Analysing the Impact of Artificial Intelligence and Computational Sciences on Student PerformanceSystematic Review and Meta-analysis

  1. Inmaculada García Martínez 1
  2. José María Fernández Batanero 2
  3. José Fernández Cerero 2
  4. Samuel P. León 3
  1. 1 Universidad de Granada
    info

    Universidad de Granada

    Granada, España

    ROR https://ror.org/04njjy449

  2. 2 Universidad de Sevilla
    info

    Universidad de Sevilla

    Sevilla, España

    ROR https://ror.org/03yxnpp24

  3. 3 Universidad de Jaén
    info

    Universidad de Jaén

    Jaén, España

    ROR https://ror.org/0122p5f64

Revista:
NAER: Journal of New Approaches in Educational Research

ISSN: 2254-7339

Año de publicación: 2023

Volumen: 12

Número: 1

Páginas: 171-197

Tipo: Artículo

DOI: 10.7821/NAER.2023.1.1240 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: NAER: Journal of New Approaches in Educational Research

Resumen

La inteligencia artificial (IA) y las ciencias computacionales han despertado un interés creciente en el campo de la educación. Pese a su historial relativamente reciente, la IA se está introduciendo cada vez más en el aula a través de distintas modalidades con el fin de mejorar los logros de los estudiantes. Así pues, el propósito de esta investigación es analizar, cuantitativa y cualitativamente, el impacto de los componentes de la IA y las ciencias computacionales sobre el rendimiento estudiantil. Para conseguir este objetivo, se han llevado a cabo una revisión sistemática y un meta-análisis en las bases de datos WoS y Scopus. Tras aplicar los criterios de inclusión y exclusión, la muestra quedó conformada por 25 artículos. Los resultados apoyan la idea del impacto positivo que producen la IA y las ciencias computacionales en el rendimiento de los estudiantes, constatándose una tendencia ascendente en su actitud hacia el aprendizaje y su motivación, especialmente en las áreas STEM (Ciencia, Tecnología, Ingeniería y Matemáticas). A pesar de los múltiples beneficios reportados, la implementación de estas tecnologías en los procesos de instrucción plantea un gran desafío educativo y ético para los docentes en relación con su diseño y puesta en práctica que requiere análisis adicionales desde la investigación educativa. Estos hallazgos aparecen de forma consistente en todas las etapas educativas.

