=Paper=
{{Paper
|id=Vol-1690/paper15
|storemode=property
|title=Constructing Curriculum Ontology and Dynamic Learning Path Based on Resource Description Framework
|pdfUrl=https://ceur-ws.org/Vol-1690/paper15.pdf
|volume=Vol-1690
|authors=Makoto Urakawa,Masaru Miyazaki,Hiroshi Fujisawa,Masahide Naemura,Ichiro Yamada
|dblpUrl=https://dblp.org/rec/conf/semweb/UrakawaMFNY16
}}
==Constructing Curriculum Ontology and Dynamic Learning Path Based on Resource Description Framework==
https://ceur-ws.org/Vol-1690/paper15.pdf
Constructing Curriculum Ontology and Dynamic
Learning Path Based on Resource Description
Framework
Makoto Urakawa, Masaru Miyazaki, Hiroshi Fujisawa,
Masahide Naemura, Ichiro Yamada
NHK (Japanese Broadcasting Corporation), 1-10-11,Kinuta,Setagaya-ku,Tokyo,Japan
{urakawa.m-gi, miyazaki.m-fk, fujisawa.h-ja, naemura.m-ei, yamada.i-hy}@nhk.or.jp
Abstract. Curriculum for school is generated based on the academic year. Be-
cause students have to study several subjects each and every year, the relative
topics are put into curricula in discrete. In this study, we propose a method to
construct a dynamic learning path which enables us to learn the relative topics
continuously. In this process, we define two kinds of similarity score, inher-
itance score and context similarity score to connect the learning path of relative
topics. We also construct curriculum ontology with Resource Description
Framework (RDF) to make the dynamic learning path accessible and to make
education materials integrated with a suitable learning step. Using the curricu-
lum ontology, we develop a learning system for school which shows a dynamic
learning path with broadcasted video clips.
Keywords: Ontology, resource description framework, knowledge graph,
learning path, linked data, curriculum, education, natural language processing
1 Introduction
In Japan, the Ministry of Education, Culture, Sports, Science and Technology
(MEXT) establishes the curricula 1 2 in HTML and PDF. A curriculum has items ac-
cording to a subject and academic year. An example of items is “to confirm the pro-
cess of cell division and relate it to the growth of creatures,” and the topic of this item
is “cell division and growth of creature.” Because students have to study several sub-
jects each and every year, the relative topics are put into curricula in discrete. For
example, the science curriculum for a junior high school covers “refraction and reflec-
tion of light,” “ cell division and growth of creature,” “weather observation,” “DNA,”
and so on, and the science curriculum for a senior high school covers “genetic infor-
mation” and “expanding universe” and so on. If a student, who cannot understand
1
http://www.mext.go.jp/a_menu/shotou/new-cs/youryou/chu/ri.htm
2
http://www.mext.go.jp/component/a_menu/education/micro_detail/__icsFiles/afieldfile/2011/0
4/11/1298356_5.pdf (referred for explanation in English)
“genetic information” in senior high school, reviews the topic of “DNA” studied in
junior high school, he/she can strengthen the foundation for learning about “genetic
information.” The primary objective of this paper is to extract learning paths based on
words from the curricula and make the paths accessible by a curriculum ontology of
Liked Open Data format. We also propose an approach integrating learning materials
with appropriate topic on learning path by utilizing the ontology.
The remainder of this paper is organized as follows. Section 2 discusses related
work. Section 3 introduces constructing the curriculum ontology and the application
we developed for students. In Section 4, we provide our conclusions.
2 Related Work
The British Broadcasting Corporation (BBC) published a curriculum ontology 3
that describes the United Kingdom (UK) national curricula [1]. It represents the im-
portance of organized learning resources. However, it does not enable us to learn the
relative subjects continuously and dynamically. Study of ontology design [2] [3] di-
vides certain ontology, for teachers, learners, syllabus and subject. These approaches
focus on a system to manage a layered ontology, and the syllabus is classified by
string similarity based on only common words [3]. To focus on building learning
sequences [4], an ontology is used to generate course learning paths. However, this
can be achieved by experts and needs external resources. We focus on the way of
extracting paths from the present curricula and making them easily integrated with
learning materials by an ontology.
3 Constructing Curriculum Ontology
To extract learning path from curricula, we interrelate the two items in the curricu-
la using an inheritance score and a context similarity score. Figure 1 depicts the inher-
itance and the context similarity score to find the relationship between two items. In
the process of calculating the inheritance score, we first arrange 232 items in the sci-
ence curriculum for junior and senior high school in order of their appearance. Then,
all words appeared in the items are classified as new entry or previously used ones.
