This paper presents a work-in-progress solution for planning hybrid Massive Open Online Courses (MOOC). The use of MOOCs in brick and mortar courses presents several design challenges. One of them is to find the most suitable online course regarding the alignment with the face-to-face course structure, timeline and syllabus. Despite there are different lesson planning and design tools which support educators in the design of their courses, there is a lack of solutions allowing to incorporate the use of MOOC or MOOC resources in the planning process of blended courses. In this paper, we present a MOOC design module for being used in design authoring tools which aim to support the planning of blended courses that incorporate MOOCs (or MOOCs resources). We discuss two different solutions for gathering information regarding existing MOOCs in the market: the creation of our own MOOC database versus the parsing of MOOC information from existing search engines on demand. Our exploration leads us to discard the first solution as maintaining the database is highly demanding. Thus, the final system uses existing MOOC search engines to extract the online courses design information to later be used in the overall hybrid-course planning. As it is a work-in-progress article, we present and discuss our future steps for supporting educators in the of design hybrid MOOCs scenarios.
The use of Massive Open Online Courses (MOOC) or MOOC resources in blended
contexts offers the potentiality of influencing higher education in several ways [
The use of MOOCs in regular university courses has led to different types of hybrid
combinations [
The planning of blended learning can be challenging in general, but the planning
process of hybrid MOOCs can be even more, especially when using external MOOC/s
or MOOC/s resources (i.e. using MOOCs that are not from our university and have
been designed by other instructors). In this context, we have developed a learning
design tool [
Our first exploration was centred on building a data base from scratch, which contained the available information about existing MOOCs in the market. The main idea was to extract the MOOCs’ information from the existing platforms and update the database periodically (e.g. daily). Our authoring tool would communicate directly with the database when information on MOOCs was required during the blended courses design process (Fig. 1-A). The main method thought for gathering the data was using the existing APIs from the MOOC providers. However, only few platforms offered an API and sometimes they did not provide all the necessary data or documentation. To face this limitation, we decided to implement a web crawler for the MOOC platforms that were not providing a satisfactory API. The diversity of platforms and the unexpected changes that could occur on the web interfaces by the providers hindered the automatic crawling development based on MOOC platforms. On that point, we looked for existing searchers engines (we selected Class Central https://www.classcentral.com/, and MOOC list https://www.mooc-list.com/) and decided to apply the crawler to both MOOC aggregators (as they had no available APIs). The implemented solution was successful but presented several issues regarding ethical and security restrictions. Despite the exploration was in a research context, the application used for the automatic crawler was at certain time automatically blocked due to prevention of Denial of Service (DoS) attacks ensured by security companies. Although, even if working, the fact of maintaining the database of MOOCs would be highly demanding for research purposes (APIs from some MOOC providers, the crawler, etc.) and would not be justified by the use that we seek to (as an integrated feature in our authoring tool). Moreover, the possibility that new platforms may appear in the market, it would also present a new scalability challenge.
There are web search engines that allow users find a desired MOOC on the market, allowing them searching courses filtering them by MOOC platform, university, topic, language, etc. Our second exploration had the aim of using the two above mentioned search engines (Class Central and MOOC list) as a source for our design module. But instead of using an automatic crawler of the complete web engines (as we already explored in our first approach) we developed a less intrusive system which use a grammatical parsing of single MOOC pages on demand. The system asks users to add the webpage Uniform Resource Locator (URL) of the online course provided by the selected search engine. Then, it extracts the required information from it to be used in the design authoring tool (Fig. 1-B). The main inconvenient of this approach is that, as well as in the crawler solution, the interfaces of the search engines may change periodically. Thus, the parsing algorithms need to be updated every time the search engine changes their web design configuration. But this would be less demanding and intrusive than our first solution. 4
This section describes the implementation of the second solution: an independent MOOC module which is integrated in our design authoring tool. Users can use the module for extracting data from MOOCs and use it for planning the blended learning unit that they are designing in the tool’s editor. The system provides an interface with the links to the two search engines and asks users to add the URL of the online course provided by the selected search engine (see Fig. 2). Then, when the users click on the 'Read Info' button, the system extracts the information from the URL and it shows it in another interface (Fig. 3). Probably, the best way of implementing this solution would be to directly provide the web of the search engines to the users using an iframe in the authoring tool and extract the information directly (so users would not need to leave the tool's webpage and switch to the search engines sites). But for security reasons, the web browsers do not allow to discover the URL that the user is viewing using code through an embedded iframe. Thus, we decided to use the URL “copy and paste” approach.
The system also allows users to introduce the information of a MOOC manually. Due
to the amount of information extracted is quite large, we needed to prioritize it and we
decided to divide it in two groups (showing them in two different tabs): the first tab
contains the ‘Basic information’ (Fig. 3-A) whereas the second tab shows the ‘Extra
Information’ (Fig. 3-B). The system provides all the extracted information in a json
file format. Table 1 presents the list of items that the system can extract from the URL
provided indicating which ones are in the Basic tab or in the Extra tab. Sometimes,
the system cannot extract all the items due to, in some cases, the MOOC URL do not
provide them all. To face this issue, the interface allows users to edit all found items
as well as introducing new information in the empty ones at any moment.
Whereas some of the items extracted from the MOOC design module can serve for
reporting general context of the MOOC (title, description, provider, university…),
others can have a more relevant role in the design process. In the context of our
authoring tool [