Agrawal, H., & Mavani, H. (2015). Student performance prediction using machine learning. International Journal of Engineering Research and Technology, 4(03), 111–113. https://doi.org/10.17577/IJERTV4IS030127
Alabdulhadi, A., & Faisal, M. (2021). Systematic literature review of STEM self-study related ITSs. Education and Information Technologies, 26, 1549–1588. https://doi.org/10.1007/s10639-020-10315-z
*Aldabe, I., & Maritxalar, M. (2014). Semantic similarity measures for the generation of science tests in Basque. IEEE Transactions on Learning Technologies, 7(4), 375–387. https://doi.org/10.1109/TLT.2014.2355831
*Alemán, J. L. F. (2011). Automated assessment in a programming tools course. IEEE Transactions on Education, 54(4), 576–581. https://doi.org/10.1109/TE.2010.2098442
Atman Uslu, N., Yavuz, G. Ö., & KoçakUsluel, Y. (2022). A systematic review study on educational robotics and robots. Interactive Learning Environments. https://doi.org/10.1080/10494820.2021.2023890
*Azcona, D., Hsiao, I. H., & Smeaton, A. F. (2019). Detecting students-at-risk in computer programming classes with learning analytics from students’ digital footprints. User Modeling and User-Adapted Interaction, 29(4), 759–788. https://doi.org/10.1007/s11257-019-09234-7
Baker, T., & Smith, L. (2019). Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges. https://media.nesta.org.uk/documents/Future_of_AI_and_education_v5_WEB.Pdf
*Balakrishnan, B. (2018). Motivating engineering students learning via monitoring in personalized learning environment with tagging system. Computer Applications in Engineering Education, 26(3), 700–710. https://doi.org/10.1002/cae.21924
Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., & Tanaka, F. (2018). Social robots for education: A review. Science Robotics, 3(21), eaat5954. https://doi.org/10.1126/scirobotics.aat5954
*Berland, M., Davis, D., & Smith, C. P. (2015). AMOEBA: Designing for collaboration in computer science classrooms through live learning analytics. International Journal of Computer-Supported Collaborative Learning, 10(4), 425–447. https://doi.org/10.1007/s11412-015-9217-z
*Bertolini, R., Finch, S. J., & Nehm, R. H. (2021). Testing the impact of novel assessment sources and machine learning methods on predictive outcome modeling in undergraduate biology. Journal of Science Education and Technology, 30(2), 193–209. https://doi.org/10.1007/s10956-020-09888-8
*Blikstein, P., Worsley, M., Piech, C., Sahami, M., Cooper, S., & Koller, D. (2014). Programming pluralism: Using learning analytics to detect patterns in the learning of computer programming. Journal of the Learning Sciences, 23(4), 561–599. https://doi.org/10.1080/10508406.2014.954750
*Buenaño-Fernández, D., Gil, D., & Luján-Mora, S. (2019). Application of machine learning in predicting performance for computer engineering students: A case study. Sustainability, 11(10), 1–18. https://doi.org/10.3390/su11102833
Bybee, R. W. (2013). The case for STEM education: Challenges and opportunities. NSTA Press.
Byrne, D., & Callaghan, G. (2014). Complexity theory and the social sciences. Routledge.
Cantú-Ortiz, F. J., Galeano Sánchez, N., Garrido, L., Terashima-Marin, H., & Brena, R. F. (2020). An artificial intelligence educational strategy for the digital transformation. International Journal on Interactive Design and Manufacturing, 14(4), 1195–1209. https://doi.org/10.1007/s12008-020-00702-8
*Cao, X., Li, Z., & Zhang, R. (2021). Analysis on academic benchmark design and teaching method improvement under artificial intelligence robot technology. International Journal of Emerging Technologies in Learning, 16(5), 58–72. https://doi.org/10.3991/ijet.v16i05.20295
Castañeda, L., & Selwyn, N. (2018). More than tools? Making sense of the ongoing digitizations of higher education. International Journal of Educational Technology in Higher Education, 15(1), 1–10. https://doi.org/10.1186/s41239-018-0109-y
Chen, D., & Stroup, W. (1993). General system theory: Toward a conceptual framework for science and technology education for all. Journal of Science Education and Technology, 2(3), 447–459. https://doi.org/10.1007/BF00694427
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
*Chrysafiadi, K., & Virvou, M. (2013). PeRSIVA: An empirical evaluation method of a student model of an intelligent e-learning environment for computer programming. Computers & Education, 68, 322–333. https://doi.org/10.1016/j.compedu.2013.05.020
*Çınar, A., Ince, E., Gezer, M., & Yılmaz, Ö. (2020). Machine learning algorithm for grading open-ended physics questions in Turkish. Education and Information Technologies, 25, 3821–3844. https://doi.org/10.1007/s10639-020-10128-0
Cohen, L., Manion, L., & Morrison, K. (2005). Research methods in education. Routledge Falmer.
