近年來，STEM教育出現在學校課程中，其目的是傳播知識和技能。然而STEM教育中廣泛使用動手作學習理論，導致傳統以教師為中心的教學方法在STEM教育上效果不彰；如何在STEM教育中有效評估動手作的動作並用以分析學習者的表現便成為教育研究中一項重要的課題。本研究提出一套自動化的系統：「ONCE」，進行學生行為編碼，分析學生行為，將學生活動情況提供給教師，讓教師可以快速追蹤學生參與狀況。兩步驟驗證系統的準確率，第一步使用mAP@.5作為系統模型評估的依據，mAP@.5可達到93.4%，顯示「ONCE」對於物件的辨識率相當高；第二步進行「ONCE」編碼與專家編碼比較，評估兩者之間的一致性，採用Cohen's Kappa係數分析，Cohen's Kappa係數為0.895，證明系統自動化編碼的有效性，這項研究還對學生參與的自動測量具有重要的方法學意義。
過往研究認為現行的AI工具皆是以研究分析為導向的，而非真正作為課堂教學環境中的實用工具。本研究採用「ONCE」監控學生活動情況，強調教師及時的教學干預對學生參與度、學習焦慮、和學生表現的影響。為此設計了一項準實驗研究，控制組與實驗組唯一區別是教師是否有根據「ONCE」反饋提供學生教學干預。結果顯示，教師及時教學干預的組別具有較高的學習表現和學生參與度，但在學習焦慮方面兩組並沒有顯著差異；學生參與度與學習表現是正相關的；學生參與度與學習焦慮也是正相關的，當系統偵測到學生參與度過低時，教師進行及時的教學干預，可以幫助學生重回課程，達到提升學習表現的目標。將AI工具導入課程中，成為教學環境中的實用工具。

In recent years, STEM education has actively developed to transmit knowledge and skills. However, the hands-on learning theories have led to the ineffectiveness of traditional teacher-centered teaching methods in STEM education. How to effectively assess the action of hands-on activities and analyze learners' performance in STEM education has become an important issue in educational research.
The system of this study is named "ONCE", which is an automated system to instantly assess the status of student engagement in the classroom so that teachers can quickly keep track of student engagement. In this study, the data is processed in two steps to verify the accuracy of the system: The first step used mAP@.5 as the evaluation benchmark for the system model, and mAP@.5 could reach 93.4%. The second step assessed the consistency of the system coding with the expert coding, using Cohen's Kappa coefficient analysis to verify whether the system model could replace expert coding in the observation method. The kappa coefficient was 0.895 when comparing "ONCE" coding with the traditional observation method of expert coding, which proved the effectiveness of the automated coding system.
In this study, we used "ONCE" to observe student and to emphasize the effect of timely pedagogical interventions on student engagement, learning anxiety, and student performance. We designed a quasi-experimental study for this purpose and the results showed that the experimental group with teacher timely pedagogical interventions had higher learning performance and higher learning engagement, but there was no significant difference between the two groups in terms of learning anxiety. Student engagement is positively correlated with learning performance and learning anxiety. When "ONCE" detected low student engagement, timely pedagogical interventions can help students return to the course and achieve the goal of improving learning performance.

李心佑（民 110）。電腦視覺技術於多人 STEAM 教育的實作學習活動之
分析與探討（碩士論文）。國立成功大學，臺南市。
Abramczyk, A., & Jurkowski, S. (2020). Cooperative learning as an evidence-based teaching strategy: What teachers know, believe, and how they use it. Journal of Education for Teaching, 46(3), 296-308.
Acar, D., Tertemiz, N., & Taşdemir, A. (2018). The Effects of STEM Training on the Academic Achievement of 4th Graders in Science and Mathematics and their Views on STEM Training. International Electronic Journal of Elementary Education, 10(4), 505-513.
