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In April, Mango Languages invited advanced Ph.D. candidates to submit applications for our second annual Dissertation Awards. These awards recognize exceptional dissertation research at the intersection of second language acquisition and educational technology.
There is so much exciting and rigorous research happening with implications for language learning technology. We can’t wait to see all of our applicants do great things!
This year, we increased the number of prizes awarded from two to three. And the winners are…
Diana Velázquez-López
Spanish & Portuguese Studies at the University of Florida
The role of technology-mediated feedback in the acquisition of phonology
Diana’s project explores how different types of feedback — speech recognition versus visual feedback — can be used to improve pronunciation for language learners. This study also compares two different groups of learners: second-language learners who primarily learned Spanish in the classroom, and heritage-language learners who were primarily exposed to Spanish at home. In the study, students enrolled in Spanish classes receive pronunciation instruction and practice with either automated speech recognition or visualization tools. Learners’ pronunciation abilities are tested, as well as their attitudes toward pronunciation and the training tools that they used.
This study has the potential to shed light on how different technologies benefit different learner populations, which has important implications for curriculum development and educational technology for language learning and teaching.
Lillian Jones
Hispanic Linguistics at the University of California, Davis
The Task is in the Text: Texting and L2 Oral Fluency
Lillian’s dissertation project examines whether and how text messaging can help learners become better second-language speakers. In this study, Spanish learners complete weekly interactive tasks with a partner, either via WhatsApp messaging or face-to-face Zoom sessions. Their speaking fluency is measured throughout the study to compare how effective the two types of communication are for second-language learning. Interestingly, previous research has shown that written practice may help with spoken proficiency — known as a positive cross-modality effect — so the text messaging group may be expected to do at least as well, if not better than, the face-to-face group.
This study paves the way for language teachers to incorporate colloquial communication tools, like WhatsApp, into the formal language curriculum in a way that is effective and engaging for learners.
Xi Chen
Communicative Sciences and Disorders at New York University
Real-time Pitch Biofeedback in Second-Language Tone Production: Effectiveness and Predictors
Tone languages such as Mandarin and Cantonese use variations in vocal pitch to distinguish word meanings, which can be a challenge for people learning these languages as second languages. Xi’s study investigates the effects of a technology-enhanced training technique, real-time pitch biofeedback, for English speakers learning to produce Mandarin tones. As they practice producing tones, learners can view and compare a visual representation of their own pitch contours with those of a native speaker. This study will compare this innovative real-time pitch biofeedback training to a more traditional imitation approach. Additionally, the study examines how learners with different abilities respond to different training conditions.
This research will provide evidence on the effectiveness of a computer-assisted speech training technique in second language pronunciation learning. It also represents a step toward a personalized learning approach that optimizes training conditions for individual learners based on their abilities.
A big congratulations to Diana, Lillian, and Xi! And thanks to everyone who applied for this award — you are all finding ways to make language learning technology better!
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