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研究生: 景義豫
Ching, Yi-Yu
論文名稱: 探討學生使用聊天機器人學習大學邏輯推理課程的使用態度與批判性思維之關聯性
Exploring the Relationship Between Students' Attitudes Toward Using Chatbots for Learning University Logic Reasoning Courses and Their Critical Thinking
指導教授: 陳明秀
Chen, Ming-Hsiu
口試委員: 陳明秀
Chen, Ming-Hsiu
邱富源
Chiu, Fu-Yuan
梁至中
Liang, Jyh-Chong
口試日期: 2024/06/26
學位類別: 碩士
Master
系所名稱: 圖文傳播學系
Department of Graphic Arts and Communications
論文出版年: 2024
畢業學年度: 112
語文別: 中文
論文頁數: 98
中文關鍵詞: 教育研究數位科技教育UTAUT模型批判性思維傾向
英文關鍵詞: Educational Research, Digital Technology Education, UTAUT Model, Critical Thinking Disposition
研究方法: 準實驗設計法問卷調查法
DOI URL: http://doi.org/10.6345/NTNU202400931
論文種類: 學術論文
相關次數: 點閱:68下載:13
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  • 近年來,資訊科技迅猛發展使教育領域有了新的變化。綜觀現今教學現場,在教學策略中融入數位科技的應用已經不在少數。ChatGPT在2022年問世後,生成式聊天機器人儼然成為人工智慧應用中重要一環;而聊天機器人整合成為課程教材與學生學習的工具,過去就曾有研究表明,能有效提升學生幸福感、學習效率等益處。儘管聊天機器人為社會帶來無盡的便利,同時也帶來了教育領域新的考驗。
    又聊天機器人題材新穎,近來還沒有高等教育中學習者對聊天機器人接受度與批判性思維關聯性的調查。但這類以學習者為觀點進行的研究在教育上卻至關重要。此外,研究指出邏輯推理(Logical Reasoning)課程能有效地提升大學生其他學科的基礎,且邏輯課程提供了培養學生邏輯思維和批判性思考能力的機會,考慮到聊天機器人技術的複雜性,本研究認為邏輯課程提供了一個理想的實驗場域。
    有鑑於此,為更深入地瞭解大學生對聊天機器人的感知態度與批判性思維傾向,本研究以台北市某大學所開設之「邏輯思考與應用」課程修課學生作為研究對象,採問卷調查法前、後測,透過授課教師一學期的教學,探究以聊天機器人整合成通識教育教材,並作為大學生學習輔助工具後,學習者對聊天機器人之使用態度與批判性思維關聯性。
    本研究在學生對機器人感知與批判性思維傾向,建構了偏最小平方法的結構方程模型(PLS-SEM)。結果顯示,學生對於聊天機器人的理想自我能成功預測他們的批判性思維傾向。此外,經過教學干擾後,學生對聊天機器人的使用行為不再影響他們的批判性思維傾向;研究者同時發現,學生對聊天機器人的行為意圖正向影響批判性思維傾向合。這突顯課堂對學生個人成長與自我效能的重要性。最後,通過配對樣本t檢定發現,學生經過新科技輔助學習邏輯思維課程後,其學習結果獲得顯著提升。

    Rapid advancements in information technology have resulted in significant advances in the field of education in recent years. Considering the state of education today, it is no longer unusual to see digital technology used into lesson plans. Generative chatbots have been an essential component of artificial intelligence applications since the launch of ChatGPT in 2022. Previous studies have demonstrated that including chatbots into course materials and student learning tools can significantly improve students' overall well-being, learning effectiveness, and other aspects. Though chatbots make society's lives easier, they also present new difficulties for the educational sector.
    There has been no investigation into the correlation between the acceptance of chatbots by learners in higher education and their critical thinking abilities. Yet, such research, conducted from the learners' perspective, is crucial in education. Additionally, studies have indicated that courses in logical reasoning can effectively enhance the foundations of university students in other subjects and provide opportunities to cultivate their logical thinking and critical thinking abilities. Considering the complexity of chatbot technology, this study believes that logic courses provide an ideal experimental field. Therefore, to gain a deeper understanding of university students' perceptions of chatbots and their tendencies toward critical thinking, this study was conducted.
    This study selected students enrolled in the "Logical Thinking and Applications" course at a university in Taipei City as the research subjects. A questionnaire survey was conducted before and after the course, and through one semester of instruction by the course instructor, the study explored the correlation between students' attitudes toward using chatbots integrated as general education materials and learning aids for university students and their critical thinking tendencies.
    This study constructed a Partial Least Squares Structural Equation Model (PLS-SEM) to examine students' perceptions of robots and their propensity for critical thinking. The results indicate that students' ideal self in relation to chatbots successfully predicts their tendency towards critical thinking. Additionally, after instructional intervention, students' use of chatbots no longer affects their propensity for critical thinking. The researchers also found that students' behavioral intentions towards chatbots positively influence their propensity for critical thinking. This highlights the importance of classroom interventions for students' personal growth and self-efficacy. Finally, through paired sample t-tests, it was found that students' learning outcomes significantly improved after taking a logic thinking course assisted by new technology.

