The Study of Applying ARCS Model and Education App to Enhance College Students' English Listening Performance

Autor: HSU, SHU-CHEN, 徐淑真
Rok vydání: 2019
Druh dokumentu: 學位論文 ; thesis
Popis: 107
This study aimed to apply English listening app and English instruction to carry out an innovative teaching program for college students' English listening performance. We adopted ARCS motivation model and ADDIE instructional systems teaching design mode through integrating English listening app and English listening teaching to enhance students’ learning motivation and improve English listening performance. The adoption of listening learning strategies in English listening instruction helped students capture keywords, think about listening content, and realize the purpose of the content. At the same time, we applied the ARCS motivation model in the teaching plan. After the implementation of the program, Therefore, the present study would examine if adopting English listening strategies could effectively improve students’ listening comprehension and motivation. Participants in this study were 43 students in a college in Taiwan. The instruments used in the study consisted of listening pre-test, and post-test and an English listening strategy questionnaire. According to the data analysis results, the major findings of the study were summarized as follows. First, the implementation of the ARCS motivation model combined with the education App teaching program can effectively improve learners' English listening ability to learn motivation. Second, the implementation of the ARCS motivation model combined with the education App teaching program can effectively improve learners' English listening performance.Third, the words used in the test and students’ vacabulary ability are the main factors that affect students’ performance. Last, learners agree that learning listening strategies can help them while handling with exams. Finally, pedagogical implications and suggestions for further study were provided at the end of the study.
Databáze: Networked Digital Library of Theses & Dissertations