近年來，由於其與提高學習效果的聯繫，以及作為評價學習的一個重要部分，低風險評估模式已經得到了關注。與高風險評估模式不同，低風險評估對學習者的學業成績幾乎沒有影響，其目的是支持以反饋為導向的學習過程。為學習者提供多次低風險評估，可以使知識和技能的長期保留率明顯提高。然而，儘管有這樣的好處，學習者在進行低風險評估時可能不會付出最大的努力，這可能會導致最少的學習成果。另一方面，在學習環境中使用新興技術，如社交機器人，可以促進互動學習、參與和學習評估的積極性。因此，整合低風險評估和機器人可能會鼓勵學習者在考試時付出更多努力。本研究旨在發現基於機器人的多種低風險評估對支持學習者的口頭表達能力、集體效能和學習態度的影響。在兩個六年級的小學生班級中進行了一個準實驗。基於機器人的多元低風險評估「Robot-MLSA」「Robot-multiple low-stake assessment」被隨機分配到一個班，而基於計算機的多重低風險評估「C-MLSA」「Computer-multiple low-stake assessment」被分配到另一個班。研究結果顯示，Robot-MLSA可以提高學生的口頭表達能力，支持集體效能，並提高他們對機器人的學習態度。此外，還對學生的學習感知和體驗進行了深入討論，以探索 Robot-MLSA的有效性。

Low-stake assessments have gained attention in recent years due to their link to increasing learning effects and as an essential part of evaluating learning. Unlike high-stake assessments, low-stake assessments hold little or no consequences for learners' academic performance and are designed to support the feedback-oriented learning process. Providing multiple low-stake assessments to learners yields significantly greater long-term retention of knowledge and skills. However, despite their benefits, learners may not give their best efforts while taking low-stake assessments, which could lead to the least learning outcomes. On the other hand, using emerging technologies, such as social robots, in the learning environment could foster interactive learning, engagement, and motivation for learning assessments. Therefore, integrating low-stake assessments and robots might encourage learners to put forth more effort while taking tests. This study aimed to discover the impacts of robot-based multiple low-stake assessments on supporting learners' oral presentation performance, collective efficacy, and learning attitude. A quasi-experiment was conducted in two sixth-grade classes of elementary students. The Robot-based Multiple Low-stakes Assessment (Robot-MLSA) was randomly assigned in one class, while the Computer-based Multiple Low-stakes Assessment (C-MLSA) was assigned in another class. The findings show that the Robot-MLSA could enhance students' oral presentation performance, support collective efficacy, and increase their learning attitude toward the robot. Furthermore, an in-depth discussion of students' learning perception and experience is provided to explore the effectiveness of the Robot-MLSA.

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
Alemi, M., Meghdari, A., & Ghazisaedy, M. (2014). Employing humanoid robots for teaching English language in Iranian junior high-schools. International Journal of Humanoid Robotics, 11(03), 1450022. https://doi.org/10.1142/S0219843614500224
Alemi, M., Meghdari, A., & Ghazisaedy, M. (2015). The impact of social robotics on L2 learners' anxiety and attitude in English vocabulary acquisition. International Journal of Social Robotics, 7(4), 523-535. https://doi.org/10.1007/s12369-015-0286-y
Al Hakim, V. G., Yang, S. H., Liyanawatta, M., Wang, J. H., & Chen, G. D. (2022). Robots in situated learning classrooms with immediate feedback mechanisms to improve students' learning performance. Computers & Education, 182, 104483. https://doi.org/10.1016/j.compedu.2022.104483
Amiri, M., & Ghonsooly, B. (2015). The relationship between English learning anxiety and the students' achievement on examinations. Journal of Language Teaching and Research, 6(4), 855-865. http://dx.doi.org/10.17507/jltr.0604.20
Anderson, L. W., & Krathwohl, D. R. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom's taxonomy of educational objectives. Longman,.