    第壹章   緒論 1 第一節      研究背景與動機 1 第二節      研究目的與研究問題 3 第三節      研究範圍與限制 4 第四節      名詞解釋 5 第貳章   文獻探討 7 第一節      教育領域中聊天機器人之相關研究 7 第二節      大學生感知和批判性思維相關研究 13 第三節      邏輯推理課之相關研究 19 第四節      文獻探討小結 23 第參章   研究設計 25 第一節      研究假設 25 第二節      研究架構 29 第三節      研究方法 30 第四節      研究對象 30 第五節      研究工具 31 第六節      研究實施 35 第七節      資料處理與分析 37 第肆章   研究結果與討論 39 第一節      聊天機器人感知與批判性思維傾向信效度分析 39 第二節      前、後測資料描述性分析 48 第三節      前、後測數據相關分析 51 第四節      前、後測路徑分析與討論及模型構建 53 第五節      學習結果分析 65 第伍章   結論與未來建議 66 第一節      研究結論 66 第二節      研究與教學建議 71 參考文獻 74 附錄 96

    中文文獻
    吳惠子、朱玉仿(2000)。教育研究法(一)。師友月刊,(392),91-94。https://doi.org/10.6437/EM.200002.0091
    國家教育研究院。(2022)。學生學習成就資料庫。https://tasal.naer.edu.tw/dsa/dsa。檢索時間:2024年1月15日
    彭亮(2023)。論大學英語教學中學生批判性思考的培養。國外英語考試教學與研究,5(4):180-184。10.12677/OETPR.2023.54019
    黃秀珠(2010)。研究問題、研究目的與待答問題之探析。家庭教育雙月刊,(28),26-42。https://doi.org/10.6422/JFEB.201011.0026
    蕭建華、張俊彥(2012)。介入自我效能對不同性別學生「自我學習評估」與「學習成效」之影響-以高一地球科學為例。科學教育月刊,(352),28-34。https://doi.org/10.6216/SEM.201209_(352).0003

    英文文獻
    Abdullah, F., & Ward, R. (2016). Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors. Computers in human behavior, 56, 238-256.https://doi.org/10.1016/j.chb.2015.11.036
    Aithal, A., & Aithal, P. S. (2020). Development and validation of survey questionnaire & experimental data–a systematical review-based statistical approach. International Journal of Management, Technology, and Social Sciences (IJMTS), 5(2), 233-251. https://doi.org/10.2139/ssrn.3724105
    Akinnuwesi, B. A., Uzoka, F. M. E., Fashoto, S. G., Mbunge, E., Odumabo, A., Amusa, O. O., ... & Owolabi, O. (2022). A modified UTAUT model for the acceptance and use of digital technology for tackling COVID-19. Sustainable Operations and Computers, 3, 118-135.https://doi.org/10.1016/j.susoc.2021.12.001
    Akinwande, M. O., Dikko, H. G., & Samson, A. (2015). Variance inflation factor: as a condition for the inclusion of suppressor variable (s) in regression analysis. Open journal of statistics, 5(07), 754. https://doi.org/10.4236/ojs.2015.57075
    Al-Mamary, Y. H., Shamsuddin, A., & Aziati, N. (2015). Investigating the key factors influencing on management information systems adoption among telecommunication companies in Yemen: The conceptual framework development. International Journal of Energy, Information and Communications, 6(1), 59-68. https://doi.org/10.14257/ijeic.2015.6.1.06
    Aldraiweesh, A., & Alturki, U. (2023). Exploring factors influencing the acceptance of e-learning and students’ cooperation skills in higher education. Sustainability, 15(12), 9363. https://doi.org/10.23956/ijermt.v6i6.279
    Almaiah, M. A., Alamri, M. M., & Al-Rahmi, W. (2019). Applying the UTAUT model to explain the students’ acceptance of mobile learning system in higher education. Ieee Access, 7, 174673-174686.https://doi.org/10.1109/ACCESS.2019.2957206
    An, X., Chai, C. S., Li, Y., Zhou, Y., & Yang, B. (2023b). Modeling students’ perceptions of artificial intelligence assisted language learning. Computer Assisted Language Learning, 1-22. https://doi.org/10.1076/call.11.5.543.5660
    An, X., Chai, C. S., Li, Y., Zhou, Y., Shen, X., Zheng, C., & Chen, M. (2023a). Modeling English teachers’ behavioral intention to use artificial intelligence in middle schools. Education and Information Technologies, 28(5), 5187-5208. https://doi.org/10.1007/s10639-022-11286-z
    Anderson, D. (2005). Peirce and the art of reasoning. Studies in Philosophy and Education, 24, 277-289. https://doi.org/10.1007/s11217-005-3849-9
    Annamalai, N., Ab Rashid, R., Hashmi, U. M., Mohamed, M., Alqaryouti, M. H., & Sadeq, A. E. (2023). Using chatbots for English language learning in higher education. Computers and Education: Artificial Intelligence, 5, 100153.https://doi.org/10.1201/9781003184157-4
    Armstrong, R. A. (2019). Should Pearson's correlation coefficient be avoided?. Ophthalmic and Physiological Optics, 39(5), 316-327.https://doi.org/10.1111/opo.12636
    Aslam, F. (2023). The impact of artificial intelligence on chatbot technology: A study on the current advancements and leading innovations. European Journal of Technology, 7(3), 62-72. https://doi.org/10.4018/JTA.20210101.oa6
    Ahvenharju, S., Lalot, F., Minkkinen, M., & Quiamzade, A. (2021). Individual futures consciousness: Psychology behind the five-dimensional Futures Consciousness scale. Futures, 128, 102708. https://doi.org/10.1016/j.futures.2021.102708
    Blut, M., Chong, A., Tsiga, Z., & Venkatesh, V. (2021). Meta-analysis of the unified theory of acceptance and use of technology (UTAUT): challenging its validity and charting A research agenda in the red ocean. Journal of the Association for Information Systems, forthcoming. https://doi.org/10.17705/1jais.00719
    Bong, M., & Skaalvik, E. M. (2003). Academic self-concept and self-efficacy: How different are they really?. Educational psychology review, 15, 1-40. https://doi.org/10.1111/j.2044-8279.1983.tb02562.x
