Embed Size (px)
DESCRIPTION
Presented at LAK Data Challenge, Linked Data Tutorial, LAK 2013 Conference April 9, Leuven, Belgium
Citation preview
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
1
Layers
These slides are licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.
A Dynamic Topic Model of
Learning Analytics Research
Past – Present – Future
Michael Derntl*, Nikou Günnemann and Ralf Klamma
RWTH Aachen University Advanced Community Information Systems (ACIS)
Informatik 5, Aachen, Germany
LAK Data Challange 2013
April 9, 2013
Leuven, Belgium
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
2
Layers
Methodology: Dynamic Topic
Modeling
Relational DB
(ID, venue, year,
title, authors,
abstract, fulltext,
hyperlink)
RDF XML
Dump
(LAK + EDM)
RDF XML to
Relational DB
Paper 1 text blah
blah blah blah
Paper N text blah
blah blah blah
…
Probabilistic
topic mining
(LDA)
Figure © ACM. Source: D. M. Blei: Probabilistic topic models. Commun. ACM 55(4): 77-84 (2012)
Dynamic topic
modeling of LAK
data (20 topics,
5 epochs)
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
3
Layers
Main Topics in LAK Dataset
"students model parameters skill" (A): most relevant and most volatile
"model data features prediction" (B) – high relevance and high stability the LAK topic?
– in 2012 "predicition" most relevant to this topic
"network community discussion analysis" (R): – quite irrelevant and volatile
– rise in relevance through LAK 2011
General prominence of students, learners, model, data in multiple topics
(bubble size: relevance in 2012)
LAK 2011
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
4
Layers
Impact of LAK Conference
Overall topic "river":
Turbulence in EDM era
More shifts through LAK 2011
Catharsis in 2012? EDM '09 EDM '08 EDM '10 EDM '11
LAK '11
EDM '12
LAK '12
ET&S SI
EDM '11
LAK '11
EDM '10
Zooming into 2010-11 transition:
Three topics reach absolute peak
(black up triangles)
– model students data probability
– network community discussion analysis
– problem students model types
Increased focus on network,
community, student models
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
5
Layers
Most Recent Topic Trends
Highest absolute rise in relevance in 2012: 1. students data courses system
2. students interaction participants analysis
3. learning analytics social learners
4. students actions learning state
5. data user learning dataset
From 11% cumulative relevance in 2009 to 42% in 2012
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
6
Layers
Summary
Past
Turbulent topic currents in EDM-only era
LAK and EDM have a shared topic foundation (data modeling, classification, clustering, …)
Present
Emphasis on learner modeling, data modeling, analysis, prediction.
1st LAK conference brought topic shifts (e.g. networks, community)
Future
Topic shifts 2012 moderate; convergence of LAK research topics
Social and interaction aspects; students as research subjects
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
7
Layers
Do It Yourself:
Topic Analytics with D-VITA
http://is.gd/laktopics
