ORAL FEEDBACK FOR LEARNER’S LANGUAGE DEVELOPMENT

In ELT there are many types of oral feedback that can be used by teachers to respond and correct the mistakes of language learners. However, there are still many teachers who have difficulty or less reflection in choosing the appropriate feedback for students. Therefore, this study aimed to identify the important role of the types and ways in giving oral feedback by an English teacher in language learner development. This study uses a content analysis method using secondary data taken from the previous study in the form of journal articles and thesis published in Indonesian and International journal sites in the past two decades. The result of this study revealed that from 2009 to 2021 there were many changes regarding the application of oral feedback. However, it was found that corrective feedback always be used continuously from year to year, while one of the most dominant types of oral feedback used was explicit feedback.&nbsp

Automated Distractor and Feedback Generation for Math Multiple-choice Questions via In-context Learning

Multiple-choice questions (MCQs) are ubiquitous in almost all levels of education since they are easy to administer, grade, and are a reliable form of assessment. An important aspect of MCQs is the distractors, i.e., incorrect options that are designed to target specific misconceptions or insufficient knowledge among students. To date, the task of crafting high-quality distractors has largely remained a labor-intensive process for teachers and learning content designers, which has limited scalability. In this work, we explore the task of automated distractor and corresponding feedback message generation in math MCQs using large language models. We establish a formulation of these two tasks and propose a simple, in-context learning-based solution. Moreover, we propose generative AI-based metrics for evaluating the quality of the feedback messages. We conduct extensive experiments on these tasks using a real-world MCQ dataset. Our findings suggest that there is a lot of room for improvement in automated distractor and feedback generation; based on these findings, we outline several directions for future work.Comment: NeurlIPS 2023 GAIED Workshop Versio

Learning processes in interactive CALL systems: Linking automatic feedback, system logs, and learning outcomes

Interactive digital tools increasingly used for language learning can provide detailed system logs (e.g., number of attempts, responses submitted), and thereby a window into the user’s learning processes. To date, SLA researchers have made little use of such data to understand the relationships between learning conditions, processes, and outcomes. To fill this gap, we analyzed and interpreted detailed logs from an ICALL system used in a randomized controlled field study where 205 German learners of English in secondary school received either general or specific corrective feedback on grammar exercises. In addition to explicit pre-/post-test results, we derived 19 learning process variables from the system log. Exploratory factor analysis revealed three latent factors underlying these process variables: effort, accuracy focus, and time on task. Accuracy focus and finish time (a process variable that did not load well on any factors) significantly predicted pre-/post-test gain scores with a medium effect size. We then clustered learners based on their process patterns and found that the specific feedback group tended to demonstrate particular learning processes and that these patterns moderate the advantage of specific feedback. We discuss the implications of analyzing system logs for SLA, CALL, and education researchers and call for more collaboration