IMASHEDU: Intelligent MAshups for EDUcation -

Towards a Data Mining Approach

Priscila Cedillo

1,2 a

, Pablo Martínez León

and Marcos Orellana

Computer Science Research & Development Lab - LIDI, Universidad del Azuay, Cuenca, Ecuador

Universidad de Cuenca, Cuenca, Ecuador

Keywords: Mashup, Learning Tool, Software, Web Applications.

Abstract: Nowadays, technological tools greatly support the work of teaching-learning tasks. In this sense, there are

various sources of information from which teachers and students rely on to complement their academic

activities. Content is sought on the web, significantly updated and easy to understand, generally in the form

of videos. As people progress in their learning, they face terms, concepts, and topics that they are not familiar

with them. However, those topics are included in the video. In this context, a complex process is generated of

alternating sections of the video with other sources of information that explain the related topics and contribute

to the understanding of the topic discussed. In this regard, and considering the possibility of systematically

consuming information from various sources, it is necessary to build a method and an application that

orchestrates the contents of these sources in a convenient, fast and automatic way, according to the person's

learning. This proposal contemplates the development of a Mashup. This mashup integrates different data

sources in a single graphical interface. Also, it is considered the construction of a core software solution based

on text mining techniques. This solution allows extracting the textual content from videos and identifying the

terms that could support the knowledge of the topic. It would significantly contribute to the fact that related

topics are presented unified in the same interface. At the same time, the learning experience is greatly

improved, avoiding losing the common thread of the observed video. Therefore, this article presents a process

of orchestrating various data sources in a Web Mashup application. It includes videos available on YouTube

channels, with other sources (e.g., Wikipedia, Pinterest) that help understand the topic better, generating

hypertext references based on the generation of terms through text mining techniques. A Mathematics

Learning mashup has been built to show the proposal’s feasibility.

1 INTRODUCTION

Nowadays, within virtual learning, several tools are

used for helping teachers and learners during their

classes’ preparation and study. Some of the most used

information platforms are YouTube (Google LLC,

2022) and Wikipedia (Wikipedia Foundation, 2022).

Those solutions are comprehensive sources of

knowledge for any topic that people want to study and

learn about. However, there is a problem when using

different platforms: the amount of related information

they provide for learners. Also, the dispersion of that

information makes searching and integrating the

knowledge difficult. In this context, when students

https://orcid.org/0000-0002-6787-0655

https://orcid.org/0000-0002-9269-346X

https://orcid.org/0000-0002-3671-9362

want to learn about a topic, they go to a specific

platform to search for a video on the subject needed;

once they find it, this video can generate more doubts

or concerns about different topics discussed, which

can be in another platform or source. This causes the

student to lose the continuity of the subject since the

student has to go to several sources or websites

containing information on a specific topic and find its

explanation.

On the other hand, mashups are Web applications

whose main objective is to integrate the contents and

services provided by third-party components (Insfran

et al., 2012). Thus, mashups have gained popularity

due to their ease of creation. Also, mashups are

Cedillo, P., León, P. and Orellana, M.

IMASHEDU: Intelligent MAshups for EDUcation - Towards a Data Mining Approach.

DOI: 10.5220/0011087500003182

In Proceedings of the 14th International Conference on Computer Supported Education (CSEDU 2022) - Volume 1, pages 383-388

ISBN: 978-989-758-562-3; ISSN: 2184-5026

383

specific applications usually presented as a single

page. Then, considering the compositional character

of mashups, they can be implemented to solve the

problem above.

Although mashups represent a good solution,

those tools have challenges related to the source’s

contents (e.g., availability, replaceability). The

sources format that can be integrated into mashups

can be RSS, ATOM feeds, Restful, SOAP Web

Services, among others. Thus, several platforms

provide convenient and free access to their

information in those formats. Several platforms offer

video and multimedia, which are very useful when

teachers and learners need to integrate information for

performing their activities. That integration needs to

be automatic and based on recommendations from the

contents displayed on the mashup (video, audio, text)

to support users when preparing information to teach

or learn. Moreover, data mining techniques can be

integrated into the mashup creation to perform that

integration. Therefore, this paper presents a process

to create mashups that combine information

conveniently from different data sources. The

resulting mashups will help teachers and learnings

during their activities. Those mashups will present

related information without searching other sources

but show all the information in a unique web

application. To display the feasibility of this proposal,

an example and a study case will be presented for

evaluating the integration process and the use of the

resulting mashup.

