بررسی نظام مند نویدها و چالش‌های هوش مصنوعی برای معلمان

Aggarwal, C. C. (2018). Neural networks and deep learning.  Springer, 10, 978-3.

Akçayır, M., & Akçayır, G. (2017). Advantages and challenges associated with augmented reality for education: A systematic review of the literature. Educational Research Review, 20, 1–11.

Akgün, E., & Demir, M. (2018). Modeling course achievements of elementary education teacher candidates with artificial neural networks. International Journal of Assessment Tools in Education, 5(3), 491–509.

Alenezi, H. S., & Faisal, M. H. (2020). Utilizing crowdsourcing and machine learning in education: Literature review. Education and Information Technologies, 1-16.

Alharbi, M. A., & Al-Hoorie, A. H. (2020). Turnitin peer feedback: Controversial vs. non-controversial essays. International Journal of Educational Technology in Higher Education, 17, 1–17.

Alloghani, M., Al-Jumeily, D., Mustafna, J., Hussain, A., & Aljaaf, A. J. (2020). A systematic review on supervised and unsupervised machine learning algorithms for data science. In Supervised and Unsupervised Learning for Data Science (pp. 3–21). Springer, Cham. https://doi.org/10.1007/978-3-030-22475-2_1

Alzahrani, H., Arif, M., Kaushik, A., Goulding, J., & Heesom, D. (2020). Artificial neural network analysis of teachers’ performance against thermal comfort. International Journal of Building Pathology and Adaptation. https://doi.org/10.1108/

 IJBPA-11-2019-0098

Annabestani, M., Rowhanimanesh, A., Mizani, A., & Rezaei, A. (2020) Fuzzy descriptive evaluation system: Real, complete and fair evaluation of students. Soft Computing, 24(4), 3025–3035.

Baker, T., & Smith, L. (2019). Education rebooted? Exploring the future of artificial intelligence in schools and colleges. Retrieved from Nesta Foundation website: https://media.nesta.org.uk/documents/Future_of_AI_and_education_v5_WEB.pdf

Baran, E. (2014). A review of research on mobile learning in teacher education. Journal of Educational Technology & Society, 17(4), 32-17.

Bonneton-Botté, N., Fleury, S., Girard, N., Le Magadou, M., Cherbonnier, A., Renault, M., ... & Jamet, E. (2020). Can tablet apps support the learning of handwriting? An investigation of learning outcomes in kindergarten classroom. Computers & Education, 151, 103831.

 

Borges, A. F., Laurindo, F. J., Spínola, M. M., Gonçalves, R. F., & Mattos, C. A. (2020). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International Journal of Information Management, 102225

Bonk, C. J., & Wiley, D. A. (2020). Preface: Refection’s on the waves of emerging learning technologies. Educational Technology Research and Development, 68(4), 1595–1612.

Buddhtha, S., Natasha, C., Irwansyah, E., & Budiharto, W. (2019). Building an artificial neural network with backpropagation algorithm to determine teacher engagement based on the Indonesian teacher engagement index and presenting the data in a Web-Based GIS. International Journal of Computational Intelligence Systems, 1575, (2) 12

Burstein, J., Chodorow, M., & Leacock, C. (2004). Automated essay evaluation: The Criterion online writing service. Ai Magazine, 27-27, (3)25 https://doi.org/10.1609/aimag.v25i3.1774

Bywater, J. B., Chiu J. l., Hong J., & Sankaranarayanan, V. (2019). The teacher responding tool: Scaffolding the teacher practice of responding to student ideas in mathematics classrooms. Computers & Education 139, 16-30.

Chai, C. S., Jong, M., & Yan, Z. (2020). Surveying Chinese teachers ‘technological pedagogical STEM knowledge: A pilot validation of STEM-TPACK survey. International Journal of Mobile Learning and Organization, 14(2), 203–214.

Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10. 1109/ACCESS.2020.2988510

Chiu, T. K., & Chai, C. S. (2020). Sustainable curriculum planning for artificial intelligence education: A self-determination theory perspective. Sustainability, 12(14), 5568.

Clark, D. (2020). Artificial ıntelligence for learning: How to use AI to support employee development. Kogan Page Publishers.

