Hi, Namaste, from Divx79

jus starting on my AI journey. please wish me luck!

divx79,

Welcome to the family! What part of A.I. interests you?

And, yes, I do wish you luck!

Katherine Moss

Hey Katherine, Namaste, Hello! I have always been fascinated by potential of A.I. to enhance human understanding and capabilities.

divx79,

I feel the same. It means a lot to talk with folks who share the same passion. For me now, I got some dermatology photos that are labeled for the affliction depicted. I am planning to do some work with them.

What are you working on?

regards,

Katherine Moss
https://www.linkedin.com/pub/katherine-moss/3/b49/228

Hey Katherine,
As a Data Scientist, I’ve been involved in the EdTech domain for around 5 years and currently, I’m working on the following projects as part of a team now:

1. Personalized Learning Pathways

  • Objective: Develop algorithms to create personalized learning experiences for students by analyzing their learning behavior, preferences, and performance data.
  • Techniques: Machine learning models, clustering, recommendation systems, and Natural Language Processing (NLP) for understanding text-based feedback.
  • Outcome: Improved student engagement and outcomes by tailoring content to individual needs.

2. Student Performance Prediction

  • Objective: Predict student success in courses or programs by analyzing historical academic performance, engagement metrics, and demographic data.
  • Techniques: Regression models, classification models (e.g., logistic regression, random forests), and deep learning.
  • Outcome: Early identification of at-risk students, allowing for timely intervention and support.

3. Adaptive Assessment Systems

  • Objective: Create adaptive testing systems that adjust the difficulty of questions in real-time based on student responses, ensuring accurate assessment of student ability.
  • Techniques: Item Response Theory (IRT), Bayesian networks, and reinforcement learning.
  • Outcome: More accurate and fair assessments that cater to individual student abilities.

4. Content Recommendation Systems

  • Objective: Develop systems to recommend educational content (videos, articles, exercises) to students based on their learning history and preferences.
  • Techniques: Collaborative filtering, content-based filtering, hybrid models, and NLP.
  • Outcome: Increased student engagement and completion rates through relevant content recommendations.

5. Learning Analytics Dashboards

  • Objective: Build dashboards that provide insights into student progress, engagement, and performance to educators, students, and administrators.
  • Techniques: Data visualization, descriptive analytics, and predictive analytics.
  • Outcome: Enhanced decision-making and support for educators, improved self-regulation for students.

6. Natural Language Processing for Automated Feedback

  • Objective: Use NLP to provide automated, personalized feedback on student submissions (e.g., essays, short answers).
  • Techniques: NLP models, sentiment analysis, and text similarity algorithms.
  • Outcome: Scalable feedback systems that provide immediate and actionable insights to students.

7. Churn Prediction for Online Courses

  • Objective: Predict which students are likely to drop out of an online course based on their engagement and interaction patterns.
  • Techniques: Classification models, survival analysis, and time series analysis.
  • Outcome: Targeted retention strategies to reduce student churn.

8. Social Learning Network Analysis

  • Objective: Analyze the interaction networks within online learning communities to understand the influence of peer interactions on learning outcomes.
  • Techniques: Network analysis, graph theory, and social network analysis (SNA).
  • Outcome: Insights into the role of peer networks in learning and strategies to foster beneficial interaction
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