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題名 具有聲調診斷與回饋機制之遊戲式華語學習系統對於促進發音成效之影響研究
The Effects of a Game-based Mandarin Learning System with Tone Diagnosis and Visualization Feedback Mechanism on Pronunciation Effectiveness
作者 辛佳珈
Xin, Jia-Jia
貢獻者 陳志銘
Chen, Chih-Ming
辛佳珈
Xin, Jia-Jia
關鍵詞 以華語為第二語言
電腦輔助發音訓練
數位遊戲式語言學習
聲調診斷與回饋機制
華語變調
華語聲調感知
華語發音學習
學業情緒
學習行為
Chinese as a second language
Computer-Assisted Pronunciation Training
Digital Game-based Language Learning
Tone Diagnosis and Visualization Feedback Mechanism
Mandarin tone sandhi
Mandarin tone perception
Mandarin pronunciation performance
Achievement emotions
Learning behaviors
日期 2023
上傳時間 2-Aug-2023 14:27:56 (UTC+8)
摘要 華語是全球廣泛使用的語言之一，相較於其他語言而言，其獨特的聲調特性是以華語為第二語言學習者進行發音學習時的困難挑戰。在語言能力訓練中，「發音」是溝通表達的關鍵要素，然而目前並沒有結合電腦輔助發音訓練，以及數位遊戲式語言學習，並將其應用於華語學習之相關研究。綜合以上所述， 本研究發展具「聲調診斷與回饋機制」之遊戲式華語學習系統，輔助華語為第二語言學習者在訓練發音時能獲得個人化即時診斷與回饋，並且透過比較自己與母語者的聲調輪廓方式來改善自己的華語發音，同時促進其華語聲調感知與發音成效，以及對於華語變調規則之理解。此外，探討學習者使用有無「聲調診斷與回饋機制」之遊戲式華語學習系統輔以學習時所經歷的學業情緒差異，並探討正負面情緒與華語發音成效之關聯性。最後，本研究進一步分析實驗組高低不同成效學習者在「遊戲行為」與「發音學習行為」的行為模式差異，以及其對於發音成效的影響。
本研究採用真實驗研究法，招募台灣大專院校母語非華語之國際學生，共計14名研究對象，並將其隨機分派為實驗組與控制組進行線上實驗。實驗組使用具有「聲調診斷與回饋機制」之遊戲式華語學習系統，控制組則使用不具「聲調診斷與回饋機制」之遊戲式華語學習系統輔以華語發音學習。實驗為期一個月，兩組學習者的系統教材皆相同，並且可自行決定每日上線時間與學習進度，藉以比較兩組在華語聲調感知成效、華語發音成效、科技接受度、學業情緒，以及學習行為之差異。另外，亦透過質性訪談分析了解兩組學習者的使用感想與建議，並將其歸納後提出教學實施與系統改善建議，以及未來研究方向。
　　研究結果顯示，使用具有「聲調診斷與回饋機制之遊戲式華語學習系統」較能有效提升華語聲調感知與華語發音成效，對於華語變調規則的理解亦有所助益。此外，實驗組的科技接受度略高於控制組，表示「聲調診斷與回饋機制」兼具有用性及易用性。在學業情緒方面，兩組在學習過程中均有偏高的正面情緒體驗，負面情緒感受則偏低。其中實驗組的華語發音先備能力與羞愧、無望呈現高度負相關；華語後測成績與焦慮、羞愧，以及無望呈現高度負相關；而其發音進步幅度則與享受、自豪呈現高度正相關，並且與無趣呈現高度負相關。除此之外，實驗組高成效學習者使用「聲調診斷與回饋機制」之遊戲式華語學習系統輔以華語發音學習之預設詞彙學習次數與發音進步幅度呈現完全正相關，證明此一機制是提升華語發音成效的關鍵。最後，透過高成效學習者的學習行為模式可知，將「聲調診斷與回饋機制」搭配其他遊戲式學習功能輔以學習，不但可以增進持續使用此一系統學習之意願，並且可同時提升其華語發音成效。本研究發展之「聲調診斷與回饋機制之遊戲式華語學習系統」具有創新性與實用性，可有效支援個人化之華語發音自主學習。
Mandarin Chinese is one of the most widely spoken languages in the world, and its unique tones pose a significant challenge for Chinese as a second language (CSL) learners. Pronunciation skills play a crucial role in language proficiency, but so far there is a lack of research combining Computer-Assisted Pronunciation Training (CAPT) and Digital Game-based Language Learning (DGBLL) to aid language pronunciation learning, specifically for Mandarin Chinese. Therefore, this study developed a Mandarin Tones Learning Game (MTLG) with a “Tone Diagnosing and Visualization Feedback Mechanism” to assist CSL learners in personalized and real-time feedback during pronunciation learning. The system enables learners to improve their pronunciation by comparing their pitch contours with those of the native speaker, hoping to enhance their Mandarin tones perception, Mandarin pronunciation performance, and Mandarin tone change rules. Additionally, this study also examines the differences in achievement emotions experienced by learners in different groups and investigates the correlations between positive/negative emotions and Mandarin pronunciation. Finally, the study analyzes the behavioral patterns of learners with different proficiency levels in the experimental group and the impact on “game behavior” and “pronunciation learning behavior” in relation to pronunciation performance.
This research adopts a true-experimental design, recruiting a total of 14 international students from universities in Taiwan whose native languages were not Mandarin Chinese to be research participants. Each participant was randomly assigned to the experimental and control group respectively using “MTLG with/without Tone Diagnosis and Visualization Feedback Mechanism” for an experiment that lasted for one month, with both groups using the same materials and having the flexibility to autonomously decide their daily online learning time and learning progress. This study compares the differences between the two groups regarding Mandarin tone perception, Mandarin pronunciation performance, technology acceptance, achievement emotions, and learning behaviors. The interview was conducted to gather participants’ feedback and suggestions, which were analyzed to provide recommendations for teaching implementation, system improvement, and future research directions.
Based on the experimental results, the experimental group of learners was found to significantly enhance Mandarin tone perception, Mandarin pronunciation performance, and understanding of tone change rules, but not being found in the control group of learners. Besides, the experimental group of learners showed slightly higher levels of technology acceptance than the control group, indicating that the Tone Diagnosis and Visualization Feedback Mechanism is useful and user-friendly in aiding Mandarin pronunciation learning. In terms of achievement emotions, both groups experienced relatively high levels of positive emotions and relatively low levels of negative emotions during the learning process. Among the experimental group of learners, there was a strong negative correlation between prior knowledge and shame, hopelessness, as well as a strong negative correlation between post-test scores and anxiety, shame, and hopelessness. Furthermore, the improvement in Mandarin pronunciation was highly positively correlated with enjoyment and pride, while showing a strong negative correlation with boredom.
Moreover, among high-performing learners in the experimental group, the frequency of learning must-learn vocabulary words with the Tone Diagnosis Mechanism and the improvement in pronunciation showed a strong positive correlation, confirming the critical role of this mechanism in enhancing Mandarin pronunciation performance. The learning behavior patterns of high-performing learners revealed that combining the "Tone Diagnosis and Visualization Feedback mechanism" with other game-based learning features can improve the willingness to continue using the system and simultaneously enhance Mandarin pronunciation performance. Overall speaking, the “MTLG with Tone Diagnosis and Visualization Feedback Mechanism” developed in this study is innovative and practical, and can effectively support personalized and autonomous learning of Mandarin pronunciation.
