Overview: 

The mini-guided project in DISC 1011, a Probability and Statistics Course integrated a real-world dataset from the 2024 Women in Data Science (WiDS) Datathon, which examined equity in healthcare for metastatic breast cancer treatment, providers, facilities, and patients. This project formed 15% of the final grade and involved a mini-report, peer review of other group projects, and a guided project mini-presentation. Students worked in groups of two to four to create a short report with guided questions, displaying results based on descriptive statistics and hypothesis testing. Each group also presented a condensed version of their project within a 10-minute timeframe and reviewed at least two of their peers’ initial submissions, providing meaningful feedback based on a grading rubric and survey questions available on myeLearning. The objectives of this assessment were for students to analyze data using R statistical software and to encourage critical analysis of the results for reporting, interpretation, and presentation. These skills are essential for the 21st-century learner, relevant to both the successful completion of this course and real-life decision-making. Communicating data-driven insights effectively in this assignment lays the foundation for data storytelling in the professional world.

Student Profile: 

DISC 1011: Probability and Statistics Course

Level of Student: Undergraduate

Age Group: 18 to 23 years old

Class Size: 11 students

Mode: Online

Background: Students have a basic knowledge of Statistics and Calculus. Most are recent secondary school graduates who focused on STEM courses.

Why integrate the datathon into my course?

• Unique Learning Experience: Integrate hands-on projects with unexplored data in a fun, competitive setting.

• Exclusive Data Access: Provide well-curated real-world data not publicly accessible.

• Curriculum Enhancement: Incorporate collaborative learning methods, suitable as capstone projects.

• Broad Applicability: Cater to both beginner and advanced data science students.

• Global Community Access: Expose students to diverse perspectives.

Understanding, processing, extracting value from, visualizing, and communicating data are crucial skills for the future, as noted by Varian (2009). Data-driven insights are vital for corporate intelligence and scientific policy evaluation. Utilizing data to create statistical reports allows for effective storytelling and practical application in various domains. 

By aligning rubrics with real-world research practices, my students developed essential research skills and gained practical experience, preparing them for academic and professional endeavors. The rubric emphasized understanding data, formulating research questions, conducting rigorous analysis, and deriving actionable insights, mirroring professional practices.

Peer review and presentation skills are also integral to the research process, reinforcing effective communication and collaboration. Peer review helped students evaluate research inquiries, organization, data analysis, and critical thinking in peer projects, ensuring the validity and rigor of research findings. Guided mini-presentations enhanced students' ability to communicate research findings concisely and engagingly, crucial for academic conferences, business meetings, and professional settings.

Transformative learning, emphasizing critical thinking, reasoning, and reflection, was applied to the mini-guided project. Students explored real-world problems, encountered new experiences, and presented their results, reflecting on peer projects. This process fostered collaboration, meaningful discussions, and the development of critical thinking and communication skills, leading to new insights and shifts in thinking (Wolff et al., 2022). The project highlighted the application of statistical knowledge to solve complex problems, demonstrating the relevance and significance of their learning in real-world contexts.

How was it done?:

The mini-guided project in DISC 1011, a Probability and Statistics course, aimed to integrate real-world data analysis skills using the WiDS Datathon dataset on metastatic breast cancer treatment. This project formed 15% of the final grade and included a mini-report, peer review, and a mini-presentation. See link to dataset here.

Objectives

• Analyze data using R statistical software.

• Encourage critical analysis and data storytelling.

• Develop essential skills for real-life decision making.

These were aligned to the Learning Outcomes: 

1. Analyse simple linear regression between two variables, describing the connection to the analysis of variance (ANOVA).

2. Interpret output from statistical software in the analysis of problems in Probability and Statistics.

Team Division and Adherence to Rubrics:

• Students were divided into three teams.

• Teams generally adhered to the rubrics.

Guided Project Details:

• All teams had suitable project titles and clear overviews of the dataset and variables of interest.

• Most teams formulated clear research questions and hypotheses.

• Good overview of analyses and tests performed.

• Extensive and thorough explanation and discussion of results.

• Relevant peer-reviewed papers included.

• Clear and concise conclusions.

Constructive Feedback:

• Need for clear labeling of research questions (e.g., RQ1, RQ2) and hypotheses (e.g., H0, H1).

• Specification of software used for data analysis and statistical tests.

• Reviews recommended for statistical tests and APA 7 format for charts and graphs.

Peer Review Participation:

• Nine out of eleven students provided helpful feedback to at least two peers’ projects.

Presentation Feedback:

• Overall fair presentation format; feedback included increasing graph sizes and reviewing hypothesis labeling.

• Slides were mostly clear and understandable.

• Suggestions to use bullet points for ease of reading and appropriate tables, charts, and graphs.

• Good condensation of written projects and adherence to the presentation timeframe.

