Abstract
Robotics education is valuable for developing students’ 21st century competencies. It is of significant importance for teachers and researchers to explore its effective pedagogies. Robotics learning is interdisciplinary, and the most effective pedagogy is problem-oriented instruction. This type of instruction requires students to independently identify heuristic problems which can be categorized into four types: paradox, critical reflection, anomaly, and practice problems. School students are too young to develop sophisticated self-regulated learning, thus requiring guidance from teachers. However, how teachers can best guide students to solve these four types of problems remains unclear. Accordingly, this study aimed to investigate teaching behavioral patterns in the instruction of a robotics course. The participants were 30 seventh-grade students and a teacher. We proposed a Teaching Behavior Coding Scheme and used Lag Sequential Analysis to analyze the course videos to identify various major teaching behaviors and their sequences in the heuristic problems. The results showed that the behaviors included indirect influence, direct influence and wait time. Indirect influence occurred more often than direct influence, and it is argued that enough wait time should be provided in the instruction. Moreover, the results further suggest that paradox is missing from this instruction. The teaching behavior patterns for the three types of problems are discussed. The findings propose a new method to analyze teaching behavior patterns, and also recommend how teachers can support students learning in interdisciplinary robotics courses. The results contribute to the literature by designing the Teaching Behavior Coding Scheme and confirming its use in a robotics course.






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References
Abell, S. K., & Pizzini, E. L. (1992). The effect of a problem solving in-service program on the classroom behaviors and attitudes of middle school science teachers. Journal of Research in Science Teaching, 29(7), 649–667. https://doi.org/10.1002/tea.3660290704.
Alimoglu, M. K., Sarac, D. B., Alparslan, D., Karakas, A. A., & Altintas, L. (2014). An observation tool for instructor and student behaviors to measure in-class learner engagement: A validation study. Medical Education Online, 19(1), 24037. https://doi.org/10.3402/meo.v19.24037.
Applebee, A. N., Adler, M., & Flihan, S. (2007). Interdisciplinary curricula in middle and high school classrooms: Case studies of approaches to curriculum and instruction. American Educational Research Journal, 44(4), 1002–1039. https://doi.org/10.3102/0002831207308219.
Bakeman, R., & Gottman, J. M. (1997). Observing interaction: An introduction to lag sequential analysis (2nd ed.). Cambridge University Press.
Cai, S., Niu, X., Wen, Y., & Li, J. (2021). Interaction analysis of teachers and students in inquiry class learning based on augmented reality by iFIAS and LSA. Interactive Learning Environments, 1–17. https://doi.org/10.1080/10494820.2021.2012808.
Çakır, R., Korkmaz, Ö., İdil, Ö., & Erdoğmuş, F. U. (2021). The effect of robotic coding education on preschoolers’ problem solving and creative thinking skills. Thinking Skills and Creativity, 40, 100812. https://doi.org/10.1016/j.tsc.2021.100812.
Casarrubea, M., Magnusson, M. S., Anguera, M. T., Jonsson, G. K., Castañer, M., Santangelo, A., Palacino, M., Aiello, S., Faulisi, F., Raso, G., Puigarnau, S., Camerino, O., Di Giovanni, G., & Crescimanno, G. (2018). T-pattern detection and analysis for the discovery of hidden features of behaviour. Journal of Neuroscience Methods, 310, 24–32. https://doi.org/10.1016/j.jneumeth.2018.06.013.
Cassidy, M., & Puttick, G. (2022). Because subjects don’t exist in a bubble: Middle school teachers enacting an interdisciplinary curriculum. Journal of Science Education and Technology, 31(2), 233–245. https://doi.org/10.1007/s10956-021-09951-y.
Cents-Boonstra, M., Lichtwarck-Aschoff, A., Lara, M. M., & Denessen, E. (2021). Patterns of motivating teaching behaviour and student engagement: A microanalytic approach. European Journal of Psychology of Education, 37(1), 227–255. https://doi.org/10.1007/s10212-021-00543-3.
Chatzichristofis, S. A. (2023). Recent advances in educational robotics. Electronics, 12(4), 925. https://doi.org/10.3390/electronics12040925.
Chiu T. K. F. (2022). Applying the self-determination theory (SDT) to explain student engagement in online learning during the COVID-19 pandemic. Journal of Research on Technology in Education, 54(Suppl 1), S14–S30. https://doi.org/10.1080/15391523.2021.1891998.
Fakaruddin, F. J., Shahali, E. H. M., & Saat, R. M. (2023). Creative thinking patterns in primary school students’ hands-on science activities involving robotic as learning tools. Asia Pacific Education Review. https://doi.org/10.1007/s12564-023-09825-5.