參考文獻 王維聰 、王建喬 (2011)。數位遊戲式學習系統。科學發展，467，46-51。
余字涵 (2021)。具有影片字彙標註機制之遊戲式華語學習系統促進華語聽力學習成效之影響研究。國立政治大學，台北市。
周郁凱 (2017)。遊戲化實戰全書：遊戲化大師教你把工作、教學、健身、行銷、產品設計……變遊戲，愈好玩就愈有吸引力！。台北市：城邦商業周刊。
胡瑞雪 (2021)。在臺國際大學生華語文學業成敗歸因與華語文學業情緒關聯之初步探究。華文世界，(127)，26-55。
郭智博 (2020)。具遊戲激勵機制之影片日語學習系統發展與應用於提升企業員工語言學習成效之影響研究。國立政治大學圖書資訊學數位碩士在職專班碩士論文，台北市。
張學謙 (2016)。走向添加式雙語主義：強化家庭與學校的母語教育。臺灣教育評論月刊，5(9)，1-9。
劉慧娟(2017)。初級越南學習者華語聲調學習之研究。華語文教學研究，14(1)，81-118。
鄭靜宜 (2012)。華語雙音節詞基頻的聲調共構效果。台灣聽力語言學會雜誌，(28)，27-48。
Agarwal, C., & Chakraborty, P. (2019). A review of tools and techniques for computer aided pronunciation training (CAPT) in English. Education and Information Technologies, 24(6), 3731-3743.
Akter, S., & Song, J. (2021). Problems in Pronunciation and Perception of Mandarin Tones: An Empirical Study on Bangladeshi Undergraduate Students in China. Journal of Higher Education Theory & Practice, 21(16).
Anderson, J. R. (2009). Cognitive Psychology and Its Implications (7th ed.). NY: Worth Publishers.
Bakar, Z. A., & Abdullah, M. R. (2015). Importance of correct pronunciation in spoken English: Dimension of second language learners’ perspective. Pertanika Journal of Social Sciences and Humanities, 23(8), 143-158.
Baran-Łucarz, M. (2016). Conceptualizing and measuring the construct of pronunciation anxiety: Results of a pilot study. In Classroom-oriented research (pp. 39-56). Springer, Cham.
Bieleke, M., Goetz, T., Yanagida, T., Botes, E., Frenzel, A. C., & Pekrun, R. (2022). Measuring emotions in mathematics: the Achievement Emotions Questionnaire—Mathematics (AEQ-M). ZDM–Mathematics Education, 1-16.
Charles, D., Charles, T., McNeill, M., Bustard, D., & Black, M. (2011). Game‐based feedback for educational multi‐user virtual environments. British Journal of Educational Technology, 42(4), 638-654.
Chen, B., & Hsu, Y. C. (2019). Mandarin Chinese mispronunciation detection and diagnosis leveraging deep neural network based acoustic modeling and training techniques. In Computational and Corpus Approaches to Chinese Language Learning (pp. 217-234). Springer, Singapore.
Chen, M. (2022). Computer-aided feedback on the pronunciation of Mandarin Chinese tones: using Praat to promote multimedia foreign language learning. Computer Assisted Language Learning, 1-26.
Chou, Y. K. (2016). Actionable gamification: Beyond points, badges and leaderboards. London: Leanpub.
Chun, D. M., Jiang, Y., & Natalia, A. Ì. (2013). Visualization of tone for learning Mandarin Chinese. Pronunciation in Second Language Learning and Teaching Proceedings, 4(1).
Chun, D. M., Jiang, Y., Meyr, J., & Yang, R. (2015). Acquisition of L2 Mandarin Chinese tones with learner-created tone visualizations. Journal of Second Language Pronunciation, 1(1), 86-114.
Cucchiarini, C., Neri, A., & Strik, H. (2009). Oral proficiency training in Dutch L2: The contribution of ASR-based corrective feedback. Speech Communication, 51(10), 853-863.
Davari, H., Karami, H., Nourzadeh, S., & Iranmehr, A. (2020). Examining the validity of the Achievement Emotions Questionnaire for measuring more emotions in the foreign language classroom. Journal of Multilingual and Multicultural Development, 1-14.
Davis, F. (1989). Technology Acceptance Model: Origins. Working Papers on Information Systems, 35-59.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319-340.
Deng, L., Wu, S., Chen, Y., & Peng, Z. (2020). Digital game‐based learning in a Shanghai primary‐school mathematics class: A case study. Journal of Computer Assisted Learning, 36(5), 709-717.
Dewaele, J. M., Witney, J., Saito, K., & Dewaele, L. (2018). Foreign language enjoyment and anxiety: The effect of teacher and learner variables. Language teaching research, 22(6), 676-697.
Dixon, D. H., Dixon, T., & Jordan, E. (2022). Second language (L2) gains through digital game-based language learning (DGBLL): A meta-analysis. Language Learning & Technology, 26(1), 1-25.
Dong, Z. R. (2021). Tone and vocabulary learning for classroom-based L2 Mandarin learners: Pedagogical implications from current tone word processing research. Chinese as a Second Language (漢語教學研究—美國中文教師學會學報).
Eberhard, David M., Gary F. Simons, and Charles D. Fennig (eds.). 2022. Ethnologue: Languages of the World. Twenty-fifth edition. Dallas, Texas: SIL International. Online version: http://www.ethnologue.com.
Economou, D., Doumanis, I., Pedersen, F., Kathrani, P., Mentzelopoulos, M., & Bouki, V. (2015). Evaluation of a dynamic role-playing platform for simulations based on Octalysis gamification framework. In Workshop Proceedings of the 11th International Conference on Intelligent Environments (pp. 388-395). IOS Press.
Erhel, S., & Jamet, E. (2013). Digital game-based learning: Impact of instructions and feedback on motivation and learning effectiveness. Computers & Education, 67, 156-167.
Fauzi, A., Wandira, R., Sepri, D., & Hafid, A. (2021). Exploring students` acceptance of google classroom during the covid-19 pandemic by using the technology acceptance model in West Sumatera Universities. Electronic Journal of E-Learning, 19(4), pp233-240.
Fierro-Suero, S., Almagro, B. J., & Sáenz-López, P. (2020). Validation of the achievement emotions questionnaire for physical education (AEQ-PE). International Journal of Environmental Research and Public Health, 17(12), 4560.
Flege, J. E. (2003). Assessing constraints on second-language segmental production and perception. Phonetics and phonology in language comprehension and production: Differences and similarities, 6, 319-355.
Fouz-González, J. (2015). Trends and directions in computer-assisted pronunciation training. Investigating English Pronunciation, 314-342.
Freitas, S. A. A., Lacerda, A. R., Calado, P. M., Lima, T. S., & Canedo, E. D. (2017, October). Gamification in education: A methodology to identify student`s profile. In 2017 IEEE Frontiers in Education Conference (FIE) (pp. 1-8). IEEE.
Frenzel, A. C., Thrash, T. M., Pekrun, R., & Goetz, T. (2007). Achievement emotions in Germany and China: A cross-cultural validation of the Academic Emotions Questionnaire—Mathematics. Journal of cross-cultural psychology, 38(3), 302-309.
Godwin-Jones, R. (2014). Emerging Technologies - Games in Language Learning: Opportunities and Challenges. Language Learning & Technology, 18(2), 9–19.
Gong, Y., Hu, X., & Lai, C. (2018). Chinese as a second language teachers’ cognition in teaching intercultural communicative competence. System, 78, 224-233.
Han, J. H., & Sa, H. J. (2022). Acceptance of and satisfaction with online educational classes through the technology acceptance model (TAM): The COVID-19 situation in Korea. Asia Pacific Education Review, 23(3), 403-415.
Harrison, C. J., Könings, K. D., Molyneux, A., Schuwirth, L., Wass, V., & van der Vleuten, C. (2015). Barriers to the uptake and use of feedback in the context of summative assessment. Advances in Health Sciences Education, 20, 229–245
Hiriart, J. F. V. (2019). Gaming the past: Designing and using digital games as historical learning contexts (Doctoral dissertation, University of Salford).
Hitosugi, C. I., Schmidt, M., & Hayashi, K. (2014). Digital game-based learning (DGBL) in the L2 classroom: The impact of the UN`s off-the-shelf videogame, Food Force, on learner affect and vocabulary retention. Calico Journal, 31(1), 19-39.
Hongnaphadol, W., & Attanak, A. (2022). Reducing Thai EFL Students’ Pronunciation Anxiety through a CAPT-Based Reading Progress Application. Journal of Liberal Arts Prince of Songkla University, 14(1), 83-122.
