探討在真實情境中利用 GPT 智慧回饋輔助 3D 幾何學習

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：145

、訪客IP：18.216.88.54

姓名

黃筠棋(Yun-Chi Huang) 查詢紙本館藏

畢業系所

網路學習科技研究所

論文名稱

探討在真實情境中利用 GPT 智慧回饋輔助 3D 幾何學習
(Investigation of Smart Feedback with GPT for Facilitating 3D Geometry Learning in Authentic Contexts)

相關論文

★ 同步表演機器人之建構與成效評估	★ 探討國小學童使用電子書多媒體註記系統結合註記分享機制對其學習行為與時間之影響
★ 先備知識對註記式多媒體電子書的影響研究：從個別環境到分享環境	★ Facilitating EFL speaking and writing with peer-tutoring and storytelling strategies in authentic learning context
★ An investigation into CKEL-supported EFL learning with TPR to reveal the importance of pronunciation and interactive sentence making	★ Investigation of Facilitating Physics Learning using Ubiquitous-Physics APP with Learning Map and Discussion Board in Authentic Contexts
★ 智慧互動SmartVpen在真實情境對於英文學習之影響	★ 利用合作虛擬化的網絡設計輔助計算機網路學習
★ 探討擴展合作式多媒體認知理論和其對EFL聽力與口語能力之影響 - 結合動覺辨識和學習者設計內容之猜謎遊戲	★ 在真實情境中利用智慧機制提升國小學生之外語口說及對話能力之評估
★ 探討在真實情境下教師回饋對學習認知與學習持續性之影響	★ 註釋、對話代理和協作概念圖支持大學生議論文寫作和後設認知的培養
★ Developing and Validating the Questionnaire and Its Model for Sustainable and Scalable Authentic Contextual Learning Supported by Mobile Apps	★ 探討個人化、情境化及社會化的智慧機制輔助真實情境國小幾何學習與其對學習成效之影響
★ Investigation of smart mechanisms for authentic contextual learning with sensor and recognition technologies	★ 探討智慧回饋如何影響學習時眼動和觸控操作的表現-以 Covid-19 快篩模擬為例

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

近年來人工智慧迅速發展，將人工智慧應用在教育領域幫助學習已成為熱門討論的研究話題。過去許多研究已經表明人工智慧能夠在許多方面幫助學習，但仍然缺乏學習者可以直接向人工智慧互動進而影響學習的相關研究。在這項研究中，開發了一款手機應用程式稱為 Smart 3D-UG，此系統可以幫助小學生利用手機測量和計算周遭環境中的真實物體，透過在真實情境中的學習認識立體幾何物體並應用在生活中，並且在系統中設計了智慧回饋機制，能提供學習者一對一的實時引導和互動。本研究的目的是探討學習者在智慧回饋的輔助下進行真實情境式學習對立體幾何能力的能力影響，進一步分析哪些學習行為會影響學習成績。本研究將桃園市某國小共四十七名六年級學生分為兩組進行為期八週的教學實驗，實驗結合幾何內容設計了四種學習主題活動和兩種學習任務來幫助學習。相關研究結果表明，實驗組學習者使用具有智慧回饋機制的 Smart 3D-UG 系統學習後在 3D 幾何學習成效上優於使用不具有智慧回饋機制的 Smart 3D-UG 系統的控制組學習者，同時實驗組學習者也在計算活動的答案正確率上優於控制組學習者。並且學習者使用智慧回饋的次數以及向智慧回饋提問的次數越多，和幾何能力呈現高度相關。這意味著結合智慧回饋的真實情境學習能有效幫助學習者的立體幾何學習。最後根據學習者的飯潰，多數學習者認為智慧回饋機制和真實情境中學習能夠幫助他們學習立體幾何概念並實際應用，同時能有效降低學習焦慮。

摘要(英)

