@WI-IAT2022

Online Discussion
Transition Analysis
for Group Learning
SupportYasunobu Sumikawa*, Akikazu Takada**, Akira Ichinose***, Ari Murakami**, Yuki Toyono**,Ryohei Ikejiri****, Meiko Sakasegawa****, Kaoru Sekine*****, and Yuhei Yamauchi***** Takushoku Univ.
** Ddrive K.K.
*** First Torrent LLC**** The University of Tokyo***** Tohoku Univ.

Background
Group work is effective for higher-order learning●OECD and Japan urge group work in classrooms

Online tools use mechanisms, such as breakout rooms for group activities●●The mechanisms limit the participation of non-group members.
Teachers ﬁnd it difﬁcult to monitor all the groups simultaneously

Background | Related works●Visualizing system
Taouﬁq et al. proposed a system using latent Dirichlet allocation (LDA) tovisualize learners’ discussions [8].

●Topic detection and tracking
A topic detection and tracking (TDT) algorithm for predicting future events aftermining causal relationships [7]It analyzes whether the entered news is on the same topic as previously
reported as well as our discussion transition analysis[8]: Taouﬁq Zarra, Raddouane Chiheb, Rdouan Faizi, and Abdellatif El Aﬁa. 2018. Student Interactions in Online Discussion Forums: Visual Analysis with LDA Topic Models. In LOPAL ’18. Association for Computing Machinery, Article 30, 5 pages.
[7] Kira Radinsky and Sagie Davidovich. 2012. Learning to Predict from Textual Data. J. Artif. Int. Res. 45, 1, 641–684.

Objective | Supporting teachers

Inspiration:
traffic lightAlgorithm overview
Google Cloud
Speech-to-Text
Entity detection
byGoogle Cloud
Natural Language API
Entity&Wikipedia Category
Analysis
Proposed algorithm

Tokenizing

Discussion
Transition
Analysis
Visualizing
Main
ThemeAnalysis

Discussion Transition Analysis

Assumption: although the discussion transition happens over time, the change is continuous
1.

Queue model
Using only the most recent N pieces

2.

Memory modelthat content discussed repeatedly for a long time is more important than themost recent content.

Queue model

A, B, C, B

A, B

A

A, A, A

A: 6
B: 4
Calculating concordance rate
too low or too high: problem

A, B, B, B

Concordance rate calcuration：Jaccard coefficeint

0.0

deviating

1.0

stalling

Memory modelForgetting curve

Our model

Main theme analysis

Using Wikipedia article as approximate solution
Helping the teacher to quickly understand what kind of conversation
the group was having

Main theme analysis

Example：If we draw this straight line, we get triangle three.

Triangle, Vertex perpendicular (triangle), Right triangle
Explicit
SemanticAnalysis

Doc2Vec
Center of gravity
Main themeTriangle

Evaluation

Dataset: W2E and real discussion text
● W2E has 1,781 event texts from Wikipedia
Baselines:● TDT: discussion transition analysis [7]● LDA: main theme analysis[7] Kira Radinsky and Sagie Davidovich. Learning to Predict from Textual Data. J. Artif. Int. Res. 45, 1 (2012), 641–684.

Evaluation

Proposed algorithms○○○

97% of properly transitioning text could be analyzed as correct.
100% of inappropriate conversation data could be analyzed as
inappropriate.
Correctness rate for main theme analysis is also 70%.

Baselines:●●

TDT: 28.5% correct ratio
LDA: 32%

Conclution●●

We proposed algorithms to support teachers in monitoringgroup learning sessions easily
The proposed queue model provided faster results fordiscussion transition analysis, whereas the proposed memory
model was more accurate

Thank you!!!