Horwitz, E. K., Horwitz, M. B., & Cope, J. (1986). Foreign language classroom anxiety. The Modern Language Journal, 70(2), 125-132.
Howie, J. M., & Howie, J. M. (1976). Acoustical studies of Mandarin vowels and tones (Vol. 18). Cambridge University Press.
Howie, John (1976). Acoustical studies of Mandarin vowels and tones. Cambridge University Press.
Hsu, L. (2016). An empirical examination of EFL learners` perceptual learning styles and acceptance of ASR-based computer-assisted pronunciation training. Computer Assisted Language Learning, 29(5), 881-900.
Hsu, Y. C., Yang, M. H., Hung, H. T., Lin, Y. J., Chen, K. Y., & Chen, B. (2016). Evaluation Metric-related Optimization Methods for Mandarin Mispronunciation Detection. In International Journal of Computational Linguistics & Chinese Language Processing, Volume 21, Number 2, December 2016.
Hung, H. T., Yang, J. C., Hwang, G. J., Chu, H. C., & Wang, C. C. (2018). A scoping review of research on digital game-based language learning. Computers & Education, 126, 89-104.
Hussein, M. H., Ow, S. H., Cheong, L. S., Thong, M. K., & Ebrahim, N. A. (2019). Effects of digital game-based learning on elementary science learning: A systematic review. IEEE Access, 7, 62465-62478.
Ibrahim, M. F., Kuan, G., Hashim, H. A., Hamzah, N. A., & Kueh, Y. C. (2021). Measuring achievement emotions questionnaire for physical education (AEQ-PE): a confirmatory study in Malay language. BMC Public Health, 21(1), 1-8.
Jeon, J. (2014). Development and construct validation of the achievement emotions questionnaire-Korean middle school science (AEQ-KMS). Journal of the Korean Association for Science Education, 34(8), 745-754.
Kalikow, D., & Swets, J. (1972). Experiments with computer-controlled displays in second-language learning. IEEE Transactions on Audio and Electroacoustics, 20(1), 23-28.
Ku, O., Chen, S. Y., Wu, D. H., Lao, A. C. C., & Chan, T. W. (2014). The Effects of game-based learning on mathematical confidence and performance: High ability vs. low ability. Educational Technology & Society, 17(3), 65–78.
Lee, W.-S. & Zee, E. (2014). “Chinese phonetics,” in The Handbook of Chinese Linguistics, eds C. J. Huang, Y. A. Li, and A. Simpson (Hoboken: John Wiley & Sons), 367–399.
Lee, Y., Kozar, K. A., & Larsen, K. R. (2003). The technology acceptance model: Past, present, and future. Communications of the Association for information systems, 12(1), 50.
Lei, H., Wang, C., Chiu, M. M., & Chen, S. (2022). Do educational games affect students` achievement emotions? Evidence from a meta‐analysis. Journal of Computer Assisted Learning.
Lewis, M. P., Simons, G. F., & Fennig, C. D. (Eds.). (2015). Ethnologue: Languages of the world (18th ed.). Dallas, TX: SIL International.
Li, W., Siniscalchi, S. M., Chen, N. F., & Lee, C. H. (2016, March). Improving non-native mispronunciation detection and enriching diagnostic feedback with DNN-based speech attribute modeling. In 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 6135-6139). IEEE.
Li, Y. (2016). English and Thai Speakers` Perception of Mandarin Tones. English language teaching, 9(1), 122-132.
Liao, H. C., Guan, Y. H., Tu, J. J., & Chen, J. C. (2014). A prototype of an adaptive Chinese pronunciation training system. System, 45, 52-66.
Lin, Mao-Ts’an. (1965). The pitch indicator and the pitch characteristics of tones in Standard Chinese. Acta Acoustica (2), 8–15.
Liu, S., & Samuel, A. G. (2004). Perception of Mandarin lexical tones when F0 information is neutralized. Language and Speech, 47, pp. 109-138.
Liu, Y. C., Wang, W. T., & Lee, T. L. (2021). An integrated view of information feedback, game quality, and autonomous motivation for evaluating game-based learning effectiveness. Journal of Educational Computing Research, 59(1), 3-40.
Llisterri, J. (1995). Relationships between speech production and speech perception in a second language. In Proceedings of the 13th International Congress of Phonetics Sciences (pp. 92–99), Stockholm.
Lundblade, K. (2021). Watch Me Make History: Reenacting and Remaking the Past in Historical Game Live Streams. Popular Culture Studies Journal, 9, 69-87.
Macías León, K., de las Heras Pérez, M. Á., Romero Fernández, R., González Castanedo, Y., & Sáenz-López, P. (2022). Validation of the Achievement Emotions Questionnaire for Experimental Science Education (AEQ-S). Behavioral Sciences, 12(12), 480.
MacIntyre, P. D., Gregersen, T., & Mercer, S. (2016). Positive psychology in SLA, Multilingual Matters. Bristol.
Marangunić, N., & Granić, A. (2015). Technology acceptance model: a literature review from 1986 to 2013. Universal access in the information society, 14(1), 81-95.
Marisa, F., Ahmad, S. S. S., Yusoh, Z. I. M., Maukar, A. L., Marcus, R. D., & Widodo, A. A. (2020). Evaluation of student core drives on e-learning during the covid-19 with Octalysis gamification framework. International Journal of Advanced Computer Science and Applications, 11(11).
McIntosh, M. J., & Morse, J. M. (2015). Situating and constructing diversity in semi-structured interviews. Global qualitative nursing research, 2.
Meng, N. (2021). “Repeat After Me”: Is there a better way to correct tone errors in teaching Mandarin Chinese as a Second. The Acquisition of Chinese as a Second Language Pronunciation: Segments and Prosody, 163.
Neri, A., Cuccharini, C., Strik, H. (2006). ASR corrective feedback on pronunciation: does it really work? In Proc. ISCA Interspeech, Pittsburgh, PA, pp. 1982–1985.
Neri, A., Cucchiarini, C., Strik, H., & Boves, L. (2002). The pedagogy-technology interface in computer assisted pronunciation training. Computer Assisted Language Learning, 15(5), 441-467.
Neri, A., Mich, O., Gerosa, M., & Giuliani, D. (2008). The effectiveness of computer assisted pronunciation training for foreign language learning by children. Computer Assisted Language Learning, 21(5), 393-408.
Norman, D. A. (1993). Things that make us smart: defending human attributes in the age of the machine, Perseus Books, Cambridge, MA.
Pekrun, R. (2000). A social cognitive, control-value theory of achievement emotions. In J. Heckhausen (Ed.), Motivational Psychology of Human Development. Oxford, UK: Elsevier.
Pekrun, R. 2006. The Control-Value Theory of Achievement Emotions: Assumptions, Corollaries, and Implications for Educational Research and Practice. Educational Psychology Review 18 (4): 315–341. doi: https://doi.org/ 10.1007/s10648-006- 9029-9
Pekrun, R., & Perry, R. P. (2014). Control-value theory of achievement emotions. In International Handbook of Emotions in Education (pp. 130-151). Routledge.
Pekrun, R., & Stephens, E. J. (2010). Achievement emotions: A control‐value approach. Social and Personality Psychology Compass, 4(4), 238-255.
Pekrun, R., Cusack, A., Murayama, K., Elliot, A. J., & Thomas, K. (2014). The power of anticipated feedback: Effects on students’ achievement goals and achievement emotions. Learning and Instruction, 29, 115–124
Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. P. (2011). Measuring emotions in students’ learning and performance: The Achievement Emotions Questionnaire (AEQ). Contemporary Educational Psychology, 36(1), 36-48.
Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic emotions in students` self-regulated learning and achievement: A program of qualitative and quantitative research. Educational Psychologist, 37(2), 91-105.
Pelzl, E. (2019). What makes second language perception of Mandarin tones hard?: A non-technical review of evidence from psycholinguistic research. Chinese as a Second Language. The Journal of the Chinese Language Teachers Association, USA, 54(1), 51-78.
