Chatbot to improve learning punctuation in Spanish and to enhance open and flexible learning environments

Referencias bibliográficas

• Ali, S. S., Amin, T., & Ishtiaq, M. (2020). Punctuation errors in writing: A comparative study of students’ performance from different Pakistani universities. Sir Syed Journal of Education & Social Research, 3(1), 165–177. https://doi.org/10.36902/sjesr-vol3-iss1-2020(165-177)
• Angelillo, J. (2002). Teaching young writers to use punctuation with precision and purpose. Profile Books.
• Araujo, T. (2018). Living up to the chatbot hype: The influence of anthropomorphic design cues and communicative agency framing on conversational agent and company perceptions. Computers in Human Behavior, 85, 183–189. https://doi.org/10.1016/j.chb.2018.03.051
• Bailey, D. (2019). Chatbots as conversational agents in the context of language learning. Proceedings of the Fourth Industrial Revolution and Education, pp 32–41. Dajeon, South Korea.
• Beale, R., & Creed, C. (2009). Affective interaction: How emotional agents affect users. International Journal of HumanComputer Studies, 67, 775–776. https://doi.org/10.1016/j.ijhcs.2009.05.001
• Benotti, L., Martinez, M. C., & Schapachnik, F. (2018). A tool for introducing computer science with automatic formative assessment. IEEE Transactions on Learning Technologies, 11(2), 179–192. https://doi.org/10.1109/TLT.2017.2682084
• Bentivoglio, C. A., Bonura, D., Cannella, V., Carletti, S., Pipitone, A., Pirrone, R., Rossi, P. G., & Russo, G. (2010). Agenti intelligenti supporto dell’interazione con l’utente all’interno di processi di apprendimento. Journal of e-Learning and Knowledge Society, 2(6), 27–36
• Bii, P. (2013). Chatbot technology: A possible means of unlocking student potential to learn how to learn. Educational Research, 4(2), 218–221
• Blei, D. M., Andrew, Y. N., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3(4–5), 993–1022
• Bram, B. (1995). Write well improving writing skills. Kanisius.
• Bruck, P. A., Motiwalla, L., & Foerster, F. (2012). Mobile learning with micro-content: A framework and evaluation. Proceedings of the 25th Bled eConference, 527–543. Bled, Slovenia.
• Bruni, E., Tran, N. K., & Baroni, M. (2014). Multimodal distributional semantics. Journal of Artificial Intelligence Research, 49, 1–47. https://doi.org/10.1613/jair.4135
• Budan, I. A., & Graeme, H. (2006). Evaluating WordNet-based measures of semantic distance. Computational Linguistics, 32(1), 13–47. https://doi.org/10.1162/coli.2006.32.1.13
• Bullinaria, J. A., & Levy, J. P. (2012). Extracting semantic representations from word cooccurrence statistics: Stop-lists, stemming and svd. Behavior Research Methods, 44, 890–907. https://doi.org/10.3758/s13428-011-0183-8
• Cabero, J., & Ruiz-Palmero, J. (2018). Technologies of information and communication for inclusion: Reformulating the “digital gap.” IJERI: International Journal of Educational Research and Innovation, 9, 16–30
• Cabero, J., Vázquez-Cano, E., López-Meneses, E., & Jaén-Martínez, A. (2020). Posibilidades formativas de la tecnología aumentada. Un estudio diacrónico en escenarios universitarios. Revista Complutense De Educación, 31(2), 143–154. https://doi.org/10.5209/rced.61934
• Caddéo, S. (1998). L’usage de la ponctuation chez les enfants. In J.-M. Defays, L. Rosier, & F. Tilkin (Eds.), Actes du colloque international et interdisciplinaire de Liège: A qui appartient la ponctuation? (pp. 255–274). De Boeck.
• Cassany, D. (1999). Puntuación: Investigaciones, concepciones y didáctica. Letras, 58, 21–54
• Chen, J. A., Tutwiler, M. S., Metcalf, S. J., Kamarainen, A., Grotzer, T., & Dede, C. (2016). A multi-user virtual environment to support students’ self-efficacy and interest in science: A latent growth model analysis. Learning and Instruction, 41, 11–22. https://doi.org/10.1016/j.learninstruc.2015.09.007
• Ciechanowski, L., Przegalinska, A., & Wegner, K. (2018). The necessity of new paradigms in measuring human–chatbot interaction. In M. Hoffman (Ed.), Advances in cross-cultural decision making. (pp. 205–214). Springer.