Cohen, I. L., Liu, X., Hudson, M., Gillis, J., Cavalari, R. N., Romanczyk, R. G., ... & Gardner, J. M. (2017). Level 2 Screening with the PDD Behavior Inventory: Subgroup Profiles and Implications for Differential Diagnosis. Canadian Journal of School Psychology, 32(3-4), 299-315.

Contreras, I., & Vehi, J. (2018). Artificial intelligence for diabetes management and decision support: Literature review. Journal of Medical Internet Research, 20(5), e10775. https://doi.org/10.10775/2196.

Cope, B., Kalantzis, M., & Searsmith, D. (2020). Artificial intelligence for education: Knowledge and its assessment in AI-enabled learning ecologies. Educational Philosophy and Theory, 1–17.

Cukurova, M., & Luckin, R. (2018). Measuring the impact of emerging technologies in education: A pragmatic approach. Springer, Cham. https://discovery.ucl.ac.uk/id/eprint/10068777

Dalvean, M., & Enkhbayar, G. (2018). Assessing the readability of fiction: a corpus analysis and readability ranking of 200 English fiction texts* 4. Linguistic Research, 35, 137–170.

Dawson, P., Sutherland-Smith, W., & Ricksen, M. (2020). Can software improve marker accuracy at detecting contract cheating? A pilot study of the Turnitin authorship investigate alpha. Assessment & Evaluation in Higher Education, 45(4), 473–482.

Demir, M. (2015). Predicting pre-service classroom teachers’ civil servant recruitment examination’s educational sciences test scores using artificial neural networks. Educational Sciences: Theory & Practice, 15(5).

Denzin, N. K. (2017). The research act: A theoretical introduction to sociological methods. Transaction publishers.

Dillenbourg, P. (2013). Design for classroom orchestration. Computers & Education, 69, 485–492.

Dillenbourg, P. (2016). The evolution of research on digital education. International Journal of Artificial Intelligence in Education, 26(2), 544–560. https://doi.org/10.1007/s40593-016-0106-z

EdTechXGlobal. (2016). EdTechXGlobal report 2016—Global EdTech industry report: a map for the future of education and work. Retrieved from http://ecosystem.edtechxeurope.com/

Farhan, M., Jabbar, S., Aslam, M., Ahmad, A., Iqbal, M. M., Khan, M., & Maria, M. E. A. (2018). A real-time data mining approach for interaction analytics assessment: based student interaction framework. International Journal of Parallel Programming. (5), 46, 903-886 .

Fitzgerald, J., Elmore, J., Koons, H., Hiebert, E. H., Bowen, K., Sanford-Moore, E. E., & Stenner, A. J. (2015). Important text characteristics for early-grades text complexity. Journal of Educational Psychology, 107(1), 4.

Gaudioso, E., Montero, M., & Hernandez-Del-Olmo, F. (2012). Supporting teachers in adaptive educational systems through predictive models: A proof of concept. Expert Systems with Applications, 39(1), 621–625.

Häkkinen, P., Järvelä, S., Mäkitalo-Siegl, K., Ahonen, A., Näykki, P., & Valtonen, T. (2017). Preparing teacher students for 21st century learning practices (PREP 21): A framework for enhancing collaborative problem solving and strategic learning skills. Teachers and Teaching: Theory and Practice, 23(1), 25–41.

Hefernan, N. T., & Hefernan, C. L. (2014). The ASSISTments ecosystem: Building a platform that brings scientists and

teachers together for minimally invasive research on human learning and teaching. International Journal of Artificial Intelligence in Education, 24(4), 470–497.

Heitink, M. C., Van der Kleij, F. M., Veldkamp, B. P., Schildkamp, K., & Kippers, W. B. (2016). A systematic review of prerequisites for implementing assessment for learning in classroom practice. Educational Research Review, 17, 50–62.

Holmes, W., Bialik, M., & Fadel, C. (2019). Artifcial intelligence in education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.

Holstein, K., McLaren, B. M., & Aleven, V. (2019). Co-designing a real-time classroom orchestration tool to support teacher–AI complementarity. Journal of Learning Analytics, 6(2), 27–52.