Fischer, F., Troendle, P., & Mandl, H. (2003). Using the internet to improve university education: Problem-oriented web-based learning with MUNICS. Interactive Learning Environments, 11(3), 193–214. https://doi.org/10.1076/ilee.11.3.193.16546.
Flanders, N. A. (1963). Intent, action and feedback: A preparation for teaching. Journal of Teacher Education, 14(3), 251–260. https://doi.org/10.1177/002248716301400305.
Gomoll, A. S., Hmelo-Silver, C. E., Tolar, E., Šabanović, S., & Francisco, M. (2017). Moving apart and coming together: Discourse, engagement, and deep learning. Educational Technology & Society, 20(4), 219–232.
Gray, S., Wheat, M., Christensen, M., & Craft, J. (2019). Snaps+: Peer-to-peer and academic support in developing clinical skills excellence in under-graduate nursing students: An exploratory study. Nurse Education Today, 73, 7–12. https://doi.org/10.1016/j.nedt.2018.10.006.
Hertling, S. F., Back, D. A., Eckhart, N., Kaiser, M., & Graul, I. (2022). How far has the digitization of medical teaching progressed in times of COVID-19? A multinational survey among medical students and lecturers in german-speaking central Europe. BMC Medical Education, 22(1), 1–10. https://doi.org/10.1186/s12909-022-03470-z.
Hindman, A. H., Wasik, B. A., & Bradley, D. E. (2019). How classroom conversations unfold: Exploring teacher-child exchanges during shared book reading. Early Education and Development, 30(4), 478–495. https://doi.org/10.1080/10409289.2018.1556009.
Hou, H. T., Sung, Y. T., & Chang, K. E. (2009). Exploring the behavioral patterns of an online knowledge-sharing discussion activity among teachers with problem-solving strategy. Teaching and Teacher Education, 25(1), 101–108. https://doi.org/10.1016/j.tate.2008.07.006.
Hu, X., He, W., Chiu, T. K., & Zhao, L. (2022). Using a teacher scheme for educational dialogue analysis to investigate student-student interaction patterns for optimal group activities in an artificial intelligence course. Education and Information Technologies, 1–25. https://doi.org/10.1007/s10639-022-11556-w.
Hu, Y. H., Xing, J., & Tu, L. P. (2018). The effect of a problem-oriented teaching method on university mathematics learning. EURASIA Journal of Mathematics Science and Technology Education, 14(5), 1695–1703. https://doi.org/10.29333/ejmste/85108.
Inagaki, K., Hatano, G., & Morita, E. (1998). Construction of mathematical knowledge through whole-class discussion. Learning and Instruction, 8(6), 503–526. https://doi.org/10.1016/S0959-4752(98)00032-2.
Islam, S. O. B., & Lughmani, W. A. (2022). A connective framework for social collaborative robotic system. Machines, 10(11), 1086. https://doi.org/10.3390/machines10111086.
Jung, S. (2013). Experiences in developing an experimental robotics course program for undergraduate education. IEEE Transactions on Education, 56(1), 129–136. https://doi.org/10.1109/TE.2012.2213601.
Kerimbayev, N., Nurym, N., Akramova, A., & Abdykarimova, S. (2023). Educational Robotics: Development of computational thinking in collaborative online learning. Education and Information Technologies, 28, https://doi.org/10.1007/s10639-023-11806-5.
Knudsen, S. (2014). Students are doing it for themselves – ‘the problem-oriented problem’ in academic writing in the humanities. Studies in Higher Education, 39(10), 1838–1859. https://doi.org/10.1080/03075079.2013.806455.
Kovacic, Z., & Marcos-Valls, A. (2023). Institutionalising interdisciplinarity in PhD training: Challenging and redefining expertise in problem-oriented research. Environmental Education Research, 29(3), 473–488. https://doi.org/10.1080/13504622.2023.2174252.
Kozar, O. (2016). Teachers’ reaction to silence and teachers’ wait time in video and audioconferencing English lessons: Do webcams make a difference? System, 62, 53–62. https://doi.org/10.1016/j.system.2016.07.002.
Kucuk, S., & Sisman, B. (2017). Behavioral patterns of elementary students and teachers in one-to-one robotics instruction. Computers & Education, 111, 31–43. https://doi.org/10.1016/j.compedu.2017.04.002.
Larkin, K., & Lowrie, T. (2023). Teaching approaches for stem integration in pre- and primary school: A systematic qualitative literature review. International Journal of Science and Mathematics Education, 21(Suppl 1), 11–39. https://doi.org/10.1007/s10763-023-10362-1.