Finally, this document is structured as follows:

Section 2 presents related work. Section 3 presents

the process of the mashup creation, Section 4 shows

the application of the process during the development

of the mashup, and Section 5 shows the conclusions

and further work.

2 RELATED WORK

Several studies focused on applying different

techniques and mechanisms to create tools that help

people improve their study methods (Burstein, 2009).

However, previous mashups that have been made

have no purpose within the field of education and do

not have the techniques proposed in this study.

Many studies provide several ways to create new

Mashups for different needs and aspects (Ghiani et

al., 2016). Various components and tools must be

considered when developing a Mashup by reusing

existing components from different applications and

combining them within the same solution.

Methodologically, some studies apply natural

language processing and text mining techniques to

find new topics from a base text (Devlin et al., 2019).

These proposals are based on a similar process since

characteristics are extracted from the transcribed

texts, or the keywords of the entered text are

classified. At the same time, some studies use Natural

Language Processing (NLP) and processes for

Artificial Intelligence (AI) to improve teaching and

learning processes.

Several practical approaches to NLP are

presented, which help in educational field and other

linguistic aspects (Ferreira-Mello et al., 2019). Also,

several effective solutions handle patterns of

grammar and other linguistic approaches. Besides,

NLP is an effective technique for improving students'

ability to identify relationships of different words and

use such within search engines to generate new

knowledge (Campos, R et al., 2019). Therefore, a

practical proposal allows students to use these tools

optimally. The search procedure requires entering the

correct information in the text to enter the next step.

NLP enables the analysis of the information gathered

from students, comparing it with the content within

the search carried out (Burstein, 2009). Also, studies

on extracting keywords and topics from a single

document were considered without a document

dictionary. From the study of YAKE! (Campos, R et

al., 2019), a tool was obtained, which extracts

keywords from a text from a simple document

without a corpus. Based on the study of the attention

mechanism (Vaswani, A et al., 2017), a technique

called zero-shot classification was carried out, which

associates observed and unobserved classes through

auxiliary information, which encodes the distinctive

observable properties of objects and detects types that

the model has never seen during training. It is

characterized by having the human-like ability to

generalize and identify new things without direct

supervision. Unlike previous studies where NLP

mechanisms are used to classify data and web

mashups that were created for different purposes

(Wong et al., 2007), our objective is to extract the

keywords from the text of a video or audio by using

various APIs and generate new learning topics as

explained in this through a mashup. In this way, it

creates a helpful tool for the student to develop a

learning path fluently and without losing attention to

the topics of main interests.

CSEDU 2022 - 14th International Conference on Computer Supported Education

384

3 CREATING INTELLIGENT

LEARNING MASHUPS

The methodology is represented with the Software &

Systems Process Engineering Meta-Model 2.0

(SPEM 2.0). The entire process is divided into three

main tasks: i) Video metadata extraction, ii) Natural

Language Processing (NLP) task, and iii) API links

task. Each of which has input and output data. The

first task (i.e., extraction of metadata from the video)

has as input the link of a YouTube video. The NLP

task transcribes the video obtained in the first task as

input. Finally, the API links task has the keywords of

the text obtained through text mining techniques as

input, which leads to the output of new topics of the

video that the user is observing.

A. Video Metadata Extraction

In this process, the multimedia information is

extracted from the video platform (e.g., YouTube);

this information serves to obtain the relevant data

during the learning o teaching processes. In this task,

the input is a transcription of the platform video,

which is enabled in several sources and will collect

the text to be analyzed.

Video transcription: The first step is extracting

information obtained through a platform API (e.g.,

YouTube API). The process should focus on the

transcription of the text of the video. It is necessary to

transform these data into a document to carry out the

respective processing of the obtained data. Once it is

performed, available data is brought to continue with

all the following steps.

Platform API: Using a platform API, all the

necessary information is obtained, which will help

continue with the next steps of the process.

B. Natural Language Processing Task

Here, the input artifacts are i) Transcribed text of the

video platform (e.g., YouTube); the transcription is

used to analyze the obtained text.

1) Transcribed text of the video platform: The

transcribed text will be used to apply text mining

techniques to search for knowledge and provide

several topics that can be interesting for final users.

2) Text mining techniques: To arrive at a coherent

result and to be able to classify the main topics that

each video is about, several text mining techniques

are used:

Stopwords: This technique reduces the

dimensionality of texts by eliminating words that are

not useful for the study.

Convert the text to lowercase: This technique is

used to standardize texts.

