Hello everyone. I am Li, a first-year doctoral student. Here, I would like to introduce a paper discussed in our recent English seminar regarding online oral practice for Chinese and learners’ affective factors.

Title: Beyond technology: the dominance of affective factors in explaining speaking ability among Chinese learners using an online platform

・Authors: Bao, W., Wu, J., Zhou, Y., & Zha, R.

・Journal: Frontiers in Psychology (2026, Vol. 17, Article 1802635)

・DOI: https://doi.org/10.3389/fpsyg.2026.1802635

When learning a foreign language, do speaking skills naturally improve if there are convenient learning apps or systems that automatically evaluate pronunciation? In recent years, there has been an increase in online learning tools that record one’s pronunciation and provide immediate feedback on accuracy. However, even when using the same tool, some learners speak actively, while others hesitate to speak for fear of making mistakes.

The study introduced here focuses on these differences and examines whether evaluations of technology or “affective factors,” such as willingness to speak and anxiety, are more strongly associated with the oral performance of Chinese language learners.

1. Research Background

While interest in learning Chinese is growing, challenges such as a shortage of teachers and educational resources, as well as difficulty in securing sufficient opportunities to speak both inside and outside the classroom, remain. Online learning platforms are expected to serve as a means for learners to repeatedly practice speaking. In Chinese, in particular, not only tones and pronunciation but also the correspondence between spoken sounds and Chinese characters is important, and feedback from Automatic Speech Recognition (ASR) plays a role unique to Chinese language learning.

In previous studies, psychological aspects such as learner anxiety and motivation, and technical aspects such as the usability and usefulness of learning systems, have often been examined separately. This study is characterized by incorporating both into the same analytical framework and comparing their relationships with oral performance.

2. Research Objectives

This study examines how (1) affective factors—such as the willingness to “try speaking in Chinese,” speaking anxiety, speaking motivation, and self-esteem—and (2) the degree to which learners perceive the learning platform as useful and easy to use, are related to oral test scores for learners who participated in a two-week ASR-supported Chinese class. Furthermore, it sought to clarify which of the two factor groups could better explain individual differences in oral performance.

What is important here is that the purpose of this study is not to prove that “using the platform improved oral proficiency.” It merely examines the relationship between the oral performance of learners who took similar classes and their respective psychological factors and perceptions of technology.

3. Research Methods

The subjects of the study were beginner learners who were studying Chinese at a university in Anhui Province, China, and were enrolled in a preparatory course for the HSKK (Hanyu Shuiping Kouyu Kaoshi) Level 3. The analysis included 113 participants, aged 18 to 35, from 34 different countries. All had at least one semester of Chinese learning experience and had not yet passed the HSKK Level 3.

In the class, the “Global Chinese Learning Platform (GCLP)” was used. Over two weeks, four 40-minute sessions were held, covering oral tasks related to HSKK Level 3, such as self-introductions, daily life, campus life, and simple interpersonal interactions. When learners spoke, the ASR function matched the audio with the target Chinese characters and displayed the accuracy of the pronunciation using color coding. This allowed learners to check which parts needed further practice.

Immediately after the final class, a questionnaire was used to survey affective factors and perceptions of the platform. Four affective factors were measured: “Willingness to Communicate (WTC),” speaking anxiety, speaking motivation, and self-esteem during speaking. WTC refers to the willingness or tendency to actually start speaking in Chinese. On the other hand, regarding perceptions of technology, “Perceived Usefulness (PU)” and “Perceived Ease of Use (PEOU)” were measured.

The questionnaire consisted of 68 items on a 5-point scale, using existing scales adapted to the context of Chinese language learning. After content validation by experts and a pilot study, the Cronbach’s α, indicating the internal consistency of each scale, was between .83 and .96. Oral proficiency was assessed using an HSKK Level 3 mock test on the GCLP, which was graded by two trained evaluators. The ICC, representing inter-rater reliability, was .92, confirming high agreement.

In the analysis, we first checked the correlation between each factor and oral performance. Furthermore, using a method called “hierarchical multiple regression analysis,” we swapped the order in which affective factors and technical perceptions were entered to compare which factor group explained more of the variance in oral performance.