Collinson, V. (1996). Reaching students: Teachers ways of knowing. Corwin Press.
Crawford, J. L. (1974). A systems approach model for the application of general systems theory principles to education [Doctoral dissertation, University of Houston]. The University of Houston Institutional Repository. https://hdl.handle.net/10657/10661
Cviko, A., McKenney, S., & Voogt, J. (2014). Teacher roles in designing technology-rich learning activities for early literacy: A cross-case analysis. Computers & Education, 72, 68–79. https://doi.org/10.1016/j.compedu.2013.10.014
*De-Marcos, L., Garcia-Cabot, A., Garcia-Lopez, E., & Medina, J. A. (2015). Parliamentary optimization to build personalized learning paths: Case study in web engineering curriculum. International Journal of Engineering Education, 31(4), 1092–1105.
*Deo, R. C., Yaseen, Z. M., Al-Ansari, N., Nguyen-Huy, T., Langlands, T. A. M., & Galligan, L. (2020). Modern artificial intelligence model development for undergraduate student performance prediction: An investigation on engineering mathematics courses. IEEE Access, 8, 136697–136724. https://doi.org/10.1109/ACCESS.2020.3010938
Douce, C., Livingstone, D., & Orwell, J. (2005). Automatic test-based assessment of programming: A review. Journal on Educational Resources in Computing, 5(3), 4-es. https://doi.org/10.1145/1163405.1163409
Drack, M., & Pouvreau, D. (2015). On the history of Ludwig von Bertalanffy’s “general systemology”, and on its relationship to cybernetics—Part III: Convergences and divergences. International Journal of General Systems, 44(5), 523–571. https://doi.org/10.1080/03081079.2014.1000642
Drigas, A. S., & Ioannidou, R. E. (2012). Artificial intelligence in special education: A decade review. International Journal of Engineering Education, 28(6), 1366. http://imm.demokritos.gr/publications/AI_IJEE.pdf
*Ferrarelli, P., & Iocchi, L. (2021). Learning Newtonian physics through programming robot experiments. Technology, Knowledge and Learning, 26, 789–824. https://doi.org/10.1007/s10758-021-09508-3
*Figueiredo, M., Esteves, L., Neves, J., & Vicente, H. (2016). A data mining approach to study the impact of the methodology followed in chemistry lab classes on the weight attributed by the students to the lab work on learning and motivation. Chemistry Education Research and Practice, 17(1), 156–171. https://doi.org/10.1039/c5rp00144g
*García-Gorrostieta, J. M., López-López, A., & González-López, S. (2018). Automatic argument assessment of final project reports of computer engineering students. Computer Applications in Engineering Education, 26(5), 1217–1226. https://doi.org/10.1002/cae.21996
Gary, K. (2019). Pragmatic standards versus saturated phenomenon: Cultivating a love of learning. Journal of Philosophy of Education, 53(3), 477–490. https://doi.org/10.1111/1467-9752.12377
*Gavrilović, N., Arsić, A., Domazet, D., & Mishra, A. (2018). Algorithm for adaptive learning process and improving learners’ skills in Java programming language. Computer Applications in Engineering Education, 26(5), 1362–1382. https://doi.org/10.1002/cae.22043
Graneheim, U. H., & Lundman, B. (2004). Qualitative content analysis in nursing research: Concepts, procedures and measures to achieve trustworthiness. Nurse Education Today, 24(2), 105–112. https://doi.org/10.1016/j.nedt.2003.10.001