Hrastinski, S., Olofsson, A. D., Arkenback, C., Ekström, S., Ericsson, E., Fransson, G., ... & Utterberg, M. (2019). Critical imaginaries and refections on artifcial intelligence and robots in post digital K-12 education. Post digital Science and Education, 1(2), 427-445. https://doi.org/10.1007/s42438-019-00046-x

Huang, C. J., Liu, M. C., Chang, K. E., Sung, Y. T., Huang, T. H., Chen, C. H., ... & Chang, T. Y. (2010). A learning assistance tool for enhancing ICT literacy of elementary school students. Journal of Educational Technology & Society, 13(3), 126-138.

Huang, C. J., Wang, Y. W., Huang, T. H., Chen, Y. C., Chen, H. M., & Chang, S. C. (2011). Performance evaluation of an online argumentation learning assistance agent. Computers & Education, (1)57, 1270-1280

Järvelä, S. & Bannert, M. (2021). Temporal and adaptive processes of regulated learning – What can multimodal data tell? Learning and Instruction, 72, https://doi.org/10.1016/j.learninstruc.2019. 101268.

Järvelä, S., Malmberg, J., Haataja, E., Sobocinski, M., & Kirschner, P. A. (2021). What multimodal data can tell us about the students’ regulation of their learning process. Learning and Instruction, 101203.

Kelly, S., Olney, A. M., Donnelly, P., Nystrand, M., & D’Mello, S. K. (2018). Automatically measuring question authenticity in real-world classrooms. Educational Researcher, 47(7), 451–464.

Kersting, N. B., Sherin, B. L., & Stigler, J. W. (2014). Automated scoring of teachers’ open-ended responses to video prompts: Bringing the classroom-video-analysis assessment to scale. Educational and Psychological Measurement, 74(6), 950–974.

Kirschner, P. A. (2015). Do we need teachers as designers of technology enhanced learning? Instructional Science, 43(2), 309–322.

Koedinger, K. R., Corbett, A. T., & Perfetti, C. (2012). The Knowledge Learning-Instruction framework: Bridging the science-practice chasm to enhance robust student learning. Cognitive Science, 798-757, (5) 36.

Kucuk, S., Aydemir, M., Yildirim, G., Arpacik, O., & Goktas, Y. (2013). Educational technology research trends in Turkey from 1990 to 2011. Computers & Education, 68, 42–50.

Lamb, R., & Premo, J. (2015). Computational modeling of teaching and learning through application of evolutionary algorithms. Computation, 3(3), 427–443.

Langran, E., Searson, M., Knezek, G., & Christensen, R. (2020). AI in Teacher Education. In Society for Information Technology & Teacher Education International Conference (pp. 735–740). Association for the Advancement of Computing in Education (AACE).

Lu, X. (2019). An empirical study on the artificial intelligence writing evaluation system in China CET. Big Data, 7(2), 121–129.

Luckin, R., & Cukurova, M. (2019). Designing educational technologies in the age of AI: A learning sciences-driven approach. British Journal of Educational Technology, 50(6), 2824–2838.

Luckin, R., Holmes, W., Grifths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education.

Luor, T., Johanson, R. E., Lu, H. P., & Wu, L. L. (2008). Trends and lacunae for future computer assisted learning (CAL) research: An assessment of the literature in SSCI journals from 1998–2006. Journal of the American Society for Information Science and Technology, 59(8), 1313–1320. https://doi.org/10.1002/asi.20836

Ma, Z. H., Hwang, W. Y., & Shih, T. K. (2020). Effects of a peer tutor recommender system (PTRS) with machine learning and automated assessment on vocational high school students’ computer application operating skills. Journal of Computers in Education, 462-435, (3) 7. https://doi.org/10.1007/s40692-020-00162-9

McCarthy, T., Rosenblum, L. P., Johnson, B. G., Dittel, J., & Kearns, D. M. (2016). An artificial intelligence tutor: A supplementary tool for teaching and practicing braille. Journal of Visual Impairment & Blindness, 110(5), 309–322.

Musso, M. F., Kyndt, E., Cascallar, E. C., & Dochy, F. (2013). Predicting general academic performance and ıdentifying the diferential contribution of participating variables using artifcial neural networks. Frontline Learning Research, 1(1), 42–71.