    Bouhnik, D., & Giat, Y. (2009). Teaching high school students applied logical reasoning. Journal of Information Technology Education. Innovations in Practice, 8, 1. https://doi.org/10.28945/169
    Braun, V., Clarke, V., Boulton, E., Davey, L., & McEvoy, C. (2021). The online survey as a qualitative research tool. International Journal of Social Research Methodology: Theory & Practice, 24(6), 641-654. https://doi.org/10.1080/13645579.2020.1805550
    Bravo, M. J., Galiana, L., Rodrigo, M. F., Navarro-Pérez, J. J., & Oliver, A. (2020). An adaptation of the critical thinking disposition scale in Spanish youth. Thinking Skills and Creativity, 38, 100748. https://doi.org/10.1016/j.tsc.2020.100748
    Bronkhorst, H., Roorda, G., Suhre, C., & Goedhart, M. (2020). Logical reasoning in formal and everyday reasoning tasks. International Journal of Science and Mathematics Education, 18, 1673-1694. https://doi.org/10.1007/s10763-019-10039-8
    Bronkhorst, H., Roorda, G., Suhre, C., & Goedhart, M. (2022). Students’ use of formalisations for improved logical reasoning. Research in Mathematics Education, 24(3), 291-323. https://doi.org/10.1080/14794802.2021.1991463
    Browne, M. N., & Freeman, K. (2000). Distinguishing features of critical thinking classrooms. Teaching in higher education, 5(3), 301-309. https://doi.org/10.1080/713699143
    Brylina, I. V., Turchevskaya, B. K., Bogoryad, N. V., Brylin, V. I., & Chaplinskaya, Y. I. (2016). Critical thinking as a cognitive educational technology. In SHS Web of Conferences (Vol. 28, p. 01018). EDP Sciences. https://doi.org/10.1051/shsconf/20162801018
    Boubker, O. (2024). From chatting to self-educating: Can AI tools boost student learning outcomes?. Expert Systems with Applications, 238, 121820. https://doi.org/10.1016/j.eswa.2011.05.048
    Chai, C. S., Chiu, T. K., Wang, X., Jiang, F., & Lin, X. F. (2022). Modeling Chinese Secondary School Students’ Behavioral Intentions to Learn Artificial Intelligence with the Theory of Planned Behavior and Self-Determination Theory. Sustainability, 15(1), 605. https://doi.org/10.3390/math8112089
    Chan, C. (2019). Using digital storytelling to facilitate critical thinking disposition in youth civic engagement: A randomized control trial. Children and Youth Services Review, 107, 104522. https://doi.org/10.1016/j.childyouth.2019.104522
    Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling. Education and Information Technologies, 25, 3443-3463. https://doi.org/10.1007/s10639-020-10159-7
    Chen, Q., Liu, D., Zhou, C., & Tang, S. (2020). Relationship between critical. thinking.disposition and research competence among clinical nurses: A cross-sectional study. Journal of Clinical Nursing, 29(7-8), 1332-1340. https://doi.org/10.1111/jocn.15201
    Cherukunnath, D., & Singh, A. P. (2022). Exploring cognitive processes of knowledge acquisition to upgrade academic practices. Frontiers in Psychology, 13, 682628. https://doi.org/10.3389/fpsyg.2022.682628
    Clark, L. A., & Watson, D. (2016). Constructing validity: Basic issues in objective. scale development. https://doi.org/10.1037/14805-012
    Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillside. https://doi.org/10.2307/2290095
    Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Routledge. https://doi.org/10.4324/9780203771587
    Cronbach, L. J. (1951). Coefficient alpha and the internal structure of. tests. psychometrika, 16(3), 297-334. https://doi.org/10.1007/BF02310555
    Chen, H. L., Vicki Widarso, G., & Sutrisno, H. (2020). A chatbot for learning Chinese: Learning achievement and technology acceptance. Journal of Educational Computing Research, 58(6), 1161-1189. https://doi.org/10.1177/0735633120929622
    Chang, C. Y., Hwang, G. J., & Gau, M. L. (2022). Promoting students' learning achievement and self‐efficacy: A mobile chatbot approach for nursing training. British Journal of Educational Technology, 53(1), 171-188. https://doi.org/10.1111/bjet.13158
    D’Silva, G., Jani, M., Jadhav, V., Bhoir, A., & Amin, P. (2020). Career counselling chatbot using cognitive science and artificial intelligence. In Advanced Computing Technologies and Applications: Proceedings of 2nd International Conference on Advanced Computing Technologies and Applications—ICACTA 2020 (pp. 1-9). https://doi.org/10.1007/978-981-15-3242-9_1
    Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information tech¬nology. Mis Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
    De Neys, W., & Pennycook, G. (2019). Logic, fast and slow: Advances in dual-process theorizing. Current directions in psychological science, 28(5), 503-509. https://doi.org/10.1177/0963721419855658
    Durand-Guerrier, V., Boero, P., Douek, N., Epp, S. S., & Tanguay, D. (2012). Examining the role of logic in teaching proof. Proof and proving in mathematics education: The 19th ICMI study, 369-389. https://doi.org/10.1007/978-94-007-2129-6_16
    Dutot, V. (2015). Factors influencing near field communication (NFC) adoption: An extended TAM approach. The Journal of High Technology Management Research, 26(1), 45-57. https://doi.org/10.1016/j.hitech.2015.04.005
    Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21, 719-734. https://doi.org/10.1007/s10796-017-9774-y
    Dwyer, C. P., Hogan, M. J., & Stewart, I. (2014). An integrated critical thinking framework for the 21st century. Thinking skills and Creativity, 12, 43-52. https://doi.org/10.1016/j.tsc.2013.12.004
    Deveci Topal, A., Dilek Eren, C., & Kolburan Geçer, A. (2021). Chatbot application in a 5th grade science course. Education and Information Technologies, 26(5), 6241-6265. https://doi.org/10.1007/s10639-021-10627-8
    Deng, X., & Yu, Z. (2023). A meta-analysis and systematic review of the effect of chatbot technology use in sustainable education. Sustainability, 15(4), 2940. https://doi.org/10.3390/su13147683
    Ennis, R. H. (2015). Critical thinking: A streamlined conception. In The Palgrave handbook of critical thinking in higher education(pp. 31-47). New York: Palgrave Macmillan US. https://doi.org/10.1057/9781137378057_2
    Essel, H. B., Vlachopoulos, D., Essuman, A. B., & Amankwa, J. O. (2024). ChatGPT. effects on cognitive skills of undergraduate students: Receiving instant responses from AI-based conversational large language models (LLMs). Computers and Education: Artificial Intelligence, 6, 100198. https://doi.org/10.1016/0360-1315(79)90008-3
    Facione, N. C., Facione, P. A., & Sanchez, C. A. (1994). Critical thinking disposition as a measure of competent clinical judgment: The development of the California Critical Thinking Disposition Inventory. Journal of Nursing education, 33(8), 345-350. https://doi.org/10.3928/0148-4834-19941001-05
    Facione, P. (1990). Critical thinking: A statement of expert consensus for purposes of educational assessment and instruction (The Delphi Report). https://doi.org/10.1002/au.3650060611
    Feine, J., Gnewuch, U., Morana, S., & Maedche, A. (2019). A taxonomy of social cues for conversational agents. International Journal of Human-Computer Studies, 132, 138–161. https://doi.org/10.1016/j.ijhcs.2019.07.009
    Ferrell, O. C., & Ferrell, L. (2020). Technology challenges and opportunities facing marketing education. Marketing Education Review, 30(1), 3-14. https://doi.org/10.1080/10528008.2020.1718510
    Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An Introduction to theory and research. Reading, MA: Addison-Wesley. https://doi.org/10.2307/2065853
    Fisher, R. A. (1970). Statistical methods for research workers. In Breakthroughs in statistics: Methodology and distribution (pp. 66-70). New York, NY: Springer New York. https://doi.org/10.1007/978-1-4612-4380-9_5
    Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with. unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.2307/3151312
    Fryer, L. K., Ainley, M., Thompson, A., Gibson, A., & Sherlock, Z. (2017). Stimulating and sustaining interest in a language course: An experimental comparison of Chatbot and Human task partners. Computers in Human Behavior, 75, 461-468. https://doi.org/10.1016/j.chb.2017.05.045
    Fu, J., Ding, Y., Nie, K., & Zaigham, G. H. K. (2023). How does self-efficacy, learner personality, and learner anxiety affect critical thinking of students. Frontiers in Psychology, 14, 1289594. https://doi.org/10.3389/fpsyg.2022.871707
    Garone, A., Pynoo, B., Tondeur, J., Cocquyt, C., Vanslambrouck, S., Bruggeman, B., & Struyven, K. (2019). Clustering university teaching staff through UTAUT: Implications for the acceptance of a new learning management system. British Journal of Educational Technology, 50(5), 2466-2483. https://doi.org/10.1111/bjet.12867
    Gefen, D., Straub, D., & Boudreau, M. C. (2000). Structural equation modeling and. regression: Guidelines for research practice. Communications of the association for information systems, 4(1), 7. https://doi.org/10.17705/1CAIS.00407
    Gold, A. H., Malhotra, A., & Segars, A. H. (2001). Knowledge management: An. organizational capabilities perspective. Journal of management information systems, 18(1), 185-214. https://doi.org/10.1080/07421222.2001.11045669
    Graesser, A. C., Chipman, P., Haynes, B. C., & Olney, A. (2005). AutoTutor: An intelligent tutoring system with mixed-initiative dialogue. IEEE Transactions on Education, 48(4), 612-618. https://doi.org/10.1109/TE.2005.856149
    Grant, D. (1996). Generalizability of findings of exploratory practice-based research on polydrug-addicted mothers. Research on Social Work Practice, 6(3), 292-307. https://doi.org/10.1177/104973159600600302
    Guilford, J. P. (1950). Fundamental statistics in psychology and education.https://doi.org/10.1086/399015
    Gupta, P., & Bamel, U. (2023). Need for metacognition and critical thinking in the e‐learning ecosystem: The new normal in post Covid era. Global Business and Organizational Excellence, 43(1), 104-120. https://doi.org/10.1002/joe.22208
    Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook (p. 197). Springer Nature. https://doi.org/10.54055/ejtr.v6i2.134
    Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2009). Multivariate data. analysis (7th ed.). Prentice-Hall.
    Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152. https://doi.org/10.2753/MTP1069-6679190202
    Hair, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business researcher. European Business Review, 26, 106–121. https://doi.org/10.1016/j.jfbs.2014.01.002
    Hair, Jr, J. F., Sarstedt, M., Matthews, L. M., & Ringle, C. M. (2016). Identifying and. treating unobserved heterogeneity with FIMIX-PLS: part I–method. European business review, 28(1), 63-76. https://doi.org/10.1108/EBR-09-2015-0094
    Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial management & data systems, 117(3), 442-458. https://doi.org/10.1108/IMDS-04-2016-0130
    Haque, M. R., & Rubya, S. (2023). An Overview of Chatbot-Based Mobile Mental Health Apps: Insights From App Description and User Reviews. JMIR mHealth and uHealth, 11(1), e44838. https://doi.org/10.2196/41282
    Harman, H. H. (1976). Modern factor analysis. University of Chicago press. https://doi.org/10.1017/S0008439500031799
    Hatzikiriakou, K., & Metallidou, P. (2009). Teaching deductive reasoning to pre-service teachers: Promises and constraints. International Journal of Science and Mathematics Education, 7, 81-101. https://doi.org/10.1007/s10763-007-9113-8
    Hedley, M., & Markowitz, L. (2001). Avoiding moral dichotomies: Teaching controversial topics to resistant students. Teaching Sociology, 195-208. https://doi.org/10.2307/1318717
    Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing. discriminant validity in variance-based structural equation modeling. Journal of the academy of marketing science, 43, 115-135. https://doi.org/10.1007/s11747-014-0403-8
    Herrero, Á., & San Martín, H. (2017). Explaining the adoption of social networks sites for. sharing user-generated content: A revision of the UTAUT2. Computers in Human Behavior, 71, 209-217. https://doi.org/10.1016/j.chb.2017.02.00
    Hew, J. J., Lee, V. H., Ooi, K. B., & Lin, B. (2016). Mobile social commerce: The booster for brand loyalty?. Computers in Human Behavior, 59, 142-154. https://doi.org/10.1016/j.chb.2016.01.027
    Hillis, D. M., & Bull, J. J. (1993). An empirical test of bootstrapping as a method for. assessing confidence in phylogenetic analysis. Systematic biology, 42(2), 182-192. https://doi.org/10.1093/sysbio/42.2.182
    Hsu, C. L., & Lin, J. C. C. (2023). Understanding the user satisfaction and loyalty of customer service chatbots. Journal of Retailing and Consumer Services, 71, 103211. https://doi.org/10.1016/j.jretconser.2022.103211
    Hwang, G. J., & Chang, C. Y. (2023). A review of opportunities and challenges of chatbots in education. Interactive Learning Environments, 31(7), 4099-4112. https://doi.org/10.1080/10494820.2021.1952615
    Igbafe, E. C. (2022). The place and role of artificial intelligence chatbots in adult education and training of adult educators. In PUPIL: International Journal of Teaching, Education and Learning (Vol. 6, Issue 1, pp. 174–191). Global Research & Development Services. https://doi.org/10.20319/pijtel.2022.61.174191
    Ikuenobe, P. (2001). Teaching and assessing critical thinking abilities as outcomes in an informal logic course. Teaching in Higher Education, 6(1), 19-32. https://doi.org/10.1080/13562510020029572
    Im, I., Hong, S., & Kang, M. S. (2011). An international comparison of technology adoption: Testing the UTAUT model. Information & management, 48(1), 1-8. https://doi.org/10.1016/j.im.2010.09.001
    Jafari, Z., Yavari, S., & Ahmadi, S. D. (2015). THE IMPACT OF SELF-ASSESSMENT ON EFL LEARNERS'CRITICAL THINKING. Modern Journal of Language Teaching Methods, 5(1), 145. https://doi.org/10.11648/j.ijll.20150304.22
    Jager, J., Putnick, D. L., & Bornstein, M. H. (2017). II. More than just convenient: The scientific merits of homogeneous convenience samples. Monographs of the Society for Research in Child Development, 82(2), 13-30. https://doi.org/10.1111/mono.12296
    Johnson, D. G., & Verdicchio, M. (2017). AI anxiety. Journal of the Association for Information Science and Technology, 68(9), 2267-2270. https://doi.org/10.1002/asi.23867
    Kerly, A., & Bull, S. (2006, June). The potential for chatbots in negotiated learner modelling: A wizard-of-oz study. In International Conference on Intelligent Tutoring Systems (pp. 443-452). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/11774303_44
    Kikalishvili, S. (2023). Unlocking the potential of GPT-3 in education: opportunities, limitations, and recommendations for effective integration. Interactive Learning Environments, 1-13. https://doi.org/10.1080/10494820.2023.2220401
    Kim, N. Y., Cha, Y., & Kim, H. S. (2019). Future english learning: Chatbots and artificial intelligence. Multimedia-Assisted Language Learning, 22(3). https://doi.org/10.15702/mall.2019.22.3.32
    Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). Guilford Press.
    Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment. approach. International Journal of e-Collaboration (ijec), 11(4), 1-10. https://doi.org/10.1007/978-3-319-64069-3_11
    Kozikoglu, I. (2019). Investigating Critical Thinking in Prospective Teachers: Metacognitive Skills, Problem Solving Skills and Academic Self-Efficacy. Journal of Social Studies Education Research, 10(2), 111-130.
    Kucherbaev, P., Bozzon, A., & Houben, G. J. (2018). Human-aided bots. IEEE Internet Computing, 22(6), 36-43. https://doi.org/10.1109/MIC.2018.252095348
    Larson, M. G. (2006). Descriptive statistics and graphical displays. Circulation, 114(1), 76-81. https://doi.org/10.1161/CIRCULATIONAHA.105.584474
    Lee, M. H., & Tsai, C. C. (2010). Exploring teachers’ perceived self efficacy and technological pedagogical content knowledge with respect to educational use of the World Wide Web. Instructional Science, 38, 1-21. https://doi.org/10.1007/s11251-008-9075-4
    Likert, R. (1932). A technique for the measurement of attitudes. Archives of psychology.