Guan, C., Mou, J., & Jiang, Z. (2020). Artificial intelligence innovation in education: A twenty-year data-driven historical analysis. International Journal of Innovation Studies, 4(4), 134–147. https://doi.org/10.1016/j.ijis.2020.09.001
*Hellings, J., & Haelermans, C. (2020). The effect of providing learning analytics on student behaviour and performance in programming: A randomised controlled experiment. Higher Education, 83(1), 1–18. https://doi.org/10.1007/s10734-020-00560-z
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial 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. https://doi.org/10.18608/jla.2019.62.3
*Hooshyar, D., Ahmad, R. B., Yousefi, M., Yusop, F. D., & Horng, S. (2015). A flowchart-based intelligent tutoring system for improving problem-solving skills of novice programmers. Journal of Computer Assisted Learning, 31, 345–361. https://doi.org/10.1111/jcal.12099
*Hooshyar, D., Binti Ahmad, R., Wang, M., Yousefi, M., Fathi, M., & Lim, H. (2018). Development and evaluation of a game-based Bayesian intelligent tutoring system for teaching programming. Journal of Educational Computing Research, 56(6), 775–801. https://doi.org/10.1177/0735633117731872
*Hsiao, I. H., Huang, P. K., & Murphy, H. (2020). Integrating programming learning analytics across physical and digital space. IEEE Transactions on Emerging Topics in Computing, 8(1), 206–217. https://doi.org/10.1109/TETC.2017.2701201
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
Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, 100001. https://doi.org/10.1016/j.caeai.2020.100001
*Ji, Y., & Han, Y. (2019). Monitoring indicators of the flipped classroom learning process based on data mining—Taking the course of “virtual reality technology” as an example. International Journal of Emerging Technologies in Learning, 14(3), 166–176. https://doi.org/10.3991/ijet.v14i03.10105
Jiao, P., Ouyang, F., Zhang, Q., & Alavi, A. H. (2022). Artificial intelligence-enabled prediction model of student academic performance in online engineering education. Artificial Intelligence Review. https://doi.org/10.1007/s10462-022-10155-y
*Jones, A., Bull, S., & Castellano, G. (2018). “I know that now, I’ m going to learn this next” Promoting self-regulated learning with a robotic tutor. International Journal of Social Robotics, 10(4), 439–454. https://doi.org/10.1007/s12369-017-0430-y
*Jones, A., & Castellano, G. (2018). Adaptive robotic tutors that support self-regulated learning: A longer-term investigation with primary school children. International Journal of Social Robotics, 10(3), 357–370. https://doi.org/10.1007/s12369-017-0458-z
*Khan, I., Ahmad, A. R., Jabeur, N., & Mahdi, M. N. (2021). Machine learning prediction and recommendation framework to support introductory programming course. International Journal of Emerging Technologies in Learning, 16(17), 42–59. https://doi.org/10.3991/ijet.v16i17.18995
Khandelwal, P., Srinivasan, K., & Roy, S. S. (2019). Surgical education using artificial intelligence, augmented reality and machine learning: A review. In A. Sengupta & P. Eng (Eds.), 2019 IEEE international conference on consumer electronics—Taiwan (pp. 1–2). IEEE.