Baccarani, C., & Bonfanti, A. (2015). Effective public speaking: a conceptual framework in the corporate-communication field. Corporate Communications: An International Journal, 20(3), 375-390. https://doi.org/10.1108/CCIJ-04-2014-0025
Balkibekov, K., Meiirbekov, S., Tazhigaliyeva, N., & Sandygulova, A. (2016, August). Should robots win or lose? Robot's losing playing strategy positively affects child learning. In 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN) (pp. 706-711). IEEE. https://doi.org/10.1109/ROMAN.2016.7745196
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.
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
Bloom, B. S. (1971). Handbook on formative and summative evaluation of student learning. McGraw-Hill.
Bourgonjon, J., Valcke, M., Soetaert, R., & Schellens, T. (2010). Students' perceptions about the use of video games in the classroom. Computers & Education, 54(4), 1145-1156. https://doi.org/10.1016/j.compedu.2009.10.022
Brame, C. J., & Biel, R. (2015). Test-enhanced learning: the potential for testing to promote greater learning in undergraduate science courses. CBE—Life Sciences Education, 14(2), es4.
Brown, L. N., & Howard, A. M. (2014, March). The positive effects of verbal encouragement in mathematics education using a social robot. In 2014 IEEE integrated STEM education conference (pp. 1-5). IEEE. https://doi.org/10.1109/ISECon.2014.6891009
Bui, H. P., & Nguyen, T. T. T. (2022). Classroom assessment and learning motivation: insights from secondary school EFL classrooms. International Review of Applied Linguistics in Language Teaching. https://doi.org/10.1515/iral-2022-0020
Butler, A. C., Godbole, N., & Marsh, E. J. (2013). Explanation feedback is better than correct answer feedback for promoting transfer of learning. Journal of Educational Psychology, 105(2), 290. https://doi.org/10.1037/a0031026
Carduner, J. (2002). Using classroom assessment techniques to improve foreign language composition courses. Foreign language annals, 35(5), 543-553. https://doi.org/10.1111/j.1944-9720.2002.tb02722.x
Chang, C. C., Hwang, G. J., & Chen, K. F. (2023). Fostering professional trainers with robot-based digital storytelling: A brainstorming, selection, forming and evaluation model for training guidance. Computers & Education, 104834.
Chen, B., Hwang, G. H., & Wang, S. H. (2021). Gender differences in cognitive load when applying game-based learning with intelligent robots. Educational Technology & Society, 24(3), 102-115. https://www.jstor.org/stable/27032859
Chen Hsieh, J., & Lee, J. S. (2021). Digital storytelling outcomes, emotions, grit, and perceptions among EFL middle school learners: robot-assisted versus PowerPoint-assisted presentations. Computer Assisted Language Learning, 1-28. https://doi.org/10.1080/09588221.2021.1969410
Creswell, J. W., & Poth, C. N. (2016). Qualitative inquiry and research design: Choosing among five approaches. Sage publications.
Cross, K. P., & Angelo, T. A. (1988). Classroom Assessment Techniques: A Handbook for Faculty. National Center for Research to Improve Postsecondary Teaching and Learning.