In recent years, the rapid development of artificial intelligence has made applying AI in the educational field to assist learning a popular research topic. Numerous studies in the past have shown that AI can help learning in many ways, but there is still a lack of research on learners directly interacting with AI to influence their learning, especially in geometry learning.
In this study, a mobile application called Smart 3D-UG was developed. This system helps elementary school students measure and calculate real objects in their surroundings using their phones. Through learning in authentic contexts, students can understand 3D geometry objects and apply this knowledge in daily life. The system also includes a smart feedback mechanism that provides real-time, one-on-one guidance and interaction for learners.
The purpose of this study is to investigate the impact of authentic contextual learning assisted by smart feedback on learners′ 3D geometry abilities and further analyze which learning behaviors affect learning outcomes. In this study, 47 sixth-grade students from an elementary school in Taoyuan City were divided into two groups for an eight-week teaching experiment. The experiment combined geometric content with four learning themes activities and two learning tasks to aid learning.
The results of the related research indicate that the experimental group using Smart 3D-UG with smart feedback outperformed learners using Smart 3D-UG without smart feedback in terms of 3D geometry learning effectiveness. The experimental group also had a higher accuracy rate in answering geometry calculations than the control group. Moreover, the frequency of learners using smart feedback and asking questions to smart feedback showed a high correlation with their geometry abilities. This implies that authentic contextual learning combined with smart feedback can effectively assist learners in their 3D geometry learning.
Finally, according to learners′ feedback, most learners believed that the smart feedback mechanism and learning in real contexts could help them understand and apply 3D geometry concepts and effectively reduce learning anxiety.

關鍵字(中)

★ 幾何學習
★ 情境學習
★ 智慧回饋
★ 生成式人工智慧
★ 適性化學習
★ 幾何能力

關鍵字(英)

★ geometry learning
★ authentic learning
★ smart feedback
★ generative AI
★ adaptive learning
★ geometry ability

論文目次

中文摘要 .................................................................................................................................... ii
Abstract .................................................................................................................................... iii
List of Contents ........................................................................................................................ iv
List of Figures .......................................................................................................................... vi
List of Tables .......................................................................................................................... viii
Chapter 1 Introduction ...................................................................................................... 1
1.1 Research Background and Motivation ....................................................................... 1
1.2 Research Questions ..................................................................................................... 3
Chapter 2 Literature Review ............................................................................................. 4
2.1 Geometry Learning in Authentic Context .................................................................. 4
2.2 Collaborative Learning in an Authentic Context ........................................................ 5
2.3 Artificial intelligence (AI) support in authentic context ............................................ 6
2.4 The Capabilities of Artificial Intelligence (AI) in Education ..................................... 8
2.5 How Learner Follow-up Questions Improve GPT Feedback Quality ........................ 9
Chapter 3 System Design and Implementation .............................................................. 11
3.1 System Design .......................................................................................................... 12
3.1.1 Activities of measuring Single Objects individually ............................................ 13
3.1.2 Activities of measuring Compound Objects individually .................................... 16
3.1.3 Activity of measuring Single Object collaboratively ........................................... 18
3.1.4 Activity of measuring Compound Object collaboratively .................................... 20
3.1.5 Map ....................................................................................................................... 23
3.1.6 Whiteboard ........................................................................................................... 24
3.2 Learning Activity Module ........................................................................................ 25
3.2.1 Learning Materials - Geometry Concepts ............................................................ 25
3.2.2 Learning Progress ................................................................................................. 26
3.2.3 Learning Task - Teacher-Designed Task .............................................................. 27
3.2.4 Learning Task - Free-Exploration Task ................................................................ 28
3.3 Smart Feedback ........................................................................................................ 28
3.3.1 Smart Feedback for Learning Progress ................................................................ 29
3.3.2 Smart Feedback for Calculation ........................................................................... 30
3.3.3 Smart feedback for learners’ questions ................................................................ 32
Chapter 4 Method ............................................................................................................. 33
4.1 Participants ............................................................................................................... 33
4.2 Research Framework ................................................................................................ 33
4.2.1 Independent variables ........................................................................................... 34
4.2.2 Control variables ................................................................................................... 34
4.2.3 Dependent variables ............................................................................................. 34
4.3 Experimental Procedure ........................................................................................... 36
4.4 Experimental Instruments ......................................................................................... 37
4.5 Data Analysis Approach ........................................................................................... 39
Chapter 5 Results .............................................................................................................. 40
5.1 Analysis of learning achievements ........................................................................... 40
5.1.1 The comparison of the Learning achievements in the pretest, posttest, and gained
score between two groups ................................................................................................. 40
5.1.2 The comparison of the task scores in learning activities between two groups ..... 41
5.2 Comparison of learning behaviors between two groups .......................................... 44
5.3 The correlation between learning behavior and learning achievement in EG .......... 46
5.4 Prediction .................................................................................................................. 49
5.4.1 Prediction of the dependent variables to learning achievements in EG ............... 49
5.4.2 Mediation Effect on The Learning Achievement in EG ....................................... 50
5.5 Learners’ Perception of Smart 3D-UG ..................................................................... 51
5.6 A qualitative analysis of learners′ use of Smart 3D-UG for measurements and
calculations. .......................................................................................................................... 58
5.6.1 Demonstrations of learners using Smart 3D-UG for object measurements. ........ 59
5.6.2 A qualitative analysis of learners′ error types while using Smart 3D-UG for
calculations. ...................................................................................................................... 60
5.6.3 Demonstrations of facilitating problem-solving with GPT in Smart 3D-UG. ..... 60
5.7 Suggestion and Implication ...................................................................................... 63
Chapter 6 Conclusion ....................................................................................................... 65
Reference ................................................................................................................................. 67
Appendix A: Pretest ............................................................................................................... 71
Appendix B: Posttest .............................................................................................................. 75
Appendix C: TAM questionnaire .......................................................................................... 76
Appendix D: Semi-structured interview question ............................................................... 80

