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and MIT OCW. BLOSSOMS had 50 educational
video lectures in a variety of topics. Because all
videos have a transcript, they are suitable for text
processing such as LDA considerably. Though MIT
OCW had more than 2, 000 lectures, we used 1, 019
lectures from which we were able to obtain lecture
notes. The proposed algorithm was applied to the
transcripts for BLOSSOMS, and text data which were
extracted from lecture notes for MIT OCW respec-
tively. While OER are provided in various formats,
the proposed approach is applicable to them as long
as they have text.
After word regularization such as lemmatize and
elimination of stop-words, we applied LDA and de-
rived concepts. We used an algorithm proposed by
Griffiths (Griffiths and Steyvers, 2004) for the imple-
mentation of LDA. LDA requires several parameters
that we set as the following.
• hyper parameter α : 0.5
• hyper parameter β : 0.5
• number of iteration: 100
• number of concept: 10 to 50 step by 5
We used multiple values for the number of concepts as
proposed in Section 4.2. By this experiment setting,
we obtained nine different layers of concept which
were generated independently.
Figure 3 shows an example of output. The system
uses the output of LDA to provide search function and
to visualize concept network and material network.
The probabilistic word distribution φ works for scor-
ing of the search function. The score of a concept
can be formulated as the summation of φ value(s) for
query word(s).
The figure “1) Concept Network” in Figure 3
shows the result of the search. For the learner’s query,
”star”, system shows a layer that has 45 concepts in
which the concept number 20 is highlighted as the
most relevant concept to the query. The summary of
the concept number 20 is shown by a tag cloud below
the concept network. The learner can browse among
other concepts by moving mouse cursor over concept
nodes in this network. Browsing concepts, the learner
may find a new keyword and add it to the query. “2)
Updated Concept Network” shows the result of up-
dating the query by adding “momentum”.
If the learner selects the concept number 30 by
clicking the node in this network, the system shows
a network of educational materials which related to
the selected concept as shown in the figure “3) Mate-
rial Network”. The size of nodes in this network re-
flects the degree of relevance to the selected concept.
The learner can grasp a summary of an educational
material with a tag cloud by moving mouse cursor
over a node in the network as well as the concept net-
work. The system can also show a similarity network
in where a selected node and its similar nodes are rep-
resented in a network as shown in “4) Similarity Net-
work”. Browsing concept network, material network
and similarity network, the learner can grasp the tar-
get area and select a appropriate material to where the
learner can jump, and then start learning with a mate-
rial as shown in “5) Material Page”.
5.2 Evaluation
The result shows that materials from both BLOS-
SOMS and MIT OCW are used all together. The
figure “3) Material Network” in Figure 3 shows the
two lectures from different resources (“0315”: MIT
OCW lecture “Extrasolar Planets: Physics and Detec-
tion Techniques”, “22”: BLOSSOMS video “Galax-
ies and Dark Matter”) are represented in one network.
The proposed approach can incorporate any OER pro-
vided by different institutes and make concept net-
works which does not depend on any existing struc-
ture. On the concept networks, learners can look for
materials which are appropriate to their educational
demand. This result shows the algorithm works to
solve the first and second problem described in Sec-
tion 2.
The proposed algorithm gives the interactive
search with abstracted concept level in which learn-
ers can grasp the target area and related ones. This
helps learners to avoid facing a long list of search
result. Learners can view OER gradationally from
bird’s-eye-view to the detail. In the interactive search,
the tag clouds over the concept network help learners
to find unexpected keywords.
Theoretically, concept networks give extensional
expression which explains a target concept by the re-
lated materials and the neighbor concepts. The ex-
tensional expression works especially in a case that
learners are looking for materials to solve their own
problem. Because learners usually lack knowledge
about the target itself. The experimental result shows
that related materials and neighbor concepts can help
learners to understand the target. From this result,
the proposed approach solved the third difficulty de-
scribed in Section 2, limit of text search.
6 RELATED WORK
In this work, we aim to provide learners suitable edu-
cational materials that relevant to their demand. This
objective is the same as what many researches are
tackling in an area of information retrieval (Manning
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