Nikiforos, S., Tzanavaris, S., & Kermanidis, K. L. (2020). Virtual learning communities (VLCs) rethinking: Influence on behavior modifcation—bullying detection through machine learning and natural language processing. Journal of Computers in Education, .551-531, 7. https://doi.org/10.1007/s40692-020-00166-5

Okada, A., Whitelock, D., Holmes, W., & Edwards, C. (2019). e-Authentication for online assessment: A mixed-method study. British Journal of Educational Technology, 50(2), 861–875.

Ozdemir, O., & Tekin, A. (2016). Evaluation of the presentation skills of the pre-service teachers via fuzzy logic. Computers in Human Behavior, 61, 288–299. https://doi.org/10.1016/j.chb.2016.03.013

Pelham, W. E., Petras, H., & Pardini, D. A. (2020). Can machine learning improve screening for targeted delinquency prevention programs? Prevention Science, 21(2), 158–170.

Popenici, S. A., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education.

Research and Practice in Technology Enhanced Learning, 12(1), 13-1.

Prieto, L. P., Sharma, K., Kidzinski, Ł, Rodríguez-Triana, M. J., & Dillenbourg, P. (2018). Multimodal teaching analytics: Automated extraction of orchestration graphs from wearable sensor data. Journal of Computer Assisted Learning, 34(2), 193–203.

Qian, L., Zhao, Y., & Cheng, Y. (2020). Evaluating China’s automated essay scoring system write. Journal of Educational Computing Research, 58(4), 771–790.

Qin, F., Li, K., & Yan, J. (2020). Understanding user trust in artificial intelligence-based educational systems: Evidence from China. British Journal of Educational Technology, 51(5), 1693–1710.

Renz, A., & Hilbig, R. (2020). Prerequisites for artificial intelligence in further education: Identification of drivers, barriers, and business models of educational technology companies. International Journal of Educational Technology in Higher Education, 17, 21-1. https://doi.org/10.1186/s41239-020-00193-3

Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial

Intelligence in Education, 26(2), 582–599.

Ruiz-Palmero, J., Colomo-Magaña, E., Ríos-Ariza, J. M., & GómezGarcía, M. (2020). Big data in education: Perception of training advisors on its use in the educational system. Social Sciences,53, (4) 9. https://doi.org/10.3390/socsci9040053

Russel, S., & Norvig, P. (2010). Artificial intelligence - a modern approach. Pearson Education.

Saa, A. A., Al-Emran, M., & Shaalan, K. (2019). Factors affecting students’ performance in higher education: A systematic

review of predictive data mining techniques. Technology, Knowledge and Learning, 24(4), 567–598. https://doi.org/10./1007/s10758-019-09408-7.

Salomon, G. (1996). Studying novel learning environments as patterns of change. In S. Vosiniadou, E. De Corte, R. Glaser & H. Mandl (Eds.). International Perspectives on the design of Technology Supported Learning. NJ: Lawrence Erlbaum Associates.

Swiecki, Z., Ruis, A. R., Gautam, D., Rus, V., & Williamson Shafer, D. (2019). Understanding when students are active-in-thinking through modeling-in-context. British Journal of Educational Technology, 50(5), 2346–2364.

Sánchez-Prieto, J. C., Cruz-Benito, J., Therón Sánchez, R., & García Peñalvo, F. J. (2020). Assessed by machines: Development of a TAM-based tool to measure ai-based assessment acceptance among students. International Journal of Interactive Multimedia and Artifcial Intelligence, 6(4), 80–86.

Schwarz, B. B., Prusak, N., Swidan, O., Livny, A., Gal, K., & Segal, A. (2018). Orchestrating the emergence of conceptual learning: A case study in a geometry class. International Journal of TechTrends (2022) 66:616–630 629 31Computer-Supported Collaborative Learning, 13(2), 189–211. https://doi.org/10.1007/s11412-018-9276-z

Seufert, S., Guggemos, J., & Sailer, M. (2020). Technology-related knowledge, skills, and attitudes of pre-and in-service teachers: The current situation and emerging trends. Computers in Human Behavior, 115, 106552.

Şimşek, H., & Yıldırım, A. (2011). Qualitative research methods in social sciences. Serkin Publishing.

Su, Y. N., Hsu, C. C., Chen, H. C., Huang, K. K., & Huang, Y. M. (2014). Developing a sensor-based learning concentration detection system.