Enriching Capstone Project-Based Learning Experiences Using a Crowdsourcing Recommender Engine

Simple item page
dc.contributor.authorDiaz-Mosquera, Juan
dc.contributor.authorSanabria Quispe, Pablo
dc.contributor.authorNeyem, Andrés
dc.contributor.authorParra Santander, Denis
dc.contributor.authorNavón Cohen, Jaime
dc.date.accessioned2022-05-11T20:05:46Z
dc.date.available2022-05-11T20:05:46Z
dc.date.issued2017
dc.description.abstractCapstone project-based learning courses generate a suitable space where students can put into action knowledge specific to an area. In the case of Software Engineering (SE), students must apply knowledge at the level of Analysis, Design, Development, Implementation and Management of Software Projects. There is a large number of supportive resources for SE that one can find on the web, however, information overload ends up saturating the students who wish to find resources more accurate depending on their needs. This is why we propose a crowdsourcing recommender engine as part of an educational software platform. This engine based its recommendations on content from StackExchange posts using the project's profile in which a student is currently working. To generate the project's profile, our engine takes advantage of the information stored by students in the aforementioned platform. Content-based algorithms based on Okapi BM25 and Latent Dirichlet Allocation (LDA) are used to provide suitable recommendations. The evaluation of the engine was held with students from the capstone course in SE of the University Catholic of Chile. Results show that Cosine similarity over traditional bag-of-words TF-IDF content vectors yield interesting results, but they are outperformed by the integration of BM25 with LDA.
dc.fuente.origenIEEE
dc.identifier.doi10.1109/CSI-SE.2017.1
dc.identifier.isbn978-1538640418
dc.identifier.urihttps://doi.org/10.1109/CSI-SE.2017.1
dc.identifier.urihttps://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7972759
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/63773
dc.information.autorucEscuela de ingeniería ; Diaz-Mosquera, Juan ; S/I ; 1030576
dc.information.autorucEscuela de ingeniería ; Sanabria, Pablo ; S/I ; 212656
dc.information.autorucEscuela de ingeniería ; Neyem, Andrés ; S/I ; 1007638
dc.information.autorucEscuela de ingeniería ; Parra Santander, Denis ; S/I ; 1011554
dc.information.autorucEscuela de ingeniería ; Navón Cohen, Jaime ; S/I ; 100018
dc.language.isoen
dc.nota.accesoContenido parcial
dc.publisherIEEE
dc.relation.ispartofIEEE/ACM International Workshop on CrowdSourcing in Software Engineering (4° : 2017 : Buenos Aires, Argentina)
dc.rightsacceso restringido
dc.subjectCrowdsourcing
dc.subjectEngines
dc.subjectSoftware
dc.subjectData mining
dc.subjectTools
dc.subjectSoftware engineering
dc.subjectMobile communication
dc.titleEnriching Capstone Project-Based Learning Experiences Using a Crowdsourcing Recommender Enginees_ES
dc.typecomunicación de congreso
sipa.codpersvinculados1030576
sipa.codpersvinculados212656
sipa.codpersvinculados1007638
sipa.codpersvinculados1011554
sipa.codpersvinculados100018
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Enriching Capstone Project-Based Learning Experiences Using a Crowdsourcing Recommender Engine.pdf
Size:
3.03 KB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Artículos de conferencia