• Colace, F., Santo, M. D., Lombardi, M., Pascale, F., Pietrosanto, A., & Lemma, S. (2018). Chatbot for e-learning: A case of study. International Journal of Mechanical Engineering and Robotics Research, 7(5), 528–533. https://doi.org/10.18178/ijmerr.7.5.528-533
• Coniam, D. (2008). Evaluating the language resources of chatbots for their potential in English as a second language. ReCALL, 20(01), 98–116. https://doi.org/10.1017/S0958344008000815
• Coniam, D. (2014). The linguistic accuracy of chatbots: Usability from an ESL perspective. Text & Talk, 34(5), 545–567. https://doi.org/10.1515/text-2014-0018
• Cordova, D. I., & Lepper, M. R. (1996). Intrinsic motivation and the process of learning: Beneficial effects of contextualization, personalization, and choice. Journal of Educational Psychology, 88(4), 715–730. https://doi.org/10.1037/0022-0663.88.4.715
• Crown, S., Fuentes, A., Jones, R., Nambiar, R., & Crown, D. (2010). Ann G. Neering: Interactive chatbot to motivate and engage engineering students. American Society for Engineering Education, 15(1), 1–13
• Daffern, T., & Mackenzie, N. (2015). Building strong writers: Creating a balance between the authorial and secretarial elements of writing. Literacy Learning: the Middle Years, 23(1), 23–32
• Fang, Z., & Wang, Z. (2011). Beyond rubrics: Using functional language analysis to evaluate student writing. Australian Journal of Language and Literacy, 34(2), 147–165
• Farkash, Z. (2018). Education Chatbot: 4 ways chatbots are revolutionizing education. Chatbot Magazine. https://chatbotsmagazine.com/education-chatbot-4-ways-chatbots-arerevolutionizing-education-33f36627964c
• Feng, Y., Bagheri, E., Ensan, F., & Jovanovic, J. (2017). The state of the art in semantic relatedness: A framework for comparison. Knowledge Engineering Review, 32, 1–30. https://doi.org/10.1017/S0269888917000029
• Ferreiro, E. (1999). Cultura escrita y educación. Conversaciones con Emilia Ferreiro. Fondo de Cultura Económica.
• Ferreiro, E., & Teberosky, A. (1979). Los sistemas de escritura en el desarrollo del niño. Siglo XXI.
• Fryer, L. K., & Carpenter, R. (2006). Bots as language learning tools. Language Learning and Technology, 10(3), 8–14. http://llt.msu.edu/vol10num3/emerging/
• Fryer, L. K., Nakao, K., & Thompson, A. (2019). Chatbot learning partners: Connecting learning experiences, interest and competence. Computers in Human Behavior, 93, 279–289. https:// doi. org/ 10. 1016/j. chb. 2018. 12. 023
• Fuente, M. (1993). Los signos de puntuación: Normativa y uso. Universidad de Valladolid. Garcia Brustenga, G., Fuertes-Alpiste, M., & Molas-Castells, N. (2018). Briefing paper: Los chatbots en educación. eLearn Center. Universitat Oberta de Catalunya.
• García-Valdecasas, J. (2011). Agent-based modelling: A new way of exploring social phenomena. Revista Española De Investigaciones Sociológicas, 136, 91–110. https://doi.org/10.5477/cis/reis.136.91
• Ghose, S., & Barua, J. (2013). Toward the implementation of a topic specific dialogue based natural language chatbot as an undergraduate advisor. Proceedings of the International Conference on Informatics, Electronics and Vision, 1–5. Dhaka, Bangladesh. doi: https://doi.org/10.1109/ICIEV.2013.65726 50
• Giurgiu, L. (2017). Microlearning an evolving elearning trend. Scientific Bulletin, 22(1), 18–23. https://doi.org/10.1515/bsaft-2017-0003
• Goda, Y., Yamada, M., Matsukawa, H., Hata, K., & Yasunami, S. (2014). Conversation with a chatbot before an online EFL group discussion and the effects on critical thinking. The Journal of Information and Systems in Education, 13(1), 1–7. https://doi.org/10.12937/ejsise.13.1
• Grossman, J., Lin, Z., Sheng, H., Wei, J. T.-Z., Williams, J. J., & Goel, S. (2019). MathBot: Transforming online resources for learning math into conversational interactions. http://logical.ai/story/papers/mathbot.pdf
• Gupta, S., & Jagannath, K. (2019). Artificially intelligently (AI) tutors in the classroom: A need assessment study of designing chatbots to support student learning. Proceedings of the Twenty-Third Pacific Asia Conference on Information Systems, 1–8. Chicago, United States.