Peng, X., Chen, H., Wang, L., Tian, F., & Wang, H. (2020). Talking head-based L2 pronunciation training: Impact on achievement emotions, cognitive load, and their relationships with learning performance. International Journal of Human–Computer Interaction, 36(16), 1487-1502.
Prasetyo, F. H., & Sofyan, H. (2020, May). Digital game-based learning model and design elements to increase student learning motivation in English listening skills. In International Conference on Online and Blended Learning 2019 (ICOBL 2019) (pp. 134-137). Atlantis Press.
Pratama, g. D., & Kusuma, g. P. (2022). Implementation of gamification framework on online learning of procedural programming. Journal of Theoretical and Applied Information Technology, 100(22).
Reinders, H., & Wattana, S. (2015). Affect and willingness to communicate in digital game-based learning. ReCALL, 27(1), 38–57.
Rogerson-Revell, P. M. (2021). Computer-assisted pronunciation training (CAPT): Current issues and future directions. RELC Journal, 52(1), 189-205.
Rogerson-Revell, P. M. (2021). Computer-Assisted Pronunciation Training (CAPT): Current Issues and Future Directions. RELC Journal, 52(1), 189–205. doi: https://doi.org/10.1177/0033688220977406
Ronimus, M., Eklund, K., Pesu, L., & Lyytinen, H. (2019). Supporting struggling readers with digital game-based learning. Educational Technology Research and Development, 67(3), 639-663.
Saito, K., Trofimovich, P., & Isaacs, T. (2016). Second language speech production: Investigating linguistic correlates of comprehensibility and accentedness for learners at different ability levels. Applied Psycholinguistics, 37(2), 217-240.
Sakai, M., & Moorman, C. (2018). Can perception training improve the production of second language phonemes? A meta-analytic review of 25 years of perception training research. Applied Psycholinguistics, 39(1), 187-224.
Schrader, C., & Grassinger, R. (2021). Tell me that I can do it better. The effect of attributional feedback from a learning technology on achievement emotions and performance and the moderating role of individual adaptive reactions to errors. Computers & Education, 161, 104028.
Schutz, P. A., & Pekrun, R. E. (2007). Emotion in education. Elsevier Academic Press.
Scovel, T. (1978). The effect of affect on foreign language learning: A review of the anxiety research. Language learning, 28(1), 129-142.
Shao, K., Pekrun, R., & Nicholson, L. J. (2019). Emotions in classroom language learning: What can we learn from achievement emotion research?. System, 86, 102121.
Shih, B. Y., Chen, C. Y., & Li, C. E. (2013). The exploration of the mobile mandarin learning system by the application of TRIZ theory. Computer Applications in Engineering Education, 21(2), 343-348.
Soleimani, E., Ismail, K., & Mustaffa, R. (2014). The acceptance of mobile assisted language learning (MALL) among post graduate ESL students in UKM. Procedia-Social and Behavioral Sciences, 118, 457-462.
Song, J. (2022, July). Review of Chinese tones acquisiton by non-tonal language speakers taking English as an example. In 2022 3rd International Conference on Language, Art and Cultural Exchange (ICLACE 2022) (pp. 322-325). Atlantis Press.
Suh, S., Kim, S., & Kim, N. (2010). Effectiveness of MMORPG‐based instruction in elementary English education in Korea. Journal of Computer Assisted Learning, 26(5), 370–378.
Sulispera, T., & Recard, M. (2020). Octalysis gamification framework for enhancing students` engagement in language learning. Jurnal Dialektika Program Studi Pendidikan Bahasa Inggris, 8(2), 103-128.
Szyszka, M. (2017). Pronunciation learning strategies and language anxiety. Switzerland: Springer.
Thompson, T., & Gaddes, M. (2005). The importance of teaching pronunciation to adult learners. Asian EFL Journal, 2(1), 1-11.
Wang, Y., Sereno, J. A., Jongman, A., & Hirsch, J. (2003). fMRI evidence for cortical modification during learning of Mandarin lexical tone. Journal of Cognitive Neuroscience, 15(7), 1019–1027. doi: https://doi.org/10.1162/89892903770007407
Wayland, R. P., & Guion, S. G. (2004). Training English and Chinese listeners to perceive Thai tones: A preliminary report. Language Learning, 54(4), 681-712.
Witt, S. M., & Young, S. J. (2000). Phone-level pronunciation scoring and assessment for interactive language learning. Speech communication, 30(2-3), 95-108.
Xu, W., Zhang, H., Sukjairungwattana, P., & Wang, T. (2022). The roles of motivation, anxiety and learning strategies in online Chinese learning among Thai learners of Chinese as a foreign language. Frontiers in Psychology, 13.
Xu, Y. (1997). Contextual tonal variations in Mandarin. Journal of Phonetics, 25(1), 61–83. doi: https://doi.org/10.1006/jpho.1996.34
Yang, J. C., & Quadir, B. (2018). Effects of prior knowledge on learning performance and anxiety in an English learning online role-playing game. Journal of Educational Technology & Society, 21(3), 174-185.
Zhang, K. J., & Chen, L. M. (2005). Tonal errors of Japanese students learning Chinese: A study of disyllabic words. In Proceedings of the 17th Conference on Computational Linguistics and Speech Processing (pp. 125-139).
Zhang, T. (2018). Foreigners’ Perception of Mandarin Tones: A Review. Modern Linguistics, 6(4), 599-606.
Zheng, A., Hirata, Y., & Kelly, S. D. (2018). Exploring the effects of imitating hand gestures and head nods on L1 and L2 Mandarin tone production. Journal of Speech, Language, and Hearing Research, 61(9), 2179-2195.
Zhong, W., Muyunda, G., & Cheng, J. (2021). Epistemological beliefs and conceptions about language teaching and learning: A study of secondary school non-native learners and teachers of Mandarin Chinese in Zambia. Asian-Pacific Journal of Second and Foreign Language Education, 6(10). doi: https://doi. org/10.1186/s40862-021-00117-2
Zion, D. B., Nevat, M., Prior, A., & Bitan, T. (2019). Prior knowledge predicts early consolidation in second language learning. Frontiers in Psychology, 10, 2312.