*Kinnebrew, J. S., Killingsworth, S. S., Clark, D. B., Biswas, G., Sengupta, P., Minstrell, J., Martinez-garza, M., & Krinks, K. (2017). Contextual markup and mining in digital games for science learning: Connecting player behaviors to learning goals. IEEE Transactions on Learning Technologies, 10(1), 93–103. https://doi.org/10.1109/TLT.2016.2521372
Kitto, K. (2014). A contextualised general systems theory. Systems, 2(4), 541–565. https://doi.org/10.3390/systems2040541
*Koć-Januchta, M. M., Schönborn, K. J., Tibell, L. A. E., Chaudhri, V. K., & Heller, H. C. (2020). Engaging with biology by asking questions: Investigating students’ interaction and learning with an artificial intelligence-enriched textbook. Journal of Educational Computing Research, 58(6), 1190–1224. https://doi.org/10.1177/0735633120921581
*Kose, U., & Arslan, A. (2017). Optimization of self-learning in computer engineering courses: An intelligent software system supported by artificial neural network and vortex optimization algorithm. Computer Applications in Engineering Education, 25(1), 142–156. https://doi.org/10.1002/cae.21787
*Krämer, N. C., Karacora, B., Lucas, G., Dehghani, M., Rüther, G., & Gratch, J. (2016). Closing the gender gap in STEM with friendly male instructors? On the effects of rapport behavior and gender of a virtual agent in an instructional interaction. Computers & Education, 99, 1–13. https://doi.org/10.1016/j.compedu.2016.04.002
Krasovskiy, D. (2020). The challenges and benefits of adopting AI in STEM education. https://upjourney.com/the-challenges-and-benefits-of-adopting-ai-in-stem-education
Krippendorff, K. (2004). Reliability in content analysis: Some common misconceptions and recommendations. Human Communication Research, 30(3), 411–433. https://doi.org/10.1093/hcr/30.3.411
*Lacave, C., Molina, A. I., & Cruz-Lemus, J. A. (2018). Learning analytics to identify dropout factors of computer science studies through Bayesian networks. Behaviour and Information Technology, 37(10–11), 993–1007. https://doi.org/10.1080/0144929X.2018.1485053
*Lamb, R., Hand, B., & Kavner, A. (2021). Computational modeling of the effects of the science writing heuristic on student critical thinking in science using machine learning. Journal of Science Education and Technology, 30(2), 283–297. https://doi.org/10.1007/s10956-020-09871-3
Law, N. W. Y. (2019). Human development and augmented intelligence. In The 20th international conference on artificial intelligence in education (AIED 2019), Chicago, IL, USA.
Le, N. T., Strickroth, S., Gross, S., & Pinkwart, N. (2013). A review of AI-supported tutoring approaches for learning programming. In N. Nguyen, T. Van Do, & H. Le Thi (Eds.), Advanced computational methods for knowledge engineering (pp. 267–279). Springer. https://doi.org/10.1007/978-3-319-00293-4_20
*Ledesma, E. F. R., & García, J. J. G. (2017). Selection of mathematical problems in accordance with student’s learning style. International Journal of Advanced Computer Science and Applications, 8(3), 101–105. https://doi.org/10.14569/IJACSA.2017.080316
Lee, J., Jang, D., & Park, S. (2017). Deep learning-based corporate performance prediction model considering technical capability. Sustainability, 9(6), 899. https://doi.org/10.3390/su9060899
Lee, J., Wu, A. S., Li, D., & Kulasegaram, K. M. (2021). Artificial intelligence in undergraduate medical education: A scoping review. Academic Medicine, 96(11S), S62–S70. https://doi.org/10.1097/ACM.0000000000004291
Liang, J. C., Hwang, G. J., Chen, M. R. A., & Darmawansah, D. (2021). Roles and research foci of artificial intelligence in language education: An integrated bibliographic analysis and systematic review approach. Interactive Learning Environments. https://doi.org/10.1080/10494820.2021.1958348
*Lin, P. H., & Chen, S. Y. (2020). Design and evaluation of a deep learning recommendation based augmented reality system for teaching programming and computational thinking. IEEE Access, 8, 45689–45699. https://doi.org/10.1109/ACCESS.2020.2977679
Liu, M., Li, Y., Xu, W., & Liu, L. (2017). Automated essay feedback generation and its impact on revision. IEEE Transactions on Learning Technologies, 10(4), 502–513. https://doi.org/10.1109/TLT.2016.2612659
