Language Instruction in Indonesian Elementary Schools Through Computer Assisted Language Learning: A Library Research Review
DOI:
https://doi.org/10.26740/nld.v4n1.p11-22Keywords:
CALL, elementary schools, language instruction, multimedia resources, technology integrationAbstract
In elementary institutions around the globe, including in Indonesia, computer-assisted language learning (CALL) has gained popularity. Typically, the CALL procedure consists of four stages: preparation, instruction, practice, and evaluation. Preparation includes setting learning goals and choosing software and multimedia. During teaching, teachers present software and multimedia tools and lead pupils. Students improve their language skills individually using software and multimedia materials. Assessing pupils' language skills is the last step. However, successful implementation of CALL in Indonesian elementary schools requires resolving several obstacles, such as the scarcity of trained instructors and the need to adapt CALL to the context of Indonesia. In addition, it is crucial to balance the use of technology with traditional teaching techniques, such as group activities and face-to-face interactions. The results show that (1) language learning results based on AI relates on computer science mastery level, (2) ICT literacy relates on capabilities in operating CALL (3) Artificial Intelligence viewed a tool to teach language learning, and (4) language learning should create learning environment based on community. CALL could be a potent instrument for enhancing language proficiency and preparing Indonesian students for the challenges of the digital era if it receives adequate funding and support.
Downloads
References
Bowker, L. (2021). Promoting Linguistic Diversity and Inclusion: Incorporating Machine Translation Literacy into Information Literacy Instruction for Undergraduate Students. The International Journal of Information, Diversity, & Inclusion (IJIDI), 5(3). https://doi.org/10.33137/ijidi.v5i3.36159
Cope, B., Kalantzis, M., & Searsmith, D. (2021). Artificial intelligence for education: Knowledge and its assessment in AI-enabled learning ecologies. Educational Philosophy and Theory, 53(12), 1229–1245. https://doi.org/10.1080/00131857.2020.1728732
Cox, A. M., Pinfield, S., & Rutter, S. (2019). The intelligent library: Thought leaders’ views on the likely impact of artificial intelligence on academic libraries. Library Hi Tech, 37(3), 418–435. https://doi.org/10.1108/LHT-08-2018-0105
Drukker, L., Noble, J. A., & Papageorghiou, A. T. (2020). Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology. Ultrasound in Obstetrics & Gynecology, 56(4), 498–505. https://doi.org/10.1002/uog.22122
Fırat, T., & Koyuncu, İ. (2021). Investigating Reading Literacy in PISA 2018 Assessment. Lnternational Electronic Journal of Elementary Education, 13(2), 263–275. https://doi.org/10.26822/iejee.2021.189
Fraseda, E., Susilawati, E., Ikhsanudin, I., Bunau, E., & Surmiyati, S. (2022). DEVELOPING A PICTURE BOOK OF LOCAL FOLKTALES TO FACILITATE READING LITERACY FOR THE 8TH GRADE STUDENTS OF SMP ANAK NEGERI SANGGAU. Journal of English Educational Study (JEES), 5(2), 172–179. https://doi.org/10.31932/jees.v5i2.1934
Hassanzadeh, S., & Nikkhoo, F. (2019). Reading Literacy Development of Deaf Students in Special Schools in Iran. International Journal of Special Education, 34.
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. The Center for Curriculum Redesign.
Huettig, F., & Pickering, M. J. (2019). Literacy Advantages Beyond Reading: Prediction of Spoken Language. Trends in Cognitive Sciences, 23(6), 464–475. https://doi.org/10.1016/j.tics.2019.03.008
Hwang, G.-J., & Tu, Y.-F. (2021). Roles and Research Trends of Artificial Intelligence in Mathematics Education: A Bibliometric Mapping Analysis and Systematic Review. Mathematics, 9(6), 584. https://doi.org/10.3390/math9060584
Kim, Y.-S. G. (2020). Interactive Dynamic Literacy Model: An Integrative Theoretical Framework for Reading-Writing Relations. In R. A. Alves, T. Limpo, & R. M. Joshi (Eds.), Reading-Writing Connections (Vol. 19, pp. 11–34). Springer International Publishing. https://doi.org/10.1007/978-3-030-38811-9_2
Knox, J. (2020). Artificial intelligence and education in China. Learning, Media and Technology, 45(3), 298–311. https://doi.org/10.1080/17439884.2020.1754236
Kruchinin, S., & Bagrova, E. (2021). Quality of Mobile Apps for Language Learning. SHS Web of Conferences, 93, 01009. https://doi.org/10.1051/shsconf/20219301009
Lan, X., & Yu, Z. (2022). A Bibliometric Review Study on Reading Literacy over Fourteen Years. Education Sciences, 13(1), 27. https://doi.org/10.3390/educsci13010027
Liu, K., Liu, X., Yang, A., Liu, J., Su, J., Li, S., & She, Q. (2020). A Robust Adversarial Training Approach to Machine Reading Comprehension. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 8392–8400. https://doi.org/10.1609/aaai.v34i05.6357
Magnusson, C. G. (2022). Reading Literacy Practices in Norwegian Lower-Secondary Classrooms: Examining the Patterns of Teacher Questions. Scandinavian Journal of Educational Research, 66(2), 321–335. https://doi.org/10.1080/00313831.2020.1869078
Martins, R. M., & Gresse Von Wangenheim, C. (2022). Findings on Teaching Machine Learning in High School: A Ten - Year Systematic Literature Review. Informatics in Education. https://doi.org/10.15388/infedu.2023.18
Minervini, P., Bošnjak, M., Rocktäschel, T., Riedel, S., & Grefenstette, E. (2020). Differentiable Reasoning on Large Knowledge Bases and Natural Language. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 5182–5190. https://doi.org/10.1609/aaai.v34i04.5962
Novela, G. T., Asrowi, A., & Widyastono, H. (2022). Student’s Reading Literacy: Opportunities and Characteristic for Instructional Media Development. Journal of Education Technology, 6(1), 140. https://doi.org/10.23887/jet.v6i1.42843
Nurkaeti, N., Aryanto, S., & Gumala, Y. (2019). READ ALOUD: AN LITERACY ACTIVITY IN ELEMENTARY SCHOOL. 3(2).