Li, M., & Faghri, A. (2016). Applying problem-oriented and project-based learning in a transportation engineering course. Journal of Professional Issues in Engineering Education and Practice, 142(3), 1–11. https://doi.org/10.1061/(ASCE)EI.1943-5541.0000274.
Liu, T. (2021). Convolutional neural network-assisted strategies for improving teaching quality of college English flipped class. Wireless Communications and Mobile Computing, 2021, https://doi.org/10.1155/2021/1929077.
Liu, E. Z. F., Lin, C. H., Liou, P. Y., Feng, H. C., & Hou, H. T. (2013). An analysis of teacher-student interaction patterns in a robotics course for kindergarten children: A pilot study. The Turkish Online Journal of Educational Technology, 12(1), 9–18.
López-Belmonte, J., Segura-Robles, A., Moreno-Guerrero, A. J., & Parra-González, M. E. (2021). Robotics in education: A scientific mapping of the literature in web of Science. Electronics, 10(3), 291. https://doi.org/10.3390/electronics10030291.
Lyon, H. C., Holzer, M., Reincke, M., Brendel, T., Ring, J., Weindl, A., Zottmann, J. M., & Fischer, M. R. (2014). Improvements in teaching behavior at two German medical schools resulting from a modified Flanders interaction analysis feedback intervention process. Medical Teacher, 36(10), 903–911. https://doi.org/10.3109/0142159x.2014.917157.
Mak, B. (2011). An exploration of speaking-in-class anxiety with Chinese ESL learners. System, 39(2), 202–214. https://doi.org/10.1016/j.system.2011.04.002.
McGarr, O., McCormack, O., & Comerford, J. (2019). Peer-supported collaborative inquiry in teacher education: Exploring the influence of peer discussions on pre-service teachers’ levels of critical reflection. Irish Educational Studies, 38(2), 245–261. https://doi.org/10.1080/03323315.2019.1576536.
Nikitina, S. (2006). Three strategies for interdisciplinary teaching: Contextualizing, conceptualizing, and problem-centring. Journal of Curriculum Studies, 38(3), 251–271. https://doi.org/10.1080/00220270500422632.
Numrich, C. (1996). On becoming a language teacher: Insights from diary studies. TESOL Quarterly, 30(1), 131–153. https://doi.org/10.2307/3587610.
Pellegrino, J. W., & Hilton, M. L. (2012). Education for life and work: Developing transferable knowledge and skills in the 21st century. Retrieved from http://www.nap.edu/catalog.php?record_id=13398.
Puntambekar, S. (2015). Distributing scaffolding across multiple levels: Individuals, small groups, and a class of students. In A. Walker, H. Leary, C. E. Hmelo-Silver, & P. A. Ertmer (Eds.), Essential readings in problem-based learning. Exploring and extending the legacy of Howard S. Barrows (pp. 207–221). Purdue University.
Reimann, P. (2009). Time is precious: Variable-and event-centred approaches to process analysis in CSCL research. International Journal of Computer-Supported Collaborative Learning, 4(3), 239–257. https://doi.org/10.1007/s11412-009-9070-z.
Reiser, B. J., & Tabak, I. (2014). Scaffolding. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 44–62). Cambridge.
Richards, J. C. (1987). The dilemma of teacher education in TESOL. TESOL Quarterly, 21(2), 209. https://doi.org/10.2307/3586732.
Rickard, A. (1995). Teaching with problem-oriented curricula: A case study of middle-school mathematics instruction. The Journal of Experimental Education, 64(1), 5–26. https://doi.org/10.1080/00220973.1995.9943792.
Riek, L. D. (2013). Embodied computation: An active-learning approach to mobile robotics education. IEEE Transactions on Education, 56(1), 67–72. https://doi.org/10.1109/te.2012.2221716.
Rochester, S. R. (1973). The significance of pauses in spontaneous speech. Journal of Psycholinguistic Research, 2(1), 51–81. https://doi.org/10.1007/BF01067111.
Rodríguez-Medina, J., Rodríguez-Navarro, H., Arias, V., Arias, B., & Anguera, M. T. (2018). Non-reciprocal friendships in a school-age boy with autism: The ties that build? Journal of Autism and Developmental Disorders, 48(9), 2980–2994. https://doi.org/10.1007/s10803-018-3575-0.
Roesch, F., Nerb, J., & Riess, W. (2015). Promoting experimental problem-solving ability in sixth-grade students through problem-oriented teaching of ecology: Findings of an intervention study in a complex domain. International Journal of Science Education, 37(4), 577–598. https://doi.org/10.1080/09500693.2014.1000427.
Sackett, G. P. (1978). Observing behavior. Theory and applications in mental retardation (Vol. 1). University Park.
