DATA1001 Project 1

DATA1001 Project 1 | University of Sydney | Semester 2, 2026

Author

Group 6

Published

16 September 2026

Executive Summary

Analysis of 5,057 DATA1X01 students found that international students spend notably more time on social media daily (median 5.0 hrs) than domestic students (median 3.5 hrs). A weak but positive relationship exists between data science interest and intended study hours, suggesting students who are more engaged plan to put in more effort.

Evidence

Overview

The data was sourced from a voluntary survey examining 29 variables, completed by 5,057 consenting DATA1X01 students across cohorts 2024S2 to 2026S2.

Variables focused on:

  • Student type – Qualitative (nominal)
  • Social media use (hours/day) – Quantitative (continuous)
  • Data science interest (0–10) – Quantitative (discrete)
  • Intended study hours per week – Quantitative (continuous)

Limitations

While the Data 1X01’s dataset was collected from students enrolled in the unit, participation was voluntary, meaning the dataset represents a sample rather than the full population. The survey is self-reported, so students may inaccurately estimate their daily social media use and intended study hours. Social media use was measured via phone screen time only, potentially excluding desktop usage. As the data is cross-sectional, causation cannot be inferred from any observed relationships.

Assumptions

We assumed that students answered the survey honestly and that phone screen time is a reasonable proxy for social media use. We assumed that lecture and tutorial hours were excluded from intended study hour. Values for social media use greater than 24 hours per day were treated as data entry errors and removed. Values of hours of studying equal to 0 were excluded as non-meaningful, and values exceeding 60 hours per week were treated as an unrealistic outliers and removed.

Data Cleaning

All 5027 responses indicated consent, so no filtering was required on that basis. 43 responses with social media use greater than 24 hours per day were removed as implausible. Responses with hours of studying equal to 0 or greater than 60 were excluded, resulting in a final cleaned dataset of 4892 rows used for analysis.

Research Question 1

Do domestic and international students differ in their daily social media use?

Code 
library(tidyverse)
library(ggplot2)

data <- read.csv("DATA1001_Project_1.csv")

#filter for RQ1

rq1_data <- data %>%
  filter(consent == "I consent to take part in the study") %>%
  filter(social_media_use >= 0 & social_media_use <= 24) %>%
  filter(student_type %in% c("Domestic", "International"))

#boxplot

ggplot(rq1_data, aes(x = student_type, y = social_media_use, fill = student_type)) +
  geom_boxplot(outlier.colour = "grey50", outlier.size = 0.8, alpha = 0.8) +
  scale_fill_manual(values = c("Domestic" = "#4E79A7", "International" = "#F28E2B")) +
  labs(
    title = "Daily Social Media Use by Student Type",
    x = "Student Type",
    y = "Social Media Use (hours/day)",
    fill = "Student Type"
  ) +
  theme_minimal() +
  theme(legend.position = "none")

Our analysis of the boxplot shows that students who work steadily all semester have a higher median intended study time ( 4hrs/week, IQR =3) compared to those who leave things to the last minute ( median=3, IQR=4 ). Both distributions are right-skewed, with the last-minute group showing a wider spread and more outliers, suggesting greater variability in that group The greatest variability is seen among students who adapt by subject, with a median of 5.5 hrs and the widest IQR, suggesting their study effort varies considerably depending on the unit. As this is an observational study, we cannot conclude that study type causes differences in study hours, other confounding variables such as subject personal motivation may also play a role.

Research Question 2 (Linear Model)

Is there a linear relationship between a student’s interest in data science and their intended study hours per week?

Code 
#scatter plot

ggplot(rq2_data, aes(x = data_interest, y = hours_studying)) +
  geom_jitter(alpha = 0.2, colour = "#4E79A7", size = 0.6, width = 0.2, height = 0) +
  geom_smooth(method = "lm", se = TRUE, colour = "#E15759", formula = y ~ x) +
  labs(
    title = "Data Science Interest vs Intended Weekly Study Hours",
    x = "Interest in Data Science (0–10)",
    y = "Intended Study Hours per Week"
  ) +
  theme_minimal()

Code 
library(plotly)
library(tidyverse)

## residual plot

p<- ggplot(model, aes(x = "fitted value", y = "residual")) +
  geom_point(alpha = 0.3, colour = "#4E79A7", size = 0.6) +
  geom_hline(yintercept = 0, linetype = "dashed", colour = "#E15759") +
  labs(
    title = "Residual Plot",
    x = "Fitted Values",
    y = "Residuals"
  ) +
  theme_minimal()

ggplotly(p)

The fitted linear model is: hours_studying = 3.45 + 0.37 × data_interest (n = 4,892, R² = 0.029).