Engwall, O., Lopes, J., Cumbal, R., Berndtson, G., Lindström, R., Ekman, P., ... & Mekonnen, M. (2022). Learner and teacher perspectives on robot-led L2 conversation practice. ReCALL, 1-16. https://doi.org/10.1017/S0958344022000027
Engwall, O., & Lopes, J. (2020). Interaction and collaboration in robot-assisted language learning for adults. Computer Assisted Language Learning, 1-37. https://doi.org/10.1080/09588221.2020.1799821
De Grez, L., Valcke, M., & Berings, D. (2010). Peer assessment of oral presentation skills. Procedia-Social and Behavioral Sciences, 2(2), 1776-1780. https://doi.org/10.1016/j.sbspro.2010.03.983
De Grez, L., Valcke, M., & Roozen, I. (2009). The impact of an innovative instructional intervention on the acquisition of oral presentation skills in higher education. Computers & Education, 53(1), 112-120. https://doi.org/10.1016/j.compedu.2009.01.005
Demirbas, O. O., & Demirkan, H. (2007). Learning styles of design students and the relationship of academic performance and gender in design education. Learning and instruction, 17(3), 345-359. https://doi.org/10.1016/j.learninstruc.2007.02.007
Fridin, M., & Belokopytov, M. (2014). Acceptance of socially assistive humanoid robot by preschool and elementary school teachers. Computers in Human Behavior, 33, 23-31. https://doi.org/10.1016/j.chb.2013.12.016
Han, J. H., Jo, M. H., Jones, V., & Jo, J. H. (2008). Comparative study on the educational use of home robots for children. Journal of Information Processing Systems, 4(4), 159-168. https://doi.org/10.3745/JIPS.2008.4.4.159
Hartwig, M. K., & Malain, E. D. (2022). Do students space their course study? Those who do earn higher grades. Learning and Instruction, 77, 101538. https://doi.org/10.1016/j.learninstruc.2021.101538
Hong, Z. W., Huang, Y. M., Hsu, M., & Shen, W. W. (2016). Authoring robot-assisted instructional materials for improving learning performance and motivation in EFL classrooms. Journal of Educational Technology & Society, 19(1), 337-349. https://www.jstor.org/stable/10.2307/jeductechsoci.19.1.337
Hsiao, H. S., Lin, Y. W., Lin, K. Y., Lin, C. Y., Chen, J. H., & Chen, J. C. (2022). Using robot-based practices to develop an activity that incorporated the 6E model to improve elementary school students' learning performances. Interactive Learning Environments, 30(1), 85-99. https://doi.org/10.1080/10494820.2019.1636090
Hsu, Y. C., Ho, H. N. J., Tsai, C. C., Hwang, G. J., Chu, H. C., Wang, C. Y., & Chen, N. S. (2012). Research trends in technology-based learning from 2000 to 2009: A content analysis of publications in selected journals. Journal of Educational Technology & Society, 15(2), 354-370. https://www.jstor.org/stable/jeductechsoci.15.2.354
Huang, L., McDonald, D., & Gillan, D. (2017, September). Exploration of human reactions to a humanoid robot in public STEM education. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 61, No. 1, pp. 1262-1266). Sage CA: Los Angeles, CA: Sage Publications. https://doi.org/10.1177/1541931213601796
Hwang, G. J., Yang, T. C., Tsai, C. C., & Yang, S. J. (2009). A context-aware ubiquitous learning environment for conducting complex science experiments. Computers & Education, 53(2), 402-413. https://doi.org/10.1016/j.compedu.2009.02.016
Iio, T., Maeda, R., Ogawa, K., Yoshikawa, Y., Ishiguro, H., Suzuki, K., ... & Hama, M. (2019). Improvement of Japanese adults' English speaking skills via experiences speaking to a robot. Journal of Computer Assisted Learning, 35(2), 228-245. https://doi.org/10.1111/jcal.12325
In, J., & Han, J. (2015, March). The acoustic-phonetics change of English learners in robot assisted learning. In Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts (pp. 39-40). https://doi.org/10.1145/2701973.2702003
Jaipal-Jamani, K., & Angeli, C. (2017). Effect of robotics on elementary preservice teachers’ self-efficacy, science learning, and computational thinking. Journal of Science Education and Technology, 26, 175-192. https://doi.org/10.1007/s10956-016-9663-z
Jakonen, T., & Jauni, H. (2022). Managing activity transitions in robot-mediated hybrid language classrooms. Computer Assisted Language Learning, 1-24. https://doi.org/10.1080/09588221.2022.2059518
Jawad, L. F., Majeed, B. H., & ALRikabi, H. T. S. (2021). The impact of CATs on mathematical thinking and logical thinking among fourth-class scientific students. International Journal of Emerging Technologies in Learning (Online), 16(10), 194. https://doi.org/10.3991/ijet.v16i10.22515
Kanda, T., Sato, R., Saiwaki, N., & Ishiguro, H. (2007). A two-month field trial in an elementary school for long-term human–robot interaction. IEEE Transactions on robotics, 23(5), 962-971. https://doi.org/10.1109/TRO.2007.904904
Kulik, J. A., & Kulik, C. L. C. (1988). Timing of feedback and verbal learning. Review of educational research, 58(1), 79-97. https://doi.org/10.3102/00346543058001079
Khalifa, A., Kato, T., & Yamamoto, S. (2017). Measuring Effect of Repetitive Queries and Implicit Learning with Joining-in-type Robot Assisted Language Learning System. In SLaTE (pp. 13-17).