The inheritance score is defined by the ratio of common words in the target item and
the following item. The parameter t in Fig. 1 shows the number of new entry word in
the target item and s is the number of succeeded word to the following item. The con-
text similarity score is defined by the average value of cosine similarities among all
combination of words appeared in the items. Here, each word is represented by a
feature vector which is calculated using their grammatical context and has been dis-
tributed by ALAGIN forum. 4 Although all the items in the curricula are written in
Japanese, the text was translated into English for the present purposes in Fig. 1.
3
http://www.bbc.co.uk/ontologies/curriculum
4
https://alaginrc.nict.go.jp/
Item X
“To identify the character of cell; to understand that physical bodies of creatures.”
(No.48)
t=2 m=4 Inheritance Score Context Similarity
cell creature body
s=1 inhe
ritan r(w3,w3)
ce
cell division growth cell
w3=(0.01,0.00,0.21,…,0.33)
ALAGIN
n=6 2000 dimensions
Item Y New entry word
(No.59) “To confirm the process of cell division and relate it to the growth.”
Previous-used word
Fig. 1. Overview of process for identifying the relationship between items
We construct the curriculum ontology in reference to item relation with the max
score of inheritance and context similarity. Figure 2 presents an example of individual
instances generated from the ontology. We defined “http://cur.nhk.or.jp” as a
namespace only for this study. The class “cur:ItemOfStudy” is the main role for gen-
erating the learning path. The object property, “cur:hasReview” functions as a con-
nector of individual instances belonging to “cur:ItemOfStudy”. For example,
“cur:Item00060” and “cur:Item00059,” “cur:Item00059” and “cur:Item00048” are
connected. That is why it is easy to get the item for review before studying “cell divi-
sion” by SPARQL query. This ontology also covers the necessary information for
such subjects, the school level and so on.
Learning materials such as videos should align with a specific item being studied. To
solve this challenge, we retrieved the words to study afterwards for each item from
the ontology. For example, we can understand that these words such as “sexual repro-
duction,” “meiosis” are the words learned afterwards for “cur:Item00059”. Therefore
a video clip explaining “meiosis” is not appropriate to integrate with “cur:Item00059”
even if the video has the words studied at “cur:Item00059” such as “cell.” After cal-
culating the videos suitable for each item, we updated the curriculum ontology. For
example, the video about “cell division and chromosome” is integrated with
“cur:Item00059” by “cur:hasClip.” We actually experimented with videos, which are
published by “Japan Broadcasting Corporation (NHK) for School,” 5 by extracting
words from them and finding a suitable learning item.
Junior high school
Science
cur:Subject001 cur:Stage001 cur:StageOfSchool
cur:Clip cur:Subject
cur:taughtAtSubject cur:taughtAtStage
cur:Filed001 Second field
Cell division and chromosome cur:Clip00011
cur:hasClip cur:taughtAtField cur:FieldOfStudy
cur:Item00060 cur:hasReview cur:Item00059 The way of procreation
cur:hasTopic rdfs:label
cur:ItemOfStudy cur:hasGoal rdfs:type
cur:hasNewKeyword cur:hasReview To confirm the process of cell division and
relate it to the growth
cur:K00032 cur:K00033 cur:hasKeyword Owl:class
cur:Item00048
cur:hasNewKeyword Instance
sexual cur:hasNewKeyword cur:K00009 cell divison
meiosis creature cur:K00020 ObjectProperty
reproduction
cell cur:K00010 DataTypeProperty
cur:Keyword Literal
Fig. 2. Example of individual instances of an ontology
5
http://www.nhk.or.jp/school/
We developed an application that generates learning paths with education videos
by retrieving them with SPARQL query. In the Figure 3, the learner can view the
relevant videos with several leaning paths from the item they study “cell.” This appli-
cation helps learners comprehend their learning stages and the subsequent steps.
Learners also can utilize educational materials, such as videos, that align with their
respective learning stage.
Fig. 3. Application utilizing dynamic learning path
4 Conclusion
In this paper, we proposed a method to construct the learning path by definition of
two kinds of similarity score, inheritance score and context similarity score. We also
constructed curriculum ontology with RDF to make the dynamic learning path acces-
sible and integrated video materials with a suitable item based on the words that
should be studied at the given item. Using the curriculum ontology, we developed a
learning system that users can dynamically navigate a learning path with broadcasted
video clips for review or preparation. In future work, we plan to experiment the effec-
tiveness for teachers and students, and launch its service in NHK. Furthermore, we
intend to link the curriculum ontology to the other subjects such as math.
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