Tokenization: This technique consists of

constructing a list of tokens that do not include

prevalent and uninformative words from a linguistic

point of view, such as conjunctions (and, or, nor,

that), prepositions (a, in, for, among others) and very

common verbs.

YAKE!: It is a lightweight, unsupervised

automatic keyword extraction method that relies on

statistical characteristics of text extracted from

individual documents to select the most important

keywords from a reader (Campos, R et al., 2019).

This system does not need to be trained with a

particular set of documents, nor does it depend on

dictionaries, external corpus, text size, language, or

domain.

Then users are able to get the video's keywords,

which will associate the relevant topics of the video

with other sources of information on the web and

generate new topics of interest.

C. API Links

The next step is to associate them with Wikipedia

articles through the API provided by this virtual

encyclopedia, which we will use to get associated

topics regarding the topics of interest which were

extracted in the previous steps via your search engine.

After finding and obtaining similar topics within the

Wikipedia platform, the zero-shot-classification

technique is used based on the attention mechanism

(Vaswani, A et al., 2017), which will help us

determine the effectiveness of the extracted results. In

this way, it will be possible to determine if the similar

topics extracted through keywords and the Wikipedia

API have a special relationship with the issues spoken

in the main video that the user enters.

All this was joined by a pipeline to build a

workflow for extracting topics associated with the

YouTube video that the user chose. The text

transcription of the video was inserted as a parameter.

This flow will be implemented through a Mashup,

which has the objective of showing the user all the

new and suggested topics that may be of interest to

them according to the video they enter, directing them

to links to other websites where they will find more

information about these recommended topics and

thus improve the users' study flow.

Figure 1 shows the entire process towards

developing a Mashup which includes data mining

techniques to accomplish the objective of this study.

Thus, the mashup development begins with selecting

the components integrated into the Web application.

It is necessary to explain that the central component

should be a media container API; once the

components that integrate the Mashup have been

selected, they maintain an orchestration or

choreography that reaches the main objective of this

proposal.

IMASHEDU: Intelligent MAshups for EDUcation - Towards a Data Mining Approach

385

Figure 1: The process to be followed during the intelligent mashup’s development.

4 APPLYING THE PROCESS

With the different techniques of text mining, NLP,

and APIs used in this study, a Mashup was obtained.

This application allows users to enter a video of

interest from YouTube and generate topics according

to the displayed video. Thus, watching a video with

support while it is being played improves the flow of

study and provides you with more information on

these topics by directing you to Wikipedia articles to

improve the way you study and answer the questions

that arise when looking at new issues.

The use of text mining and NLP were critical

aspects for verifying if the recommended topics were

relevant to the main video. Therefore, it is possible to

avoid showing issues that are not relevant or do not

correspond to the theme of the video. Therefore, it is

need a tool that provides more knowledge of various

topics for learning. A proof of concept was carried out

with different stages to demonstrate the usefulness

and benefits of the Mashup obtained, which is

presented below.

A. Linking the processes of the methodology in

the Mashup. This study seeks to generate a Mashup

that obtains new topics of interest related to a

YouTube video that a person is watching. Here, it is

expected to shorten study periods and create a linear

study flow when searching for information within

videos on YouTube.

With the Mashup made, it is possible to create this

study flow and at the same time generate new topics

from a related topic. This will be reflected in the next

step from the workflow tests. The following shows the

anchoring of the process designed in Figure 1 through

the Mashup interface to reach the objectives and thus

visualize a friendly and straightforward approach to

show the results of the flow process to the user.

1) As shown in Figure 2, an interface was

designed in which the user is allowed to enter the link

to the YouTube video of interest. In this way, the flow

enters the Video Metadata Extraction process, where

the output is the transcribed text of the video entered

by the user. This process is done by linking the

YouTube API, where the transcription of the video

and its different components are extracted. In this

way, the flow is directed to the following process.

2) After obtaining the transcript as the output in

the previous process, text mining and natural

language processing techniques are performed to get

the main topics related to the video entered by the

user. These are linked through the Wikipedia API to

obtain articles associated with the different topics

extracted and thus orchestrate the different APIs.

Therefore, the process flow shown in Figure 1 is

completed, and the results are shown below.

3) As shown in Figure 3, the results obtained

through the process flow of the methodology are

displayed so that each of the main topics of the video

entered by the user contains several Wikipedia

articles related to it, where the user can automatically

redirect to each of them by clicking on any of the

displayed topics. Through all these steps, the process

flow is linked through a Mashup, which finalizes the

process of the methodology and achieves the

objectives.