Referencias bibliográficas

  • Anderson, J. & Barnett, M. (2011). Using video games to support pre-service elementary teachers learning of basic physics principles. Journal of Science Education and Technology, 20(4), 347–362. https://doi.org/10.1007/s10956-010-9257-0
  • Anderson J., L. & Barnett, M. (2013). Learning physics with digital game simulations in middle school science. Journal of Science Education and Technology, 22(6), 914–926. https://doi.org/10.1007/s10956-013-9438-8
  • Aromataris, E. & Munn, Z. (2020). Chapter 1: JBI Systematic Reviews. In JBI manual for evidence synthesis. Joanna Briggs Institute: Joanna Briggs Institute. Retrieved from https://doi.org/10.46658/JBIMES-20-02 https://doi.org/10.46658/JBIMES-20-02
  • Baker, T., Smith, L. & Anissa, N. (2019). Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges. Retrieved from https://media.nesta.org.uk/documents/Future_of_AI_and_education_v5_WEB.pdf
  • Barak, M. & Zadok, Y. (2009). Robotics projects and learning concepts in science, technology and problem solving. International Journal of Technology and Design Education, 19(3), 289–307. https://doi.org/10.1007/s10798-007-9043-3
  • Barbalios, N., Ioannidou, I., Tzionas, P. & Paraskeuopoulos, S. (2013). A model supported interactive virtual environment for natural resource sharing in environmental education. Computers & Education, 62, 231–248. https://doi.org/10.1016/j.compedu.2012.10.029
  • Baxter, G. & Hainey, T. (2019). Student perceptions of virtual reality use in higher education. Journal of Applied Research in Higher Education, 12(3), 413–424. https://doi.org/10.1108/jarhe-06-2018-0106
  • Beck, D. (2019). Augmented and Virtual Reality in Education: Immersive Learning Research. Journal of Educational Computing Research, 57(7), 1619–1625. https://doi.org/10.1177/0735633119854035
  • Bernardo, A. (2017). Virtual reality and simulation in neurosurgical training. World Neurosurgery, 106, 1015–1029. https://doi.org/10.1016/j.wneu.2017.06.140
  • Borenstein, M., Hedges, L. V., Higgins, J. & Rothstein, H. R. (2009). When does it make sense to perform a meta-analysis. Introduction to Meta-Analysis, 357–364. https://doi.org/10.1002/9780470743386.ch2
  • Bortnik, B., Stozhko, N., Pervukhina, I., Tchernysheva, A. & Belysheva, G. (1968). Effect of virtual analytical chemistry laboratory on enhancing student research skills and practices. Research in Learning Technology, 25, 1968. http://doi.org/10.25304/rlt.v25.1968
  • Bower, M., Dewitt, D. & Lai, J. W. (2020). Reasons associated with preservice teachers’ intention to use immersive virtual reality in education. British Journal of Educational Technology, 1–19. https://doi.org/10.1111/bjet.13009
  • Bozkurt, E. & Ilik, A. (2010). The effect of computer simulations over students’ beliefs on physics and physics success. Procedia-Social and Behavioral Sciences, 2(2), 4587–4591. https://doi.org/10.1016/j.sbspro.2010.03.735
  • Butt, S., Hannan, F. E., Rafiq, M., Hussain, I., Faisal, C. N. & Younas, W. (2020). Say-It & Learn: Interactive Application for Children with ADHD. In International Conference on Human-Computer Interaction. (pp. 213–223). Springer. https://doi.org/10.1007/978-3-030-49913-6_18
  • Cabero-Almenara, J. & Costas, J. (2016). Simulators use for students training. Prisma Social, 7, 343–372.
  • Castrillón, O., Sarache, & Herrera, . R. (2020). Predicción del rendimiento académico por medio de técnicas de inteligencia artificial. Formación Universitaria, 13(1), 93–102. http://doi.org/10.4067/S0718-50062020000100093
  • Chin, D. B., Dohmen, I. M., Cheng, B. H., Oppezzo, M. A., Chase, C. C. & Schwartz, D. L. (2010). Preparing students for future learning with teachable agents. Educational Technology Research and Development, 58(6), 649–669. https://doi.org/10.1007/s11423-010-9154-5
