Course: EECS4080

Supervisor: Uyen Trang Nguyen

Supervisor's email address: utn@eecs.yorku.ca

Project Description: How much should I spend and how much should I save (for a car/house and my children’s education)? How much do I need to save for a comfortable retirement? What types of investment should I use for a specific saving goal? What is the investment return rate needed to reach my saving goals? How much will my savings grow? In this project we will build an app to assist people with their personal finance planning. The students will read books/articles on personal finance (provided by the supervisor) to acquire background knowledge for the project.

Required skills or prerequisites: Experience in software development; completion of a co-op or internship in software development.

Instructions:

• This project will need 2-3 students for the front end, back end and user interface.
• Email Prof. Uyen T. Nguyen (utn@eecs.yorku.ca) a resume listing courses, projects and prior experience relevant to the project.
• Students can enrol in summer 2023 (EECS4080) or FW 2023-2024 (EECS 4088/4090).

Course: EECS4080

Supervisor: Pooja Vashisth; Lab Website: https://lassonde.yorku.ca/users/pvashisth

Supervisor's email address: vashistp@yorku.ca

Project Description: Problems and Needs: Moodle – York University Learning Management System (LMS) – provides instructors with rich data sets for students’ activities and performance. However, while the data comes in bulk, some important information (e.g. course activities) is automatically deleted by the system and replaced with new data each week. This issue prevents instructors from using such data effectively to enhance their teaching. Moreover, the bulk data may prevent professors from taking away useful insights to improve course quality. Ideas: This project aims to address the main problems below with the following solutions: Retrieve full data set using scripts/integrations/apps that automatically pull course data from Moodle; Provide instructors with useful data visualizations from Moodle’s enormous data to support their teaching; Provide a quick summary and insights from those data sets. To be able to identify and address the issues related to: (1) Pull data – quiz statistics, proposal, weekly course activity, new analytics → report, course activity, course grades. (2) Data visualization: visualize general data, data of assessments (quizzes, assignments,…), summary for all assessments and summary for each assessment and for each question, mean, mode, median, average time overall and for each assessment, average number of attempts, grade distribution, time distribution (when and how long students spend on) → clusters, respondents distribution (based on best attempt and compared to class) for each question, correlation between the number of attempts and accuracy, highlight hard questions and corresponding materials/reading views and engagement, extract question labels/keywords, what are the types of questions (MCQ, pseudocode,…) that students are generally not performing well on, data of exam, mean, mode, median, grade distribution, data of engagement, time distribution (when and how long students spend on) per course material, most and least viewed/engaged materials, average views and engagement per material, is there any correlation between students’ engagement and performance (i.e. do students actually achieve higher scores if they engage with course materials more frequently), visualize individual data, current avg. mark, all marks of that student up to that moment, performance as a graph (quizzes, assignments, test 1, test 2, final), attempts and accuracy per quiz, time spent on each assessment, time a student started an assessment and the last submission timestamp, questions/topics that each student is doing well/struggling with engagement: what content does this student engage with? how often? when? how long? times opened a document? correlation between engagement and performance? feedback and insights, individual feedback – assess whether a student is at risk or not, if a student does well on assignments, how do they perform on midterms and finals? how about the opposite case? [tentative] predict examination results? interventions, when and how instructors should deploy their interventions to students, based on the results provided? [tentative] option to email/inform those low-performing students?

Deliverables: Research and Proposal for a website to show statistical visualizations (high priority), sign in with Moodle, all students’ data graphs individuals’ data graphs feedback by words | insights for each student, automation on data collection from Moodle (medium priority) instructors will be able to share those results with students (optional – low priority).

The extent of implementation depends on the complexity and scope of the tasks mentioned above.

Required skills or prerequisites: The position is open for third and fourth year students in a computer science, statistics, data science, or engineering degree.

Recommended skills or prerequisites: Good at programming, statistics, research, and has a willingness to explore the unknown.

Research and statistics skills; web development (applying OOP design, design pattern, and SOLID principles in developing backend API (using Python Flask)); data analysis: using Python framework and library (pandas, NumPy, seaborn, matplotlib, statsmodel) in analyzing the data

Instructions: Send your CV, transcript, statement of interest in the project, and your suitability to the same to Professor Vashisth.

Course: EECS4080 or EECS4480

Supervisor: Yan Shvartzshnaider

Supervisor's email address: rhythm.lab@yorku.ca

Project Description: Modern sociotechnical systems share and collect vast amounts of information. These systems violate users’ privacy by ignoring the context in which the information is shared and failing to incorporate contextual information norms.

Using techniques in natural language processing, machine learning, network, and data analysis, this project is set to explore the privacy implications of mobile apps, online platforms, and other systems in different social contexts/settings.

To tackle this challenge, the project will operationalize a cutting-edge privacy theory and methodologies to conduct an analysis of existing technologies and design privacy-enhancing tools.

Students will help analyze information handling practices of online services and design privacy-enhancing tools.

Specific tasks include: comprehensive literature review of existing methodologies and tools, analysis of privacy policies and regulations, visualization of information collection practices, and design of a web-based interface for analyzing extracted privacy statements to identify vague, misleading, or incomplete privacy statements.

For prior project, see this link

Required skills or prerequisites: Good programming and data analysis skills overall, and experience in using Jupyter and/or R for data analysis.  Ability to work indecently. Interest in usable privacy, critical analysis of privacy policies and privacy related regulation.

Recommended skills or prerequisites: Experience with Machine Learning, Natural Language Processing techniques, HCI design. Students with diverse backgrounds, including in technical fields, social sciences and humanities are encouraged to apply.

Instructions: Please fill in this form