Download CIDM 07

Mining the Students’ Learning
Interest in Browsing
Web-Streaming Lectures
Long Wang, Christoph Meinel
CIDM 07
組員:
69721012 陳子玉
69821002 朱玉棠
1/17
Outline






Introduction
Motivation
Data preparation
Methods
Analysis
Conclusions
2/17
Introduction

Web-based e-learning system is missing:






Eye contact
Body language
Facial expression
Voice tone
Atmosphere
and so-on…
3/17
Introduction
http://www.tele-task.de/
4/17
Motivation



Mine students’ learning interest form their
browsing behaviors
Know the learners and their learning interest
Optimize the organization of the web site and
the lectures
5/17
Data preparation

Unifying heterogeneous learning usage data


Log format、user
Model student browsing profile
6/17
Methods

Q1: Are the online lectures welcomed by students?

Method: check if NAS,L + NPS,L > NCS,L
NAS,l :Number of accessing live streaming version of L
NPS,l :Number of accessing post edited version of L
NCS,l :Number of students that attend L in classroom

Question:
 Who can choose the learning method?
 Who is sitting in front of computer?
7/17
Methods

Q2: Is there any difference between viewing the live
broadcasting lectures and browsing lectures after they are
recorded and edited?

Method: compare NAS,L and NPS,L
Reasons of that NAS,L is always less than NPS,L :
 Schedule
 Disharmony of teaching & learning tempo

8/17
Methods

Q3: Is there any preference on the different lectures in a
course and preference on different pieces of one lecture

method: observe NDS,L & NOS,L
NDS,l :Average time duration of viewing L
NOS,l :Average Number of Operations of viewing L



Bigger NDS,L & NOS,L tells that students would like to spend
more efforts on it
9/17
Methods

Q4: Did the students view other lectures when they
accessed one lecture?

method:

the set including all the learning sessions are named as P where
P consists of ps :


From P, we try to mine the relations each of which is formed as :
Suppr is the number of sessions that viewed all the lectures in r
(by frequent)
Integrates and mines on FP-Tree
10/17
Methods

Q5: Is there any relation between the exercise marks and
the usage on lectures?
method:

USS,L : usage score

where α + β + γ + δ + θ = 1 and the values of these

five coefficients are assigned based on the statistical
observations or the expert experiences.
11/17
Methods


Q6: For the same named courses supplied for
different years, is there any changes on the students’
interest?
method:
12/17
Methods

a: From Lecture Level:
S: a set of students
L: lecture
C: course
K1, K2 : the sets of the knowledge elements of L1 & L2
13/17
Methods

b: From Chapter Level:
？
14/17
Analysis
15/17
Analysis (cont.)
16/17
Conclusions



We explain the learning interest by raising 6 questions from
the teacher’s view, and different mining methods.
The learning interest of a set of students on a lecture is
multi linear regressed from four learning attributes of this
lecture.
The average time of a student spending on a lecture is
about 10 minutes, while the normal length of a lecture is
about 90 minutes.
17/17