cleaning, manipulation, and analysis tasks. Write scripts to improve efficiency and avoid repetitive manual work. Leverage libraries like NumPy, Pandas, and Matplotlib for data manipulation and visualization. Frame research questions and hypotheses based on data exploration. Critically evaluate the quality and potential biases within datasets. Apply data analysis techniques to solve problems and draw evidence-based conclusions.
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Stage 2 – Assessment Evidence |
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Performance Task Hands-on coding exercises: Write Python code to import, clean, manipulate, and visualize data. Data visualization project: Choose a dataset of interest, analyze it, and create visualizations to communicate insights.Open-ended question: Assess understanding of the data analysis process.Project presentations: Encourage students to discuss the implications of their data analysis findings. Case studies: Analyze real-world examples of how data is used to inform decision-making. |
Other Evidence Develop a tutoring and coaching plan for supporting other learners, providing feed-forward reviews to improve their works and a sharing platform.Develop a framework for learning data analysis and visualizations.Share their learning experience and process with others through Collaboration, group discussion and group work evaluation |
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Stage 3 – Learning Plan |
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Learning Activities Interactive activity: Students participate in a data-driven game/simulation highlighting real-world applications of data analysis. H, T, O |
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Legend meaning The part of the UbD template is the WHERETO elements, which helps the teachers to assess how the design will do list below. This acronym kept me on track when completing the Learning activities section. |
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References
- Fink, L. D. (2003). Creating significant learning experiences: An integrated approach to designing college courses. Jossey-Bass.
- Fink, L. D. (2013). A self-directed guide to designing courses for significant learning. Jossey-Bass.
- Wiggins, G., & McTighe, J. (2005). Understanding by design (Expanded 2nd edition). Association for Supervision and Curriculum Development.