AlAmer, R. A., Al-Doweesh, W. A., Al-Khalifa, H. S., & Al-Razgan, M. S. (2015). Programming unplugged: Bridging CS unplugged activities gap for learning key programming concepts. 2015 Fifth International Conference on e-Learning (econf),
Amer, H., & Ibrahim, W. (2014). Using the iPad as a pedagogical tool to enhance the learning experince for novice programing students. 2014 IEEE Global Engineering Education Conference (EDUCON),
Angeli, C., & Valanides, N. (2020). Developing young children's computational thinking with educational robotics: An interaction effect between gender and scaffolding strategy. Computers in human behavior, 105, 105954.
Anwar, S., & Menekse, M. (2021). A systematic review of observation protocols used in postsecondary STEM classrooms. Review of Education, 9(1), 81-120.
Appleton, J. J., Christenson, S. L., & Furlong, M. J. (2008). Student engagement with school: Critical conceptual and methodological issues of the construct. Psychology in the Schools, 45(5), 369-386.
Bakeman, R., & Quera, V. (2011). Sequential analysis and observational methods for the behavioral sciences. Cambridge University Press.
Barron, B. (2003). When smart groups fail. The journal of the learning sciences, 12(3), 307-359.
Bers, M. U., Flannery, L., Kazakoff, E. R., & Sullivan, A. (2014). Computational thinking and tinkering: Exploration of an early childhood robotics curriculum. Computers & education, 72, 145-157.
Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934.
Bryce, D., & Whitebread, D. (2012). The development of metacognitive skills: Evidence from observational analysis of young children’s behavior during problem-solving. Metacognition and Learning, 7(3), 197-217.
Burke, L. M., & McNeill, J. B. (2011). Educate to innovate: How the Obama plan for STEM education falls short. Backgrounder, 2504, 1-8.
Bybee, R. W. (2013). The case for STEM education: Challenges and opportunities. NSTA press.
Cai, Y., Wang, Z., Luo, Z., Yin, B., Du, A., Wang, H., Zhang, X., Zhou, X., Zhou, E., & Sun, J. (2020). Learning delicate local representations for multi-person pose estimation. European Conference on Computer Vision,
Çankaya, S., Durak, G., & Yünkül, E. (2017). Education on programming with robots: Examining students’ experiences and views. Turkish Online Journal of Qualitative Inquiry, 8(4), 428-445.
Cao, Z., Simon, T., Wei, S.-E., & Sheikh, Y. (2017). Realtime multi-person 2d pose estimation using part affinity fields. Proceedings of the IEEE conference on computer vision and pattern recognition,
Chen, C.-S., & Lin, J.-W. (2019). A practical action research study of the impact of maker-centered STEM-PjBL on a rural middle school in Taiwan. International Journal of Science and Mathematics Education, 17(1), 85-108.
Chen, G., Shen, J., Barth-Cohen, L., Jiang, S., Huang, X., & Eltoukhy, M. (2017). Assessing elementary students’ computational thinking in everyday reasoning and robotics programming. Computers & education, 109, 162-175.
Chen, J., Wang, M., Kirschner, P. A., & Tsai, C.-C. (2018). The role of collaboration, computer use, learning environments, and supporting strategies in CSCL: A meta-analysis. Review of Educational Research, 88(6), 799-843.
Chen, X., Xie, H., & Hwang, G.-J. (2020). A multi-perspective study on artificial intelligence in education: Grants, conferences, journals, software tools, institutions, and researchers. Computers and Education: Artificial Intelligence, 1, 100005.
Chen, X., Xie, H., Zou, D., & Hwang, G.-J. (2020). Application and theory gaps during the rise of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence, 1, 100002.
Chi, M. T., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational psychologist, 49(4), 219-243.
Cotton, D. R., Stokes, A., & Cotton, P. A. (2010). Using observational methods to research the student experience. Journal of Geography in Higher Education, 34(3), 463-473.
D'Mello, S., Dieterle, E., & Duckworth, A. (2017). Advanced, analytic, automated (AAA) measurement of engagement during learning. Educational psychologist, 52(2), 104-123.
Dang, J., King, K. M., & Inzlicht, M. (2020). Why are self-report and behavioral measures weakly correlated? Trends in cognitive sciences, 24(4), 267-269.