    Lin, C. C., Huang, A. Y., & Yang, S. J. (2023). A review of ai-driven conversational chatbots implementation methodologies and challenges (1999–2022). Sustainability, 15(5), 4012. https://doi.org/10.3390/su15054012
    Lin, H. M., Lee, M. H., Liang, J. C., Chang, H. Y., Huang, P., & Tsai, C. C. (2020). A review of using partial least square structural equation modeling in e‐learning research. British Journal of Educational Technology, 51(4), 1354-1372. https://doi.org/10.1111/bjet.12890
    Lin, M. P. C., & Chang, D. (2020). Enhancing post-secondary writers’ writing skills with a chatbot. Journal of Educational Technology & Society, 23(1), 78-92.
    Liu, Y., & Pásztor, A. (2022). Design and validate the employer-employee-supported critical thinking disposition inventory (2ES-CTDI) for undergraduates. Thinking Skills and Creativity, 46, 101169. https://doi.org/10.1016/j.tsc.2022.101169
    Liu, Y., & Pásztor, A. (2023). Survey on the influential demographic factors of Chinese undergraduate students’ critical thinking disposition: Evidence from plausible values. Thinking Skills and Creativity, 50, 101397.educational assessment and instruction (The Delphi Report). https://doi.org/10.1016/j.tsc.2023.101397
    Lorencová, H., Jarošová, E., Avgitidou, S., & Dimitriadou, C. (2019). Critical thinking practices in teacher education programmes: a systematic review. Studies in Higher Education, 44(5), 844-859. https://doi.org/10.1080/03075079.2019.1586331
    Lupu, E., & Özcan, D. (2014). Aspects Of Logical Reasoning For Candidates Enrolled In The Admission Programme For Higher Education (18-27 Years). Procedia-Social and Behavioral Sciences, 131, 420-425. https://doi.org/10.1016/j.sbspro.2014.04.141
    Luce, C., & Kirnan, J. P. (2016). Using indirect vs. direct measures in the summative assessment of student learning in higher education. Journal of the Scholarship of Teaching and Learning, 16(4), 75-91. https://doi.org/10.14434/josotl.v16i4.19371
    Medeiros, M., Ozturk, A., Hancer, M., Weinland, J., & Okumus, B. (2022). Understanding travel tracking mobile application usage: An integration of self determination theory and UTAUT2. Tourism Management Perspectives, 42, 100949. https://doi.org/10.1016/j.tmp.2022.100949
    Mikic-Fonte, F. A., Llamas-Nistal, M., & Caeiro-Rodríguez, M. (2018, October). Using a Chatterbot as a FAQ Assistant in a Course about Computers Architecture. In 2018 IEEE Frontiers in Education Conference (FIE) (pp. 1-4). IEEE. https://doi.org/10.1109/FIE.2018.8659174
    Mytnyk, A., Matvienko, O., Guraliuk, A., Mykhalchuk, N., & Ivashkevych, E. (2021, May). The development of constructive interaction skill as a component of social success of junior pupil. In SOCIETY. INTEGRATION. EDUCATION. Proceedings of the International Scientific Conference (Vol. 2, pp. 387-401). https://doi.org/10.17770/sie2021vol2.6406
    Menon, D., & Shilpa, K. (2023). “Chatting with ChatGPT”: Analyzing the factors influencing users' intention to Use the Open AI's ChatGPT using the UTAUT model. Heliyon, 9(11). https://doi.org/10.1016/j.heliyon.2023.e20962
    NGunes, T., Bryant, P., Evans, D., Bell, D., Gardner, S., Gardner, A., & Carraher, J. (2007). The contribution of logical reasoning to the learning of mathematics in primary school. British Journal of Developmental Psychology, 25(1), 147-166. https://doi.org/10.1348/026151006X153127
    Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory. New York. NY: McGraw-Hill.
    Nunnally, J.C. and Bernstein, I.H. (1994) The Assessment of Reliability. Psychometric Theory, 3, 248-292.
    Nwarogu, I. A., & Lormbagah, A. J. (2020). Effect Of Indirect Cost On The Profitability Of Listed Firms In Nigeria. Nigerian Journal of Management Sciences, 6(2).
    Nwarogu, O. (2020). The Nexus Between Logic And General Studies. Journal of
    Humanities, Social Science and Creative Arts, 15(1).
    O’brien, R. M. (2007). A caution regarding rules of thumb for variance inflation. factors. Quality & quantity, 41, 673-690. https://doi.org/10.1007/s11135-006-9018-6
    Okonkwo, C. W., & Ade-Ibijola, A. (2021). Chatbots applications in education: A systematic. review. Computers and Education: Artificial Intelligence, 2, 100033. https://doi.org/10.1016/j.caeai.2021.100033
    Okonkwo, C. W., Huisman, M., & Taylor, E. (2019, April). The adoption of m-commerce. applications: rural dwellers perspectives. In 12th, IADIS, international conference. Information systems. https://doi.org/10.33965/is2019_201905L013
    Ooi, K. B., Hew, J. J., & Lee, V. H. (2018). Could the mobile and social perspectives. of mobile social learning platforms motivate learners to learn continuously?. Computers & Education, 120, 127-145. https://doi.org/10.1016/j.compedu.2018.01.017
    Osei, H. V., Kwateng, K. O., & Boateng, K. A. (2022). Integration of personality trait, motivation and UTAUT 2 to understand e-learning adoption in the era of COVID-19 pandemic. Education and Information Technologies, 27(8), 10705-10730. https://doi.org/10.1007/s10639-022-11047-y
    Ozili, P. K. (2023). The acceptable R-square in empirical modelling for social. science. research. In Social research methodology and publishing results: A guide to non-native English speakers (pp. 134-143). IGI global. https://doi.org/10.2139/ssrn.4128165
    Paschoal, L. N., de Oliveira, M. M., & Chicon, P. M. M. (2018, October). A. chatterbot. sensitive to student's context to help on software engineering education. In 2018 XLIV Latin American computer conference (CLEI) (pp. 839-848). IEEE. https://doi.org/10.1109/CLEI.2018.00105
    Peterson, R. A. (1994). A meta-analysis of Cronbach's coefficient alpha. Journal of consumer research, 21(2), 381-391. https://doi.org/10.1086/209405
    Peterson, R. A., & Merunka, D. R. (2014). Convenience samples of college students and research reproducibility. Journal of Business Research, 67(5), 1035-1041. https://doi.org/10.1016/j.jbusres.2013.08.010
    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. https://doi.org/10.1177/0013164493053003024
    Quinn, S., Hogan, M., Dwyer, C., Finn, P., & Fogarty, E. (2020). Development and validation of the student-educator negotiated critical thinking dispositions scale (SENCTDS). Thinking Skills and Creativity, 38, 100710. https://doi.org/10.1016/j.tsc.2020.100710
    Rajeswari, P. (2023). Attitudes and Motives of Using Social Media. Based Learning Lead to Critical Thinking Skills: Survey Among College Students. Annamalai International Journal of Business Studies & Research, 14(2).