Loveless, A. (2011). Technology, pedagogy and education: Reflections on the accomplishment of what teachers know, do and believe in a digital age. Technology, Pedagogy and Education, 20(3), 301–316. https://doi.org/10.1080/1475939X.2011.610931
*Maestrales, S., Zhai, X., Touitou, I., Baker, Q., Schneider, B., & Krajcik, J. (2021). Using machine learning to score multi-dimensional assessments of chemistry and physics. Journal of Science Education and Technology, 30(2), 239–254. https://doi.org/10.1007/s10956-020-09895-9
*Mahboob, K., Ali, S. A., & Laila, U. E. (2020). Investigating learning outcomes in engineering education with data mining. Computer Applications in Engineering Education, 28(6), 1652–1670. https://doi.org/10.1002/cae.22345
*Matthew, F. T., Adepoju, A. I., Ayodele, O., Olumide, O., Olatayo, O., Adebimpe, E., Bolaji, O., & Funmilola, E. (2018). Development of mobile-interfaced machine learning-based predictive models for improving students’ performance in programming courses. International Journal of Advanced Computer Science and Applications, 9(5), 105–115. https://doi.org/10.14569/IJACSA.2018.090514
McLaren, B. M., Scheuer, O., & Mikšátko, J. (2010). Supporting collaborative learning and e-discussions using artificial intelligence techniques. International Journal of Artificial Intelligence in Education, 20(1), 1–46. https://doi.org/10.3233/JAI-2010-0001
McLurkin, J., Rykowski, J., John, M., Kaseman, Q., & Lynch, A. J. (2013). Using multi-robot systems for engineering education: Teaching and outreach with large numbers of an advanced, low-cost robot. IEEE Transactions on Education, 56(1), 24-33. https://doi.org/10.1109/TE.2012.2222646
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., Prisma Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097.t001
Muhisn, Z. A. A., Ahmad, M., Omar, M., & Muhisn, S. A. (2019). The Impact of socialization on collaborative learning method in e-learning management system (eLMS). International Journal of Emerging Technologies in Learning, 14(20), 137–148.
Murray, T. (2003). An overview of intelligent tutoring system authoring tools: Updated analysis of the state of the art. In T. Murray, S. B. Blessing, & S. Ainsworth (Eds.), Authoring tools for advanced technology learning environments (pp. 491–544). Springer. https://doi.org/10.1007/978-94-017-0819-7_17
*Myneni, L. S., Narayanan, N. H., & Rebello, S. (2013). An interactive and intelligent learning system for physics education. IEEE Transactions on Learning Technologies, 6(3), 228–239. https://doi.org/10.1109/TLT.2013.26
Mystakidis, S., Christopoulos, A., & Pellas, N. (2021). A systematic mapping review of augmented reality applications to support STEM learning in higher education. Education and Information Technologies, 27, 1883–1927. https://doi.org/10.1007/S10639-021-10682-1/FIGURES/10
*Nehm, R. H., Ha, M., & Mayfield, E. (2012). Transforming biology assessment with machine learning: Automated scoring of written evolutionary explanations. Journal of Science Education and Technology, 21(1), 183–196. https://doi.org/10.1007/s10956-011-9300-9
Ouyang, F., & Jiao, P. (2021). Artificial intelligence in education: The three paradigms. Computers and Education: Artificial Intelligence, 2, 100020. https://doi.org/10.1016/j.caeai.2021.100020
Ouyang, F., Zheng, L., & Jiao, P. (2022). Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020. Education and Information Technologies. https://doi.org/10.1007/s10639-022-10925-9
*Pereira, F. D., Oliveira, E. H. T., Oliveira, D. B. F., Cristea, A. I., Carvalho, L. S. G., Fonseca, S. C., Toda, A., & Isotani, S. (2020). Using learning analytics in the Amazonas: Understanding students’ behaviour in introductory programming. British Journal of Educational Technology, 51(4), 955–972. https://doi.org/10.1111/bjet.12953
Pimthong, P., & Williams, J. (2018). Preservice teachers’ understanding of STEM education. Kasetsart Journal of Social Sciences, 41(2), 1–7. https://doi.org/10.1016/j.kjss.2018.07.017
Rapoport, A. (1986). General system theory: Essential concepts & applications. CRC Press.
*Rodríguez Corral, J. M., Morgado-Estévez, A., Cabrera Molina, D., Pérez-Peña, F., Amaya Rodríguez, C. A., & CivitBalcells, A. (2016). Application of robot programming to the teaching of object-oriented computer languages. International Journal of Engineering Education, 32(4), 1823–1832.