• Hasler, B. S., Tuchman, P., & Friedman, D. (2013). Virtual research assistants: Replacing human interviewers by automated avatars in virtual worlds. Computers in Human Behavior, 29, 1608–1616. https://doi.org/10.1016/j.chb.2013.01.004
• Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77, 81–112
• Heidig, S., & Clarebout, G. (2011). Do pedagogical agents make a difference to student motivation and learning? Educational Research Review, 6, 27–54. https://doi.org/10.1016/j.edurev.2010.07.004
• Hill, J., Ford, W. R., & Farreras, I. G. (2015). Real conversations with artificial intelligence: A comparison between humanehuman online conversations and humanechatbot conversations. Computers in Human Behavior, 49, 245–250. https://doi.org/10.1016/j.chb.2015.02.026
• Ho, A., Hancock, J., & Miner, A. S. (2018). Psychological, relational, and emotional effects of self-disclosure after conversations with a chatbot. Journal of Communication, 68(4), 712–733. https://doi.org/10.1093/joc/jqy026
• Hsu, H.-C.K., Wang, C. V., & Levesque-Bristol, C. (2019). Reexamining the impact of self-determination theory on learning outcomes in the online learning environment. Education and Information Technologies, 24(3), 2159–2174. https://doi.org/10.1007/s10639-019â-09863-w
• Huang, W., Hew, K. F., & Gonda, D. E. (2019). Designing and evaluating three chatbot enhanced activities for a flipped graduate. International Journal of Mechanical Engineering and Robotics Research, 8(5), 813–818. https://doi.org/10.18178/ijmerr.8.5.813-818
• Io, H. N., & Lee, C. B. (2018). Chatbots and conversational agents: A bibliometric analysis. Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management, 215–219. Singapore.
• Jeno, L. M., Adachi, P. J., Grytnes, J. A., Vandvik, V., & Deci, E. L. (2019). The effects of m-learning on motivation, achievement and well-being: A self-determination theory approach. British Journal of Educational Technology, 50(2), 669–683. https://doi.org/10.1111/bjet.12657
• Johnson, W. L., & Lester, J. C. (2016). Face-to-face interaction with pedagogical agents, twenty years later. International Journal of Artificial Intelligence in Education, 26, 25–36
• Jomah, O., Masoud, A. K., Kishore, X. P., & Aurelia, S. (2016). Micro learning: A modernized education system. BRAIN Broad Research in Artificial Intelligence and Neuroscience, 7(1), 103–110
• Jones, M., & Mewhort, D. (2007). Representing word meaning and order information in a composite holographic lexicon. Psychological Review, 114(1), 1–37. https://doi.org/10.1037/0033-295X.114.1.1
• Klevjer, R. (2006). What is the avatar? Fiction and embodiment in avatar-based single player computer games. Dissertation for the degree doctor rerum politicarum. University of Bergen.
• Klopfenstein, L. C., Delpriori, S., Malatini, S., & Bogliolo, A. (2017). The rise of bots: A survey of conversational interfaces, patterns, and paradigms. Proceedings of the 2017 Conference on Designing Interactive Systems, DIS ’17, 555–565. New York, United States. https://doi.org/https://doi.org/10.1145/3064663.3064672
• Labuhn, A. S., Zimmerman, B. J., & Hasselhorn, M. (2010). Enhancing students’ self-regulation and mathematics performance: The influence of feedback and self‑evaluative standards. Metacognition and Learning, 5(2), 173–194. https://doi.org/101007/s11409-010-9056-2
• Liew, T., Mat Zin, N., & Sahari, N. (2017). Exploring the affective, motivational and cognitive effects of pedagogical agent enthusiasm in a multimedia learning environment. Human-Centric Computing and Information Sciences, 7(1), 1–21. https://doi.org/10.1186/s13673-017- 0089-2
• Liu, Q., Huang, J., Wu, L., Zhu, K., & Ba, S. (2019). CBET: Design and evaluation of a domain-specific chatbot for mobile learning. Universal Access in the Information Society. https://doi.org/10.1007/s10209-019-00666-x
• López-Meneses, E., Sirignano, F. M., Vázquez-Cano, E., & Ramírez-Hurtado, J. M. (2020). University students’ digital com-petence in three areas of the DigCom 2.1 model: A comparative study at three European universities. Australasian Journal of Educational Technology, 36(3), 69–88. https://doi.org/10.14742/ajet.5583
• Macken-Horarik, M., & Sandiford, C. (2016). Diagnosing development: A grammatics for tracking student progress in narrative composition. International Journal of Language Studies, 10(3), 61–94
• Mohammed, G. S., & Wakil, K. (2018). The effectiveness of microlearning to improve students’ learning ability. International Journal of Educational Research Review, 3(3), 32–38
• Nikou, S. A. (2019). A micro-learning based model to enhance student teachers’ motivation and engagement in blended learning. Proceedings of the SITE 2019, Society for Information Technology and Teacher Education, 255–260. Las Vegas, United States.