Du, J., Wimmer, H., & Rada, R. (2016). " Hour of Code”: Can It Change Students’ Attitudes Toward Programming? Journal of Information Technology Education: Innovations in Practice, 15, 53.
Duckworth, A. L., & Yeager, D. S. (2015). Measurement matters: Assessing personal qualities other than cognitive ability for educational purposes. Educational Researcher, 44(4), 237-251.
Durak, H. Y., Yilmaz, F. G. K., & Yilmaz, R. (2019). Computational thinking, programming self-efficacy, problem solving and experiences in the programming process conducted with robotic activities. Contemporary Educational Technology, 10(2), 173-197.
Ellis, R. A., Han, F., & Pardo, A. (2017). Improving learning analytics–combining observational and self-report data on student learning. Journal of Educational Technology & Society, 20(3), 158-169.
English, L. D., King, D., & Smeed, J. (2017). Advancing integrated STEM learning through engineering design: Sixth-grade students’ design and construction of earthquake resistant buildings. The Journal of Educational Research, 110(3), 255-271.
Fagerlund, J., Häkkinen, P., Vesisenaho, M., & Viiri, J. (2021). Computational thinking in programming with Scratch in primary schools: A systematic review. Computer Applications in Engineering Education, 29(1), 12-28.
Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59-109.
Gao, X., Li, P., Shen, J., & Sun, H. (2020). Reviewing assessment of student learning in interdisciplinary STEM education. International journal of STEM education, 7(1), 1-14.
García-Santillán, A., Escalera-Chávez, M. E., Moreno-García, E., & del Carmen Santana-Villegas, J. (2016). Factors that explains student anxiety toward mathematics. EURASIA Journal of Mathematics, Science and Technology Education, 12(2), 361-372.
Girshick, R. (2015). Fast r-cnn. Proceedings of the IEEE international conference on computer vision,
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition,
Gomes, A., Correia, F. B., & Abreu, P. H. (2016). Types of assessing student-programming knowledge. 2016 IEEE Frontiers in Education Conference (FIE),
Gonzalez, G. (2006). A systematic approach to active and cooperative learning in CS1 and its effects on CS2. Proceedings of the 37th SIGCSE technical symposium on Computer science education,
Greene, B. A. (2015). Measuring cognitive engagement with self-report scales: Reflections from over 20 years of research. Educational psychologist, 50(1), 14-30.
Hainey, T., Baxter, G., & Ford, A. (2019). An evaluation of the introduction of games-based construction learning in upper primary education using a developed game codification scheme for scratch. Journal of Applied Research in Higher Education.
Harris, A. H., & Cox, M. F. (2003). Developing an observation system to capture instructional differences in engineering classrooms. Journal of Engineering Education, 92(4), 329-336.
Hartmann, D. P., & Wood, D. D. (1990). Observational methods. In International handbook of behavior modification and therapy (pp. 107-138). Springer.
He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask r-cnn. Proceedings of the IEEE international conference on computer vision,
Hennessy, S., Rojas-Drummond, S., Higham, R., Márquez, A. M., Maine, F., Ríos, R. M., García-Carrión, R., Torreblanca, O., & Barrera, M. J. (2016). Developing a coding scheme for analysing classroom dialogue across educational contexts. Learning, Culture and Social Interaction, 9, 16-44.
Henrie, C. R., Halverson, L. R., & Graham, C. R. (2015). Measuring student engagement in technology-mediated learning: A review. Computers & education, 90, 36-53.
Hora, M. T., & Ferrare, J. J. (2013). Instructional systems of practice: A multidimensional analysis of math and science undergraduate course planning and classroom teaching. Journal of the Learning Sciences, 22(2), 212-257.
Ifenthaler, D., & Yau, J. Y.-K. (2020). Utilising learning analytics to support study success in higher education: a systematic review. Educational Technology Research and Development, 68(4), 1961-1990.
Janssen, J., Erkens, G., Kirschner, P. A., & Kanselaar, G. (2012). Task-related and social regulation during online collaborative learning. Metacognition and Learning, 7(1), 25-43.
