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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