Reflective Analysis of the Airbnb Business Project (Gibbs’ Reflective Cycle)

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Description

This assignment was my second group project in the MSc. Drawing on lessons from my first, I reached out to my new teammates as soon as the groups were announced, sharing my number and setting up a WhatsApp group so we could start brainstorming early. Out of five members, only one replied straight away and another joined a few days later. The remaining two were silent for nearly a week, and one even questioned if they were actually in the group despite the email announcement.

We eventually held a Zoom meeting, but turnout was inconsistent, with one member never attending at all. The others were reluctant to handle the technical elements of the project, so I allocated simpler tasks to keep them involved while I focused on exploring the dataset, combining location data with subway and landmark information, and experimenting with regression and K-Means clustering. The bulk of the work fell to me, and I submitted the report less than a day before the deadline. Despite this, our group earned a strong grade and positive feedback on the quality of analysis and visualisations.

Feelings

At first I felt optimistic. Coming off a decent result in my previous group project, I was eager to put that experience to use and raise the bar. I felt confident enough to take the initiative in forming the group and setting a proactive tone. That enthusiasm faded when replies trickled in slowly and some classmates appeared disengaged or unsure about the brief. Their hesitation with Python and data analytics eroded my confidence in their contribution. By our first meeting, I sensed that I might carry most of the workload, which left me frustrated but also determined to see the project succeed.

Evaluation

Starting early was the right call, giving me time to explore the dataset and research supporting material like New York City subway data and Local Law 18. The downside was that my early sense of having to “do it all” shaped how I interacted with the group. I often accepted their reluctance to engage with the technical side instead of challenging them constructively. As a result, two members barely contributed, and the quality of collaboration never matched its potential. On the positive side, I gained hands-on experience with regression, clustering, and geographical visualisation — skills directly relevant to my work with exam data at the aviation college.

Analysis

Looking back, the uneven participation stemmed from several factors: differing confidence levels with analytics, slow communication, and my own tendency to “rescue” the group rather than push for shared responsibility. Research on teamwork suggests that early clarity of roles, clear expectations, and gentle accountability improve engagement, particularly in remote groups. My choice to focus on the technical core while assigning lighter tasks created a comfort zone that limited others’ growth. At the same time, their lack of initiative meant opportunities to balance the workload were missed. Professor feedback praised our analysis and visuals but highlighted that the business impact and referencing could have been stronger — areas I might have improved with more time for discussion and review had collaboration been better.

Conclusion

I learned that effective group work is not only about dividing tasks, but also about encouraging participation and setting standards for quality and pace. While technical independence is useful, it should not become a barrier to guiding peers out of their comfort zones. On the technical side, I deepened my understanding of cleaning and modelling data with Python, as well as the value of supplementing a dataset with external sources to enrich analysis. Personally, I recognised that trying to “be nice” by shielding others from challenges can reduce the overall quality of the work and prevent the team from developing.

Action Plan

In future group projects, I will establish clear roles and expectations at the start, encouraging peers to handle meaningful parts of the analysis, even if it requires extra support or coaching. I will also schedule progress checkpoints to maintain momentum and create space for reviewing business implications and academic referencing before submission. Beyond university, I intend to use my improved Python and data-cleaning skills to develop semi-automated pipelines for processing daily exam results in my department. I also plan to adopt a firmer but supportive approach with colleagues, promoting accountability so that our combined output exceeds what any of us could achieve alone.

References

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