How to pronounce goo的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列包括賽程、直播線上看和比分戰績懶人包

探討結合語音辨識技術開發電腦輔助英語學習系統之效益

為了解決How to pronounce goo的問題，作者王怡萱 這樣論述：

This is an interdisciplinary study to explore whether a self-developed ASR-based CALL system integrating different learning factor designs, corrective feedback, multi-modality content presentation and game strategy in the ASR-based learning environment could facilitate students’ English learning.

In order to achieve the above mentioned purpose, three sub-studies that adopted the self-developed ASR-based CALL system in an educational context were conducted in the dissertation. The developed ASR-based CALL system aims to provide learners with a web-based speaking scenario in which students hav

e learner-centered and self-paced English practicing opportunities through ASR interaction. The participants of the three sub-studies were 32 seventh graders, 52 third graders and 32 fourth graders. A quasi-experimental design with pretest-posttest nonequivalent-group design was carried out in each

sub-study. The duration of the data collection session of each study was 4 weeks, and paper-based tests and the pronunciation tests were conducted before and after the experiment as the learning pre-test and post-test. In addition, questionnaires and interviews were administered to the students.

Several valuable outcomes are concluded from the study: 1) the web-based ASR program benefits flexible personal learning and achieves the goal of lifelong language learning; 2) three-level feedback with implicit and explicit formats in the ASR programs enhances speaking learning; 3) juxtaposing gra

phic with auditory information enhanced the learners’ speaking motivation, while juxtaposing text with auditory information helped the learners pronounce the target vocabulary correctly; 4) adding gamification elements in ASR programs enhances students’ speaking engagement and self-learning motivati

on, while adding drill practice speaking activities affirmed vocabulary retention; and 5) learners of various achievement levels have different preferences for ASR programs. The study further proposes an integrated ASR-based CALL model based on the research findings focusing on adding multi-modal

ity of learning contents, designing gamification of activities and including multiple levels of corrective feedback to enhance the English learning of learners of different backgrounds. The multi-modalities could be adopted as speaking scaffolding to help beginners connect the referential links of t

he targeted word and pronunciation. Both gamification speaking activities and traditional practice-and-drill activities should be included in the ASR-based model, and the proportion of the two activities can be adjusted according to the students’ levels progressively. The immediate corrective feedba

ck could be designed for advanced learners, and personal speaking files with immediate corrective feedback as selective items could be provided to less-advanced learners. The study concludes from the current research findings that the adoption of ASR technology into e-learning systems has great p

otential to promote students’ educative engagement so as to facilitate language acquisition for learners of various language proficiencies. The implementation of the ASR-based CALL system serves as a helpful tool in educational settings for learners to achieve the goal of self-paced learning and a o

ne-to-one language learning scenario. Suggestions for educators and instructional designers are also proposed in this study.