  • Civelek, T., Ucar, E., Ustunel, H. & Aydın, M. K. (2014). Effects of a haptic augmented simulation on K-12 students’ achievement and their attitudes towards physics. Science and Technology Education, 10(6), 565–574. https://doi.org/10.12973/eurasia.2014.1122a
  • Crompton, H., Bernacki, M. & Greene, J. A. (2020). Psychological foundations of emerging technologies for teaching and learning in higher education. Current Opinion in Psychology, 36, 101–105. https://doi.org/10.1016/j.copsyc.2020.04.011
  • Deng, R., Benckendorff, P. & Gannaway, D. (2019). Progress and new directions for teaching and learning in MOOCs. Computers & Education, 129, 48–60. https://doi.org/10.1016/j.compedu.2018.10.019
  • Dickerson, S. J. & Clark, R. M. (2018). A classroom-based simulation-centric approach to microelectronics education. Computer Applications in Engineering Education, 26(4), 768–781. https://doi.org/10.1002/cae.21918
  • Drigas, A. S. & Ioannidou, R. E. (2013). A review on artificial intelligence in special education. Communications in Computer and Information Science, 385–391. https://doi.org/10.1007/978-3-642-35879-1_46
  • Dunleavy, G., Nikolaou, C. K., Nifakos, S., Atun, R., Law, G. C. Y. & Car, L. T. (2019). Mobile digital education for health professions: systematic review and meta-analysis by the digital health education collaboration. Journal of Medical Internet Research, 21(2). https://doi.org/10.2196/12937
  • Elliot, L., Gehret, A., Valadez, M. S., Carpenter, R. & Bryant, L. (2020). Supporting Autonomous Learning Skills in Developmental Mathematics Courses with Asynchronous Online Resources. American Behavioral Scientist, 64(7), 1012–1030. https://doi.org/10.1177/0002764220919149
  • Fabregas, E., Farias, G., Dormido-Canto, S., Guinaldo, M., Sánchez, J. & Bencomo, S. D. (2016). Platform for teaching mobile robotics. Journal of Intelligent & Robotic Systems, 81(1), 131–143. https://doi.org/10.1007/s10846-015-0229-8
  • Fang, N. & Guo, Y. (2016). Interactive computer simulation and animation for improving student learning of particle kinetics. Journal of Computer Assisted Learning, 32(5), 443–455. https://doi.org/10.1111/jcal.12145
  • Fidan, M. & Tuncel, M. (2019). Integrating augmented reality into problem based learning: The effects on learning achievement and attitude in physics education. Computers & Education, 142, 103635. https://doi.org/10.1016/j.compedu.2019.103635
  • Flores-Vivar, J. M. & García-Peñalvo, F. J. (2023). Reflexiones sobre la ética, potencialidades y retos de la Inteligencia Artificial en el marco de la Educación de Calidad (ODS4) Comunicar, 31(74). https://doi.org/10.3916/C74-2023-03
  • Gao, P., Li, J. & Liu, S. (2021). An Introduction to Key Technology in Artificial Intelligence and big Data Driven e-Learning and e-Education. Mobile Networks and Applications, 26(5), 2123–2126. https://doi.org/10.1007/s11036-021-01777-7
  • Guilherme, A. (2017). AI and education: the importance of teacher and student relations. AI & Society, 34(1), 47–54. https://doi.org/10.1007/s00146-017-0693-8
  • Halili, S. H. (2019). Technological advancements in education 4.0. The Online Journal of Distance Education and E-Learning, 7, 63–69.
  • Han, J., Zhao, W., Jiang, Q., Oubibi, M. & Hu, X. (2019). Intelligent Tutoring System Trends 2006-2018: A Literature Review. In 2019 Eighth International Conference on Educational Innovation through Technology (EITT). (pp. 153–159) IEEE. https://doi.org/10.1109/eitt.2019.00037
  • Harrison, N. (1986). Patterns of participation in higher education for care-experienced students in England: why has there not been more progress? Studies in Higher Education, 45(9), 1986–2000. https://doi.org/10.1080/03075079.2019.1582014
  • Hooshyar, D., Yousefi, M. & Lim, H. (2019). A systematic review of data-driven approaches in player modeling of educational games. Artificial Intelligence Review, 52(3), 1997–2017. https://doi.org/10.1007/s10462-017-9609-8
