Edmundo  Tovar,  Nelson  Piedra,  Jorge  López,  Janeth  Chicaiza  Bali  Indonesia  May  8-­‐10,  2013  

Serendipity  a  Faceted  Search  engine  for  OpenCourseWare  Content  

@nopiedra  #ocwcglobal  #OCW  #OER  #Serendipity  #UTPL  

The main purpose of the service developed is to provide students, teachers and self-learners with an faceted search engine that allow them to find and discover open educational resources related to OCW from OpenCourseWareConsortium and OCW-Universia.

Purpose  of  Serendipity  

Faceted search, also called faceted navigation, is a technique for accessing content organized according to a faceted classification system, allowing users to explore a collection of information by applying dynamic and multiple filters. With the benefit of search results diversification, no need for a priori knowledge, and never leading to zero result, it can significantly reduce information overload.

About  Faceted  ExploraDon  

Facets refer to categories used to characterize information items in a collection. A faceted classification system classifies each information element along multiple explicit dimensions, enabling the classifications to be accessed and ordered in multiple ways rather than in a single, pre-determined, and taxonomic order.

Facets  and  taxonomic  order  

Faceted exploration is a proven technique for supporting flexible exploration and discovery through the information space. The user can refine queries based on facets such as size, language, knowledge-domain, geographic localization and neighbourhood characteristics. The facets are extracted by applying a parser specialized for parsing classified content.

Refine  queries  based  on  facets  

Serendipity  is  for  people  Serendipity is an faceted search engine based on Semantic Web Technologies. Open Linked Data from Open Educational

Content. Current version of Serendipity is based on Flamenco.

Serendipity is an faceted search engine based on Semantic Web Technologies. Serendipity is based on Flamenco. The main objectives of faceted navigation are to support flexible navigation through the information space: refining and expanding, provide suggestions of exploration choices at each point in the search process, prevent empty result sets, and provide a sense of control and reduce confusion in the use.

About  Serendipity  

Serendipity use Linked Data Design Issues to retrieve information that is semantically described and related to open educational resources (OER) that are accessible via Internet. Linked data have the potential of create bridges between OER data silos.  

The collection under study consisted of approximately 8000 OpenCourseWare in the collection of the OCW-Dataset of LOCWD Project. This collection contained standard OER metadata facets, including creators names, language, licenses of OCW, repositories, tags, knowledge areas, universities, countries and dates.

CollecDon  PreparaDon  

The data extracted by Serendipity been validated through the sponsorship and collaboration of OpenCourseWare Consortium.

Sponsorship  from  OCWC  

i.  Faceted  Query  of  OCW    based  on  Linked  OpenCourseWare  data    

CASE:    FIND,  OpenCourseWare  about  “Web”  

The Serendipity faceted exploration is a guided and exploratory search mechanism, which provides an iterative way to refine search results by a faceted taxonomy or a schema of classification.

Serendipity  facet  exploraDon  

hRp://serendipity.utpl.edu.ec    Serendipity  is  an  OCW  faceted  search  engine  based  on  

SemanVc  Web  Technologies.    

Guided navigation Previews of Results

Refine search

Add keywords With  Serendipity  Explore  OCW  in  an  integrated  and  incremental  manner,  from  any  of  the  repositories  of  insVtuVons  that  publish  OpenCourseWare.  

The user, when presented with the facets, is likely to discover new facets of the query that they were not aware of before. When clicking on a facet, they will narrow down their search by expanding the original query with the suggested facet.

Discover  new  facets  by  expanding    the  original  query  

Facets

Share this OCW

Inspect current OCW

Refine your search

Access  the  full  descripDon  of  the  courses  as  published  by  the  home  insVtuVon,  along  with  complimentary  informaVon  such  as  language,  license,  author,  country,  geographic  locaVon  of  the  insVtuVon  and  other  semanVcally  related  informaVon  available  via  the  Web.    

Propose change

Inspect the result from OCW original site

Get  more  accurate  and  complete  results,  since  it  locates  OCWs  using  different  metadatas  and  data  elements,  providing  the  user  with  visible  opVons  that  help  clarify  and  refine  the  queries.    