描述 碩士
國立政治大學
圖書資訊與檔案學研究所
110155005
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110155005
資料類型 thesis
dc.contributor.advisor 陳志銘zh_TW
dc.contributor.advisor Chen, Chih-Mingen_US
dc.contributor.author (Authors) 辛佳珈zh_TW
dc.contributor.author (Authors) Xin, Jia-Jiaen_US
dc.creator (作者) 辛佳珈zh_TW
dc.creator (作者) Xin, Jia-Jiaen_US
dc.date (日期) 2023en_US
dc.date.accessioned 2-Aug-2023 14:27:56 (UTC+8)-
dc.date.available 2-Aug-2023 14:27:56 (UTC+8)-
dc.date.issued (上傳時間) 2-Aug-2023 14:27:56 (UTC+8)-
dc.identifier (Other Identifiers) G0110155005en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/146674-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 圖書資訊與檔案學研究所zh_TW
dc.description (描述) 110155005zh_TW
dc.description.abstract (摘要) 華語是全球廣泛使用的語言之一，相較於其他語言而言，其獨特的聲調特性是以華語為第二語言學習者進行發音學習時的困難挑戰。在語言能力訓練中，「發音」是溝通表達的關鍵要素，然而目前並沒有結合電腦輔助發音訓練，以及數位遊戲式語言學習，並將其應用於華語學習之相關研究。綜合以上所述， 本研究發展具「聲調診斷與回饋機制」之遊戲式華語學習系統，輔助華語為第二語言學習者在訓練發音時能獲得個人化即時診斷與回饋，並且透過比較自己與母語者的聲調輪廓方式來改善自己的華語發音，同時促進其華語聲調感知與發音成效，以及對於華語變調規則之理解。此外，探討學習者使用有無「聲調診斷與回饋機制」之遊戲式華語學習系統輔以學習時所經歷的學業情緒差異，並探討正負面情緒與華語發音成效之關聯性。最後，本研究進一步分析實驗組高低不同成效學習者在「遊戲行為」與「發音學習行為」的行為模式差異，以及其對於發音成效的影響。
本研究採用真實驗研究法，招募台灣大專院校母語非華語之國際學生，共計14名研究對象，並將其隨機分派為實驗組與控制組進行線上實驗。實驗組使用具有「聲調診斷與回饋機制」之遊戲式華語學習系統，控制組則使用不具「聲調診斷與回饋機制」之遊戲式華語學習系統輔以華語發音學習。實驗為期一個月，兩組學習者的系統教材皆相同，並且可自行決定每日上線時間與學習進度，藉以比較兩組在華語聲調感知成效、華語發音成效、科技接受度、學業情緒，以及學習行為之差異。另外，亦透過質性訪談分析了解兩組學習者的使用感想與建議，並將其歸納後提出教學實施與系統改善建議，以及未來研究方向。
　　研究結果顯示，使用具有「聲調診斷與回饋機制之遊戲式華語學習系統」較能有效提升華語聲調感知與華語發音成效，對於華語變調規則的理解亦有所助益。此外，實驗組的科技接受度略高於控制組，表示「聲調診斷與回饋機制」兼具有用性及易用性。在學業情緒方面，兩組在學習過程中均有偏高的正面情緒體驗，負面情緒感受則偏低。其中實驗組的華語發音先備能力與羞愧、無望呈現高度負相關；華語後測成績與焦慮、羞愧，以及無望呈現高度負相關；而其發音進步幅度則與享受、自豪呈現高度正相關，並且與無趣呈現高度負相關。除此之外，實驗組高成效學習者使用「聲調診斷與回饋機制」之遊戲式華語學習系統輔以華語發音學習之預設詞彙學習次數與發音進步幅度呈現完全正相關，證明此一機制是提升華語發音成效的關鍵。最後，透過高成效學習者的學習行為模式可知，將「聲調診斷與回饋機制」搭配其他遊戲式學習功能輔以學習，不但可以增進持續使用此一系統學習之意願，並且可同時提升其華語發音成效。本研究發展之「聲調診斷與回饋機制之遊戲式華語學習系統」具有創新性與實用性，可有效支援個人化之華語發音自主學習。		zh_TW
dc.description.abstract (摘要) Mandarin Chinese is one of the most widely spoken languages in the world, and its unique tones pose a significant challenge for Chinese as a second language (CSL) learners. Pronunciation skills play a crucial role in language proficiency, but so far there is a lack of research combining Computer-Assisted Pronunciation Training (CAPT) and Digital Game-based Language Learning (DGBLL) to aid language pronunciation learning, specifically for Mandarin Chinese. Therefore, this study developed a Mandarin Tones Learning Game (MTLG) with a “Tone Diagnosing and Visualization Feedback Mechanism” to assist CSL learners in personalized and real-time feedback during pronunciation learning. The system enables learners to improve their pronunciation by comparing their pitch contours with those of the native speaker, hoping to enhance their Mandarin tones perception, Mandarin pronunciation performance, and Mandarin tone change rules. Additionally, this study also examines the differences in achievement emotions experienced by learners in different groups and investigates the correlations between positive/negative emotions and Mandarin pronunciation. Finally, the study analyzes the behavioral patterns of learners with different proficiency levels in the experimental group and the impact on “game behavior” and “pronunciation learning behavior” in relation to pronunciation performance.
This research adopts a true-experimental design, recruiting a total of 14 international students from universities in Taiwan whose native languages were not Mandarin Chinese to be research participants. Each participant was randomly assigned to the experimental and control group respectively using “MTLG with/without Tone Diagnosis and Visualization Feedback Mechanism” for an experiment that lasted for one month, with both groups using the same materials and having the flexibility to autonomously decide their daily online learning time and learning progress. This study compares the differences between the two groups regarding Mandarin tone perception, Mandarin pronunciation performance, technology acceptance, achievement emotions, and learning behaviors. The interview was conducted to gather participants’ feedback and suggestions, which were analyzed to provide recommendations for teaching implementation, system improvement, and future research directions.
Based on the experimental results, the experimental group of learners was found to significantly enhance Mandarin tone perception, Mandarin pronunciation performance, and understanding of tone change rules, but not being found in the control group of learners. Besides, the experimental group of learners showed slightly higher levels of technology acceptance than the control group, indicating that the Tone Diagnosis and Visualization Feedback Mechanism is useful and user-friendly in aiding Mandarin pronunciation learning. In terms of achievement emotions, both groups experienced relatively high levels of positive emotions and relatively low levels of negative emotions during the learning process. Among the experimental group of learners, there was a strong negative correlation between prior knowledge and shame, hopelessness, as well as a strong negative correlation between post-test scores and anxiety, shame, and hopelessness. Furthermore, the improvement in Mandarin pronunciation was highly positively correlated with enjoyment and pride, while showing a strong negative correlation with boredom.
Moreover, among high-performing learners in the experimental group, the frequency of learning must-learn vocabulary words with the Tone Diagnosis Mechanism and the improvement in pronunciation showed a strong positive correlation, confirming the critical role of this mechanism in enhancing Mandarin pronunciation performance. The learning behavior patterns of high-performing learners revealed that combining the "Tone Diagnosis and Visualization Feedback mechanism" with other game-based learning features can improve the willingness to continue using the system and simultaneously enhance Mandarin pronunciation performance. Overall speaking, the “MTLG with Tone Diagnosis and Visualization Feedback Mechanism” developed in this study is innovative and practical, and can effectively support personalized and autonomous learning of Mandarin pronunciation.		en_US
dc.description.tableofcontents 謝辭 i
摘要 ii
Abstract iv
目次 vi
圖次 ⅷ
表次 x
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 6
第三節 研究問題 7
第四節 研究範圍與限制 8
第五節 重要名詞解釋 9
第二章 文獻探討 12
第一節 電腦輔助語言發音訓練 12
第二節 數位遊戲式語言學習 14
第三節 影響第二語言學習成效的因素 18
第三章 系統設計 23
第一節 系統架構 23
第二節 系統介面與功能說明 26
第三節 系統開發環境 43
第四章 研究設計 45
第一節 研究架構 45
第二節 研究方法 48
第三節 研究對象 49
第四節 實驗設計與流程 51
第五節 研究工具 55
第六節 資料處理與分析 59
第七節 研究實施步驟 60
第五章 實驗結果分析 62
第一節 使用有無「聲調診斷與回饋機制」之MTLG輔助華語聲調感知學習成效之差異分析 62
第二節 使用有無「聲調診斷與回饋機制」之MTLG輔助華語發音成效之差異分析 65
第三節 使用有無「聲調診斷與回饋機制」之MTLG輔助華語聲調學習之科技接受度分析 69
第四節 使用有無「聲調診斷與回饋機制」之MTLG輔助華語聲調學習之華語學業情緒分析 71
第五節 使用具「聲調診斷與回饋機制」之MTLG輔助華語發音學習的學習行為分析 75
第六節 質性訪談內容分析 107
第七節 綜合討論 117
第六章 結論與建議 126
第一節 結論 126
第二節 教學實施與改善建議 132