參考文獻

Aditama, K. (2020). Pemanfaatan Natural Language Processing Dan Pattern Matching Dalam
Pembelajaran Melalui Guru Virtual. Elkom: Jurnal Elektronika dan Komputer, 13(1),
121-133. doi:https://doi.org/10.51903/elkom.v13i1.187
Aleven, V., Baraniuk, R., Brunskill, E., Crossley, S., Demszky, D., Fancsali, S., . . . Ritter, S.
(2023). Towards the Future of AI-Augmented Human Tutoring in Math Learning. Paper
presented at the International Conference on Artificial Intelligence in Education.
Alvin, C., Gulwani, S., Majumdar, R., & Mukhopadhyay, S. (2015). Automatic synthesis of
geometry problems for an intelligent tutoring system. arXiv preprint arXiv:1510.08525.
doi:https://doi.org/10.48550/arXiv.1510.08525
Cao, M., Zhang, Q., Cao, M., & Zhang, Q. (2013). Collaborative advantage as consequences.
Supply Chain Collaboration: Roles of Interorganizational Systems, Trust, and
Collaborative Culture, 77-91. doi:https://doi.org/10.1007/978-1-4471-4591-2_5
Carless, D. (2022). Feedback for student learning in higher education. International
Encyclopedia of Education, 623-629.
Ezzaim, A., Dahbi, A., Assad, N., & Haidine, A. (2022). AI-Based Adaptive Learning-State of
the Art. Paper presented at the International Conference on Advanced Intelligent
Systems for Sustainable Development.
Firdaus, M. (2021). Enhancing Students Multivariable Calculus Learning Through Online
Formative Feedback. Paper presented at the Journal of Physics: Conference Series.
Flores-Bascuñana, M., Diago, P. D., Villena-Taranilla, R., & Yáñez, D. F. (2019). On
Augmented Reality for the Learning of 3D-Geometric Contents: A Preliminary
Exploratory Study with 6-Grade Primary Students. Education Sciences.
doi:https://doi.org/10.3390/educsci10010004
Fu, Q.-K., & Hwang, G.-J. (2018). Trends in mobile technology-supported collaborative
learning: A systematic review of journal publications from 2007 to 2016. Computers &
Education, 119, 129-143. doi:https://doi.org/10.1016/j.compedu.2018.01.004
Gasarch, W. I., & Smith, C. H. (1992). Learning via queries. J. ACM, 39(3), 649–674.
doi:10.1145/146637.146670
Graefen, B., & Fazal, N. (2023). GPTEACHER: EXAMINING THE EFFICACY OF
CHATGPT AS A TOOL FOR PUBLIC HEALTH EDUCATION. 2023, 10(8).
doi:10.46827/ejes.v10i8.4926
Halim, L., Buang, N. A., & Meerah, T. S. M. (2011). Guiding Student Teachers to be Reflective.
Procedia - Social and Behavioral Sciences, 18, 544-550.
doi:https://doi.org/10.1016/j.sbspro.2011.05.080
Hong-Quan, L., & Jee-In, K. (2017, 2017). An augmented reality application with hand
gestures for learning 3D geometry.
Hwang, W.-Y., Hoang, A., & Tu, Y.-H. (2020). Exploring Authentic Contexts with Ubiquitous
Geometry to Facilitate Elementary School Students′ Geometry Learning. The Asia-
Pacific Education Researcher, 29(3), 269-283. doi:10.1007/s40299-019-00476-y
Hwang, W.-Y., & Hu, S.-S. (2013). Analysis of peer learning behaviors using multiple
representations in virtual reality and their impacts on geometry problem solving.
Computers & Education, 62, 308-319.
doi:https://doi.org/10.1016/j.compedu.2012.10.005
Hwang, W.-Y., Lin, L.-K., Ochirbat, A., Shih, T. K., & Kumara, W. G. C. W. (2015).
Ubiquitous Geometry: Measuring Authentic Surroundings to Support Geometry
Learning of the Sixth-Grade Students. Journal of Educational Computing Research,
52(1), 26-49. doi:10.1177/0735633114568852