  • Hoplock, L. B., Lobchuk, M. M. & Lemoine, J. (2020). Perceptions of an evidence-based empathy mobile app in post-secondary education. Education and Information Technologies, 26, 1273–1292. https://doi.org/10.1007/s10639-020-10311-3
  • Ibáñez, M. B., Serio, Á. D., Villarán, D. & Kloos, C. D. (2014). Experimenting with electromagnetism using augmented reality: Impact on flow student experience and educational effectiveness. Computers & Education, 71, 1–13. https://doi.org/10.1016/j.compedu.2013.09.004
  • Jawaid, I., Javed, M. Y., Jaffery, M. H., Akram, A., Safder, U. & Hassan, S. (2020). Robotic system education for young children by collaborative-project-based learning. Computer Applications in Engineering Education, 28(1), 178–192. https://doi.org/10.1002/cae.22184
  • Jee, C. (2019). Best chatbot building platforms. Techworld. Retrieved from https://bit.ly/2Ate94F
  • Jiménez, E., Bravo, E. & Bacca, E. (2010). Tool for experimenting with concepts of mobile robotics as applied to children´s education. IEEE Transactions on Education, 53(1), 88–95. https://doi.org/10.1109/TE.2009.2024689
  • Jiménez-Hernández, E. M., Oktaba, H., Díaz-Barriga, F. & Piattini, M. (2020). Using web-based gamified software to learn Boolean algebra simplification in a blended learning setting. Computer Applications in Engineering Education, 28(6), 1591–1611. https://doi.org/10.1002/cae.22335
  • Kavanagh, S., Luxton-Reilly, A., Wuensche, B. & Plimmer, B. (2017). A systematic review of Virtual Reality in education. Themes in Science and Technology Education, 10(2), 85–119.
  • Lau, K. W. & Lee, P. Y. (2015). The use of virtual reality for creating unusual environmental stimulation to motivate students to explore creative ideas. Interactive Learning Environments, 23(1), 3–18. https://doi.org/10.1080/10494820.2012.745426
  • Law, G. C., Dutt, A. & Neihart, M. (2019). Increasing intervention fidelity among special education teachers for autism intervention: A pilot study of utilizing a mobile-app-enabled training program. Research in Autism Spectrum Disorders, 67, 101411. https://doi.org/10.1016/j.rasd.2019.101411
  • López-Rodríguez, M. I. & Barac, M. (2019). Valoración del alumnado sobre el uso de Clickers y vídeo tutoriales en educación superior. Research in Education and Learning Innovation Archives, 22, 29–44. Retrieved from https://doi.org/10.7203/realia.22.14582 https://doi.org/10.7203/realia.22.14582
  • Madathil, K. C., Frady, K., Hartley, R., Bertrand, J., Alfred, M. & Gramopadhye, A. (2017). An empirical study investigating the effectiveness of integrating virtual reality-based case studies into an online asynchronous learning environment. Computers in Education Journal, 8(3), 1–10.
  • Martínez, D. L., Karanik, M., Giovannini, M. & Pinto, N. (2015). Perfiles de Rendimiento Académico: Un Modelo basado en Minería de datos. Campus Virtuales, 4(1), 12–30.
  • Masson, R. & Rennie, F. (2006). ELearning. The key concepts. Routledge
  • McCormick, K. I. & Hall, J. A. (2022). Computational thinking learning experiences, outcomes, and research in preschool settings: a scoping review of literature. Education and Information Technologies, 27, 1–36. https://doi.org/10.1007/s10639-021-10765-z
  • Merino-Armero, J. M., González-Calero, J. A. & Cozar-Gutierrez, R. (2022). Computational thinking in K-12 education. An insight through meta-analysis. Journal of Research on Technology in Education, 54(3), 410–437. https://doi.org/10.1080/15391523.2020.1870250
  • Moreno, R. D. (2019). The arrival of artificial intelligence to education. RITI Journal, 7(14), 260–270. https://doi.org/10.36825/RITI.07.14.022
  • Morris, S. B. (2008). Estimating effect sizes from pretest-posttest-control group designs. Organizational Research Methods, 11, 364–386. https://doi.org/10.1177/1094428106291059
  • Neri, L., Noguez, J., Robledo-Rella, V., Escobar-Castillejos, D. & Gonzalez-Nucamendi, A. (2018). Teaching of Classical Mechanics Concepts using Visuo-haptic Simulators. Educational Technology & Society, 21(2), 85–97.