Johnson, D. W., & Johnson, R. T. (2009). An educational psychology success story: Social interdependence theory and cooperative learning. Educational Researcher, 38(5), 365-379.
Järvelä, S., & Renninger, K. (2014). Designing for learning: Interest, motivation, and engagement.
Kaendler, C., Wiedmann, M., Rummel, N., & Spada, H. (2015). Teacher competencies for the implementation of collaborative learning in the classroom: A framework and research review. Educational Psychology Review, 27(3), 505-536.
Kalelioglu, F., & Gülbahar, Y. (2014). The Effects of Teaching Programming via Scratch on Problem Solving Skills: A Discussion from Learners' Perspective. Informatics in Education, 13(1), 33-50.
Keiler, L. S. (2018). Teachers’ roles and identities in student-centered classrooms. International journal of STEM education, 5(1), 1-20.
Kennedy, T. J., & Odell, M. R. (2014). Engaging students in STEM education. Science Education International, 25(3), 246-258.
Kern, A. L., Moore, T. J., & Akillioglu, F. C. (2007). Cooperative learning: Developing an observation instrument for student interactions. 2007 37th Annual Frontiers In Education Conference-Global Engineering: Knowledge Without Borders, Opportunities Without Passports,
Kim, N. J., Belland, B. R., & Walker, A. E. (2018). Effectiveness of computer-based scaffolding in the context of problem-based learning for STEM education: Bayesian meta-analysis. Educational Psychology Review, 30(2), 397-429.
Kintsakis, D., & Rangoussi, M. (2017, 25-28 April 2017). An early introduction to STEM education: Teaching computer programming principles to 5^th graders through an e-learning platform: A game-based approach. 2017 IEEE Global Engineering Education Conference (EDUCON),
Koç, A. (2019). Okul öncesi ve temel fen eğitiminde robotik destekli ve basit malzemelerle yapılan STEM uygulamalarının karşılaştırılması.
Korb, K. A. (2011). Self-report questionnaires: Can they collect accurate information? Journal of Educational Foundations, 1, 5-12.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25.
Lai, E. R. (2011). Metacognition: A literature review. Always learning: Pearson research report, 24, 1-40.
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. biometrics, 159-174.
Le, H., Janssen, J., & Wubbels, T. (2018). Collaborative learning practices: teacher and student perceived obstacles to effective student collaboration. Cambridge Journal of Education, 48(1), 103-122.
Lee, H.-Y., Chien, Y.-C., Chang, P.-Y., Hooshyar, D., & Huang, Y.-M. (2021). Use Object-Detection to Identify Materials and Tools for STEAM Hands-on Activity. International Conference on Innovative Technologies and Learning,
Lee, Y. (2018). Python-based software education model for non-computer majors. Journal of the Korea Convergence Society, 9(3), 73-78.
Li, F. W., & Watson, C. (2011). Game-based concept visualization for learning programming. Proceedings of the third international ACM workshop on Multimedia technologies for distance learning,
Li, S., Lajoie, S. P., Zheng, J., Wu, H., & Cheng, H. (2021). Automated detection of cognitive engagement to inform the art of staying engaged in problem-solving. Computers & education, 163, 104114.
Li, S., Zheng, J., Poitras, E., & Lajoie, S. (2018). The allocation of time matters to students’ performance in clinical reasoning. International Conference on Intelligent Tutoring Systems,
Li, Y., Wang, K., Xiao, Y., & Froyd, J. E. (2020). Research and trends in STEM education: A systematic review of journal publications. International journal of STEM education, 7(1), 1-16.
Lister, R., Simon, B., Thompson, E., Whalley, J. L., & Prasad, C. (2006). Not seeing the forest for the trees: novice programmers and the SOLO taxonomy. ACM SIGCSE Bulletin, 38(3), 118-122.
Liu, Z., Hu, H., Lin, Y., Yao, Z., Xie, Z., Wei, Y., Ning, J., Cao, Y., Zhang, Z., & Dong, L. (2021). Swin Transformer V2: Scaling Up Capacity and Resolution. arXiv preprint arXiv:2111.09883.
