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Files to support: "Prior Experience with Unlabeled Actions Promotes 3-Year-Old Children’s Verb Learning" ...
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Ludolinguistica e formazione di insegnanti di italiano LS/L2 ...
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Ludolinguistica e formazione di insegnanti di italiano LS/L2
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In: Studi di glottodidattica; V. 6, N. 2 (2021); 14 - 23 ; 1970-1861 (2021)
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Hmong American Charter Schools: An Exploratory and Descriptive Study
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In: Journal of Southeast Asian American Education and Advancement (2021)
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LIFELONG FOREIGN LANGUAGE LEARNING FOR SPECIAL PURPOSES IN THE INFORMATION SOCIETY ; ЦІЛЬОВЕ ВИВЧЕННЯ ІНОЗЕМНИХ МОВ ВПРОДОВЖ ЖИТТЯ В УМОВАХ ІНФОРМАЦІЙНОГО СУСПІЛЬСТВА
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In: Youth & market; No. 4/190 (2021) ; Молодь і ринок; № 4/190 (2021) ; 2617-0825 ; 2308-4634 ; 10.24919/2617-0825.4/190.2021 (2021)
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MOTIVATIONAL ASPECTS OF LEARNING ENGLISH AS A FOREIGN LANGUAGE IN ERA OF GLOBALIZATION ; МОТИВАЦІЙНІ АСПЕКТИ ВИВЧЕННЯ АНГЛІЙСЬКОЇ МОВИ ЯК ІНОЗЕМНОЇ В ЕПОХУ ГЛОБАЛІЗАЦІЇ
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In: Youth & market; No. 4/190 (2021) ; Молодь і ринок; № 4/190 (2021) ; 2617-0825 ; 2308-4634 ; 10.24919/2617-0825.4/190.2021 (2021)
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Real Pandemic: Institutional Neglect
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In: Teaching Culturally and Linguistically Diverse International Students in Open or Online Learning Environments: A Research Symposium (2021)
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Supporting International Graduate Students: Lessons from a Fall 2020 Non-Credit Course
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In: Teaching Culturally and Linguistically Diverse International Students in Open or Online Learning Environments: A Research Symposium (2021)
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Empathy Mapping: Bridging cultural and linguistic divides in international online education
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In: Teaching Culturally and Linguistically Diverse International Students in Open or Online Learning Environments: A Research Symposium (2021)
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Cross-Domain Polarity Models to Evaluate User eXperience in E-learning
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Abstract:
[EN] Virtual learning environments are growing in importance as fast as e-learning is becoming highly demanded by universities and students all over the world. This paper investigates how to automatically evaluate User eXperience in this domain using sentiment analysis techniques. For this purpose, a corpus with the opinions given by a total of 583 users (107 English speakers and 476 Spanish speakers) about three learning management systems in different courses has been built. All the collected opinions were manually labeled with polarity information (positive, negative or neutral) by three human annotators, both at the whole opinion and sentence levels. We have applied our state-of-the-art sentiment analysis models, trained with a corpus of a different semantic domain (a Twitter corpus), to study the use of cross-domain models for this task. Cross-domain models based on deep neural networks (convolutional neural networks, transformer encoders and attentional BLSTM models) have been tested. In order to contrast our results, three commercial systems for the same task (MeaningCloud, Microsoft Text Analytics and Google Cloud) were also tested. The obtained results are very promising and they give an insight to keep going the research of applying sentiment analysis tools on User eXperience evaluation. This is a pioneering idea to provide a better and accurate understanding on human needs in the interaction with virtual learning environments and a step towards the development of automatic tools that capture the feed-back of user perception for designing virtual learning environments centered in user's emotions, beliefs, preferences, perceptions, responses, behaviors and accomplishments that occur before, during and after the interaction. ; Partially supported by the Spanish MINECO and FEDER founds under Project TIN2017-85854-C4-2-R. Work of J.A. Gonzalez is financed under Grant PAID-01-17 ; Sanchis-Font, R.; Castro-Bleda, MJ.; González-Barba, JÁ.; Pla Santamaría, F.; Hurtado Oliver, LF. (2021). Cross-Domain Polarity Models to Evaluate User eXperience in E-learning. Neural Processing Letters. 