Hwang, W. Y., Hoang, A., & Lin, Y. H. (2021). Smart mechanisms and their influence on
geometry learning of elementary school students in authentic contexts. Journal of
computer assisted learning, 37(5), 1441-1454. doi:10.1080/09588221.2022.2095406
Hwang, W. Y., Lin, Y. J., Utami, I. Q., & Nurtantyana, R. (2023a). Smart Geometry Learning
in Authentic Contexts with Personalization, Contextualization, and Socialization. IEEE
Transactions on Learning Technologies, 1-18. doi:10.1109/TLT.2023.3307614
Hwang, W. Y., Nurtantyana, R., Purba, S. W. D., Hariyanti, U., & Suprapto. (2023b).
Augmented Reality With Authentic GeometryGo App to Help Geometry Learning and
Assessments. IEEE Transactions on Learning Technologies, 16(5), 769-779.
doi:10.1109/TLT.2023.3251398
Hwang, W. Y., Purba, S. W. D., Liu, Y. f., Zhang, Y. Y., & Chen, N. S. (2019). An Investigation
of the Effects of Measuring Authentic Contexts on Geometry Learning Achievement.
IEEE Transactions on Learning Technologies, 12(3), 291-302.
doi:10.1109/TLT.2018.2853750
Inventado, P. S., Scupelli, P., Heffernan, C., & Heffernan, N. (2017). Feedback design patterns
for math online learning systems. Paper presented at the Proceedings of the 22nd
European Conference on Pattern Languages of Programs.
Irwin, B., Hepplestone, S., Holden, G., Parkin, H. J., & Thorpe, L. (2013). Engaging students
with feedback through adaptive release. Innovations in Education and Teaching
International, 50(1), 51-61. doi:https://doi.org/10.1080/14703297.2012.748333
Jannah, M. (2018). The Application of Assignment and Feedback Method to Improve Students’
Achievement in Learning English. UIN Ar-Raniry Banda Aceh,
Jones, J. (2021). Integrating machine learning in secondary geometry. Mathematics Teacher:
Learning and Teaching PK-12, 114(4), 325-329.
doi:https://doi.org/10.5951/MTLT.2020.0187
Joshi, A., Grana, J., Richard, D., & Haidet, P. (2020). Learning in Context: a Model for
Collaborative, Longitudinal Learning for medical students. Academic Psychiatry, 44(4),
502-503. doi:10.1007/s40596-020-01251-8
Jukić Matić, L., & Glasnović Gracin, D. (2021). How do teacher guides give support to
mathematics teachers? Analysis of a teacher guide and exploration of its use in teachers′
practices. Research in mathematics education, 23(1), 1-20.
doi:https://doi.org/10.1080/14794802.2019.1710554
Kabudi, T., Pappas, I., & Olsen, D. H. (2021). AI-enabled adaptive learning systems: A
systematic mapping of the literature. Computers and Education: Artificial Intelligence,
2, 100017. doi:https://doi.org/10.1016/j.caeai.2021.100017
Khaidir, C., & Suhaili, N. (2023). Pengaruh Bimbingan Konseling dalam Upaya Mengatasi
Rendahnya Motivasi Belajar Matematika Siswa SMP. Journal on Education, 6(1),
2244-2253. doi:https://doi.org/10.31004/joe.v6i1.3226
Kochmar, E., Vu, D. D., Belfer, R., Gupta, V., Serban, I. V., & Pineau, J. (2020). Automated
personalized feedback improves learning gains in an intelligent tutoring system. Paper
presented at the Artificial Intelligence in Education: 21st International Conference,
AIED 2020, Ifrane, Morocco, July 6–10, 2020, Proceedings, Part II 21.
Lawley, L., & MacLellan, C. J. (2023). Interactive Learning of Hierarchical Tasks from Dialog
with GPT. arXiv preprint arXiv:2305.10349.
doi:https://doi.org/10.48550/arXiv.2305.10349
Li, X., Zhou, Y., & Shen, Z. (2022). Improve the teaching effect of plane geometry in primary
