This study focused on students’ experience with a mobile multimedia application for a pronunciation course. An app was used for after-class pronunciation practice, through which the instructor gave out multimodal feedback to students. Student engagement with teacher feedback in the app was investigated from multiple data sources. Data collected in the form of the instructor’s response and analytics gathered through the mobile system revealed that students demonstrated a high level of engagement in the app, behaviorally, cognitively, and emotionally. Specifically, the instructor observed more submissions from and interactions with students compared with previous classes. Records of student-teacher interactions suggested that students reacted positively and actively to multimodal feedback from the teacher and expressed willingness to use the app for future learning. Questionnaire results further confirmed that a large number of the students perceived the app to be useful for practicing pronunciation tasks, as it made it easy for them to interact with the teacher, receive course materials, and submit recordings. Interviews among selected students revealed more details about students’ experiences and views. Some students reported accessing their peers’ readings and feedback from the teacher as a way of informing their own learning, which is made possible by the app. This study provides implications to enrich pronunciation instruction practice to maximize student engagement, which ultimately contributes to positive learning experiences and gains.

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Personality is an inherent rater’s characteristic influencing rating severity, but very few studies examined their relationship and the findings were inconclusive. This study aimed to re-investigate the relationship between raters’ personality and rating severity with more control on relevant variables and more reliable analysis of rating severity. Female novice raters ( n = 28) from a demographically homogeneous background were recruited to rate on two occasions essays written by 111 students in an intermediate-level Chinese as a foreign language program. Raters’ personality traits were measured using the complete version of NEO-PI-R. Many-faceted Rasch measurement model and repeated measurement were applied to yield more robust estimates of rating severity. In addition, rating order effect was carefully controlled. Extroversion was found to be positively correlated with severity, r(26) = .495, p = .010. Furthermore, Extroversion was found to be a valid predictor of severity, t(24) = 2.792, p = .010, R2 = .21, Cohen’s d = .77, Hattie’s r = .37. Practical implications for developing more individualized online rater calibration for large-scale writing assessments were discussed, followed by limitations of the present study.

The prospect of automated scoring for interpreting fluency has prompted investigations into the predictability of human raters’ perceived fluency based on acoustically measured utterance fluency. Recently, Han, Chen, Fu and Fan (2020) correlated ten utterance fluency measures with raters’ perceived fluency ratings. To verify previous correlational patterns, the present study partially replicated Han et al. (2020). Our analysis shows that most of the correlations observed in Han et al. (2020) were successfully replicated. To produce overall interim estimates of the true relationships, we conducted a mini meta-analysis of correlation coefficients reported in six relevant studies, informed by the “continuously cumulating meta-analysis” approach (Braver et al. 2014). We found that phonation time ratio, mean length of run, and speech rate had relatively strong correlations with perceived fluency. We discuss these findings in light of automated fluency assessment and the need for replication and meta-analysis in translation and interpreting studies.