# Summer Undergraduate Research Fair (SURF 2024)

Established by UTM's Office of the Vice-Principal, Research & Innovation (OVPRI), the Summer Undergraduate Research Fair (SURF) celebrates the research conducted by undergraduate students over the summer.

The event was held on Tuesday, August 20, 2024 in the Instructional Building/Centre (IB). Below are pictures from the events and summary of abstracts.

Decoding Survivorship Bias: Insights from Biased Survival Data
Xiaoyu Cai, Kunyi Gao (Supervisor: Professor Omidali Aghababaei Jazi)

This project addresses the analysis of length-biased survival data using the Cox proportional hazards model, where individuals with longer survival times are over-represented. To correct the bias in parameter estimates, we employed two advanced estimation methods: the Composite Partial Likelihood (CP) approach and the Weighted Estimation (WE) method. Through a series of simulation studies, these methods were compared against the traditional Partial Likelihood (PL) method, with results demonstrating that both CPL and WE provided more efficient estimates. The CP method has the lowest standard errors, indicating higher efficiency. The methods were then applied to the Channing House dataset, where we analyzed the impact of gender on hazard rates. This research shows the efficient of CP and WE in correcting bias in length-biased data.

A Multi-Step Tutorial Structure to Enhance Students’ Learning Experience in Applied Statistics
Zhengyang Fei, Dylan Galego, Yusuf Kenaroglu (Supervisor: Professor Asal Aslemand)

Statistics with applied probability is one of the core courses in our undergraduate statistics curriculum focusing on developing statistical communication in a variety of disciplines. This course is the first course in which our students develop practical skills in data analysis. With the focus on incorporating active learning strategies into this course, students were required to prepare for their weekly tutorial sessions by working on a statistical activity using R (a pre-task tutorial activity). During the tutorials, students worked in small groups accompanied by their teaching assistants to discuss the activity, complete a worksheet, and submit their statistical outputs produced in R. They were also given an opportunity to reflect on that week’s material, identifying their struggles and plans to overcome challenges. Students reported that they found weekly tutorials stimulating and engaging, providing them with a deeper understanding of statistical concepts and opportunities to demonstrate an understanding of the course material in an atmosphere that was conducive to their learning. They were encouraged to keep up with their studies on a weekly basis in a less stressful way.

Using Storytelling and Physical Analogues to Enhance Engagement and Accessibility in CS1
Karmjot Girn, Khushi Malik, Moazzam Reza (Supervisor: Professor Tingting Zhu, Professor Andrew Petersen)

With the return of in-person classes after the pandemic, imparting education has transformed significantly. However, these changes have led to decreased student participation and engagement. Studies have highlighted the positive impact of engaging videos in helping students retain and better understand course material. This study created videos based on engagement theory to enhance student learning and cognitive abilities. We developed emotionally engaging material to help students comprehend the educational content and produced videos to explain complex concepts in an introductory CS course. We used storytelling and physical analogs to make challenging topics more relatable and easier to grasp. After identifying these concepts, we developed a storyline and scripts by creating a relationship between chosen physical examples and corresponding programming concepts. Additionally, we designed storyboards and included visual graphics of some video scenes and the characters in the story. In conclusion, the project’s outcomes include enhancing student performance and memory retention, fostering a deeper connection with the material, and improving retention rates in computer science courses by making learning more accessible.

Introducing Critical Algorithmic Literacies in Computing Education
Alisha Hasan, Adelina Patlatii, Lanz Angeles, Sana Sarin (Supervisor: Professor Rutwa Engineer)

This project explores the notion of critical algorithmic literacies in an Introduction to Computer Programming course. Critical algorithmic literacy is an important stepping stone toward understanding how algorithms can create power hierarchies and imbalances. Our goal is to help students explore critical algorithmic literacies through various pedagogies, such as accessibility, inclusive, culturally relevant, and Indigenous pedagogies. Each of the modules we have created contains learning objectives, interactive elements, and open coding questions related to introductory computer programming concepts. The interactive modules challenge the traditional norms in which these concepts are taught. At various steps of our project, we have consulted with the Equity, Diversity, and Inclusion Office, and for the Indigenous module, we consulted with the Office of Indigenous Initiatives.