Kolb, D. A. (2014). Experiential learning: Experience as the source of learning and development (2nd ed.). Upper Saddle River: Pearson Education.
Konstantakis, M., Lykiardopoulou, A., Lykiardopoulou, E., Tasiouli, G., & Heliades, G. (2022). An Exploratory Study of Mobile-Based Scenarios for Foreign Language Teaching in Early Childhood. Education Sciences, 12(5), 306. https://doi.org/10.3390/educsci12050306
Lee, S., Noh, H., Lee, J., Lee, K., Lee, G. G., Sagong, S., & Kim, M. (2011). On the effectiveness of robot-assisted language learning. ReCALL, 23(1), 25-58. https://doi.org/10.1017/S0958344010000273
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, 1-27. https://doi.org/10.1080/10494820.2021.1958348
Li, J. (2015). The benefit of being physically present: A survey of experimental works comparing copresent robots, telepresent robots and virtual agents. International Journal of Human-Computer Studies, 77, 23-37. https://doi.org/10.1016/j.ijhcs.2015.01.001
Li, Y., Sekino, H., Sato-Shimokawara, E., & Yamaguchi, T. (2022). The Influence of Robot's Expressions on Self-Efficacy in Erroneous Situations. Journal of Advanced Computational Intelligence and Intelligent Informatics, 26(4), 521-530. https://doi.org/10.20965/jaciii.2022.p0521
Liu, E. Z. F. (2010). Early adolescents' perceptions of educational robots and learning of robotics. British Journal of Educational Technology, 41(3), E44-E47. https://doi.org/10.1111/j.1467-8535.2009.00944.x
Liu, Y., & Hau, K. T. (2020). Measuring motivation to take low-stakes large-scale test: New model based on analyses of "Participant-Own-Defined" missingness. Educational and Psychological Measurement, 80(6), 1115-1144. https://doi.org/10.1177/0013164420911972
Luria, M., Hoffman, G., & Zuckerman, O. (2017, May). Comparing social robot, screen and voice interfaces for smart-home control. In Proceedings of the 2017 CHI conference on human factors in computing systems (pp. 580-628). https://doi.org/10.1145/3025453.3025786
Manolis, C., Burns, D. J., Assudani, R., & Chinta, R. (2013). Assessing experiential learning styles: A methodological reconstruction and validation of the Kolb Learning Style Inventory. Learning and individual differences, 23, 44-52. https://doi.org/10.1016/j.lindif.2012.10.009
Mazzoni, E., & Benvenuti, M. (2015). A robot-partner for preschool children learning English using socio-cognitive conflict. Journal of Educational Technology & Society, 18(4), 474-485. https://www.jstor.org/stable/10.2307/jeductechsoci.18.4.474
McCarthy, P. M., & Jarvis, S. (2010). MTLD, vocd-D, and HD-D: A validation study of sophisticated approaches to lexical diversity assessment. Behavior research methods, 42(2), 381-392. https://doi.org/10.3758/BRM.42.2.381
Mercer, N., Warwick, P., & Ahmed, A. (2017). An oracy assessment toolkit: Linking research and development in the assessment of students' spoken language skills at age 11-12. Learning and Instruction, 48, 51-60. https://doi.org/10.1016/j.learninstruc.2016.10.005
Mertler, C. (2016). Classroom assessment: A practical guide for educators. Routledge.