第三節 未來研究方向 134
參考資料 136
中文文獻 136
英文文獻 137
附錄一 實驗同意書 147
附錄二 受試者背景問卷 148
附錄三 兩組使用系統輔以華語學習之總時間 149
附錄四 華語發音測驗 150
附錄五 預設詞彙與發音測驗詞彙分級表 152
附錄六 (實驗組)科技接受度問卷 153
附錄七 (控制組)科技接受度問卷 155
附錄八 華語學業情緒問卷 157
附錄九 訪談大綱 161		zh_TW
dc.format.extent 6602494 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110155005en_US
dc.subject (關鍵詞) 以華語為第二語言zh_TW
dc.subject (關鍵詞) 電腦輔助發音訓練zh_TW
dc.subject (關鍵詞) 數位遊戲式語言學習zh_TW
dc.subject (關鍵詞) 聲調診斷與回饋機制zh_TW
dc.subject (關鍵詞) 華語變調zh_TW
dc.subject (關鍵詞) 華語聲調感知zh_TW
dc.subject (關鍵詞) 華語發音學習zh_TW
dc.subject (關鍵詞) 學業情緒zh_TW
dc.subject (關鍵詞) 學習行為zh_TW
dc.subject (關鍵詞) Chinese as a second languageen_US
dc.subject (關鍵詞) Computer-Assisted Pronunciation Trainingen_US
dc.subject (關鍵詞) Digital Game-based Language Learningen_US
dc.subject (關鍵詞) Tone Diagnosis and Visualization Feedback Mechanismen_US
dc.subject (關鍵詞) Mandarin tone sandhien_US
dc.subject (關鍵詞) Mandarin tone perceptionen_US
dc.subject (關鍵詞) Mandarin pronunciation performanceen_US
dc.subject (關鍵詞) Achievement emotionsen_US
dc.subject (關鍵詞) Learning behaviorsen_US
dc.title (題名) 具有聲調診斷與回饋機制之遊戲式華語學習系統對於促進發音成效之影響研究zh_TW
dc.title (題名) The Effects of a Game-based Mandarin Learning System with Tone Diagnosis and Visualization Feedback Mechanism on Pronunciation Effectivenessen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 王維聰 、王建喬 (2011)。數位遊戲式學習系統。科學發展，467，46-51。
余字涵 (2021)。具有影片字彙標註機制之遊戲式華語學習系統促進華語聽力學習成效之影響研究。國立政治大學，台北市。
周郁凱 (2017)。遊戲化實戰全書：遊戲化大師教你把工作、教學、健身、行銷、產品設計……變遊戲，愈好玩就愈有吸引力！。台北市：城邦商業周刊。
胡瑞雪 (2021)。在臺國際大學生華語文學業成敗歸因與華語文學業情緒關聯之初步探究。華文世界，(127)，26-55。
郭智博 (2020)。具遊戲激勵機制之影片日語學習系統發展與應用於提升企業員工語言學習成效之影響研究。國立政治大學圖書資訊學數位碩士在職專班碩士論文，台北市。
張學謙 (2016)。走向添加式雙語主義：強化家庭與學校的母語教育。臺灣教育評論月刊，5(9)，1-9。
劉慧娟(2017)。初級越南學習者華語聲調學習之研究。華語文教學研究，14(1)，81-118。
鄭靜宜 (2012)。華語雙音節詞基頻的聲調共構效果。台灣聽力語言學會雜誌，(28)，27-48。
Agarwal, C., & Chakraborty, P. (2019). A review of tools and techniques for computer aided pronunciation training (CAPT) in English. Education and Information Technologies, 24(6), 3731-3743.
Akter, S., & Song, J. (2021). Problems in Pronunciation and Perception of Mandarin Tones: An Empirical Study on Bangladeshi Undergraduate Students in China. Journal of Higher Education Theory & Practice, 21(16).
Anderson, J. R. (2009). Cognitive Psychology and Its Implications (7th ed.). NY: Worth Publishers.
Bakar, Z. A., & Abdullah, M. R. (2015). Importance of correct pronunciation in spoken English: Dimension of second language learners’ perspective. Pertanika Journal of Social Sciences and Humanities, 23(8), 143-158.
Baran-Łucarz, M. (2016). Conceptualizing and measuring the construct of pronunciation anxiety: Results of a pilot study. In Classroom-oriented research (pp. 39-56). Springer, Cham.
Bieleke, M., Goetz, T., Yanagida, T., Botes, E., Frenzel, A. C., & Pekrun, R. (2022). Measuring emotions in mathematics: the Achievement Emotions Questionnaire—Mathematics (AEQ-M). ZDM–Mathematics Education, 1-16.
Charles, D., Charles, T., McNeill, M., Bustard, D., & Black, M. (2011). Game‐based feedback for educational multi‐user virtual environments. British Journal of Educational Technology, 42(4), 638-654.
Chen, B., & Hsu, Y. C. (2019). Mandarin Chinese mispronunciation detection and diagnosis leveraging deep neural network based acoustic modeling and training techniques. In Computational and Corpus Approaches to Chinese Language Learning (pp. 217-234). Springer, Singapore.
Chen, M. (2022). Computer-aided feedback on the pronunciation of Mandarin Chinese tones: using Praat to promote multimedia foreign language learning. Computer Assisted Language Learning, 1-26.
Chou, Y. K. (2016). Actionable gamification: Beyond points, badges and leaderboards. London: Leanpub.
Chun, D. M., Jiang, Y., & Natalia, A. Ì. (2013). Visualization of tone for learning Mandarin Chinese. Pronunciation in Second Language Learning and Teaching Proceedings, 4(1).
Chun, D. M., Jiang, Y., Meyr, J., & Yang, R. (2015). Acquisition of L2 Mandarin Chinese tones with learner-created tone visualizations. Journal of Second Language Pronunciation, 1(1), 86-114.
Cucchiarini, C., Neri, A., & Strik, H. (2009). Oral proficiency training in Dutch L2: The contribution of ASR-based corrective feedback. Speech Communication, 51(10), 853-863.
Davari, H., Karami, H., Nourzadeh, S., & Iranmehr, A. (2020). Examining the validity of the Achievement Emotions Questionnaire for measuring more emotions in the foreign language classroom. Journal of Multilingual and Multicultural Development, 1-14.
Davis, F. (1989). Technology Acceptance Model: Origins. Working Papers on Information Systems, 35-59.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319-340.
Deng, L., Wu, S., Chen, Y., & Peng, Z. (2020). Digital game‐based learning in a Shanghai primary‐school mathematics class: A case study. Journal of Computer Assisted Learning, 36(5), 709-717.
Dewaele, J. M., Witney, J., Saito, K., & Dewaele, L. (2018). Foreign language enjoyment and anxiety: The effect of teacher and learner variables. Language teaching research, 22(6), 676-697.
Dixon, D. H., Dixon, T., & Jordan, E. (2022). Second language (L2) gains through digital game-based language learning (DGBLL): A meta-analysis. Language Learning & Technology, 26(1), 1-25.
Dong, Z. R. (2021). Tone and vocabulary learning for classroom-based L2 Mandarin learners: Pedagogical implications from current tone word processing research. Chinese as a Second Language (漢語教學研究—美國中文教師學會學報).
Eberhard, David M., Gary F. Simons, and Charles D. Fennig (eds.). 2022. Ethnologue: Languages of the World. Twenty-fifth edition. Dallas, Texas: SIL International. Online version: http://www.ethnologue.com.
Economou, D., Doumanis, I., Pedersen, F., Kathrani, P., Mentzelopoulos, M., & Bouki, V. (2015). Evaluation of a dynamic role-playing platform for simulations based on Octalysis gamification framework. In Workshop Proceedings of the 11th International Conference on Intelligent Environments (pp. 388-395). IOS Press.
Erhel, S., & Jamet, E. (2013). Digital game-based learning: Impact of instructions and feedback on motivation and learning effectiveness. Computers & Education, 67, 156-167.
Fauzi, A., Wandira, R., Sepri, D., & Hafid, A. (2021). Exploring students` acceptance of google classroom during the covid-19 pandemic by using the technology acceptance model in West Sumatera Universities. Electronic Journal of E-Learning, 19(4), pp233-240.
Fierro-Suero, S., Almagro, B. J., & Sáenz-López, P. (2020). Validation of the achievement emotions questionnaire for physical education (AEQ-PE). International Journal of Environmental Research and Public Health, 17(12), 4560.
Flege, J. E. (2003). Assessing constraints on second-language segmental production and perception. Phonetics and phonology in language comprehension and production: Differences and similarities, 6, 319-355.
Fouz-González, J. (2015). Trends and directions in computer-assisted pronunciation training. Investigating English Pronunciation, 314-342.