Seeding Forums with Curated Questions
Khushi Malik, Amber Richardson, Naaz Sibia, Angela Zaveltea Bernuy, Prjna Pendharkar, Carolina Nobre (Supervisor: Professor Andrew Petersen, Professor Michael Liut)

Fostering a supportive community in introductory programming courses is essential for student success and retention. Tools like Q&A forums, meant to aid students, can induce stress and discourage participation due to social comparisons. This study investigates whether simulated student posts can enhance engagement and belonging on these platforms.  We implemented a strategy of ‘seeding’ questions on Q&A platforms for two introductory courses (CS1 and CS2) at a major North American research university. Researchers posted a mix of programming assistance, emotional support, student experiences, study tips, and general queries using fictitious student accounts. Student reactions were analyzed through forum interactions and surveys to assess the impact on feelings of isolation and confidence.  Student reactions were predominantly positive, with increased engagement and a stronger sense of connection reported. Women resonated more with all curated posts, while men with prior experience gained confidence from simpler content questions. Qualitative data revealed a mix of empathy and competitiveness; while students wanted to help peers, competitive undercurrents sometimes fueled anxiety.  Seeding questions effectively encourages student engagement and enhances their sense of belonging by offering relatable content. Although competition can arise, students generally became more willing to participate and reflect on their knowledge.

Hidden Markov Model on Stock Price Prediction
Ian Quan (Supervisor: Professor Omidali Aghababaei Jazi)

Predicting stock market trends remains a complex challenge due to its inherent volatility and the potential for substantial financial returns. In this project, we used the Hidden Markov Model (HMM) to predict stock prices by representing market dynamics through hidden states. The study's analysis concludes that the prediction results are relatively accurate, particularly in forecasting the trend of the company's stock fluctuations. However, the variance is significant, indicating a need to incorporate more control variables and combine different prediction methods to achieve more accurate stock prediction results.

Understanding Undergraduate Students' Attitudes Toward Statistics
Selena Gangaram, Louisianne Saldagna (Supervisor: Professor Asal Aslemand)

Students often enter introductory statistics courses with fear, largely due to a lack of confidence in their mathematical abilities. This fear can make it challenging for educators to effectively engage students in learning statistics. This study aimed to examine undergraduate students' attitudes towards statistics by the end of an introductory statistics course designed for non-statistics majors (e.g., psychology, biology, neuroscience) and to assess how a semester-long scaffolded collaborative project contributed to their statistical learning. Throughout the course project, students developed their own research questions, collected and analyzed data using R, and presented their findings both orally and in writing. A mixed methods approach was used to survey students’ attitudes toward statistics at the end of the course. Quantitative analysis indicated that students who felt more positive about statistics were more interested, invested more effort, and valued the subject more highly. Additionally, their increased positive feelings correlated with higher statistics achievement, accounting for their self-perceived mathematical abilities. Thematic analysis of qualitative data revealed that students believed the group project improved their statistical literacy, made learning statistics more engaging and meaningful, and facilitated the application of their knowledge to other academic areas.

Enhancing Self-Explanation in Student Learning Through Large Language Models
Jessica Wen (Supervisor: Professor Michael Liut)

Self-explanation (SE) plays a crucial role in student learning, particularly in understanding abstract material where deeper comprehension is required. However, many students struggle to effectively employ SE strategies, often submitting summaries instead of reflective explanations. To address this challenge, we conducted an experiment involving two groups: a control group receiving a single prompt to compare their explanations with expert ones, and a treatment group interacting with a Large Language Model (LLM) for a dialogue about their explanations.  While we didn’t find any significant differences in the groups overall, we did see some differences demographically, such as gender and English proficiency. Students in the LLM group did perform significantly better on some questions, and had a higher grade on average. However, students reported some issues with the LLM chat feature which needs further investigation.  This study contributes to the integration of advanced technologies in education, highlighting the practical application of LLMs to improve teaching strategies and learning
outcomes.