Min, Q., Wang, Z., & Liu, N. (2019). Integrating a cloud learning environment into English-medium instruction to enhance non-native English-speaking students' learning. Innovations in Education and Teaching International, 56(4), 493-504. https://doi.org/10.1080/14703297.2018.1483838
Mubin, O., Shahid, S., Bartneck, C. (2013) Robot Assisted Language Learning through Games: A Comparison of Two Case Studies. Australian Journal of Intelligent Information Processing Systems, 13(3), pp. 9-14. https://hdl.handle.net/10092/17651
Nadolski, R. J., Hummel, H. G., Rusman, E., & Ackermans, K. (2021). Rubric formats for the formative assessment of oral presentation skills acquisition in secondary education. Educational Technology Research and Development, 69(5), 2663-2682. https://doi.org/10.1007/s11423-021-10030-7
Nomura, T., Kanda, T., & Suzuki, T. (2006). Experimental investigation into influence of negative attitudes toward robots on human–robot interaction. Ai & Society, 20(2), 138-150. https://doi.org/10.1007/s00146-005-0012-7
Nomura, T., Suzuki, T., Kanda, T., & Kato, K. (2006). Measurement of negative attitudes toward robots. Interaction Studies. Social Behaviour and Communication in Biological and Artificial Systems, 7(3), 437-454.
Papastergiou, M. (2009). Digital game-based learning in high school computer science education: Impact on educational effectiveness and student motivation. Computers & education, 52(1), 1-12. https://doi.org/10.1016/j.compedu.2008.06.004
Park, S. J., Han, J. H., Kang, B. H., & Shin, K. C. (2011, October). Teaching assistant robot, ROBOSEM, in English class and practical issues for its diffusion. In Advanced Robotics and its Social Impacts (pp. 8-11). IEEE. https://doi.org/10.1109/ARSO.2011.6301971
Pashler, H., Cepeda, N. J., Wixted, J. T., & Rohrer, D. (2005). When does feedback facilitate learning of words?. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31(1), 3. https://doi.org/10.1037/0278-7393.31.1.3
Pintrich, P. R., Smith, D. A., Garcia, T., & McKeachie, W. J. (1993). Reliability and predictive validity of the Motivated Strategies for Learning Questionnaire (MSLQ). Educational and psychological measurement, 53(3), 801-813.
Rassaei, E. (2015). The effects of foreign language anxiety on EFL learners' perceptions of oral corrective feedback. Innovation in Language Learning and Teaching, 9(2), 87-101. https://doi.org/10.1080/17501229.2013.837912
Rios, J. (2021). Improving test-taking effort in low-stakes group-based educational testing: A meta-analysis of interventions. Applied Measurement in Education, 34(2), 85-106. https://doi.org/10.1080/08957347.2021.1890741
Robles, M. M. (2012). Executive perceptions of the top 10 soft skills needed in today's workplace. Business communication quarterly, 75(4), 453-465. https://doi.org/10.1177/1080569912460400
Roediger III, H. L., & Butler, A. C. (2011). The critical role of retrieval practice in long-term retention. Trends in cognitive sciences, 15(1), 20-27. https://doi.org/10.1016/j.tics.2010.09.003
Rosenberg-Kima, R. B., Koren, Y., & Gordon, G. (2020). Robot-supported collaborative learning (RSCL): Social robots as teaching assistants for higher education small group facilitation. Frontiers in Robotics and AI, 6, 148. https://doi.org/10.3389/frobt.2019.00148
Schut, S., van Tartwijk, J., Driessen, E., van der Vleuten, C., & Heeneman, S. (2020). Understanding the influence of teacher–learner relationships on learners' assessment perception. Advances in Health Sciences Education, 25(2), 441-456. https://doi.org/10.1007/s10459-019-09935-z
Schüttpelz-Brauns, K., Hecht, M., Hardt, K., Karay, Y., Zupanic, M., & Kämmer, J. E. (2020). Institutional strategies related to test-taking behavior in low stakes assessment. Advances in Health Sciences Education, 25(2), 321-335. https://doi.org/10.1007/s10459-019-09928-y
Seau, L. S., Azman, H., & Noor, N. M. (2018). A Responsive Pedagogical Initiative for Multimodal Oral Presentation Skills: An Action Research Study. 3L: Southeast Asian Journal of English Language Studies, 24(2). http://doi.org/10.17576/3L-2018-2402-03
Serholt, S., Barendregt, W., Leite, I., Hastie, H., Jones, A., Paiva, A., ... & Castellano, G. (2014, August). Teachers' views on the use of empathic robotic tutors in the classroom. In The 23rd IEEE International Symposium on Robot and Human Interactive Communication (pp. 955-960). IEEE.