• Nikou, S. A., & Economides, A. A. (2017). Mobile-based assessment: Integrating acceptance and motivational factors into a combined model of self-determination theory and technology acceptance. Computers in Human Behavior, 68, 83–95. https://doi.org/10.1016/j.chb.2016.11.020
• Nikou, S. A., & Economides, A. A. (2018). Mobile-based micro-learning and assessment: Impact on learning performance and motivation of high school students. Journal of Computer Assisted Learning, 34(3), 269–278. https://doi.org/10.1111/jcal.12240
• Paschoal, L. N., Turci, L. F., Conte, T. U., & Souza, S. R. S. (2019). Towards a conversational agent to support the software testing education. Proceedings of the XXXIII Brazilian Symposium on Software Engineering, 57–66. Curitiba, Brazil.
• Polo, J. (1990). Manifiesto ortográfico de la lengua española. Visor.
• Procter, M., Lin, F., & Heller, B. (2012). Intelligent intervention by conversational agent through chatlog analysis. Smart Learning Environments, 5(30), 1–15. https://doi.org/10.1186/s40561-018-0079-5
• Reyes-Reina, D., Vilaça, L., Spolidorio, S., & Martins, M. (2019). El desarrollo sociotécnico de un chatbot o ¿Cómo se construye una caja negra? Revista Tecnologia e Sociedade, 16(39), 23–40
• Rosenthal, R. (1991). Effect sizes: Pearson’s correlation, its display via the BESD, and alternative indices. American Psychologist, 46(10), 1086–1087
• Rosenthal, R., & Rubin, D. B. (1982). A simple, general purpose display of magnitude of experimental effect. Journal of Educational Psychology, 74(2), 166–169. https://doi.org/10.1037/0022-0663.74.2.166.
• Ruan, S., Willis, A., Xu, Q., Davis, G. M., Jiang, L., Brunskill, E., & Landay, J. A. (2019). BookBuddy. Proceedings of the Sixth ACM Conference on Learning @ Scale L@S ’19, 1–4. New York, United States. https://doi.org/https://doi.org/10.1145/3330430.3333643
• Schroeder, N., Adesope, O., & Gilbert, R. (2013). How effective are pedagogical agents for learning? A metaanalytic review. Journal of Educational Computing Research, 49(1), 1–39. https://doi.org/10.2190/ec.49.1.a
• Schroeder, N. L., Romine, W. L., & Craig, S. D. (2017). Measuring pedagogical agent persona and the influence of agent persona on learning. Computers & Education, 109, 176–186. https://doi.org/10.1016/j.compedu.2017.02.015
• Scull, J., & Mackenzie, N. M. (2018). Developing authorial skills: Child language leading to text construction, sentence construction and vocabulary development. In N. M. Mackenzie & J. Scull (Eds.), Understanding and supporting young writers from birth to 8. (pp. 89–115). Routledge.