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
*Saito, T., & Watanobe, Y. (2020). Learning path recommendation system for programming education based on neural networks. International Journal of Distance Education Technologies, 18(1), 36–64. https://doi.org/10.4018/IJDET.2020010103
*Sapounidis, T., Stamovlasis, D., & Demetriadis, S. (2019). Latent class modeling of children’s preference profiles on tangible and graphical robot programming. IEEE Transactions on Education, 62(2), 127–133. https://doi.org/10.1109/TE.2018.2876363
Segal, M. (2019). A more human approach to artificial intelligence. Nature, 571(7766), S18–S18. https://doi.org/10.1038/d41586-019-02213-3
Selwyn, N. (2016). Is technology good for education? Polity Press.
Skinner, B. F. (1953). Science and human behavior. Macmillan.
*Spikol, D., Ruffaldi, E., Dabisias, G., & Cukurova, M. (2018). Supervised machine learning in multimodal learning analytics for estimating success in project-based learning. Journal of Computer Assisted Learning, 34(4), 366–377. https://doi.org/10.1111/jcal.12263
*Suh, S. C., Anusha Upadhyaya, B. N., & Ashwin Nadig, N. V. (2019). Analyzing personality traits and external factors for stem education awareness using machine learning. International Journal of Advanced Computer Science and Applications, 10(5), 1–4. https://doi.org/10.14569/ijacsa.2019.0100501
Tang, K. Y., Chang, C. Y., & Hwang, G. J. (2021). Trends in artificial intelligence supported e-learning: A systematic review and co-citation network analysis (1998–2019). Interactive Learning Environments. https://doi.org/10.1080/10494820.2021.1875001
*Tehlan, K., Chakraverty, S., Chakraborty, P., & Khapra, S. (2020). A genetic algorithm-based approach for making pairs and assigning exercises in a programming course. Computer Applications in Engineering Education, 28(6), 1708–1721. https://doi.org/10.1002/cae.22349
*Thai, K. P., Bang, H. J., & Li, L. (2021). Accelerating early math learning with research-based personalized learning games: A cluster randomized controlled trial. Journal of Research on Educational Effectiveness, 15(1), 28–51. https://doi.org/10.1080/19345747.2021.1969710
*Troussas, C., Krouska, A., & Sgouropoulou, C. (2021). A novel teaching strategy through adaptive learning activities for computer programming. IEEE Transactions on Education, 64(2), 103–109. https://doi.org/10.1109/TE.2020.3012744
*Tüfekçi, A., & Köse, U. (2013). Development of an artificial intelligence based software system on teaching computer programming and evaluation of the system. Hacettepe Üniversitesi Eğitim Fakültesi Dergisi, 28(2), 469–481.
*Verner, I. M., Cuperman, D., Gamer, S., & Polishuk, A. (2020). Exploring affordances of robot manipulators in an introductory engineering course. International Journal of Engineering Education, 36(5), 1691–1707.
Von Bertalanffy, L. (1950). An outline of general system theory. British Journal for the Philosophy of Science, 1, 134–165. https://doi.org/10.1093/bjps/I.2.134
Von Bertalanffy, L. (1968). General system theory: Foundations, development, applications. George Braziller.
*Vyas, V. S., Kemp, B., & Reid, S. A. (2021). Zeroing in on the best early-course metrics to identify at-risk students in general chemistry: An adaptive learning pre-assessment vs. traditional diagnostic exam. International Journal of Science Education, 43(4), 552–569. https://doi.org/10.1080/09500693.2021.1874071
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
*Wang, T., Su, X., Ma, P., Wang, Y., & Wang, K. (2011). Ability-training-oriented automated assessment in introductory programming course. Computers & Education, 56(1), 220–226. https://doi.org/10.1016/j.compedu.2010.08.003
*Wang, X. (2016). Course-taking patterns of community college students beginning in STEM: Using data mining techniques to reveal viable STEM transfer pathways. Research in Higher Education, 57(5), 544–569. https://doi.org/10.1007/s11162-015-9397-4
Wohlin, C. (2014). Guidelines for snowballing in systematic literature studies and a replication in software engineering. In C. Wohlin (Ed.), Proceedings of the 18th international conference on evaluation and assessment in software engineering (pp. 1–10). ACM Press. https://doi.org/10.1145/2601248.2601268
*Wu, P., Hwang, G., & Tsai, W. (2013). An expert system-based context-aware ubiquitous learning approach for conducting science learning activities. Journal of Educational Technology & Society, 16(4), 217–230.