  • Ocaña, Y., Valenzuela, L. & Garro, L. (2019). Artificial Intelligence and its Implications in Higher Education. Propósito y Representaciones, 7(2), 536–568. http://doi.org/10.20511/pyr2019.v7n2.274
  • Olde, G.-C.-V.-D., Jong, T. D. & Gijlers, H. (2013). Learning by Designing Instruction in the Context of Simulation-based Inquiry Learning. Educational Technology & Society, 16(4), 47–58.
  • Page, M. J., Mckenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D. & Moher, D. (2021). Declaración PRISMA 2020: una guía actualizada para la publicación de revisiones sistemáticas. Revista Española de Cardiología, 74(9), 790–799. https://doi.org/10.1016/j.recesp.2021.06.016
  • Pareto, L. (2014). A teachable agent game engaging primary school children to learn arithmetic concepts and reasoning. International Journal of Artificial Intelligence in Education, 24(3), 251–283. https://doi.org/10.1007/s40593-014-0018-8
  • Păsărelu, C. R., Andersson, G. & Dobrean, A. (2020). Attention-deficit/hyperactivity disorder mobile apps: A systematic review. International Journal of Medical Informatics, 138. https://doi.org/10.1016/j.ijmedinf.2020.104133
  • Pellas, N. & Vosinakis, S. (2018). The effect of simulation games on learning computer programming: A comparative study on high school students’ learning performance by assessing computational problem-solving strategies. Education and Information Technologies, 23(3), 2423–2452. https://doi.org/10.1007/s10639-018-9724-4
  • Petko, D., Schmid, R., Müller, L. & Hielscher, M. (2019). Metapholio: A mobile app for supporting collaborative note taking and reflection in teacher education. Technology, Knowledge and Learning, 24(4), 699–710. https://doi.org/10.1007/s10758-019-09398-6
  • Popenici, S. A. D. & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Technology Enhanced Learning, 12(1). ). https://doi.org/10.1186/s41039-017-0062-8
  • Pyörälä, E., Mäenpää, S., Heinonen, L., Folger, D., Masalin, T. & Hervonen, H. (2019). The art of note taking with mobile devices in medical education. BMC medical education, 19(1), 96. https://doi.org/10.1186/s12909-019-1529-7
  • Reister, M. & Blanchard, S. B. (2020). Tips and Tools for Implementing Progress Monitoring. Kappa Delta Pi Record, 56(3), 128–134. https://doi.org/10.1080/00228958.2020.1770006
  • Riess, W. & Mischo, C. (2010). Promoting systems thinking through biology lessons. International Journal of Science Education, 32(6), 705–725. https://doi.org/10.1080/09500690902769946
  • Roll, I. & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582–599. https://doi.org/10.1007/s40593-016-0110-3
  • Schachner, T., Keller, R. & Wangenheim, F. V. (2020). Artificial intelligence-based conversational agents for chronic conditions: systematic literature review. Journal of Medical Internet Research, 22(9). https://doi.org/10.2196/20701
  • Shegog, R., Lazarus, M. M., Murray, N. G., Diamond, P. M., Sessions, N. & Zsigmond, E. (2012). Virtual transgenics: Using a molecular biology simulation to impact student academic achievement and attitudes. Research in Science Education, 42(5), 875–890. https://doi.org/10.1007/s11165-011-9216-7
  • Singer-Brodowski, M., Brock, A., Etzkorn, N. & Otte, I. (2019). Monitoring of education for sustainable development in Germany-insights from early childhood education, school and higher education. Environmental Education Research, 25(4), 492–507. . https://doi.org/10.1080/13504622.2018.1440380
  • Song, P. & Wang, X. (2020). A bibliometric analysis of worldwide educational artificial intelligence research development in recent twenty years. Asia Pacific Education Review, 21(3), 473–486. https://doi.org/10.1007/s12564-020-09640-2
  • Stieff, M. (2011). Improving representational competence using molecular simulations embedded in inquiry activities. Journal of Research in Science Teaching, 48(10), 1137–1158. https://doi.org/10.1002/tea.20438