53:3199-3215. https://doi.org/10.1007/s11063-020-10260-5 ; S ; 3199 ; 3215 ; 53 ; Ba J, Kiros JR, Hinton GE (2016) Layer normalization. arxiv:1607.06450 ; Bahdanau D, Cho K, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. In: 3rd international conference on learning representations, ICLR 2015, San Diego, CA, USA, May 7–9, 2015, conference track proceedings. arxiv:1409.0473 ; Baziotis C, Pelekis N, Doulkeridis C (2017) Datastories at SemEval-2017 task 4: deep LSTM with attention for message-level and topic-based sentiment analysis. In: Proceedings of the 11th international workshop on semantic evaluation (SemEval-2017). Association for Computational Linguistics, Vancouver, Canada, pp 747–754 ; Cliche M (2017) BB\_twtr at SemEval-2017 task 4: Twitter sentiment analysis with CNNs and LSTMs. In: Proceedings of the 11th international workshop on semantic evaluation (SemEval-2017). Association for Computational Linguistics, Vancouver, Canada, pp 573–580. https://doi.org/10.18653/v1/S17-2094. https://www.aclweb.org/anthology/S17-2094 ; Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20(1):37 ; Devlin J, Chang MW, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the North American chapter of the Association for Computational Linguistics: Human Language Technologies, volume 1 (long and short papers). Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186. https://doi.org/10.18653/v1/N19-1423. https://www.aclweb.org/anthology/N19-1423 ; Diaz-Galiano MC, et al (2019) Overview of TASS 2019: one more further for the global Spanish sentiment analysis corpus. In: Proceedings of the Iberian languages evaluation forum (IberLEF 2019), CEUR-WS, Bilbao, Spain, CEUR workshop proceedings, pp 550–560 ; Godin F, Vandersmissen B, De Neve W, Van de Walle R (2015) Multimedia lab @ ACL WNUT NER shared task: named entity recognition for Twitter microposts using distributed word representations. In: Proceedings of the workshop on noisy user-generated text. Association for Computational Linguistics, Beijing, China, pp 146–153. https://doi.org/10.18653/v1/W15-4322. https://www.aclweb.org/anthology/W15-4322 ; González J, Pla F, Hurtado L (2018) Elirf-upv en TASS 2018: Análisis de sentimientos en twitter basado en aprendizaje profundo (elirf-upv at TASS 2018: sentiment analysis in Twitter based on deep learning). In: Proceedings of TASS 2018: workshop on semantic analysis at SEPLN, TASS@SEPLN 2018, co-located with 34nd SEPLN conference (SEPLN 2018), Sevilla, Spain, September 18th, 2018, pp 37–44. http://ceur-ws.org/Vol-2172/p2_elirf_tass2018.pdf ; González J, Hurtado L, Pla F (2019) Elirf-upv at TASS 2019: transformer encoders for Twitter sentiment analysis in Spanish. In: Proceedings of the Iberian languages evaluation forum co-located with 35th conference of the Spanish Society for Natural Language Processing, IberLEF@SEPLN 2019, Bilbao, Spain, September 24th, 2019, pp 571–578. http://ceur-ws.org/Vol-2421/TASS_paper_2.pdf ; González JÁ, Pla F, Hurtado LF (2017) ELiRF-UPV at SemEval-2017 task 4: sentiment analysis using deep learning. In: Proceedings of the 11th international workshop on semantic evaluation (SemEval-2017). Association for Computational Linguistics, Vancouver, Canada, pp 723–727. https://doi.org/10.18653/v1/S17-2121. https://www.aclweb.org/anthology/S17-2121 ; González JÁ, Hurtado LF, Pla F (2019) ELiRF-UPV at TASS 2019: transformer encoders for Twitter sentiment analysis in Spanish. In: Proceedings of the Iberian languages evaluation forum (IberLEF 2019), CEUR-WS, Bilbao, Spain, CEUR workshop proceedings ; GoogleCloud (2019) Cloud natural language API. https://cloud.google.com/natural-language/. Accessed 27 Dec 2019 ; Hassenzahl M, Tractinsky N (2006) User experience—a research agenda. Behav Inf Technol 25(2):91–97. https://doi.org/10.1080/01449290500330331 ; Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735 ; Hurtado Oliver LF, Pla F, González Barba J (2017) ELiRF-UPV at TASS 2017: sentiment analysis in Twitter based on deep learning. In: TASS 2017: workshop on semantic analysis at SEPLN, pp 29–34 ; IBM (2019) Natural language understanding. https://www.ibm.com/watson/services/natural-language-understanding/. Accessed 27 Dec 2019 ; ISO 9241-210:2019 (2019) Ergonomics of human-system interaction—part 210: human-centred design for interactive systems. International Standardization Organization (ISO). https://www.iso.org/standard/77520.html. Accessed 27 Dec 2019 ; Kim Y (2014) Convolutional neural networks for sentence classification. In: Proceedings of the 2014 conference on empirical methods in natural language processing, EMNLP 2014, October 25–29, 2014, Doha, Qatar, a meeting of SIGDAT, a special interest group of the ACL, pp 1746–1751. http://aclweb.org/anthology/D/D14/D14-1181.pdf ; Krippendorff K (2004) Reliability in content analysis. Hum Commun Res 30(3):411–433 ; Kujala S, Roto V, Väänänen-Vainio-Mattila K, Karapanos E, Sinnelä A (2011) UX curve: a method for evaluating long-term user experience. Interact Comput 23(5):473–483 ; Liu B (2012) Sentiment analysis and opinion mining. A comprehensive introduction and survey. Morgan & Claypool Publishers, San Rafael ; Liu B, Hu M, Cheng J (2005) Opinion observer: analyzing and comparing opinions on the web. In: Proceedings of the 14th international conference on world wide web. ACM, New York, NY, USA, WWW ’05, pp 342–351. https://doi.org/10.1145/1060745.1060797 ; Luque FM (2019) Atalaya at TASS 2019: data augmentation and robust embeddings for sentiment analysis. In: Proceedings of the Iberian languages evaluation forum (IberLEF 2019), CEUR-WS, Bilbao, Spain, CEUR workshop proceedings ; Manning CD, Surdeanu M, Bauer J, Finkel J, Bethard SJ, McClosky D (2014) The Stanford CoreNLP natural language processing toolkit. In: Association for computational linguistics (ACL) system demonstrations, pp 55–60. http://www.aclweb.org/anthology/P/P14/P14-5010 ; Martínez-Cámara E, Díaz-Galiano M, García-Cumbreras M, García-Vega M, Villena-Román J (2017) Overview of TASS 2017. In: Proceedings of TASS 2017: workshop on semantic analysis at SEPLN (TASS 2017), CEUR-WS, Murcia, Spain, CEUR workshop proceedings, vol 1896 ; MeaningCloud (2019) Demo de Analítica de Textos. https://www.meaningcloud.com/es/demos/demo-analitica-textos. Accessed 27 Dec 2019 ; MeaningCloud (2019) MeaningCloud: Servicios web de analítica y minería de textos. https://www.meaningcloud.com/. Accessed 27 Dec 2019 ; MicrosoftAzure (2019) Text analytics API. https://azure.microsoft.com/es-es/services/cognitive-services/text-analytics/. Accessed 27 Dec 2019 ; Pang B, Lee L, Vaithyanathan S (2002) Thumbs up? sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on empirical methods in natural language processing, vol 10. Association for Computational Linguistics, pp 79–86 ; Pla F, Hurtado LF (2018) Spanish sentiment analysis in Twitter at the TASS workshop. Lang Resour Eval 52(2):645–672. https://doi.org/10.1007/s10579-017-9394-7 ; Rauschenberger M, Schrepp M, Cota MP, Olschner S, Thomaschewski J (2013) Efficient measurement of the user experience of interactive products. How to use the user experience questionnaire (UEQ). Example: Spanish language version. Int J Interact Multimed Artif Intell 2(1):39–45. https://doi.org/10.9781/ijimai.2013.215 ; Rosenthal S, Farra N, Nakov P (2017) SemEval-2017 task 4: sentiment analysis in Twitter. In: Proceedings of the 11th international workshop on semantic evaluation (SemEval-2017). Association for Computational Linguistics, Vancouver, Canada, pp 502–518. https://doi.org/10.18653/v1/S17-2088. https://www.aclweb.org/anthology/S17-2088 ; Sadr H, Pedram MM, Teshnehlab M (2019) A robust sentiment analysis method based on sequential combination of convolutional and recursive neural networks. Neural Process Lett 50:2745–2761. https://doi.org/10.1007/s11063-019-10049-1 ; Sanchis-Font R, Castro-Bleda M, González J (2019) Applying sentiment analysis with cross-domain models to evaluate user experience in virtual learning environments. In: Rojas I, Joya G, Catala A (eds) Advances in computational intelligence. IWANN (2019). Lecture notes in computer science, vol 11506. Springer, Cham, pp 609–620 ; Schuster M, Paliwal K (1997) Bidirectional recurrent neural networks. Trans Signal Process 45(11):2673–2681. https://doi.org/10.1109/78.650093 ; Scott WA (1955) Reliability of content analysis: the case of nominal scale coding. Public Opin Q 19(3):321–325. https://doi.org/10.1086/266577 ; Socher R, Perelygin A, Wu J, Chuang J, Manning CD, Ng A, Potts C (2013) Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 conference on empirical methods in natural language processing. Association for Computational Linguistics, Seattle, Washington, USA, pp 1631–1642. https://www.aclweb.org/anthology/D13-1170 ; Turney PD (2002) Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: ACL, pp 417–424. http://www.aclweb.org/anthology/P02-1053.pdf ; Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Proceedings of the 31st international conference on neural information processing systems, NIPS’17. Curran Associates Inc., USA, pp 6000–6010. http://dl.acm.org/citation.cfm?id=3295222.3295349 ; Wilson T, Hoffmann P, Somasundaran S, Kessler J, Wiebe J, Choi Y, Cardie C, Riloff E, Patwardhan S (2005) OpinionFinder: a system for subjectivity analysis. In: Proceedings of HLT/EMNLP on interactive demonstrations. Association for Computational Linguistics, pp 34–35 ; Zaharias P, Mehlenbacher B (2012) Editorial: exploring user experience (UX) in virtual learning environments. Int J Hum Comput Stud 70(7):475–477. https://doi.org/10.1016/j.ijhcs.2012.05.001 ; Zhang L, Wang S, Liu B (2018) Deep learning for sentiment analysis: a survey. Wiley Interdiscip Rev Data Min Knowl Discov 8(4):e1253
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Keyword:
Artificial neural networks; Learning management systems; LENGUAJES Y SISTEMAS INFORMATICOS; Machine learning; Sentiment analysis; User experience; Virtual learning environments
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URL: https://doi.org/10.1007/s11063-020-10260-5
http://hdl.handle.net/10251/176382
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Efectos de la Pandemia de Coronavirus COVID-19 en Estudiantes Extranjeros y su Posible Impacto en los Programas de Estudio en el Extranjero en el Futuro
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In: World Languages and Cultures (2020)
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Understanding Teacher Experience with Instructional Coaching to Inform Program Improvement Reflecting the Principles of Adult Learning Theory: A Mixed Method Case Study
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In: Graduate Theses & Dissertations (2020)
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"Langsam vermisse ich die Schule .". Schule während und nach der Corona-Pandemie
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In: Münster ; New York : Waxmann 2020, 229 S. - (Die Deutsche Schule, Beiheft; 16) (2020)
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"Les fondements communs de nos démocraties modernes". Persönliche Eindrücke aus einem französisch-deutsch-ungarischen Erasmus+ Projekt
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In: Quoi de neuf : nouvelle du bilingue (2020), S. 61-75 (2020)
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Designing and using gamification elements to improve students’ user experience in a video-based mobile language learning app
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Galle, Thor. - : KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020
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Breaking silence: The voices of Syrian refugee children in the Canadian classroom
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In: Journal of Global Education and Research (2020)
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Redes de aprendizaje personalizadas en contextos universitarios de aprendizaje semipresencial
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In: Comunicar: Revista científica iberoamericana de comunicación y educación, ISSN 1134-3478, Nº 62, 2020, pags. 19-30 (2020)
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Trying Self- and Peer-assessment with Intermediate Spanish Students of English
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In: Applied Linguistics Research Journal, Vol 4, Iss 2, Pp 40-53 (2020) (2020)
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