Karshi state university

Chaptrer II. Teaching culture in EFL classes
Second language acquisition (SLA) is studied under a wide range of perspectives, and depending on the perspective and the research question pursued, different methods are meaningfully employed to empirically ground the research. This rich landscape is also reflected in instructed SLA (ISLA, Loewen & Sato, Reference Loewen and Sato2017), but the ISLA focus on the best way to teach and learn a second language (L2) brings with it a particular concern for the generalizability of laboratory research to classroom contexts (cf. Loewen, Reference Loewen, Phakiti, De Costa, Plonsky and Starfield2018, p. 672). The generalizability and replicability of results from experimental research is increasingly also a concern throughout the field of SLA, as recently illustrated by the call for replication studies with nonacademic subjects (Andringa & Godfroid, Reference Andringa and Godfroid2019) designed to broaden the traditional focus on experiments with academic, college-age subjects.
Combining those two lines of thought, a population that arguably is underresearched in SLA are school children (K–12) in their authentic learning context. In 2016, there were almost 22 million school children in upper secondary schools (ISCED level 3, age ≈ 14–18) in Europe, with 94% learning English and almost 60% of them studying two or more foreign languages.Footnote1 Conducting more research in regular foreign language classrooms arguably could help increase the impact of SLA on real-life language teaching and learning in school, which so far seems to be rather limited. While in many countries the language aspects to be taught at a given grade level are regulated by law, where are school curricula actually based on empirically grounded L2 research? Where is it informed by what can be acquired by which type of student at which time using which explicit/implicit instruction methods? In the same vein, textbook writers in practice mostly follow publisher traditions rather than empirical research about developmental sequences, effective task and exercise design, or the differentiation needed to accommodate individual differences. While political and practical issues will always limit the direct throughput between research and practice, scaling up SLA research from the lab to authentic classrooms to explore and establish the generalizability and relevance of the SLA findings in real-life contexts would clearly strengthen the exchange. Note that scaling up as a term from educational science is not just about numbers, but about “adapting an innovation successful in some local setting to effective usage in a wide range of contexts” (Dede, Reference Dede and Sawyer2005, p. 551), which requires “evolving innovations beyond ideal settings to challenging contexts of practice.” This has much to offer, in both directions, given that the data from such ecologically valid formal education settings could arguably be an important vehicle for more integration of SLA perspectives focusing on aspects of learning at different levels of granularity. In real-life learning, all social, cognitive, task, and language factors are simultaneously present and impact the process and product of learning. In sum, we conclude with Mackey (Reference Mackey, Loewen and Sato2017) that “in order to better understand the relationship between instructional methods, materials, treatments, and L2 learning outcomes, research needs to be carried out within the instructional settings where learning occurs”.But how can we scale up ISLA research to real-life contexts where many factors cannot be controlled and the intervention itself is carried out by others, with many practicality issues and a range of educational stakeholders (teachers, students, parents, administrators, teacher educators, and politicians)? While it seems crucial to establish that the effects piloted in lab studies still show up and are strong enough to be relevant under real-world conditions, how can we methodologically deal with the loss of focus and control this entails and successfully set up intervention experiments that support valid interpretations related to SLA theory when carried out in a real-life
2.1 General characteristics and components of culture in teaching classroom
However, the fact that an ITS such as the FeedBook supports immediate learner interaction provides the opportunity to build on feedback research arguing for the effectiveness of scaffolded feedback provide cues in order to scaffold the use of forms that the learner could not yet handle entirely on their own for successful completion of an exercise. Since variables at all the different levels of granularity play a role in real-life language learning, it is unproductive to maintain a divide between sociocultural and cognitive-interactionist perspectives on language learning. Opting to conceptualize the interaction offered by an as scaffolded feedback in the learner's is cognitively well-grounded ( Reference Finn and Metcalfe and, as far as we can see, compatible with our plans to later investigate the impact of a range of individual difference measures.In the next step, we expanded the automatic feedback in the FeedBook to also provide feedback on meaning, as needed for meaning-based reading/listening comprehension exercises (Ziai, Rudzewitz, De Kuthy, Nuxoll, & Meurers, Reference Rudzewitz, Ziai, De Kuthy, Möller, Nuxoll and Meurers2018).⁹ Including such meaning-based activities in the FeedBook also provides opportunities for the system to give (incidental) focus on form feedback (Ellis, Reference Ellis2016). In the current system, meaning feedback is always prioritized over form feedback, though in the future we plan to individually prioritize feedback for a given learner and task using machine learning based on information from learner and task models and learning analytics.In contrast to traditional computer-assisted language learning (CALL) systems, the does not require explicit encoding of different answer options and linkage to the feedback. As transparently illustrated by Nagata (Reference Nagata2009), manual encoding would not be feasible for many exercise types where the potential paraphrases and error types quickly combinatorially explode into thousands of learner responses that the system needs to be able to respond to. In line with the intelligent CALL (ICALL; Heift & Schulze, Reference Heift and Schulze2007) perspective, we therefore employ computational linguistic methods to characterize the space of possible language realizations and link them to parameterized feedback templates. Different from typical ICALL approaches generalizing the language analysis away from the task properties and learner characteristics, for the reasons depicted in Meurers and Dickinson (Reference Meurers and Dickinson2017), we would argue that valid analysis and interpretation of learner language requires task and learner characteristics. This is reflected by the FeedBook in two ways: First, two active English teachers with experience teaching seventh-grade students in this school form were hired on secondment as part of the project, one after the other, to ensure a firm link to the real-life teaching context. This includes the formulation of 188 types of feedback messages designed to express the scaffolding hints that teachers would give students on the language targeted by the seventh-grade curriculum. In addition to the learner characteristics implicitly encoded in the exercise materials and feedback templates, an inspectable learner model was developed to record individual competency facets. Second, the exercise properties are directly taken into account by the computational modeling of the well-formed and ill-formed variability of the learner language. The approach in Rudzewitz et al. (Reference Rudzewitz, Ziai, De Kuthy, Möller, Nuxoll and Meurers2018) derives the structure of the answer space that the system can respond to, which is based on the target hypotheses provided by the publisher in the teacher version of the workbook, combined with a flexible online matching mechanism.
