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 01/08-10
  • Course Overview
  • Recent Case Studies
  • Data Collection and Abstraction
  • slides
  • slides
  • slides
  • Learning Analytics in Higher Education - A Literature Review (Leitner et al. 2017) paper
  • Research Evidence on the Use of Learning Analytics (Ferguson et al. 2016) appendix 1
  • Lumiere paper
  • paper to estimate and learn user behaviours in a dvorak user study
  • 2 01/15-17
  • Content Based Recommendation Systems
  • Collaborative Filtering
  • Decision Trees
  • slides
  • slides
  • slides
  • TF-IDF and cosine similarity
  • Chapter on classification and decision trees (from Intro to Data Mining by Tan, Steinbach, & Kumar 2006)
  • A1 due - personalized learning
    3 01/22-24
  • Clustering
  • Cluster Validation
  • Quiz #1 expectations
  • slides
  • slides
  • slides
  • slides
  • Chapter on cluster analysis (from Intro to Data Mining by Tan, Steinbach, & Kumar 2006)
  • 4 01/29-31
  • Modeling Uncertainty using Probability
  • Friday: class cancelled due to illness
  • slides
  • A2 due - document similarity
    5 02/05-07
  • Quiz #1 (Wednesday)
  • Independence Assumptions and Bayesian Networks
  • Overview of A3
  • slides
  • Intro to Bayes Nets
  • What is cURL
  • Getting started with GitHub API
  • List commits on a repo
  • What is JSON?
  • Graphviz
  • 6 02/12-14
  • Building Bayesian Networks
  • review Quiz 1
  • Inference in Bayesian Networks
  • Bayes nets with Matlab
  • Dynamic Bayesian Networks
  • slides
  • slides
  • slides
  • slides
  • slides
  • Clique inference algorithm
  • Online Matlab reference
  • How to use the BNT
  • Visualizing graph structures in Matlab
  • A3 due - GitHub collaboration
    7 02/19-21 No class due to Reading week
    8 02/26-28
  • Overview of A4
  • Dynamic Bayesian Networks (cont.)
  • Expected Utility
  • Real Decision Problems
  • slides
  • slides
  • 9 03/04-06
  • Quiz #2 expectations
  • Preference Elicitation
  • DBN Simulations
  • DBN an intelligent tutoring
  • slides
  • slides
  • slides
  • slides
  • study using bounded queries in PowerPoint
  • a DBN intelligent tutoring system for Physics
  • mk_hints.m, sim_hints.m, sim_hints_decision.m
  • A4 due - project proposal and Bayes nets
    10 03/11-13
  • Quiz #2 (Wednesday)
  • Plagiarism Detection (Essays)
    Video lectures on Canvas under "Module"
  • slides
  • Challenges in automatic plagiarism detection (Clough 2003)
  • Measuring Text Reuse (Clough et. al. 2002)
  • 11 03/18-20
  • GPLAG
  • Expectations for Project presentation and Final exam
  • Online office hours 2:30-3:30
  • slides
  • slides
  • GPLAG: Detection of Software Plagiarism by Program Dependence Graph Analysis (Liu et al. 2006)
  • A5 due - probabilistic learner models
    12 03/25-27
  • Online office hours on demand only
  • 13 04/01-03
  • Online office hours on demand only
  • Project Presentation Video due Apr 2
    A6 due - plagiarism detection
    14 04/08
  • Online office hours 2:30-3:30
  • Project Presentation Voting due Apr 7