Sisman, B., Gunay, D., & Kucuk, S. (2019). Development and validation of an educational robot attitude scale (ERAS) for secondary school students. Interactive Learning Environments, 27(3), 377-388. https://doi.org/10.1080/10494820.2018.1474234
Sotola, L. K., & Crede, M. (2021). Regarding class quizzes: A meta-analytic synthesis of studies on the relationship between frequent low-stakes testing and class performance. Educational Psychology Review, 33(2), 407-426. https://doi.org/10.1007/s10648-020-09563-9
Spanos, T., Hansen, C. M., & Daines, E. (2001). Integrating technology and classroom assessment. Foreign Language Annals, 34(4), 318-324. https://doi.org/10.1111/j.1944-9720.2001.tb02063.x
Spaulding, S., Gordon, G., and Breazeal, C. (2016). "Affect-aware student models for robot tutors," in Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems, 864–872. https://hdl.handle.net/1721.1/137902
Stordeur, M. F., Nils, F., & Colognesi, S. (2022). No, an oral presentation is not just something you prepare at home! Elementary teachers' practices supporting preparation of oral presentations. L1-Educational Studies in Language and Literature, 22, 1-29. https://doi.org/10.21248/l1esll.2022.22.1.417
Sweller, J. (2010). Cognitive load theory: Recent theoretical advances. In J. L. Plass, R. Moreno, & R. Brünken (Eds.), Cognitive load theory (pp. 29–47). Cambridge University Press. https://doi.org/10.1017/CBO9780511844744.004
Tanaka, F., & Matsuzoe, S. (2012). Children teach a care-receiving robot to promote their learning: Field experiments in a classroom for vocabulary learning. Journal of Human-Robot Interaction, 1(1), 78-95. https://doi.org/10.5898/JHRI.1.1.Tanaka
Timpe-Laughlin, V., Sydorenko, T., & Dombi, J. (2022). Human versus machine: investigating L2 learner output in face-to-face versus fully automated role-plays. Computer Assisted Language Learning, 1-30. https://doi.org/10.1080/09588221.2022.2032184
Tung, F. W. (2016). Child perception of humanoid robot appearance and behavior. International Journal of Human-Computer Interaction, 32(6), 493-502. https://doi.org/10.1080/10447318.2016.1172808
van den Berghe, R., Verhagen, J., Oudgenoeg-Paz, O., Van der Ven, S., & Leseman, P. (2019). Social robots for language learning: A review. Review of Educational Research, 89(2), 259-295.