Assessment Components

Guided Mini-Report:

1. Title of Report (1 Mark)

2. Overview of Dataset (2 Marks)

3. Research Questions (2 Marks)

4. Statistical Hypotheses (2 Marks)

5. Methodology (3 Marks)

6. Results (3 Marks)

7. Discussion (2 Marks)

8. Conclusion (2 Marks)

9. References (1 Mark)

10. Appendix (2 Marks)

Peer Review of Other Group Projects:

• Content of Report (2 Marks)

• Formulation of Research Inquiry (1 Mark)

• Data Analysis with Critical Inquiry (2 Marks)

• Structure of Report (1 Mark)

Guided Project Mini-Presentation:

• Overall Presentation Format (1 Mark)

• Clarity of Slides (1 Mark)

• Content (1 Mark)

• Staying within Time Limit (1 Mark)

Reflecting on Students' Approach

Students were divided into three teams, each adhering to the rubric. They formulated clear research questions, conducted thorough analyses, and provided meaningful feedback through peer review. Constructive feedback included labeling research questions and hypotheses clearly, specifying the software used, and reviewing statistical tests and APA format.

Impact on Student Learning

Through this project, students gained a comprehensive understanding of the research process, applied statistical methods to real-world data, and developed critical thinking skills. The peer review and mini-presentation components enhanced their ability to communicate findings effectively.

Impact on Teaching Practice

This project helped provide a clear structure for student work, aligning instructional activities with assessment criteria. It encouraged the incorporation of inquiry-based learning and real-world datasets, illustrating the relevance of course concepts and engaging students in deeper learning.

Impact on Students Learning:

• Students gained a comprehensive understanding of the research process, from formulating research questions to drawing conclusions, and learned to apply statistical methods and analytical techniques to real-world data. 

• This hands-on application reinforced their understanding of statistical concepts, research design principles, and data interpretation methods, demonstrating the practical relevance of their coursework.

• By critically evaluating data, formulating research questions, and interpreting results, students developed essential critical thinking skills. Peer review and mini-presentations enhanced their ability to communicate findings, provide constructive feedback, and practice public speaking. 

• Engaging in rigorous research projects prepared students for further study in graduate programs or research-oriented careers, developing highly transferable skills such as data analysis, critical thinking, and effective communication, which are sought after in various fields and industries.

Student Feedback: A student on one of the team’s noted the importance of “selecting leaders based on their leadership capabilities and commitment to the task at hand” since they expressed that their team leader exhibited “a disengaged and indifferent attitude throughout the project”. Nevertheless, the student said they “appreciated your [the lecturer’s] dedication to fostering a positive learning environment and your [the lecturer’s] efforts to encourage women in STEM.”

Meeting Diverse Leaners' Needs: 

The assignment allowed for group collaboration, with students working in teams, catering to different preferences for teamwork and fostering peer learning. The assignment's structure which included a brief report with structured questions and peer assessments, provided clear guidance and facilitated understanding for students with varying levels of statistical proficiency. Additionally, the assignment emphasized critical thinking and analysis, encouraging my students to formulate research questions, analyze data, interpret results, and discuss recommendations, which accommodated different learning styles and promoted higher-order thinking skills. The inclusion of a rubric ensured transparency in grading criteria, helping them to understand expectations and work towards achieving success. 

Impact on my Teaching Practice 

By being able to provide a clear structure and set of expectations for my students,  this aided the effective planning of my class content. Knowing the components of the mini-report and the associated rubric criteria allowed me to design instructional activities and assignments that aligned with these expectations.

Particularly through the use of the rubrics, I provided targeted feedback to students. Upon reviewing their work, I referred to specific criteria in the rubric to identify areas of strength and improvement. This feedback helped my students understand how they can enhance their skills and meet the learning objectives of the assignment.

This assessment also encouraged critical thinking skills in my students and prompted me to incorporate more inquiry-based learning activities and discussions into my lessons.

Additionally,  by incorporating real-world datasets in the assessment, I was able to connect course concepts to practical applications. This illustrated the relevance of the material and engaged students in deeper learning.

Ultimately, as I reflect on the outcomes of the assessment and gather feedback from students, I was able to identify areas for improvement in my teaching practice. This enabled me to adjust the assessment design and even encourage the ethical use of AI regarding the Guided Project Proposal for the PSYC 6013 student’s assessment. Thus, the postgraduate students were provided with a statement on why/how AI can be incorporated in the assessment as well the ethical use of AI that pertained to human agency, fairness, humanity and justified choice/academic honesty. They were also provided with an example of an AI prompt as well as the AI’s response to the question. 

My teaching philosophy centers on fostering an inclusive and engaging learning environment where students can develop critical thinking skills and apply theoretical concepts to real-world scenarios. This project exemplified these principles by encouraging data-driven storytelling and peer collaboration.