  • Sun, L., Guo, Z. & Hu, L. (2021). Educational games promote the development of students’ computational thinking: a meta-analytic review. Interactive Learning Environments, 1–15. https://doi.org/10.1080/10494820.2021.1931891
  • Tatli, Z. & Ayas, A. (2013). Effect of a Virtual Chemistry Laboratory on Students' Achievement. Educational Technology & Society, 16(1), 159–170.
  • Tondeur, J., Roblin, N. P., Van Braak, J., Voogt, J. & Prestridge, S. (2017). Preparing beginning teachers for technology integration in education: Ready for take-off? Technology, Pedagogy and Education, 26(2), 157–177. https://doi.org/10.1080/1475939X.2016.1193556
  • UNESCO. (2019). The Sustainable Development Goals Report. Retrieved from https://bit.ly/34nbq60
  • UNESCO. (2021). International Forum on AI and the futures of education developing competencies for the AI era. Retrieved from https://bit.ly/3zoB6AS
  • Veredas, F. J., Ruiz-Bandera, E., Villa-Estrada, F., Rufino-González, J. F. & Morente, L. (2014). A web-based e-learning application for wound diagnosis and treatment. Computer Methods and Programs in Biomedicine, 116(3), 236–248. https://doi.org/10.1016/j.cmpb.2014.06.005
  • Vesisenaho, M., Juntunen, M., Häkkinen, P., Pöysä-Tarhonen, J., Fagerlund, J., Miakush, I. & Parviainen, T. (2019). Virtual Reality in Education: Focus on the Role of Emotions and Physiological Reactivity. Journal of Virtual Worlds Research, 12(1). https://doi.org/10.4101/jvwr.v12i1.7329
  • Viechtbauer, W. (2010). Conducting Meta-Analyses in R with the metafor Package. Journal of Statistical Software, 36(3), 1–48. https://doi.org/10.18637/jss.v036.i03
  • Vilkova, K. & Shcheglova, I. (2020). Deconstructing self-regulated learning in MOOCs: In search of help-seeking mechanisms. Education and Information Technologies, 26, 17–33. https://doi.org/10.1007/s10639-020-10244-x
  • Vlachopoulos, D. & Makri, A. (2017). The effect of games and simulations on higher education: a systematic literature review. International Journal of Educational Technology in Higher Education, 14(1), 22. https://doi.org/10.1186/s41239-017-0062-1
  • Walker, E., Rummel, N. & Koedinger, K. R. (2014). Adaptive intelligent support to improve peer tutoring in algebra. International Journal of Artificial Intelligence in Education, 24(1), 33–61. https://doi.org/10.1007/s40593-013-0001-9
  • Wilkie, B. & Liefeith, A. (2020). Student experiences of live synchronised video feedback in formative assessment. Teaching in Higher Education, 27(3), 403–416. https://doi.org/10.1080/13562517.2020.1725879
  • Wirjawan, J. V. D., Pratama, D., Pratidhina, E., Wijaya, A., Untung, B. & Herwinarso, (2020). Development of Smartphone App as Media to Learn Impulse-Momentum Topics for High School Students. International Journal of Instruction, 13(3), 17–30. https://doi.org/10.29333/iji.2020.1332a
  • Yang, Y., Zhuang, Y. & Pan, Y. (2021). Multiple knowledge representation for big data artificial intelligence: framework, applications, and case studies. Frontiers of Information Technology & Electronic Engineering, 22(12), 1551–1558. https://doi.org/10.1631/FITEE.2100463
  • Yelamarthi, K. & Drake, E. (2014). A flipped first-year digital circuits course for engineering and technology students. IEEE Transactions on Education, 58(3), 179–186. https://doi.org/10.1109/TE.2014.2356174
  • Zacharia, Z. C. & Olympiou, G. (2011). Physical versus virtual manipulative experimentation in physics learning. Learning and Instruction, 21(3), 317–331. https://doi.org/10.1016/j.learninstruc.2010.03.001
  • Zawacki-Richter, O., Marín, V. I., Bond, M. & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education-where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0
  • Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., ... Y. (2010). A Review of Artificial Intelligence (AI) in Education from. Complexity, 8812542. https://doi.org/10.1155/2021/8812542