Park, H. W., Grover, I., Spaulding, S., Gomez, L., & Breazeal, C. (2019). A Model-Free Affective Reinforcement Learning Approach to Personalization of an Autonomous Social Robot Companion for Early Literacy Education. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 687–694. https://doi.org/10.1609/aaai.v33i01.3301687
Pitri, R., & Sofia, A. (2022). Factor Analysis for Increasing Reading Literacy in Indonesia. Parameter: Journal of Statistics, 2(2), 18–25. https://doi.org/10.22487/27765660.2022.v2.i2.15898
Prabowo, A., Suparman, S., Li, C. S., Janan, D., & Damayanti, T. D. (2023). The effect of reading literacy to mathematics comprehension of elementary school students in Indonesia and Malaysia. International Journal of Evaluation and Research in Education (IJERE), 12(1), 546. https://doi.org/10.11591/ijere.v12i1.25714
Rashidi, H. H., Tran, N. K., Betts, E. V., Howell, L. P., & Green, R. (2019). Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods. Academic Pathology, 6, 2374289519873088. https://doi.org/10.1177/2374289519873088
Ribeiro, J., Lima, R., Eckhardt, T., & Paiva, S. (2021). Robotic Process Automation and Artificial Intelligence in Industry 4.0 – A Literature review. Procedia Computer Science, 181, 51–58. https://doi.org/10.1016/j.procs.2021.01.104
Schwendicke, F., Samek, W., & Krois, J. (2020). Artificial Intelligence in Dentistry: Chances and Challenges. Journal of Dental Research, 99(7), 769–774. https://doi.org/10.1177/0022034520915714
Shara, A. M., Andriani, D., Ningsih, A. W., & Shinoda, K. (2020). CORRELATING READING LITERACY AND WRITING LITERACY IN JUNIOR HIGH SCHOOL PEMATANGSIANTAR. Journal of English Education, 5(2), 72–85. https://doi.org/10.31327/jee.v5i2.1249
Shukla, V. K., & Verma, A. (2019). Enhancing LMS Experience through AIML Base and Retrieval Base Chatbot using R Language. 2019 International Conference on Automation, Computational and Technology Management (ICACTM), 561–567. https://doi.org/10.1109/ICACTM.2019.8776684
Valtonen, T., Tedre, M., Mäkitalo, Ka., & Vartiainen, H. (2019). Media Literacy Education in the Age of Machine Learning. Journal of Media Literacy Education, 11(2). https://doi.org/10.23860/JMLE-2019-11-2-2
Xu, Y., Wang, D., Collins, P., Lee, H., & Warschauer, M. (2021). Same benefits, different communication patterns: Comparing Children’s reading with a conversational agent vs. a human partner. Computers & Education, 161, 104059. https://doi.org/10.1016/j.compedu.2020.104059
Zhang, Z., Wu, Y., Zhao, H., Li, Z., Zhang, S., Zhou, X., & Zhou, X. (2020). Semantics-Aware BERT for Language Understanding. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 9628–9635. https://doi.org/10.1609/aaai.v34i05.6510
Zhang, Z., Wu, Y., Zhou, J., Duan, S., Zhao, H., & Wang, R. (2020). SG-Net: Syntax-Guided Machine Reading Comprehension. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 9636–9643. https://doi.org/10.1609/aaai.v34i05.6511
Zhang, Z., Yang, J., & Zhao, H. (2021). Retrospective Reader for Machine Reading Comprehension. Proceedings of the AAAI Conference on Artificial Intelligence, 35(16), 14506–14514. https://doi.org/10.1609/aaai.v35i16.17705
Downloads
Published
Issue
Section
Abstract views: 120
,
PDF Downloads: 78