Sisman, B., Kucuk, S., & Ozcan, N. (2022). Collaborative behavioural patterns of elementary school students working on a robotics project. Journal of Computer Assisted Learning, 38(4), 1018–1032. https://doi.org/10.1111/jcal.12659.
Su, K. (2022). Implementation of innovative artificial intelligence cognitions with problem-based learning guided tasks to enhance students’ performance in science. Journal of Baltic Science Education, 21(2), 245–257. https://doi.org/10.33225/jbse/22.21.245.
Tan, J. S. H., & Chen, W. (2022). Peer feedback to support collaborative knowledge improvement: What kind of feedback feed-forward? Computers & Education, 187, 104467. https://doi.org/10.1016/j.compedu.2022.104467.
Tlili, A., Wang, H., Gao, B., Shi, Y., Zhiying, N., Looi, C. K., & Huang, R. (2021). Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: A lag sequential analysis approach. Interactive Learning Environments, 1–20. https://doi.org/10.1080/10494820.2021.1946565.
Tramonti, M., Dochshanov, A. M., & Zhumabayeva, A. S. (2023). Design thinking as an auxiliary tool for educational robotics classes. Applied Sciences, 13(2), 858. https://doi.org/10.3390/app13020858.
Tricio, J. A., Woolford, M. J., & Escudier, M. P. (2016). Fostering dental students’ academic achievements and reflection skills through clinical peer assessment and feedback. Journal of Dental Education, 80(8), 914–923.
Vezzosi, T., Lubas, G., & Caldin, M. (2012). The fundamental basis for developing a correct problem-oriented approach to the management of clinical cases. Veterinaria, 26(4), 9–15.
Wang, Y. (2023). The role of computer supported project-based learning in students’ computational thinking and engagement in robotics courses. Thinking Skills and Creativity, 48, 101269. https://doi.org/10.1016/j.tsc.2023.101269.
Warrens, M. J. (2013). Conditional inequalities between Cohen’s kappa and weighted kappas. Statistical Methodology, 10(1), 14–22. https://doi.org/10.1016/j.stamet.2012.05.004.
Webb, N. M. (1991). Task-related verbal interaction and mathematical learning in small groups. Journal for Research in Mathematics Education, 22, 366–389.
Yang, F. Y., & Wang, H. Y. (2023). Tracking visual attention during learning of complex science concepts with augmented 3D visualizations. Computers & Education, 193, 104659. https://doi.org/10.1016/j.compedu.2022.104659.
Yang, W., Ng, D. T. K., & Gao, H. (2022). Robot programming versus block play in early childhood education: Effects on computational thinking, sequencing ability, and self-regulation. British Journal of Educational Technology, 53(6). https://doi.org/10.1111/bjet.13215.
Yuan, Y. (2022). Quantitative analysis of Chinese classroom teaching activity under the background of artificial intelligence. Education and Information Technologies, 27(8), 11161–11177. https://doi.org/10.1007/s10639-022-11080-x.
Zhan, Z., Wu, Q., Lin, Z., & Cai, J. (2021a). Smart classroom environments affect teacher-student interaction: Evidence from a behavioural sequence analysis. Australasian Journal of Educational Technology, 37(2), 96–109. https://doi.org/10.14742/ajet.6523.
Zhan, Z., Zhong, B., Shi, X., Si, Q., & Zheng, J. (2021b). The design and application of IRobotQ3D for simulating robotics experiments in K-12 education. Computer Applications in Engineering Education, 30(2), 532–549. https://doi.org/10.1002/cae.22471.
Zhang, Y., & Zhu, Y. (2022). Effects of educational robotics on the creativity and problem-solving skills of K-12 students: A meta-analysis. Educational Studies, 1–19. https://doi.org/10.1080/03055698.2022.2107873.
Zhang, J., Gao, M., Holmes, W., Mavrikis, M., & Ma, N. (2019). Interaction patterns in exploratory learning environments for mathematics: A sequential analysis of feedback and external representations in Chinese schools. Interactive Learning Environments, 29(7), 1211–1228. https://doi.org/10.1080/10494820.2019.1620290.
Zhong, B., & Si, Q. (2021). Troubleshooting to learn via scaffolds: Effect on students’ ability and cognitive load in a robotics course. Journal of Educational Computing Research, 59(1), 95–118. https://doi.org/10.1177/0735633120951871.
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This research is supported by National Social Science Fund of China, “Teacher Portrait and Application Research from the Perspective of Artifcial Intelligence”, grant No. BCA 220206.
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Mu, S., Xu, K., He, W. et al. Teaching behaviors in problem-oriented instruction for robotics education. Educ Inf Technol 29, 17943–17964 (2024). https://doi.org/10.1007/s10639-024-12578-2
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DOI: https://doi.org/10.1007/s10639-024-12578-2


