Language Teaching in Context

Sida Cong, 2nd year

Evaluation of Oral Production and Explanations for Learning Activities Chosen to Develop Daniel’s Complexity, Accuracy and Fluency

I. Introduction

According to Housen and Kuiken (2009), complexity, accuracy and fluency (CAF) have been generally used in linguistic research to evaluate oral and written productions, which can reflect learners’ language proficiency. Based on Daniel’s oral production video, which is excerpted from his IELTS speaking test (Ross IELTS Academy, 2022), this essay will evaluate how the author measured Daniel’s CAF and why she chose the activities (displayed in Appendix B) from the textbook (Liz & Soars, 2000) to develop Daniel’s CAF specifically. Besides, some discussion about elements in the test prompt which related to Global Englishes ideals will be included.

II. Definition of CAF

Housen and Kuiken (2009) argued that there exist plenty of definitions of CAF and summarized a definition which is believed by most linguistic researchers. Complexity is the hardest one to comprehend among the three concepts. It contains cognitive complexity and linguistic complexity. Cognitive complexity is mainly determined by learners’ subjective feelings, while linguistic complexity focuses on the system and features of L2 itself. The definition of accuracy has the least controversy, which can be described as the evaluation on the amount of errors. Fluency can represent overall language proficiency, which especially measures “smoothness” of oral and written production (Housen & Kuiken, 2009).

III. Evaluation of Daniel’s oral production

Considering the definition of CAF and calculations of the measurement applied by Ogawa (2022), the author calculated each index of CAF. The statistics helped the author to evaluate Daniel’s CAF comprehensively. Table 1 shows the factors the author considered in the evaluation and the statistics. There are two terms needing explanations in the table.As-unit refers to TheAnalysis of

Speech, which can be defined as “a single speaker’s utterance” (Foster et al., 2000). According to McCarthy and Jarvis (2010), MTLD is known as the measure of textual lexical diversity, which can be calculated automatically by a web tool called TEXTINSPECTOR (http://textinspector.com/workflow).

Note. Adapted from Ogawa (2022, p. 7).

In the study done by Ogawa (2022), the score of CAF can be relatively accurate after using the same calculations on a large amount of data. The author didn’t rely on the statistics in Table 1 completely because she had to evaluate Daniel’s CAF only through one cut of his oral production. However, the statistics provided different aspects of Daniel’s linguistic performance in some ways.

IV. Problems and limitations

The first problem the author met in the evaluation process is it is difficult to measure cognitive complexity. There is little linguistic background information of Daniel. Besides, external factors of complexity like the topic of the test question can also influence his oral production. The statistics cannot be reliable because there exist fortuities in one set of data. Especially, accuracy was just confirmed by percent of error-free AS-units. There were selfrepairs, pauses and repetitions which can influence the judgement on accuracy. Another problem worthwhile to be mentioned is the author could not ensure the degree of Daniel’s confidence on the speaking test. If he felt nervous, he probably could not perform as naturally as he practiced by himself. It’s another tough issue for the author to consider. Finally, Daniel was given one minute to take notes for his monologue task in the test. What if he could just answer that question immediately?