school by Hawgent dynamic information technology—take the derivation of" Area of
The Circle" formula as an example. Paper presented at the 2022 3rd International
Conference on Education, Knowledge and Information Management (ICEKIM).
Martínez-Sevilla, Á., & Alonso, S. (2022). AI and mathematics interaction for a new learning
paradigm on monumental heritage. In Mathematics Education in the Age of Artificial
Intelligence: How Artificial Intelligence can Serve Mathematical Human Learning (pp.
107-136): Springer.
Mataheru, W., Laurens, T., & Taihuttu, S. M. (2023). The development of geometry learning
using traditional dance context assisted by GeoGebra. Jurnal Elemen, 9(1), 65-83.
doi:10.29408/jel.v9i1.6628
Mays, T. (2015). Using spreadsheets to develop applied skills in a business math course:
Student feedback and perceived learning. Spreadsheets in Education, 8(3).
Morton, K., & Qu, Y. (2015). A feedback effectiveness oriented math word problem E-Tutor
for E-Learning environment. Paper presented at the 2015 IEEE 15th International
Conference on Advanced Learning Technologies.
Mugambi, M. M., Mwove, D. K., & Musalia, F. G. (2015). Influence of teachers on classroom
learning environment. Journal of Educational Policy and Entrepreneurial Research, 2,
90-102.
Nadimpalli, V. K., Hauser, F., Bittner, D., Grabinger, L., Staufer, S., & Mottok, J. (2023).
Systematic Literature Review for the Use of AI Based Techniques in Adaptive Learning
Management Systems. Paper presented at the Proceedings of the 5th European
Conference on Software Engineering Education.
Orland‐Barak, L., & Yinon, H. (2007). When Theory Meets Practice: What Student Teachers
Learn from Guided Reflection on Their Own Classroom Discourse. Teaching and
Teacher Education, 23, 957-969. doi:https://doi.org/10.1016/j.tate.2006.06.005
Osmond, E., & Couto, C. (2022). Asking great questions. Archives of disease in childhood -
Education & practice edition, 107(3), 227-230. doi:10.1136/archdischild-2020-
320484
Pappas, M., & Drigas, A. (2016). Incorporation of artificial intelligence tutoring techniques in
mathematics.
Park, A., Colantonio II, J., Reyes, L. D., Sharp, S., Bonawitz, E., & Mackey, A. (2022).
Question asking fosters curiosity and learning in children.
doi:https://doi.org/10.31234/osf.io/qzekc
Pataranutaporn, P., Leong, J., Danry, V., Lawson, A. P., Maes, P., & Sra, M. (2022). AI-
generated virtual instructors based on liked or admired people can improve motivation
and foster positive emotions for learning. Paper presented at the 2022 IEEE Frontiers
in Education Conference (FIE).
Petrescu, A.-M. A., Gorghiu, G., & DRĂGHICESCU, L. M. (2017). The advantages of
collaborative learning in science lessons. LUMEN Proceedings, 2, 326-333.
doi:https://doi.org/10.18662/lumproc.icsed2017.36
Qiu, Y., Pan, J., & Ishak, N. A. (2022). Effectiveness of artificial intelligence (AI) in improving
pupils’ deep learning in primary school mathematics teaching in Fujian Province.
Computational intelligence and neuroscience, 2022.
doi:https://doi.org/10.1155/2022/1362996
Rajaram, K. (2021). Learning Interventions: Collaborative Learning, Critical Thinking and
Assessing Participation Real-Time. In K. Rajaram (Ed.), Evidence-Based Teaching for
the 21st Century Classroom and Beyond: Innovation-Driven Learning Strategies (pp.
77-120). Singapore: Springer Singapore.
Salzman, H., & Lynn, L. (2023). Collaborative advantage: creating global commons for science,