• Sha, G. (2009). AI-based chatterbots and spoken English teaching: A critical analysis. Computer Assisted Language Learning, 22(3), 269–281. https://doi.org/10.1080/09588220902920284
• Shail, M. S. (2019). Using micro-learning on mobile applications to increase knowledge retention and work performance: A review of literature. Cureus, 11(8), e5307. https://doi.org/10.7759/cureus.5307
• Sheth, A., Yip, H. Y., Iyengar, A., & Tepper, P. (2019). Cognitive services and intelligent chatbots: Current perspectives and special issue introduction. IEEE Internet Computing, 23(2), 6–12
• Shum, H.-Y., He, X., & Li, D. (2018). From Eliza to XiaoIce: Challenges and opportunities with social chatbots. Frontiers of Information Technology & Electronic Engineering, 19(1), 10–16. https://doi.org/10.1631/FITEE.1700826
• Smutny, P., & Schreiberova, P. (2020). Chatbots for learning: A review of educational chatbots for the Facebook Messenger. Computers & Education, 151, 103862. https://doi.org/10.1016/j.compedu.2020.103862
• Stickler, U., & Hampel, R. (2015). Transforming teaching: New skills for online language learning spaces. Palgrave Macmillan.
• Subramaniam, N. K. (2019). Teaching & learning via chatbots with immersive and machine learning capabilities. Proceedings of the ICE 2019 Conference Proceedings, 145–156. Jyväskylä, Finland.
• Tamayo, P. A., Herrero, A., Martín, J., Navarro, C., & Tránchez, J. M. (2020). Design of a chatbot as a distance learning assistant. Open Praxis, 12(1), 145–153. https://doi.org/10.5944/openpraxis.12.1.1063
• Taraban, R. (2018). Practicing metacognition on a chatbot. Improve with metacognition. http://www.improvewithmetacognition.com/2035–2/
• Tegos, S., Demetriadis, S., & Tsiatsos, T. (2014). A configurable conversational agent to trigger students’ productive dialogue: A pilot study in the CALL domain. International Journal of Artificial Intelligence in Education, 24(1), 62–91. https://doi.org/10.1007/s40593-013-0007-3
• Tegos, S., Psathas, G., Tsiatsos, T., & Demetriadis, S. (2019). Designing conversational agent interventions that support collaborative chat activities in MOOCs. Proceedings of EMOOCs 2019: Work in Progress Papers of the Research, Experience and Business Tracks, 66–71. Naples, Italy.
• Thompson, A., Gallacher, A., & Howarth, M. (2018). Stimulating task interest: Human partners or chatbots? Proceedings of the Future-proof CALL: language learning as exploration and encounters, 302–306. Jyväskylä, Finland.
• Van Rosmalen, P., Eikelboom, P., Bloemers, E., Van Winzum, K., & Spronck, P. (2012). Towards a game-chatbot: Extending the interaction in serious games. Proceedings of 6th European Conference on Games Based Learning, 1–8. Cork, Ireland.
• Vázquez-Cano, E. (2012). Mobile learning with Twitter to improve linguistic competence at secondary schools. The New Educational Review, 29(3), 134–147
• Vázquez-Cano, E. (2014). Mobile distance learning with smartphones and apps in higher education. Educational Sciences: Theory & Practice, 14(4), 1–16. https://doi.org/10.12738/est.2014.4.2012
• Vázquez-Cano, E., Fombona, J., & Fernández, A. (2013). Virtual attendance: Analysis of an audiovisual over IP system for distance learning in the Spanish Open University (UNED). The International Review of Research in Open and Distance Learning (IRRODL), 14(3), 402–426. https://doi.org/10.19173/irrodl.v14i3.1430
• Vázquez-Cano, E., Holgueras, A. I., & Sáez-López, J. M. (2018). An analysis of the ortographic error found in university students’ asynchronous digital writing. Journal of Computing in Higher Education, 31(1), 1–20. https://doi.org/10.1007/s12528-018-9189-x
• Vijayakumar, R., Bhuvaneshwari, B., Adith, S., & Deepika, M. (2019). AI based student bot for academic information system using machine learning. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 5(2), 590–596. https://doi.org/10.32628/CSEIT1952171
• Wang, N., Johnson, W. L., Mayer, R. E., Rizzo, P., Shaw, E., & Collins, H. (2008). The politeness effect: Pedagogical agents and learning outcomes. International Journal of Human Computer Studies, 66, 96–112. https://doi.org/10.1016/j.ijhcs.2007.09.003
• Weizenbaum, J. (1966). ELIZA—A computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36–45
• Wing Jan, L. (2009). Write ways: Modelling writing forms. Oxford University Press.
• Winkler, R., & Soellner, M. (2018). Unleashing the potential of chatbots in education: A state-of-the-art analysis. Proceedings of the 78th Academy of Management Annual Meeting, 1–40. Chicago, Illinois.