van der Lans, R. M., & Maulana, R. (2018). The use of secondary school student ratings of their teacher's skillfulness for low-stake assessment and high-stake evaluation. Studies in Educational Evaluation, 58, 112-121. https://doi.org/10.3102/0034654318821286
Van Doremalen, J., Boves, L., Colpaert, J., Cucchiarini, C., & Strik, H. (2016). Evaluating automatic speech recognition-based language learning systems: A case study. Computer Assisted Language Learning, 29(4), 833-851. https://doi.org/10.1080/09588221.2016.1167090
van Ginkel, S., Gulikers, J., Biemans, H., & Mulder, M. (2017). The impact of the feedback source on developing oral presentation competence. Studies in Higher Education, 42(9), 1671-1685. https://doi.org/10.1080/03075079.2015.1117064
Van der Wilt, F., van Kruistum, C., van der Veen, C., & van Oers, B. (2016). Gender differences in the relationship between oral communicative competence and peer rejection: An explorative study in early childhood education. European Early Childhood Education Research Journal, 24(6), 807-817. https://doi.org/10.1080/1350293X.2015.1073507
Veldhuis, M., & Van den Heuvel-Panhuizen, M. (2020). Supporting primary school teachers' classroom assessment in mathematics education: Effects on student achievement. Mathematics Education Research Journal, 32(3), 449-471. https://doi.org/10.1007/s13394-019-00270-5
Vogt, P., De Haas, M., De Jong, C., Baxter, P., & Krahmer, E. (2017). Child-robot interactions for second language tutoring to preschool children. Frontiers in human neuroscience, 11, 73. https://doi.org/10.3389/fnhum.2017.00073
Wang, S. L., & Lin, S. S. (2007). The effects of group composition of self-efficacy and collective efficacy on computer-supported collaborative learning. Computers in human behavior, 23(5), 2256-2268. https://doi.org/10.1016/j.chb.2006.03.005
Wellsby, M., & Pexman, P. M. (2014). Developing embodied cognition: Insights from children's concepts and language processing. Frontiers in psychology, 5, 506. https://doi.org/10.3389/fpsyg.2014.00506
Westlund, J. M. K., Martinez, M., Archie, M., Das, M., & Breazeal, C. (2016, August). Effects of framing a robot as a social agent or as a machine on children's social behavior. In 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN) (pp. 688-693). IEEE. https://doi.org/10.1109/ROMAN.2016.7745193
Wiggins, G., Wiggins, G. P., & McTighe, J. (2005). Understanding by design. Association for supervision and curriculum development ASCD.
Wiklund‐Hörnqvist, C., Jonsson, B., & Nyberg, L. (2014). Strengthening concept learning by repeated testing. Scandinavian journal of psychology, 55(1), 10-16. https://doi.org/10.1111/sjop.12093
Wise, S. L., & DeMars, C. E. (2003). Examinee motivation in low-stakes assessment: problems and potential solutions: Paper Presented At the Annual Meeting of the American Association of Higher Education Assessment Conference. Seattle, Canada.
Wu, W. C. V., Wang, R. J., & Chen, N. S. (2015). Instructional design using an in-house built teaching assistant robot to enhance elementary school English-as-a-foreign-language learning. Interactive Learning Environments, 23(6), 696-714. https://doi.org/10.1080/10494820.2013.792844
Yang, F. C. O., Lai, H. M., & Wang, Y. W. (2023). Effect of augmented reality-based virtual educational robotics on programming students’ enjoyment of learning, computational thinking skills, and academic achievement. Computers & Education, 195, 104721.
Yang, Y. T. C., Chen, Y. C., & Hung, H. T. (2022). Digital storytelling as an interdisciplinary project to improve students' English speaking and creative thinking. Computer Assisted Language Learning, 35(4), 840-862. https://doi.org/10.1080/09588221.2020.1750431
You, Z. J., Shen, C. Y., Chang, C. W., Liu, B. J., & Chen, G. D. (2006, July). A robot as a teaching assistant in an English class. In Sixth IEEE international conference on advanced learning technologies (ICALT'06) (pp. 87-91). IEEE. https://doi.org/10.1109/ICALT.2006.1652373
Zhao, X., van den Heuvel-Panhuizen, M., & Veldhuis, M. (2019). Insights Chinese primary mathematics teachers gained into their students' learning from using classroom assessment techniques. Education Sciences, 9(2), 150. https://doi.org/10.3390/educsci9020150