Amid the growth of educational services and content on the Internet, it is expected that an increase in services with material suited to the path of learning and systems that bundle materials together. There is research being done on ways to systematize connections between preparatory and review items and to coordinate the content of educational materials. The authors have developed Rikamap(“Science Map”),1 an application that utilizes resource description framework (RDF)-formatted learning paths to connect these stages of learning and systematically organize and present educational video content relevant to each stage for the user. In this paper, we will discuss the underlying data structure and system of the Rikamap, as well as the importance and issues of using a linked structure as revealed by the usage of the system.
The Ministry of Education, Culture, Sports, Science, and Technology (MEXT) publishes curriculum guidelines for each grade level and subject in HTML and PDF formats. 2 3 Textbook publishers and other companies that provide educational services develop their content and plan their lessons based on these guidelines. AS a public broadcaster, NHK not only produces and broadcasts television programs with content developed according to the guidelines, but publishes educational video content on the web as well. 4 However, because the curriculum guidelines are not made available as linked open data or even as structured data, it is difficult for a single company’s educational materials to be organized within an system and with other companies’ content.
2 http://www.mext.go.jp/a_menu/shotou/new-cs/youryou/chu/ri.htm 3http://www.mext.go.jp/component/a_menu/education/micro_detail/__icsFiles/afieldfile/2011/0 4/11/1298356_5.pdf (referred for explanation in English) 4 http://www.nhk.or.jp/school/
We have been done to parse the text of the curriculum guidelines to estimate the
relationship of new words to previously appearing words, then extract learning
connections and write them in the resource description framework (RDF) format [
Section 2 of this paper gives a brief overview of related work. Section 3 describes the structure of the backend system. Section 4 introduces Rikamap and its examination results. Finally, Section 5 summarizes this research. 2.
The British Broadcasting Corporation (BBC) published a curriculum ontology5
that describes the United Kingdom (UK) national curricula [
Rikamap’s backend data structure consists of information from the national
curriculum guidelines and NHK’s video. The leaning paths does not need to be updated
as long as the guidelines do not change, however new videos are produced daily.
Therefore, we designed a system to allows video content to be connected to learning
paths. Figure 1 shows the structure of the system, which is deployed on Amazon web
services (AWS). It takes information about the videos from a pre-existing digital
archive system in TSV. The video information has a pointer written in character string
to related to items of the curriculum. The system converts it and ties the video
information to the learning path data that is already stored in an RDF store. Meanwhile,
queries from the web service Rikamap are converted to SPARQL format on the API
server and return a response with data from the RDF store. This is done to hide the
SPARQL query and to handle the log data in a unified way. For the RDF store, we
chose Stardog after performing speed tests on the response to property path queries
for learning path extraction. Please refer to past paper [
Digital Archive System
Video information
FTP(PUT)
TSV
Rika Map “Science Map”
Data Processing (TSV->RDF) “Search items to review and preview this item”
Query (WEB-API)
UPDATE
RDF
API Server
Query (SPARQL)
RDF Store
IMPORT
OWL Learning paths A learning path with related videos based on a query
AWS 4.
Overview of Rikamap and examination of usage patterns In this section, we will introduce Rikamap, which not only allows learners to learn with videos whose content suits their interests, but also allows them to navigate between and watch videos corresponding to preparatory and review items. As the screenshots in Figure 2 show, when the learner clicks on an icon representing a learning item in the Top Screen, they are taken to the Navigation Screen. The Navigation Screen displays relevant learning paths corresponding to that icon (marked “hasReview/hasPreview” in the figure) and related videos (marked “hasVideo”). These relationships are dynamically generated from the learning path data. Using Rikamap, learners can prepare and review according to their own understanding.
An item for learning from the curricula click hasPreview hasPreview hasReview hasVideo hasVideo hasVideo click
hasVideo
We analyzed the first 2 weeks of system logs following the April 27, 2017 launch of the application. The Top Screen had 3319 views, and there were 1731 clickthroughs to the Navigation Screen. On the Navigation Screen, users clicked on preparatory and review items 2149 times, with an average of 1.2 such items being clicked per session. Furthermore, upon checking whether users navigated more to preparatory or to review items, we found that review items were clicked 1.2 times more than preparatory items. That is to say, it seems that, rather than developmental content, learners need content that will help them understand in greater depth what they are already learning. Moreover, upon reaching the Navigation Screen, users played 3.2 times as many preparatory and review videos along the learning path in comparison with other related videos that were shown when they reached at the Navigation Screen. This implies that users more frequently watch videos that have a meaningful relationship to what they are learning. From the content producer perspective, this means that rather than simply displaying related videos, it is more useful to provide content designated for specific use cases such as preparation and review. However, the video service on the existing NHK for School website had 3.1 times as many completed video plays than Rikamap. We believe this is because Rikamap also focuses on the user interface to enable and prompt users to view various videos smoothly. As a result, they do not finish watching videos that are even a slight mismatch with what they were looking for. The way of providing the appropriate videos and making them view with concentration should be needed. 5.
In this paper, we described the structure of an application that uses RDF-formatted learning paths extracted from existing curriculum guidelines which the Japanese ministry published, and examined the usage patterns of this application. This application “Rikamap” enables users to explore learning items and relevant videos. RDF, which expresses data as triples, enables the flexible expression of learning paths, which actively change depending on what is being learned. The query about the property path from RDF data is especially effective for this application. In examining the usage logs, we saw that Rikamap users searched for content to review rather than preview what they were studying. It is also revealed that there is a greater tendency to watch videos that have meaningful “preparatory” and “review” connections to their learning items. These results prove the usefulness of semantically-constructed learning paths, and give a pedagogical insight to educational content providers. In the future, we will try to improve the algorithm that bundles a learning item and videos in order to offer more appropriate videos what users want to study.