Freitas, S. A. A., Lacerda, A. R., Calado, P. M., Lima, T. S., & Canedo, E. D. (2017, October). Gamification in education: A methodology to identify student`s profile. In 2017 IEEE Frontiers in Education Conference (FIE) (pp. 1-8). IEEE.
Frenzel, A. C., Thrash, T. M., Pekrun, R., & Goetz, T. (2007). Achievement emotions in Germany and China: A cross-cultural validation of the Academic Emotions Questionnaire—Mathematics. Journal of cross-cultural psychology, 38(3), 302-309.
Godwin-Jones, R. (2014). Emerging Technologies - Games in Language Learning: Opportunities and Challenges. Language Learning & Technology, 18(2), 9–19.
Gong, Y., Hu, X., & Lai, C. (2018). Chinese as a second language teachers’ cognition in teaching intercultural communicative competence. System, 78, 224-233.
Han, J. H., & Sa, H. J. (2022). Acceptance of and satisfaction with online educational classes through the technology acceptance model (TAM): The COVID-19 situation in Korea. Asia Pacific Education Review, 23(3), 403-415.
Harrison, C. J., Könings, K. D., Molyneux, A., Schuwirth, L., Wass, V., & van der Vleuten, C. (2015). Barriers to the uptake and use of feedback in the context of summative assessment. Advances in Health Sciences Education, 20, 229–245
Hiriart, J. F. V. (2019). Gaming the past: Designing and using digital games as historical learning contexts (Doctoral dissertation, University of Salford).
Hitosugi, C. I., Schmidt, M., & Hayashi, K. (2014). Digital game-based learning (DGBL) in the L2 classroom: The impact of the UN`s off-the-shelf videogame, Food Force, on learner affect and vocabulary retention. Calico Journal, 31(1), 19-39.
Hongnaphadol, W., & Attanak, A. (2022). Reducing Thai EFL Students’ Pronunciation Anxiety through a CAPT-Based Reading Progress Application. Journal of Liberal Arts Prince of Songkla University, 14(1), 83-122.
Horwitz, E. K., Horwitz, M. B., & Cope, J. (1986). Foreign language classroom anxiety. The Modern Language Journal, 70(2), 125-132.
Howie, J. M., & Howie, J. M. (1976). Acoustical studies of Mandarin vowels and tones (Vol. 18). Cambridge University Press.
Howie, John (1976). Acoustical studies of Mandarin vowels and tones. Cambridge University Press.
Hsu, L. (2016). An empirical examination of EFL learners` perceptual learning styles and acceptance of ASR-based computer-assisted pronunciation training. Computer Assisted Language Learning, 29(5), 881-900.
Hsu, Y. C., Yang, M. H., Hung, H. T., Lin, Y. J., Chen, K. Y., & Chen, B. (2016). Evaluation Metric-related Optimization Methods for Mandarin Mispronunciation Detection. In International Journal of Computational Linguistics & Chinese Language Processing, Volume 21, Number 2, December 2016.
Hung, H. T., Yang, J. C., Hwang, G. J., Chu, H. C., & Wang, C. C. (2018). A scoping review of research on digital game-based language learning. Computers & Education, 126, 89-104.
Hussein, M. H., Ow, S. H., Cheong, L. S., Thong, M. K., & Ebrahim, N. A. (2019). Effects of digital game-based learning on elementary science learning: A systematic review. IEEE Access, 7, 62465-62478.
Ibrahim, M. F., Kuan, G., Hashim, H. A., Hamzah, N. A., & Kueh, Y. C. (2021). Measuring achievement emotions questionnaire for physical education (AEQ-PE): a confirmatory study in Malay language. BMC Public Health, 21(1), 1-8.
Jeon, J. (2014). Development and construct validation of the achievement emotions questionnaire-Korean middle school science (AEQ-KMS). Journal of the Korean Association for Science Education, 34(8), 745-754.
Kalikow, D., & Swets, J. (1972). Experiments with computer-controlled displays in second-language learning. IEEE Transactions on Audio and Electroacoustics, 20(1), 23-28.
Ku, O., Chen, S. Y., Wu, D. H., Lao, A. C. C., & Chan, T. W. (2014). The Effects of game-based learning on mathematical confidence and performance: High ability vs. low ability. Educational Technology & Society, 17(3), 65–78.
Lee, W.-S. & Zee, E. (2014). “Chinese phonetics,” in The Handbook of Chinese Linguistics, eds C. J. Huang, Y. A. Li, and A. Simpson (Hoboken: John Wiley & Sons), 367–399.
Lee, Y., Kozar, K. A., & Larsen, K. R. (2003). The technology acceptance model: Past, present, and future. Communications of the Association for information systems, 12(1), 50.
Lei, H., Wang, C., Chiu, M. M., & Chen, S. (2022). Do educational games affect students` achievement emotions? Evidence from a meta‐analysis. Journal of Computer Assisted Learning.
Lewis, M. P., Simons, G. F., & Fennig, C. D. (Eds.). (2015). Ethnologue: Languages of the world (18th ed.). Dallas, TX: SIL International.
Li, W., Siniscalchi, S. M., Chen, N. F., & Lee, C. H. (2016, March). Improving non-native mispronunciation detection and enriching diagnostic feedback with DNN-based speech attribute modeling. In 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 6135-6139). IEEE.
Li, Y. (2016). English and Thai Speakers` Perception of Mandarin Tones. English language teaching, 9(1), 122-132.
Liao, H. C., Guan, Y. H., Tu, J. J., & Chen, J. C. (2014). A prototype of an adaptive Chinese pronunciation training system. System, 45, 52-66.
Lin, Mao-Ts’an. (1965). The pitch indicator and the pitch characteristics of tones in Standard Chinese. Acta Acoustica (2), 8–15.
Liu, S., & Samuel, A. G. (2004). Perception of Mandarin lexical tones when F0 information is neutralized. Language and Speech, 47, pp. 109-138.
Liu, Y. C., Wang, W. T., & Lee, T. L. (2021). An integrated view of information feedback, game quality, and autonomous motivation for evaluating game-based learning effectiveness. Journal of Educational Computing Research, 59(1), 3-40.
Llisterri, J. (1995). Relationships between speech production and speech perception in a second language. In Proceedings of the 13th International Congress of Phonetics Sciences (pp. 92–99), Stockholm.
Lundblade, K. (2021). Watch Me Make History: Reenacting and Remaking the Past in Historical Game Live Streams. Popular Culture Studies Journal, 9, 69-87.
Macías León, K., de las Heras Pérez, M. Á., Romero Fernández, R., González Castanedo, Y., & Sáenz-López, P. (2022). Validation of the Achievement Emotions Questionnaire for Experimental Science Education (AEQ-S). Behavioral Sciences, 12(12), 480.
MacIntyre, P. D., Gregersen, T., & Mercer, S. (2016). Positive psychology in SLA, Multilingual Matters. Bristol.
Marangunić, N., & Granić, A. (2015). Technology acceptance model: a literature review from 1986 to 2013. Universal access in the information society, 14(1), 81-95.
Marisa, F., Ahmad, S. S. S., Yusoh, Z. I. M., Maukar, A. L., Marcus, R. D., & Widodo, A. A. (2020). Evaluation of student core drives on e-learning during the covid-19 with Octalysis gamification framework. International Journal of Advanced Computer Science and Applications, 11(11).
McIntosh, M. J., & Morse, J. M. (2015). Situating and constructing diversity in semi-structured interviews. Global qualitative nursing research, 2.
Meng, N. (2021). “Repeat After Me”: Is there a better way to correct tone errors in teaching Mandarin Chinese as a Second. The Acquisition of Chinese as a Second Language Pronunciation: Segments and Prosody, 163.
Neri, A., Cuccharini, C., Strik, H. (2006). ASR corrective feedback on pronunciation: does it really work? In Proc. ISCA Interspeech, Pittsburgh, PA, pp. 1982–1985.
Neri, A., Cucchiarini, C., Strik, H., & Boves, L. (2002). The pedagogy-technology interface in computer assisted pronunciation training. Computer Assisted Language Learning, 15(5), 441-467.