    Ricketts, J. C., & Rudd, R. D. (2005). Critical Thinking Skills Of Selected Youth. Leaders: The Efficacy Of Critical Thinking Dispositions, Leadership, And Academic Performance. In Journal of Agricultural Education, 46(1), 32–43. American Association for Agricultural Education. https://doi.org/10.5032/jae.2005.01032
    Russell, D. W. (2002). In search of underlying dimensions: The use (and abuse) of. factor analysis in Personality and Social Psychology Bulletin. Personality and social psychology bulletin, 28(12), 1629-1646. https://doi.org/10.1177/014616702237645
    Saadé, R. G., Morin, D., & Thomas, J. D. (2012). Critical thinking in E-learning. environments. Computers in Human Behavior, 28(5), 1608-1617. https://doi.org/10.1016/j.chb.2012.03.025
    Schmaltz, R. M., Jansen, E., & Wenckowski, N. (2017). Redefining critical thinking: Teaching students to think like scientists. Frontiers in psychology, 459. https://doi.org/10.3389/fpsyg.2017.00459
    Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: appropriate use and interpretation. Anesthesia & analgesia, 126(5), 1763-1768. https://doi.org/10.1213/ANE.0000000000002864
    Shaw, N., & Sergueeva, K. (2019). The non-monetary benefits of mobile commerce: Extending UTAUT2 with perceived value. International journal of information management, 45, 44-55. https://doi.org/10.1016/j.ijinfomgt.2018.10.024
    Shorey, S., Ang, E., Yap, J., Ng, E. D., Lau, S. T., & Chui, C. K. (2019). A virtual. counseling. application using artificial intelligence for communication skills training in nursing education: Development study. Journal of Medical Internet Research, 21(10), e14658. https://doi.org/10.2196/14658
    Siburian, J., Corebima, A. D., & Saptasari, M. (2019). The correlation between. critical and. creative thinking skills on cognitive learning results. Eurasian Journal of Educational Research, 19(81), 99-114. https://doi.org/10.14689/ejer.2019.81.6
    Sinha, S., Basak, S., Dey, Y., & Mondal, A. (2020). An educational chatbot for answering. queries. In Emerging Technology in Modelling and Graphics: Proceedings of IEM Graph 2018 (pp. 55-60). https://doi.org/10.1007/978-981-13-7403-6_7
    Smutny, P., & Schreiberova, P. (2020). Chatbots for learning: A review of educational. chatbots for the facebook messenger. Computers & Education, 151. https://doi.org/10.1016/j.compedu.2020.103862
    Soper, D. S. (2010). The free statistics calculators website. Online software.
    Sosu, E. M. (2013). The development and psychometric validation of a Critical. Thinking. Disposition Scale. Thinking skills and creativity, 9, 107-119. https://doi.org/10.1016/j.tsc.2012.09.002
    Steel, R. G. D., & Torrie, J. H. (1960). Principles and procedures of. statistics. Principles and procedures of statistics. https://doi.org/10.2307/2530180
    Suliman, W. A., & Halabi, J. (2007). Critical thinking, self-esteem, and state anxiety. of nursing students. Nurse education today, 27(2), 162-168. https://doi.org/10.1016/j.nedt.2006.04.008
    Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting. research instruments in science education. Research in science education, 48, 1273-1296. https://doi.org/10.1007/s11165-016-9602-2
    Taherdoost, H. (2018). A review of technology acceptance and adoption models and. theories. Procedia manufacturing, 22, 960-967. https://doi.org/10.1016/j.promfg.2018.03.137
    Tam, W., Huynh, T., Tang, A., Luong, S., Khatri, Y., & Zhou, W. (2023). Nursing education. in the age of artificial intelligence powered Chatbots (AI-Chatbots): Are we ready yet?. Nurse Education Today, 129, 105917. https://doi.org/10.1016/j.nedt.2023.105917
    Tamilmani, K., Rana, N. P., & Dwivedi, Y. K. (2021). Consumer acceptance and use. of information technology: A meta-analytic evaluation of UTAUT2. Information https://doi.org/10.1007/s10796-020-10007-6
    Teo, T., Lee, C. B., & Chai, C. S. (2008). Understanding pre‐service teachers' computer attitudes: applying and extending the technology acceptance model. Journal of computer assisted learning, 24(2), 128-143. https://doi.org/10.1111/j.1365-2729.2007.00247.x
    Upadhyay, N., Upadhyay, S., Abed, S. S., & Dwivedi, Y. K. (2022). Consumer adoption of mobile payment services during COVID-19: Extending meta-UTAUT with perceived severity and self-efficacy. International Journal of Bank Marketing, 40(5), 960-991. https://doi.org/10.1108/IJBM-06-2021-0262
    Van Griethuijsen, R. A., van Eijck, M. W., Haste, H., Den Brok, P. J., Skinner, N. C., Mansour, N., ... & BouJaoude, S. (2015). Global patterns in students’ views of science and interest in science. Research in science education, 45, 581-603. https://doi.org/10.1007/s11165-014-9438-6
    Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance. of information tech¬nology: Toward a unified view. Mis Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
    Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 157-178. https://doi.org/10.2307/41410412