Link current OCW to

Other LinkedData Source

ii.  Map  to  visualize  OER  Points  of  Interest  

As an important feature of Serendipity, Serendipity POIs (Points of Interest), allows users visualize data of OCW/OER/MOOC/OEP/Projects/Repositories from an dataset based on Linked Data technologies.

About  Serendipity  POIs  

Serendipity POIs use icons to represent different categories of POI on a map graphically. A  point  of  interest,  or  POI,  is  a  OER  specific  point  locaVon  that  someone  may  find  useful  or  interesVng.    

A  point  of  interest  specifies,  at  minimum,  the  laDtude  and  longitude  of  the  POI,  assuming  a  certain  map  datum  (extrated  from  serendipity  datasource).  A  name  or  descripVon  for  the  POI  is  usually  included,  and  other  informaVon  such  as  descripVon,  number  of  resources,  contact  informaVon,  language,  license  or  a  link  to  dbpedia/freebase  may  also  be  aRached.    

 An  example  is  a  point  on  the  Earth  represenVng  the  locaVon  of  the  MassachuseRs  InsVtute  of  Technology,  or  a  point  on  Spain  represenVng  the  locaVon  of  an  OCW  University.      

Other  example  is  a  point  on  the  Earth  represenVng  the  locaVon  of  the  University  of  Cape  Town  

Serendipity use the term POIs when referring to Open Repositories of OCW/OER/OEP/Projects, Open Data for educational content, MOOCs or any other categories used in open educational systems.

iii.  Open  Data  from  Open  EducaDonal  Content  

Serendipity POIs seeks to become a repository for the information about OER that the Serendipity Multiagent Environment collects. Therefore, the site would publish to the public any data collected that is not private or restricted.

CollecDng  and  downloading  OER  Data  

Serendipity Open  Data   is   the   idea   that   certain   data   should   be   freely   available   to  everyone   to  use  and   republish  as   they  wish,  without   restricVons   from  copyright,  or  other  mechanisms  of  control.  

Why  publish  Linked  OER  Data?  

•  Because  LinkedData  holds  the  potenVal  to  move  our  OER  collecVons  out  of  their  silos  

•  Open  the  data  and  content  silos,  to  leverage  the  knowledge  capital  represented  by  our  OER  repositories  

•  To  enrich  our  informaVon  landscape,  to  improve  visibility  •  To  improve  ease  of  discovery  open  academic  resources  •  To  improve  ease  of  consumpVon  and  reuse  of  OCW  •  To  reduce  redundancy  in  searched  of  OER  •  PromoVng  innovaVon  and  Added  Value  to  Open  EducaVonal  

Content  

iv.  Suggest  new  Points  of  InformaDon  

The purpose of Serendipity POIs - Open  data  is to increase public access to high value, machine readable datasets generated by volunteers and data extracted from Serendipity multiagent environment.

v.  Data  visualizaDon  

Why  OER  DataViz?  

Serendipity POIs - Open   data   contains   valuable  informaVon   that   will   drive   insights,   innovaVons,   and  discoveries,  but  it  can  be  difficult  to  access  and  digest.      Using   data   visualizaVon,   we’re   simplify   the   complexity  and   drive   a   deeper   understanding   of   the   open  educaVonal  context.  

The  main  goal  of  data  visualizaDon  is  its  ability  to  visualize  OER  data,  communicaVng  informaVon  clearly  and  effecVvelty.  

Data  VisualizaDon  1.  It  shows  informaVon  of  UniversiVes  classified  hierarchically,  taken  starVng  point  to  conVnents,  then  countries,  ciVes  and  universiVes  

Data  VisualizaVon  2:  Tree  structures  to  show  another  way  to  visualize  the  informaVon  the  universiVes  members  of  OCW  iniVaVves.  

Data  VisualizaVon  3:  Search  courses  by  tag  and  use  geographic  informaVon  to  show  courses  of  universiVes  and  social  network  analysis  (SNA)  to  form  networks  of  collaboraVon  and  recommend  related  tags  

@nopiedra  #ocwcglobal  #OCW  #OER  #LOCWD  #LinkedData  #UTPL  

Thanks!�@nopiedra,  @etc91