Our analysis of the scatter plot shows a weak positive trend between the explanatory variable (data science interest) and the response variable (intended study hours) among Data1X01 students, with data points spread widely and no clear linear pattern. The regression line is relatively flat, and for every 1-unit increase in data science interest, students intend to study approximately 0.37 more hours per week. The residual plot shows no strong curve, supporting the linearity assumption, though mild heteroscedasticity is present at higher fitted values. With R²= 0.029, the model explains only ~3% of variation in intended study hours, indicating a weak linear relationship. This suggest there may exist some potential confounding variables — such as course load, work hours and study habits — likely have a stronger influence. To conclude, this suggest the linear model is reasonable but imperfect fit for this data.

Articles

Yu, J. (2011). Shifting roles: International students’ social media use and cultural adaptation. Journal of International Students, 1(2), 19–27.

Krause, K., & Coates, H. (2008). Students’ engagement in first-year university. Assessment & Evaluation in Higher Education, 33(5), 493–505. https://doi.org/10.1080/02602930701698892

Acknowledgements

Group Meetings:

12/08/2026

All member attended

09/09/2026

All member attended

11/09/2026

All member attended

13/09/2026

All member attended

16/09/2026

All member attended

Contributions:

Leo

  • Clean the dataset, identifying and removing 43 social media outliers and excluding unrealistic study hour response

  • Conducted RQ1 analysis

  • Built the RQ2 linear model and interpreted the slope, R² and residual plot

  • Created all visualisations including an interactive residual plot using plotly in the HD examplar

  • Implemented tabbed panel layout for RQ2 plots

  • Formatted the final report using Quarto with flaty theme, table of contents and code folding

  • Rebugged rendering errors including working directory issues, YAML formatting errors and chunk syntax errors (eg. include=FALSE, message=FALSE, setwd() )

  • Wrote all written analysis including EDA,RQ1 and RQ2 interpretations,limitations, assumptioons and data cleaning sections

Tanay

  • reviewed and proofread the written anakysis for clarity and accuracy

  • contributed to selecting the research questions

  • Reviewed data cleaning decisions and provided feedback on outlier removal

Manthan

  • Assited in interpreting the RQ1 boxplot findings

  • Reviewed the executive summary and suggested edits

  • Contributed to the limitations and assumptions discussion

Haoxuan

  • Reviewed the RQ2 linear model interpretation

  • Assisted in selecting appropriate variables for analysis

  • Proofread the final before submission

Anoushka

  • Assited in interpreting the RQ1 boxplot findings

  • Reviewed the executive summary and suggested edits

  • Contributed to the limitations and assumptions discussion

Dhairya

  • Reviewed the RQ2 linear model interpretation

  • Assisted in selecting appropriate variables for analysis

  • Proofread the final before submission

Resources Used:

  • Data1001 project 1 Rubric

  • Data 1001 R Template

  • Data1001 project 1 HD examplar

  • Tutor Guidance (Kevin, Anina)

  • R documentation, ggplot2,tidyverse,plotly package documentation

  • Quarto documentation, for tabset panel layout and YAML formatting

  • Data 1001 lecture notes for statistical terminology (observational study, confounding variables, linearity assumption and hereroscedasticity)

AI Usage Statement:

Claude, Anthropic, https://claude.ai was used to assist in planning the structure of this report, selecting appropriate research questions from the dataset, computing preliminary statistics, and drafting the written analysis. All R code was written and verified by the students. All statistical interpretation and conclusions are the group’s own.

Shared Values and Ethical Principles:

Shared Value — Integrity:

We ensured that our analysis was directed by the data rather than desired outcomes. Outliers were removed transparently with documented reasoning, and we reported findings including the weak R² without exaggerating the strength of the relationship.

Ethical Principle — Respect:

We recognised that the survey data involves real students’ personal information. We did not attempt to identify individuals and handled sensitive variables (e.g., drug_use) with care, choosing not to include them in our analysis.