*Xing, W., Pei, B., Li, S., Chen, G., & Xie, C. (2019). Using learning analytics to support students’ engineering design: The angle of prediction. Interactive Learning Environments. https://doi.org/10.1080/10494820.2019.1680391
Xu, W., & Ouyang, F. (2022). A systematic review of AI role in the educational system based on a proposed conceptual framework. Education and Information Technologies, 27, 4195–4223. https://doi.org/10.1007/s10639-021-10774-y
*Yahya, A. A., & Osman, A. (2019). A data-mining-based approach to informed decision-making in engineering education. Computer Applications in Engineering Education, 27(6), 1402–1418. https://doi.org/10.1002/cae.22158
*Yang, J., Devore, S., Hewagallage, D., Miller, P., Ryan, Q. X., & Stewart, J. (2020). Using machine learning to identify the most at-risk students in physics classes. Physical Review Physics Education Research, 16(2), 20130. https://doi.org/10.1103/PhysRevPhysEducRes.16.020130
Yang, J., & Zhang, B. (2019). Artificial intelligence in intelligent tutoring robots: A systematic review and design guidelines. Applied Sciences, 9(10), 2078. https://doi.org/10.3390/app9102078
*Yannier, N., Hudson, S. E., & Koedinger, K. R. (2020). Active learning is about more than hands-on: A mixed-reality AI system to support STEM education. International Journal of Artificial Intelligence in Education, 30(1), 74–96. https://doi.org/10.1007/s40593-020-00194-3
*Yu, Y. (2017). Teaching with a dual-channel classroom feedback system in the digital classroom environment. IEEE Transactions on Learning Technologies, 10(3), 391–402. https://doi.org/10.1109/TLT.2016.2598167
*Zabriskie, C., Yang, J., Devore, S., & Stewart, J. (2019). Using machine learning to predict physics course outcomes. Physical Review Physics Education Research, 15(2), 20120. https://doi.org/10.1103/PhysRevPhysEducRes.15.020120
*Zampirolli, F. A., BorovinaJosko, J. M., Venero, M. L. F., Kobayashi, G., Fraga, F. J., Goya, D., & Savegnago, H. R. (2021). An experience of automated assessment in a large-scale introduction programming course. Computer Applications in Engineering Education, 29(5), 1284–1299. https://doi.org/10.1002/cae.22385
*Zapata-Cáceres, M., & Martin-Barroso, E. (2021). Applying game learning analytics to a voluntary video game: Intrinsic motivation, persistence, and rewards in learning to program at an early age. IEEE Access, 9, 123588–123602. https://doi.org/10.1109/ACCESS.2021.3110475
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(39), 1–27. https://doi.org/10.1186/s41239-019-0171-0
*Zhang, Z., Liu, H., Shu, J., Nie, H., & Xiong, N. (2020). On automatic recommender algorithm with regularized convolutional neural network and IR technology in the self-regulated learning process. Infrared Physics and Technology, 105, 103211. https://doi.org/10.1016/j.infrared.2020.103211
Zheng, R., Jiang, F., & Shen, R. (2020). Intelligent student behavior analysis system for real classrooms. In P. C. Center (Ed.), 2020 IEEE international conference on acoustics, speech and signal processing (pp. 9244–9248). IEEE.
*Zulfiani, Z., Suwarna, I. P., & Miranto, S. (2018). Science education adaptive learning system as a computer-based science learning with learning style variations. Journal of Baltic Science Education, 17(4), 711–727. https://doi.org/10.33225/jbse/18.17.711
Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629