Neri, A., Mich, O., Gerosa, M., & Giuliani, D. (2008). The effectiveness of computer assisted pronunciation training for foreign language learning by children. Computer Assisted Language Learning, 21(5), 393-408.
Norman, D. A. (1993). Things that make us smart: defending human attributes in the age of the machine, Perseus Books, Cambridge, MA.
Pekrun, R. (2000). A social cognitive, control-value theory of achievement emotions. In J. Heckhausen (Ed.), Motivational Psychology of Human Development. Oxford, UK: Elsevier.
Pekrun, R. 2006. The Control-Value Theory of Achievement Emotions: Assumptions, Corollaries, and Implications for Educational Research and Practice. Educational Psychology Review 18 (4): 315–341. doi: https://doi.org/ 10.1007/s10648-006- 9029-9
Pekrun, R., & Perry, R. P. (2014). Control-value theory of achievement emotions. In International Handbook of Emotions in Education (pp. 130-151). Routledge.
Pekrun, R., & Stephens, E. J. (2010). Achievement emotions: A control‐value approach. Social and Personality Psychology Compass, 4(4), 238-255.
Pekrun, R., Cusack, A., Murayama, K., Elliot, A. J., & Thomas, K. (2014). The power of anticipated feedback: Effects on students’ achievement goals and achievement emotions. Learning and Instruction, 29, 115–124
Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. P. (2011). Measuring emotions in students’ learning and performance: The Achievement Emotions Questionnaire (AEQ). Contemporary Educational Psychology, 36(1), 36-48.
Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic emotions in students` self-regulated learning and achievement: A program of qualitative and quantitative research. Educational Psychologist, 37(2), 91-105.
Pelzl, E. (2019). What makes second language perception of Mandarin tones hard?: A non-technical review of evidence from psycholinguistic research. Chinese as a Second Language. The Journal of the Chinese Language Teachers Association, USA, 54(1), 51-78.
Peng, X., Chen, H., Wang, L., Tian, F., & Wang, H. (2020). Talking head-based L2 pronunciation training: Impact on achievement emotions, cognitive load, and their relationships with learning performance. International Journal of Human–Computer Interaction, 36(16), 1487-1502.
Prasetyo, F. H., & Sofyan, H. (2020, May). Digital game-based learning model and design elements to increase student learning motivation in English listening skills. In International Conference on Online and Blended Learning 2019 (ICOBL 2019) (pp. 134-137). Atlantis Press.
Pratama, g. D., & Kusuma, g. P. (2022). Implementation of gamification framework on online learning of procedural programming. Journal of Theoretical and Applied Information Technology, 100(22).
Reinders, H., & Wattana, S. (2015). Affect and willingness to communicate in digital game-based learning. ReCALL, 27(1), 38–57.
Rogerson-Revell, P. M. (2021). Computer-assisted pronunciation training (CAPT): Current issues and future directions. RELC Journal, 52(1), 189-205.
Rogerson-Revell, P. M. (2021). Computer-Assisted Pronunciation Training (CAPT): Current Issues and Future Directions. RELC Journal, 52(1), 189–205. doi: https://doi.org/10.1177/0033688220977406
Ronimus, M., Eklund, K., Pesu, L., & Lyytinen, H. (2019). Supporting struggling readers with digital game-based learning. Educational Technology Research and Development, 67(3), 639-663.
Saito, K., Trofimovich, P., & Isaacs, T. (2016). Second language speech production: Investigating linguistic correlates of comprehensibility and accentedness for learners at different ability levels. Applied Psycholinguistics, 37(2), 217-240.
Sakai, M., & Moorman, C. (2018). Can perception training improve the production of second language phonemes? A meta-analytic review of 25 years of perception training research. Applied Psycholinguistics, 39(1), 187-224.
Schrader, C., & Grassinger, R. (2021). Tell me that I can do it better. The effect of attributional feedback from a learning technology on achievement emotions and performance and the moderating role of individual adaptive reactions to errors. Computers & Education, 161, 104028.
Schutz, P. A., & Pekrun, R. E. (2007). Emotion in education. Elsevier Academic Press.
Scovel, T. (1978). The effect of affect on foreign language learning: A review of the anxiety research. Language learning, 28(1), 129-142.
Shao, K., Pekrun, R., & Nicholson, L. J. (2019). Emotions in classroom language learning: What can we learn from achievement emotion research?. System, 86, 102121.
Shih, B. Y., Chen, C. Y., & Li, C. E. (2013). The exploration of the mobile mandarin learning system by the application of TRIZ theory. Computer Applications in Engineering Education, 21(2), 343-348.
Soleimani, E., Ismail, K., & Mustaffa, R. (2014). The acceptance of mobile assisted language learning (MALL) among post graduate ESL students in UKM. Procedia-Social and Behavioral Sciences, 118, 457-462.
Song, J. (2022, July). Review of Chinese tones acquisiton by non-tonal language speakers taking English as an example. In 2022 3rd International Conference on Language, Art and Cultural Exchange (ICLACE 2022) (pp. 322-325). Atlantis Press.
Suh, S., Kim, S., & Kim, N. (2010). Effectiveness of MMORPG‐based instruction in elementary English education in Korea. Journal of Computer Assisted Learning, 26(5), 370–378.
Sulispera, T., & Recard, M. (2020). Octalysis gamification framework for enhancing students` engagement in language learning. Jurnal Dialektika Program Studi Pendidikan Bahasa Inggris, 8(2), 103-128.
Szyszka, M. (2017). Pronunciation learning strategies and language anxiety. Switzerland: Springer.
Thompson, T., & Gaddes, M. (2005). The importance of teaching pronunciation to adult learners. Asian EFL Journal, 2(1), 1-11.
Wang, Y., Sereno, J. A., Jongman, A., & Hirsch, J. (2003). fMRI evidence for cortical modification during learning of Mandarin lexical tone. Journal of Cognitive Neuroscience, 15(7), 1019–1027. doi: https://doi.org/10.1162/89892903770007407
Wayland, R. P., & Guion, S. G. (2004). Training English and Chinese listeners to perceive Thai tones: A preliminary report. Language Learning, 54(4), 681-712.
Witt, S. M., & Young, S. J. (2000). Phone-level pronunciation scoring and assessment for interactive language learning. Speech communication, 30(2-3), 95-108.
Xu, W., Zhang, H., Sukjairungwattana, P., & Wang, T. (2022). The roles of motivation, anxiety and learning strategies in online Chinese learning among Thai learners of Chinese as a foreign language. Frontiers in Psychology, 13.
Xu, Y. (1997). Contextual tonal variations in Mandarin. Journal of Phonetics, 25(1), 61–83. doi: https://doi.org/10.1006/jpho.1996.34
Yang, J. C., & Quadir, B. (2018). Effects of prior knowledge on learning performance and anxiety in an English learning online role-playing game. Journal of Educational Technology & Society, 21(3), 174-185.
Zhang, K. J., & Chen, L. M. (2005). Tonal errors of Japanese students learning Chinese: A study of disyllabic words. In Proceedings of the 17th Conference on Computational Linguistics and Speech Processing (pp. 125-139).
Zhang, T. (2018). Foreigners’ Perception of Mandarin Tones: A Review. Modern Linguistics, 6(4), 599-606.
Zheng, A., Hirata, Y., & Kelly, S. D. (2018). Exploring the effects of imitating hand gestures and head nods on L1 and L2 Mandarin tone production. Journal of Speech, Language, and Hearing Research, 61(9), 2179-2195.
Zhong, W., Muyunda, G., & Cheng, J. (2021). Epistemological beliefs and conceptions about language teaching and learning: A study of secondary school non-native learners and teachers of Mandarin Chinese in Zambia. Asian-Pacific Journal of Second and Foreign Language Education, 6(10). doi: https://doi. org/10.1186/s40862-021-00117-2
Zion, D. B., Nevat, M., Prior, A., & Bitan, T. (2019). Prior knowledge predicts early consolidation in second language learning. Frontiers in Psychology, 10, 2312.		zh_TW