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE/CVF International Conference on Computer Vision,
Masters AO, G. N. (2016). Five challenges in Australian school education.
Matcha, W., Gašević, D., & Pardo, A. (2019). A systematic review of empirical studies on learning analytics dashboards: A self-regulated learning perspective. IEEE Transactions on Learning Technologies, 13(2), 226-245.
Mathrani, A., Christian, S., & Ponder-Sutton, A. (2016). PlayIT: Game based learning approach for teaching programming concepts. Journal of Educational Technology & Society, 19(2), 5-17.
McWhorter, W. I., & O'Connor, B. C. (2009). Do LEGO® Mindstorms® motivate students in CS1? Proceedings of the 40th ACM technical symposium on Computer science education,
Miailhe, N., & Hodes, C. (2017). The third age of artificial intelligence. Field Actions Science Reports. The journal of field actions(Special Issue 17), 6-11.
Mohamed, A. (2019). Designing a CS1 programming course for a mixed-ability class. Proceedings of the Western Canadian Conference on Computing Education,
Monkaresi, H., Bosch, N., Calvo, R. A., & D'Mello, S. K. (2016). Automated detection of engagement using video-based estimation of facial expressions and heart rate. IEEE Transactions on Affective Computing, 8(1), 15-28.
Mullins, D., Rummel, N., & Spada, H. (2011). Are two heads always better than one? Differential effects of collaboration on students’ computer-supported learning in mathematics. International Journal of Computer-Supported Collaborative Learning, 6(3), 421-443.
Murti, A. T., Wu, T.-T., & Huang, Y.-M. (2020). Combining EEG Feedback on Student Performance and Self-efficacy. International Conference on Innovative Technologies and Learning,
Ngoc Anh, B., Tung Son, N., Truong Lam, P., Le Chi, P., Huu Tuan, N., Cong Dat, N., Huu Trung, N., Umar Aftab, M., & Van Dinh, T. (2019). A computer-vision based application for student behavior monitoring in classroom. Applied Sciences, 9(22), 4729.
Paulhus, D. L., & Vazire, S. (2007). The self-report method. Handbook of research methods in personality psychology, 1(2007), 224-239.
Pekruna, R. (2020). Commentary: Self-Report Is Indispensable to Assess Students' Learning. Frontline Learning Research, 8(3), 185-193.
Pope, C., & Allen, D. (2020). Observational methods. Qualitative research in health care, 67-81.
Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28.
Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K., Millner, A., Rosenbaum, E., Silver, J., & Silverman, B. (2009). Scratch: programming for all. Communications of the ACM, 52(11), 60-67.
Rojas-Drummond, S., Torreblanca, O., Pedraza, H., Vélez, M., & Guzmán, K. (2013). ‘Dialogic scaffolding’: Enhancing learning and understanding in collaborative contexts. Learning, Culture and Social Interaction, 2(1), 11-21.
Rosicka, C. (2016). Translating STEM education research into practice.
Rosiene, C. P., & Rosiene, J. A. (2015). Flipping a programming course: The good, the bad, and the ugly. 2015 IEEE Frontiers in Education Conference (FIE),
Roth, A., Ogrin, S., & Schmitz, B. (2016). Assessing self-regulated learning in higher education: a systematic literature review of self-report instruments. Educational Assessment, Evaluation and Accountability, 28(3), 225-250.
Russell, J.-E., Smith, A., & Larsen, R. (2020). Elements of Success: Supporting at-risk student resilience through learning analytics. Computers & education, 152, 103890.
Ryu, S., & Lombardi, D. (2015). Coding classroom interactions for collective and individual engagement. Educational psychologist, 50(1), 70-83.
Sanders, M. E. (2008). Stem, stem education, stemmania.
Schumacher, C., & Ifenthaler, D. (2018). Features students really expect from learning analytics. Computers in human behavior, 78, 397-407.