B. Testing and Process Flow

Next, the integration and flow of the Mashup

processes as a final product will be demonstrated.

CSEDU 2022 - 14th International Conference on Computer Supported Education

386

As shown in Figure 4, the first step is to extract the

information from the URL of the YouTube video of

the user's choice is inserted. Then, it has been linked

the Video Metadata Extraction process, which will

obtain the transcript of the video to connect with the

NLP Task process.

Figure 2: Home page and YouTube link text field.

Figure 3: Results obtained within the Mashup.

Also, it is possible to extract the topics related to

the video and their respective associated Wikipedia

articles. After entering the YouTube video URL, it

automatically redirects to the results page, where the

keywords of the video are displayed with their related

topics that may be of interest to the user. These topics

have associated articles that automatically redirect to

Wikipedia. Therefore, it is possible to connect the

YouTube API with the Wikipedia API through text

mining techniques and natural language processing,

obtaining expected results and a Mashup that helps

both the student and the teacher generate more

sources of information on a topic they need. Finally

achieved this process, all the explained steps are

linked to complete the objective of the Mashup

developing the desired tool. Therefore, the API Links

process is finished, and this study’s process flow

culminates. Following this, the respective tests and

analyzes that were carried out to verify the validity

and usefulness of this study are explained, which

demonstrates that the objectives of the study are met.

Optimal construction of the mashup was reached; in

this way, a tool for a better flow of study is available

for both students and teachers.

C. Feedback

The participants belong to a group of Software and

Data Engineers to collect their arguments and

experiences with the final product. Different

functional tests of the Mashup were carried out. It was

determined that the results presented within the

methodology obtained were consistent and accurate

according to the entered search topics. As a general

experience, it was argued that the reviewed product

meets its primary objectives, both for integration and

its use. The usability section contends that the

Mashup generates a determined and fast study flow

for direct purposes.

Figure 4: Flow Process.

D. Final Product

Once the Mashup meets the objectives and

purposes of this study, a tool is provided to the

community to help them search for information on the

Web.

5 CONCLUSIONS

It was concluded that when using text mining

techniques within the Mashups functions, beneficial

information can be linked, which supports learners.

Using the YAKE! Method: The extracted data

analysis facilitated the analyzed transcripts’ different

topics, and the keywords can be identified with this

technique. Also, NLP is essential when determining

topics that will be taught as a final result of the study.

With the use of the transcripts, new topics associated

with the video were obtained from what was

discussed. Once the result has been obtained, it is

possible to expand the field of study and knowledge

of subjects so that the flow of analysis of a person is

improved. It is recommended that for future work,

through the data collected with the use of the Mashup,

methodologies can be applied to analyze the behavior

of users when using the tool to improve the flow and

results according to each type of search and topic. At

the same time, a tool helps stakeholders to have a

better understanding of the interest topics. Finally, it

was possible to verify that, using techniques and

methods of text mining and NLP, the obtained

mashup combines those mentioned above with an

orchestration of APIs, generating valuable topics for

the user. Based on the results obtained, it was

concluded that it is a great option to reach the

objective and purpose of this study.

ACKNOWLEDGEMENTS

The authors wish to thank the Vice rector for

Research of the Universidad del Azuay for the

IMASHEDU: Intelligent MAshups for EDUcation - Towards a Data Mining Approach

387

financial and academic support and all the Laboratory

for Research and Development in Informatics (LIDI)

staff and the Department of Computer Science,

Universidad de Cuenca. Specifically to the research

project “Fog Computing applied to monitor devices

used in assisted living environments.”, DIUC for its

academic and financial support.

REFERENCES

Burstein, J. (2009). Opportunities for natural language

processing research in education. Lecture Notes in

Computer Science, 5449 LNCS, 6–27.

https://doi.org/10.1007/978-3-642-00382-0_2

Campos, R., Mangaravite, V., Pasquali, A., Jorge, A.,

Nunes, C., & Jatowt, A. (2020). YAKE! Keyword

extraction from single documents using multiple local

features. Information Sciences, 509, 257–289.

https://doi.org/10.1016/j.ins.2019.09.013

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019).

Bert: Pre-training of deep bidirectional transformers for

language understanding. Conf. of the North American

Association for Computational Linguistics, 2019:

Human Language Technologies, 4171–4186.

