COSC 419: Topics in Computer Science

Learning Analytics


Administration

Download course outline and review it carefully. It covers course objectives, grading, and various course and university policies.

Important Dates See http://okanagan.students.ubc.ca/calendar/

Tentative Schedule

Week Date Topics Online "Textbook" Readings Deadlines
Slides Supplemental Readings Programming Resources
1 09/08
  • Course Overview
  • Recent Case Studies
  • slides
  • slides
  • Penetrating the Fog: Learning Analytics in Education
  • Analytics in Higher Education: Benefits, Barriers, Progress, and Recommendations
  • 2 09/13-15
  • Data Collection and Abstraction
  • Content Based User Profiles
  • Collaborative Filtering
  • slides
  • slides
  • slides
  • Lumiere paper
  • TF-IDF and cosine similarity
  • A1 due - what is personalized learning
    3 09/20-22
  • Video Prototyping and Group Exercise
  • Decision Trees
  • slides
  • slides
  • Videos: Paper prototype in action, Scenario and paper prototype, and Scenario and high fidelity prototype
  • Chapter on classification and decision trees (from Intro to Data Mining by Tan, Steinbach, & Kumar 2006)
  • 4 09/27-29
  • Modeling Uncertainty (Probability)
  • Reasoning Under Uncertainty and Bayesian Networks
  • slides
  • slides
  • Intro to Bayes Nets
  • Online Matlab reference
  • A2 due - video prototyping
    5 10/04-06
  • Review A3 and Quiz #1 expectations
  • Building Bayesian Networks
  • Building BNs (cont.)
  • live demo
  • slides
  • slides
  • 6 10/11-13
  • Class cancelled due to conference travel
  • Quiz #1 (Thursday)
  • What is cURL
  • Getting started with GitHub API
  • List commits on a repo
  • What is JSON?
  • Graphviz
  • A3 due - GitHub collaboration
    7 10/18-20
  • Building BNs (cont.) and Course feedback
  • Inference in Bayesian Networks
  • Quiz #1 Review
  • Expected Utility
  • slides
  • slides
  • slides
  • slides
  • Clique inference algorithm
  • How to use the BNT
  • 8 10/25-27
  • Real Decision Problems
  • Preference Elicitation
  • Dynamic Bayes Nets
  • exercises
  • slides
  • slides
  • How to use the BNT
  • Visualizing graph structures in Matlab
  • A4 due - project proposal and Bayes nets
    9 11/01-03
  • DBN Simulations
  • Plagiarism Detection (Essays)
  • slides
  • slides
  • mk_hints.m, sim_hints.m, sim_hints_decision.m
  • Challenges in automatic plagiarism detection (Clough 2003)
  • Measuring Text Reuse (Clough et. al. 2002)
  • 10 11/08-10
  • GPLAG
  • No class due to Midterm break
  • slides
  • GPLAG: Detection of Software Plagiarism by Program Dependence Graph Analysis (Liu et al. 2006)
  • 11 11/15-17
  • GPLAG (cont.)
  • Expectations for Quiz #2 and Project presentation
  • Quiz #2 (Thursday)
  • slides
  • A5 due - probabilistic learner models
    12 11/22-24
  • Project Support and Quiz #2 Review
  • Project Presentations (Thursday):
    • GPLAG (Ashley S., Bronson, Kento)
    • Document Profiling (Becca, Charlotte, James M., Nino)
    • Other (Koki, Matt, Ashley W.)
  • slides
  • 13 11/29-12/01
  • Project Presentations (Tuesday):
    • Clique Inference (Kelsey M., Jeff, Levi)
    • Social Media (Alvina, Jaren, Paul, Tapiwa, Liam, Dennis)
  • Project Presentations (Thursday):
    • Clique Inference (Max S.)
    • Conjoint Analysis (Alex, Max M., Kelsey D., Lam, Stephane, Sam, Travis)
    • Other (James R.)
  • Course Summary
  • A6 due - plagiarism detection