Schwichow, M., Zimmerman, C., Croker, S., & Härtig, H. (2016). What students learn from hands‐on activities. Journal of research in science teaching, 53(7), 980-1002.
Sedrakyan, G., Malmberg, J., Verbert, K., Järvelä, S., & Kirschner, P. A. (2020). Linking learning behavior analytics and learning science concepts: Designing a learning analytics dashboard for feedback to support learning regulation. Computers in human behavior, 107, 105512.
Sergis, S., & Sampson, D. G. (2017). Teaching and learning analytics to support teacher inquiry: A systematic literature review. Learning analytics: Fundaments, applications, and trends, 25-63.
Shahali, E. H. M., Halim, L., Rasul, M. S., Osman, K., & Zulkifeli, M. A. (2016). STEM learning through engineering design: Impact on middle secondary students’ interest towards STEM. EURASIA Journal of Mathematics, Science and Technology Education, 13(5), 1189-1211.
Shibani, A., Knight, S., & Shum, S. B. (2020). Educator perspectives on learning analytics in classroom practice. The Internet and Higher Education, 46, 100730.
Shulman, L. S. (2002). Making differences: A table of learning. Change: The Magazine of Higher Learning, 34(6), 36-44.
Sigayret, K., Tricot, A., & Blanc, N. (2022). Unplugged or plugged-in programming learning: A comparative experimental study. Computers & education, 184, 104505.
Smith, B. L., & MacGregor, J. T. (1992). What is collaborative learning. In.
Smith, M. K., Jones, F. H., Gilbert, S. L., & Wieman, C. E. (2013). The Classroom Observation Protocol for Undergraduate STEM (COPUS): A new instrument to characterize university STEM classroom practices. CBE—Life Sciences Education, 12(4), 618-627.
Smith, M. K., Vinson, E. L., Smith, J. A., Lewin, J. D., & Stetzer, M. R. (2014). A campus-wide study of STEM courses: New perspectives on teaching practices and perceptions. CBE—Life Sciences Education, 13(4), 624-635.
Spector, P. E. (1994). Using self-report questionnaires in OB research: A comment on the use of a controversial method. Journal of organizational behavior, 385-392.
Stohlmann, M., Moore, T. J., & Roehrig, G. H. (2012). Considerations for teaching integrated STEM education. Journal of Pre-College Engineering Education Research (J-PEER), 2(1), 4.
Sun, D., Ouyang, F., Li, Y., & Zhu, C. (2021). Comparing learners’ knowledge, behaviors, and attitudes between two instructional modes of computer programming in secondary education. International journal of STEM education, 8(1), 1-15.
Thomas, C., & Jayagopi, D. B. (2017). Predicting student engagement in classrooms using facial behavioral cues. Proceedings of the 1st ACM SIGCHI international workshop on multimodal interaction for education,
Topalli, D., & Cagiltay, N. E. (2018). Improving programming skills in engineering education through problem-based game projects with Scratch. Computers & education, 120, 64-74.
Trowler, V. (2010). Student engagement literature review. The higher education academy, 11(1), 1-15.
Van de Pol, J., Volman, M., & Beishuizen, J. (2010). Scaffolding in teacher–student interaction: A decade of research. Educational Psychology Review, 22(3), 271-296.
Van de Pol, J., Volman, M., Oort, F., & Beishuizen, J. (2015). The effects of scaffolding in the classroom: support contingency and student independent working time in relation to student achievement, task effort and appreciation of support. Instructional Science, 43(5), 615-641.
Van Leeuwen, A., & Janssen, J. (2019). A systematic review of teacher guidance during collaborative learning in primary and secondary education. Educational research review, 27, 71-89.
Van Leeuwen, A., Janssen, J., Erkens, G., & Brekelmans, M. (2014). Supporting teachers in guiding collaborating students: Effects of learning analytics in CSCL. Computers & education, 79, 28-39.
Veenman, M. V. (2016). Learning to self-monitor and self-regulate. In Handbook of research on learning and instruction (pp. 249-273). Routledge.
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information systems research, 11(4), 342-365.