4. Research Results

The most notable result was a strong positive correlation (r = .746) between Willingness to Communicate (WTC) and oral performance. This means that learners with a stronger tendency to actively try to speak in Chinese tended to have higher oral test scores. There were also moderate positive correlations with speaking motivation and self-esteem. On the other hand, there was a moderate negative correlation (r = −.429) with speaking anxiety, confirming that learners with higher anxiety tended to have lower scores.

Regarding technical factors, a weak positive correlation (ρ = .345) was found between the degree to which the platform was perceived as “useful for learning” (PU) and oral performance. However, the correlation between the degree to which it was perceived as “easy to use” (PEOU) and oral performance was not statistically significant. In other words, simply feeling that it was easy to operate did not sufficiently explain the level of oral performance.

Furthermore, in the hierarchical multiple regression analysis, the four affective factors alone could statistically explain 58.7% of the individual differences in oral performance. Adding the two technical perceptions only increased the explanatory power slightly from 58.7% to 59.2%, and this increase was not statistically significant. Conversely, the two technical perceptions alone had an explanatory power of 11.8%, but when affective factors were added, it reached 59.2%, confirming a significant increase.

From these results, it can be said that for the subjects and class conditions of this study, the learners’ affective factors—such as “wanting to speak,” “being afraid to speak,” and “having confidence”—were more strongly linked to individual differences in oral performance than how they evaluated the platform. However, based on the correlation and regression analysis, it is not possible to determine a causal relationship where those factors directly improved performance.

5. Research Limitations and Future Challenges

This study has several limitations. First, the participants were beginner learners enrolled in an HSKK Level 3 preparatory class at one university, and it is unclear whether the same results would apply to other learning environments or to intermediate/advanced learners. Second, since oral proficiency was evaluated by a mock test, it does not necessarily mean that the ability to interact with a partner in actual conversation was fully measured.

Also, psychological factors and perceptions of technology were measured by self-report questionnaires. It was a short period of two weeks, and it was not a study that continuously tracked how learners’ feelings changed. Furthermore, since everyone completed the same tasks during class, it was not possible to analyze individual differences in the number of self-practice sessions or actual usage behavior. It should also be noted that prior oral proficiency was not controlled for in the main regression analysis.

Future research requires surveys spanning more than one semester and verification across different proficiency levels and educational institutions. By combining not only questionnaires but also actual practice counts, audio recordings, system usage history, and speaking performance in conversation tasks, it is believed that the relationship between learners’ feelings, behavior, and oral proficiency can be captured more concretely.

6. Implications for Chinese Language Education

This study shows that when introducing new learning technologies, focusing only on ease of operation is insufficient. Ensuring that learners can start speaking with peace of mind, practice repeatedly without fear of mistakes, and maintain their willingness and confidence to speak is also important when designing environments for oral practice.

For example, for learners who feel nervous in front of a teacher, an environment where they can record and practice repeatedly on their own may reduce the psychological burden of speaking. Feedback that not only clearly indicates pronunciation errors but also helps them understand which parts to correct and how, as well as mechanisms that allow them to feel improvement, are also considered beneficial. However, the effect of such support on actually reducing anxiety or improving ability needs to be verified separately.

In Chinese, in particular, it is necessary to consider technical support for checking the accuracy of tones and syllables and affective support for learners to speak without hesitation as inseparable. “Creating a high-performance system” and “learners actually becoming able to speak” are not necessarily the same thing.

7. Reasons for Referencing This Paper

The reason I referenced this paper is that it targets Chinese speaking learning using online learning platforms, and there are many points in common with my own research. In the research I am currently working on, I am focusing on the learning process of how beginner Chinese learners who receive AI feedback understand that content, correct their own speech, and speak again.

On the other hand, this paper examines how learners’ affective factors and perceptions of technology are related to oral performance in an ASR-based oral practice environment. Although the central research questions differ, the broad framework of incorporating online platforms into classes and analyzing learners’ oral proficiency and learning experiences is common.

What was particularly helpful was the intervention method of incorporating oral practice into regular classes, the use of existing questionnaire scales, the grading of oral tests and confirmation of inter-rater reliability, and the analysis method of comparing the explanatory power of multiple factors. Also, the result that learning outcomes cannot be fully captured by evaluations of technology alone provided an important perspective for designing and evaluating AI feedback.