RT Journal Article
T1 Adaptive learning module for a conversational agent to support MOOC learners
A1 Gonzalez Castro, Nuria
A1 Muñoz Merino, Pedro José
A1 Alario-Hoyos, Carlos
A1 Delgado Kloos, Carlos
AB Massive open online courses (MOOCs) pose a challenge for instructors when trying to provide personalised support to learners, due to large numbers of registered participants. Conversational agents can be of help to support learners when working with MOOCs. This article presents an adaptive learning module for JavaPAL, a conversational agent that complements a MOOC on Java programming, helping learners review the key concepts of the MOOC. This adaptive learning module adapts the difficulty of the questions provided to learners considering their level of knowledge using item response theory (IRT) and also provides recommendations of video fragments extracted from the MOOC for when learners fail questions. The adaptive learning module for JavaPAL has been evaluated showing good usability and learnability through the system usability scale (SUS), reasonably suitable video fragments recommendations for learners, and useful visualisations generated as part of the IRT-based adaptation of questions for instructors to better understand what is happening in the course, to design exams, and to redesign the course content.
PB ASCILITE: Australasian Society for Computers in Learning in Tertiary Education
SN 1449-3098
YR 2021
FD 2021-05-10
LK https://hdl.handle.net/10016/32797
UL https://hdl.handle.net/10016/32797
LA eng
NO This  work  was  supported  in  part  by  the  FEDER/Ministerio  de  Ciencia,  Innovación  y  Universidades–Agencia Estatal de Investigación, through the Smartlet Project under Grant TIN2017-85179-C3-1-R, and in part by the Madrid Regional Government through the e-Madrid-CM Project under Grant S2018/TCS-4307, a project which is co-funded by the European Structural Funds (FSE and FEDER). Partial support has also been received from the European Commission through Erasmus+ Capacity Building in the Field of Higher Education projects, more specifically through projects LALA, InnovaT and PROF-XXI (586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP),  (598758-EPP-1-2018-1-AT-EPPKA2-CBHE-JP),  (609767-EPP-1-2019-1-ES-EPPKA2-CBHE-JP). This  work  has  also  been  supported  by  the  Madrid  Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with UC3M in the line of Excellence of University Professors (EPUC3M21), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation).This publication reflects the views only of the authors and funders cannot be held responsible for any use which may be made of the information contained therein.
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RD 27 jul. 2024