technology, and innovation. Issues in science and technology.
doi:https://doi.org/10.58875/ADGU5787
Sarsa, S., Pettersson, J., & Hellas, A. (2022, 8-11 Oct. 2022). How to Help to Ask for Help?
Help Request Prompt Structure Influence on Help Request Quantity and Course
Retention. Paper presented at the 2022 IEEE Frontiers in Education Conference (FIE).
Singh, S. V. (2023). The Prospects for Advancing Adaptive Learning Technology through AI
Methods. Paper presented at the 2023 Future of Educational Innovation-Workshop
Series Data in Action.
Sutirna, S., Musa, S., Suprananto, S., & Intisari, I. (2023). Implementation of the Guidance and
Counseling Services Principles in Mathematics Learning. Jurnal Pendidikan MIPA,
24(1), 135-151. doi:http://dx.doi.org/10.23960/jpmipa/v24i1.pp135-151
Thamrongrat, P., & Law, E. L.-C. (2019). Design and Evaluation of an Augmented Reality App
for Learning Geometric Shapes in 3D. Paper presented at the IFIP TC13 International
Conference on Human-Computer Interaction.
VARZARU, I. M., NICA, B. E., & TOMA, A. (2022). ViTeach: Artificial Intelligence
Algorithms to Improve E-learning through Virtual Teachers. doi:10.5171/2022.343795
Wardat, Y., Tashtoush, M. A., AlAli, R., & Jarrah, A. M. (2023). ChatGPT: A revolutionary
tool for teaching and learning mathematics. Eurasia Journal of Mathematics, Science
and Technology Education, 19(7), em2286. doi:https://doi.org/10.29333/ejmste/13272
Zhang, M., Wang, Z., Baraniuk, R., & Lan, A. (2021). Math operation embeddings for open-
ended solution analysis and feedback. arXiv preprint arXiv:2104.12047.
doi:https://doi.org/10.48550/arXiv.2104.12047
Zhang, Y., Zhao, S., Tian, X., & Sun, H. (2023). Design and Development of" Virtual AI
Teacher" System Based on NLP. Paper presented at the 2023 11th International Conference on
Information and Education Technology (ICIET).
Gasarch, W. I., & Smith, C. H. (1992). Learning via queries. J. ACM, 39(3), 649–674.
doi:10.1145/146637.146670
Hwang, W.-Y., Hoang, A., & Tu, Y.-H. (2020). Exploring Authentic Contexts with Ubiquitous
Geometry to Facilitate Elementary School Students′ Geometry Learning. The Asia-
Pacific Education Researcher, 29(3), 269-283. doi:10.1007/s40299-019-00476-y
Hwang, W. Y., Purba, S. W. D., Liu, Y.-f., Zhang, Y.-Y., & Chen, C. H. (2019). An
Investigation of the Effects of Measuring Authentic Contexts on Geometry Learning
Achievement. IEEE Transactions on Learning Technologies, 12(3), 291-302.
doi:10.1109/TLT.2018.2853750
Mataheru, W., Laurens, T., & Taihuttu, S. M. (2023). The development of geometry learning
using traditional dance context assisted by GeoGebra. Jurnal Elemen, 9(1), 65-83.
doi:10.29408/jel.v9i1.6628
Osmond, E., & Couto, C. (2022). Asking great questions. Archives of disease in childhood -
Education & practice edition, 107(3), 227-230. doi:10.1136/archdischild-2020-
320484
Park, A., Colantonio II, J., Reyes, L. D., Sharp, S., Bonawitz, E., & Mackey, A. (2022).
Question asking fosters curiosity and learning in children.
doi:https://doi.org/10.31234/osf.io/qzekc
Sarsa, S., Pettersson, J., & Hellas, A. (2022, 8-11 Oct. 2022). How to Help to Ask for Help?
Help Request Prompt Structure Influence on Help Request Quantity and Course
Retention. Paper presented at the 2022 IEEE Frontiers in Education Conference (FIE).

指導教授

黃武元(Wu-Yuin Hwang)

審核日期

2024-7-27

推文