Venter, M. (2020). Gamification in STEM programming courses: State of the art. 2020 IEEE Global Engineering Education Conference (EDUCON),
Verbert, K., Govaerts, S., Duval, E., Santos, J. L., Van Assche, F., Parra, G., & Klerkx, J. (2014). Learning dashboards: an overview and future research opportunities. Personal and Ubiquitous Computing, 18(6), 1499-1514.
Volet, S., & Vauras, M. (2012). Interpersonal regulation of learning and motivation. Routledge New York.
Wahono, B., Lin, P.-L., & Chang, C.-Y. (2020). Evidence of STEM enactment effectiveness in Asian student learning outcomes. International journal of STEM education, 7(1), 1-18.
Wan, H., Liu, K., Yu, Q., & Gao, X. (2019). Pedagogical intervention practices: Improving learning engagement based on early prediction. IEEE Transactions on Learning Technologies, 12(2), 278-289.
Wang, C.-Y., Bochkovskiy, A., & Liao, H.-Y. M. (2021). Scaled-yolov4: Scaling cross stage partial network. Proceedings of the IEEE/cvf conference on computer vision and pattern recognition,
Wang, C.-Y., Yeh, I.-H., & Liao, H.-Y. M. (2021). You only learn one representation: Unified network for multiple tasks. arXiv preprint arXiv:2105.04206.
Wang, M.-T., Fredricks, J. A., Ye, F., Hofkens, T. L., & Linn, J. S. (2016). The math and science engagement scales: Scale development, validation, and psychometric properties. Learning and Instruction, 43, 16-26.
Webb, N. M. (2009). The teacher's role in promoting collaborative dialogue in the classroom. British Journal of Educational Psychology, 79(1), 1-28.
Whitehill, J., Serpell, Z., Lin, Y.-C., Foster, A., & Movellan, J. R. (2014). The faces of engagement: Automatic recognition of student engagementfrom facial expressions. IEEE Transactions on Affective Computing, 5(1), 86-98.
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35.
Xu, B., Chen, N.-S., & Chen, G. (2020). Effects of teacher role on student engagement in WeChat-Based online discussion learning. Computers & education, 157, 103956.
Yang, S. J., Ogata, H., Matsui, T., & Chen, N.-S. (2021). Human-centered artificial intelligence in education: Seeing the invisible through the visible. Computers and Education: Artificial Intelligence, 2, 100008.
Yildirim, B. (2016). An Analyses and Meta-Synthesis of Research on STEM Education. Journal of Education and Practice, 7(34), 23-33.
Yu, J., Kreijkes, P., & Salmela-Aro, K. (2022). Students’ growth mindset: Relation to teacher beliefs, teaching practices, and school climate. Learning and Instruction, 80, 101616.
Zaletelj, J., & Košir, A. (2017). Predicting students’ attention in the classroom from Kinect facial and body features. EURASIP journal on image and video processing, 2017(1), 1-12.
Zepke, N., & Leach, L. (2010). Improving student engagement: Ten proposals for action. Active learning in higher education, 11(3), 167-177.
Zhang, H., Li, F., Liu, S., Zhang, L., Su, H., Zhu, J., Ni, L. M., & Shum, H.-Y. (2022). DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection. arXiv preprint arXiv:2203.03605.
Zhao, D., Chis, A., Muntean, G., & Muntean, C. (2018). A large-scale pilot study on game-based learning and blended learning methodologies in undergraduate programming courses. Proc. Int. Conf. Educ. New Learn. Technol.(EDULEARN),
Zheng, J., Huang, L., Li, S., Lajoie, S. P., Chen, Y., & Hmelo-Silver, C. E. (2021). Self-regulation and emotion matter: A case study of instructor interactions with a learning analytics dashboard. Computers & education, 161, 104061.
Ziaeefard, S., Miller, M. H., Rastgaar, M., & Mahmoudian, N. (2017). Co-robotics hands-on activities: A gateway to engineering design and STEM learning. Robotics and Autonomous Systems, 97, 40-50.