Formative Feedback in Online Distance Language Learning: Boosting Motivation With Automated Feedback

Formative Feedback in Online Distance Language Learning: Boosting Motivation With Automated Feedback

Ayse Taskiran, Mujgan Yazici
Copyright: © 2021 |Pages: 26
DOI: 10.4018/978-1-7998-7681-6.ch005
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Abstract

Today, in open and distance learning environments, employing different technologies and methods for individual formative feedback is possible with artificial intelligence. Artificial intelligence-based software can contribute to the learning processes by providing effective and immediate automated feedback (AF). This chapter highlights the importance of formative feedback in the context of distance foreign language teaching and elaborates the use of AF technology as a mean to provide effective, efficient, and attractive feedback. The effectiveness of artificial intelligence-based AF tools in increasing achievement, motivation, and self-sufficiency in English writing activities in distance foreign language learning is discussed within Keller's ARCS (attention, relevance, confidence, and satisfaction) motivation model. The potential advantages of AF tools and suggestions for their effective use in distance language teaching and learning processes are elaborated within the framework of the ARCS model.
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Introduction

For thousands of years, education and training that existed within a triangle of school-teacher-student has now benefited from new, multifaceted, multi-channel alternatives with the help of technologies in the education system (Durnalı et al., 2018; Durnalı et al., 2019; Orakcı, 2020). One of these alternatives is considered as “Online Learning”. With the developing technology, increasing demands and rapid changes in the learner profile, open and distance learning has been increasingly adopted by higher education institutions. Increasing number of students in large classes has given way to implementation of instructional technologies for feedback and evaluation mechanisms. This chapter highlights the importance of formative feedback in the context of distance foreign language teaching, and elaborates the use of automated feedback (AF) technology as a mean to provide effective, efficient and attractive feedback for the increasing number of students in distance learning contexts.

Daniel (1996, p.95) defines higher education institutions with more than 100,000 distance learners as 'Mega Universities'. With the increasing number of learners, it is observed that these institutions often do not make the necessary changes to maximize the effectiveness and efficiency of online distance learning (Howard, Schenk, & Discenza, 2004, p. vi). It is emphasized that teachers have important roles in the application of digital technologies in providing continuous feedback to learners in online teaching to be able to monitor learning processes closely and solve problems that can be experienced between teacher and learner (Guri- Rosenblit, 2019). In the digital age, where learners expect to interact directly with teachers, it is not possible to take on increasing responsibilities with a small number of academic staff. Given the higher learner numbers, open universities make significant investments in learning materials, learner support and administrative systems to provide quality distance education. Thanks to modern technologies, teachers can update learning activities and revise learning materials more frequently, while learners can get feedback about their work faster (Daniel, 2019). One of the important problems faced by many institutions offering open and distance learning services is the ability to interact with learners, teachers and programs. Due to the high number of learners and insufficient academic staff, problems such as delay and disruption arise in giving continuous, quick, and effective feedback to learners, which is among the important elements of interaction.

Moore and Kearsley (2012) emphasize the importance of providing timely and effective feedback and evaluation in the context of open and distance learning by stating that feedback and evaluation mechanisms are vital because if any part of the system breaks down, the entire system is endangered (p. 19). By drawing attention to the feedback processes in distance education institutions, the authors emphasize that learners submit their homework by using communication technologies to obtain evaluation and feedback, and this process creates a sense of participation in the course for both the teacher and the learner. Although some learners can tolerate delays in this process, most learners want feedback to be sent immediately. The number of learners who will be satisfied with the one-way communication that continues without feedback is in a minority (Moore & Kearsley, 2012, p.115).

Similarly, in distance foreign language teaching, large classes result in a summative assessment of language proficiency, often with multiple choice tests. However, making more learner-centered, more individual, effective and continuous evaluations instead of summative evaluations in language teaching can positively contribute to the development of foreign language writing skills and can boost motivation. Common factors pointed out by foreign language learning theories for learners to develop their productive skills are; intelligible input and output (Krashen, 1985; Swain, 1985), corrective feedback (Long, 2000), and motivation and attitude (Masgoret & Gardner, 2003). In this regard, significance of formative feedback in foreign language learning process becomes more prominent.

Key Terms in this Chapter

Automated Feedback: A type of feedback that is generated by artificial intelligence- based software and delivered to learners upon completing any written task.

Formative Feedback: Any kind of summative, indirect, or semi-corrective remedial interpretation, sign or indicator presented at the level of activity, process, development, and planning of the learner.

ARCS Motivation Model: A type of motivational design process that includes a synthesis of motivational concepts and theories that can be classified into four categories: attention (A), relevance (R), confidence (C), and satisfaction (S).

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