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Big Data analytics to assess personality based on voice analysis
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Refugees Welcome? Online Hate Speech and Sentiments in Twitter in Spain during the Reception of the Boat Aquarius
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Conversational agent for supporting learners on a MOOC on programming with Java
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Abstract:
One important problem in MOOCs is the lack of personalized support from teachers. Conversational agents arise as one possible solution to assist MOOC learners and help them to study. For example, conversational agents can help review key concepts of the MOOC by asking questions to the learners and providing examples. JavaPAL, a voice-based conversational agent for supporting learners on a MOOC on programming with Java offered on edX. This paper evaluates JavaPAL from different perspectives. First, the usability of JavaPAL is analyzed, obtaining a score of 74.41 according to a System Usability Scale (SUS). Second, learners’ performance is compared when answering questions directly through JavaPAL and through the equivalent web interface on edX, getting similar results in terms of performance. Finally, interviews with JavaPAL users reveal that this conversational agent can be helpful as a complementary tool for the MOOC due to its portability and flexibility compared to accessing the MOOC contents through the web interface. ; This work was supported in part by the FEDER/Ministerio de Ciencia, Innovación y Universidades-Agencia Estatal de Investigación, through the Smartlet and H2O Learning projects under Grant TIN2017-85179-C3-1-R and PID2020-112584RB-C31, and in part by the Madrid Regional Government through the e-Madrid-CM Project under Grant S2018/TCS-4307 and 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), 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 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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Keyword:
Computer science; Conversational agent; Java; MOOC; Programming; Telecomunicaciones
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URL: https://doi.org/10.2298/CSIS200731020C
http://hdl.handle.net/10016/33463
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An auditory saliency pooling-based LSTM model for speech intelligibility classification
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Desarrollo de una plataforma para reconocimiento de gestos basada en Tensor Flow Lite sobre el dispositivo IoT Argon de Particle
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Analyzing learners' engagement and behavior in MOOCs on programming with the Codeboard IDE
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Reconocimiento de voz basado en características DNN Bottleneck
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Redesigning a Freshman Engineering Course to Promote Active Learning by Flipping the Classroom through the Reuse of MOOCs
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Análisis y aplicación de técnicas de aprendizaje automático para clasificación de reseñas en redes sociales
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Implementación y evaluación de un sistema QbE-STD (Query-by-Example Spoken Term Detection)
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Assessing EPAP lexical features: A corpus-based study ; Análisis de los rasgos léxicos de IFE: Un estudio de corpus ; Una análisi dels trets lèxics d'AFE: Un estudi de corpus
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In: Quaderns de Filologia - Estudis Lingüístics; Vol. 22 (2017): Words, Corpus and Back to Words; 165-186 ; 2444-1449 ; 1135-416X (2018)
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Detección automática de paráfrasis sobre un corpus de preguntas en inglés
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Uncovering flipped-classroom problems at an engineering course on Systems Architecture through data-driven learning design
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Deep Neural Network Architectures for Large-scale, Robust and Small-Footprint Speaker and Language Recognition
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Estimación de la normalidad de los lenguados atendiendo a su morfología
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An end-to-end approach to language identification in short utterances using convolutional neural networks
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Linguistically-constrained formant-based i-vectors for automatic speaker recognition
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