Ennals, R., & Gay, D. (2007). User-friendly functional

programming for web mashups. ACM SIGPLAN

Notices, 42(9), 223–233. https://doi.org/10.1145/

1291220.1291187

Ferreira-Mello, R., André, M., Pinheiro, A., Costa, E., &

Romero, C. (2019). Text mining in education. Wiley

Int. Reviews: Data Mining and Knowledge Discovery,

9(6). https://doi.org/10.1002/widm.1332

Ghiani, G., Paternò, F., Spano, L. D., & Pintori, G. (2016).

An environment for End-User Dev. of Web mashups.

Int. Journal of Human Comp. Studies, 87, 38–64.

https://doi.org/10.1016/j.ijhcs.2015.10.008

Grammel, L., & Storey, M. A. (2010). A survey of mashup

development environments. Lecture Notes in Computer

Science (Including Subseries Lecture Notes in

Artificial Intelligence and Lecture Notes in

Bioinformatics), 6400, 137–151. https://doi.org/10.10

07/978-3-642-16599-3_10

Tuchinda, R., Knoblock, C. A., & Szekely, P. (2011).

Building mashups by demonstration. ACM

Transactions on the Web, 5(3).

https://doi.org/10.1145/1993053.19 93058

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones,

L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017).

Attention is all you need. Advances in Neural

Information Processing Systems, 2017-December

(Nips), 5999–6009.

Wong, J., & Hong, J. I. (2007). Making mashups with

marmite. https://doi.org/10.1145/1240624.1240842

Belleau, F., Nolin, M. A., Tourigny, N., Rigault, P., &

Morissette, J. (2008). Bio2RDF: Towards a mashup to

build bioinformatics knowledge systems. Journal of

Biomedical Informatics, 41(5), 706–716.

https://doi.org/10.1016/j.jbi.2008.03.004

Chary, M., Parikh, S., Manini, A. F., Boyer, E. W., &

Radeos, M. (2019). A review of natural language

processing in medical education. Western Journal of

Emergency Medicine, 20(1), 78–86.

https://doi.org/10.5811/westjem.2018.11.39725

Google LLC. (2022). YouTube. www.youtube.com

Grobelnik, M., Mladenic, D., & Jermol, M. (2002).

Exploiting text mining in publishing and education.

ICML-2002 Workshop on Data Mining Lessons

Learned, 34–39, 2002.

Insfran, E., Cedillo, P., Fernández, A., Abrahão, S., &

Matera, M. (2012). Evaluating the usability of mashups

applications. Proceedings - 2012 8th Int. Conference on

the Quality of Information and Communications

Technology, QUATIC, 323–326.

https://doi.org/10.1109/QUATIC.2012.28

Karami, A., White, C. N., Ford, K., Swan, S., & Yildiz

Spinel, M. (2020). Unwanted advances in higher

education:Uncovering sexual harassment experiences

in academia with text mining. Information Processing

and Management, 57(2), 102167.

https://doi.org/10.1016/j.ipm.2019.102167

Litman, D. (2016). Natural language processing for

enhancing teaching and learning. 30th Conference on

Artificial Intelligence, AAAI 2016, 4170–4176.

Odden, T. O. B., Marin, A., & Caballero, M. D. (2020).

Thematic analysis of 18 years of physics education

research conference proceedings using natural language

processing Thematic Analysis of 18 Years ... Odden,

Marin, And Caballero. Physical Review Physics

Education Research, 16(1).

Petersen, S. (2007). Natural Language Processing Tools for

Reading Level Assessment and Text Simplification for

Bilingual Education. 145.

Romero, C., & Ventura, S. (2013). Data mining in

education. Data Mining and Knowledge Discovery,

3(1), 12–27. https://doi.org/10.1002/widm.1075

Wikipedia Foundation. (2022). Wikipedia.

https://es.wikipedia.org/wiki/Wikipedia:Portada

Yunanto, A. A., Herumurti, D., Rochimah, S., &

Kuswardayan, I. (2019). English education game using

non-player characters based on natural language

processing. Procedia Computer Science, 161, 502–508.

https://doi.org/10.1016/j.procs. 2019.11.158

Ngu, A. H. H., Carlson, M. P., Sheng, Q. Z., & Paik, H. Y.

(2010). Semantic-Based Mashup of Composite

Applications. IEEE Transactions on Services

Computing, 3(1), 2–15. https://doi.org/10.1109/TSC.

2010.8

CSEDU 2022 - 14th International Conference on Computer Supported Education

388