    Villacís, C., Fuertes, W., Bustamante, A., Almachi, D., Procel, C., Fuertes, S., & Toulkeridis, T. (2014, October). Multi-player educational video game over cloud to stimulate logical reasoning of children. In 2014 IEEE/ACM 18th International Symposium on Distributed Simulation and Real Time Applications (pp. 129-137). IEEE. https://doi.org/10.1109/DS-RT.2014.24
    Walldén, S., Mäkinen, E., & Raisamo, R. (2016). A review on objective measurement. of. usage in technology acceptance studies. Universal Access in the Information Society, 15, 713-726. https://doi.org/10.1007/s10209-015-0443-y
    Wang, X., Lin, X., & Shao, B. (2022). How does artificial intelligence create business agility? Evidence from chatbots. International journal of information management, 66, 102535. https://doi.org/10.1016/j.ijinfomgt.2022.102535
    Wang, Y. Y., & Wang, Y. S. (2022). Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interactive Learning Environments, 30(4), 619-634. https://doi.org/10.1080/10494820.2019.1674887
    Westland, J. C. (2010). Lower bounds on sample size in structural equation modeling. Electronic commerce research and applications, 9(6), 476-487. https://doi.org/10.1016/j.elerap.2012.06.001
    Wichadee, S. (2014). Students' Learning Behavior, Motivation and Critical Thinking in Learning Management Systems. Journal of Educators Online, 11(3), n3. https://doi.org/10.9743/JEO.2014.3.3
    Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015). The unified theory of acceptance and use of technology (UTAUT): a literature review. Journal of enterprise information management, 28(3), 443-488. https://doi.org/10.1108/JEIM-09-2014-0088
    Wu, E. H. K., Lin, C. H., Ou, Y. Y., Liu, C. Z., Wang, W. K., & Chao, C. Y. (2020). Advantages and constraints of a hybrid model K-12 E-Learning assistant chatbot. Ieee Access, 8, 77788-77801. https://doi.org/10.1109/ACCESS.2020.2988252
    Wu, R., & Yu, Z. (2023). Do AI chatbots improve students learning outcomes? Evidence from a meta‐analysis. British Journal of Educational Technology. https://doi.org/10.1111/jcal.12664
    Yang, H. (2023). How I use ChatGPT responsibly in my teaching. Nature.
    Yang, K. M., Tsai, C. H., Lee, L. C., & Chen, C. T. (2016, May). The employment of. online. self-learning coding course to enhance logical reasoning ability for fifth and sixth grader students. In 2016 International Conference on Applied System Innovation (ICASI) (pp. 1-4). IEEE. https://doi.org/10.1109/ICASI.2016.7539890
    Yulian, R. (2021). The flipped classroom: Improving critical thinking for critical reading of. EFL learners in higher education. Studies in English Language and Education, 8(2), 508-522. https://doi.org/10.24815/siele.v8i2.18366
    Zhao, X., Lynch Jr, J. G., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths. and truths about mediation analysis. Journal of consumer research, 37(2), 197-206. https://doi.org/10.1086/651257
    Zhao, Y., Liu, Y., & Wu, H. (2024). Relationships among critical thinking. disposition. components of Chinese undergraduates: A moderated mediating effect analysis. International Journal of Educational Research, 124, 102306. https://doi.org/10.1016/j.ijer.2023.102306
    Zheng, J., & Li, S. (2020). What drives students’ intention to use tablet computers: An extended technology acceptance model. International Journal of Educational Research, 102, 101612. https://doi.org/10.1016/j.ijer.2020.101612
    Zhou, L., Gao, J., Li, D., & Shum, H. Y. (2020). The design and implementation of. xiaoice, an empathetic social chatbot. Computational Linguistics, 46(1), 53-93. https://doi.org/10.1162/coli_a_00368
    Zhou, Y., An, X., Li, X., Li, L., Gong, X., Li, Y., Chai, C. S., Liang, J., & Tsai, C. (2022). A. literature review of questionnaires for the assessment of online learning with a specific focus on the factors and items employed. Australasian Journal of Educational Technology, 38(1), 182–204. https://doi. org/10.14742/ajet.6719
    Zhou, Y., Chai, C. S., Liang, J. C., Jin, M., & Tsai, C. C. (2017). The relationship between teachers’ online homework guidance and technological pedagogical content knowledge about educational use of web. The Asia-Pacific Education Researcher, 26, 239-247. https://doi.org/10.1007/s40299-017-0344-3
    Zoubir, A. M., & Boashash, B. (1998). The bootstrap and its application in signal processing. IEEE signal processing magazine, 15(1), 56-76.https://doi.org/10.1109/79.647043
    Zimmerman, B. J. (2000). Self-efficacy: An essential motive to learn. Contemporary educational psychology, 25(1), 82-91.https://doi.org/10.1006/ceps.1999.1016
    Zhan, Y., Yan, Z., Wan, Z. H., Wang, X., Zeng, Y., Yang, M., & Yang, L. (2023). Effects of online peer assessment on higher‐order thinking: A meta‐analysis. British Journal of Educational Technology, 54(4), 817-835.https://doi.org/10.1111/bjet.13310

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