More discussion of the technical side of the FeedBook development can be found in Rudzewitz et al. (Reference Rudzewitz, Ziai, De Kuthy and Meurers2017) and Ziai et al. (Reference Ziai, Rudzewitz, De Kuthy, Nuxoll and Meurers2018). We focus here on the conceptual side of the system and its use as an experimental platform, which we can parametrize in different ways to study the effect on language learning of school children in their regular formal education setting. The curriculum and the design of the FeedBook as a tool interactively supporting individual homework that prepares the student for the classroom sessions delineates the type of research questions that can be explored on this platform. Considering the breadth of research perspectives ISLA is engaged with (Loewen & Sato, Reference Loewen and Sato2017), this naturally only covers a small part of that spectrum—but this subspectrum arguably still includes a substantial number of research issues that such a platform can help settle in an empirically rich way. This includes the effectiveness of different types of feedback in different types of exercises, the reality and impact of developmental sequences and teachability (Pienemann, Reference Pienemann2012) on what can be taught to learners at what point, precise parametrization of exercise and task complexity including alignment with learner proficiency characteristics supporting adaptive individual differentiation, the impact of input materials differing in linguistic complexity and input enhancement in reading comprehension, or the role of individual learner differences and aptitude-treatment interactions, including measures of cognitive ability, motivation, self-regulation, and social characteristics of the students and their families—a broad range of issues at the heart of individual differences in ISLA and classroom research (Li, Reference Li, Loewen and Sato2017; Mackey, Reference Mackey, Loewen and Sato2017).¹⁰
As the first study using the FeedBook, we are investigating the effectiveness of immediate formative feedback incrementally scaffolding the completion of homework, embedded in a regular school context. We chose this relatively traditional topic since it is well motivated by the challenges teachers and students face in real-life classroom, and there is a rich discussion of this topic in SLA pointing out the need for more systematic research, discussed below. Teachers typically are the only reliable source of feedback for foreign language students in secondary school, but their time and the time teachers and students spend together in class is very limited. So, there is little opportunity for students to obtain individual formative feedback, even though the substantial individual differences in aptitude and proficiency would make individual feedback particularly valuable. When students are systematically supported in homework exercises at their individual level, these exercises may also function as pretask activities allowing more students to actively participate in joint language tasks later in the classroom.
Throughout education, feedback is established as an important factor supporting learning, especially where it helps overcome insufficient or false hypotheses (Hattie & Timperley, Reference Hattie and Timperley2007). In its summary of evidence-based research on education, the Education Endowment Foundation includes feedback as the strongest factor influencing learning overall.Footnote2 In SLA there is a long tradition of research and interest in feedback, for which the edited volume of Nassaji and Kartchava (Reference Nassaji and Kartchava2017) provides a current overview. They highlighted the need for further investigations and also mentioned the role that technology could play (p. 181). Sheen (Reference Sheen2011, p. 108) pointed out that empirical studies were often limited to corrective feedback on few linguistic features, limiting the generalizability. In a similar vein, Russell and Spada (Reference Russell, Spada, Norris and Ortega2006, p. 156) concluded their meta study on the effectiveness of corrective feedback for the acquisition of L2 grammar, stating that more studies investigating “similar variables in a consistent manner” were needed. Ferris (Reference Ferris2004) concluded years of debate started by Truscott (Reference Truscott1996) with a call for more systematic studies: “Though it may be difficult for the ethical and methodological reasons I have already described, we need to think of ways to carry out longitudinal, carefully designed, replicable studies that compare
Linking these issues to the role of computer-generated feedback on language learning, Heift and Hegelheimer (Reference Heift, Hegelheimer, Nassaji and Kartchava2017, p. 62) discussed studies in tutorial CALL and concluded that the “key in the future development of computer-generated feedback is to equip the tools with mechanisms that allow for research of vast and reliable user data.” In sum, there is a rich landscape well worth exploring, both to investigate parameters and their interaction from the perspective of SLA research and to effectively support real-life teaching and learning. A software platform such as the FeedBook arguably can help research some of these issues by supporting systematic studies of different types of feedback in a range of exercises fully integrated in real-life school. The study in this article on scaffolded feedback supporting focus on forms to students working on their regular homework provides a first illustration of this, with the envisaged integration of individual difference measures and the functionality for (incidental) focus on form illustrating relevant and realistic next steps.
Let us illustrate the meta-linguistic cue feedback that the system currently provides to incrementally scaffold the student's work on homework exercises. All exercises are web-based versions of the workbook exercises of the Camden Town textbook made available by the publisher. Figure 1 shows a typical, minimally contextualized FIB activity targeting the formation of simple past forms. We see that the first gaps were already completed and automatically marked as correct by the system, indicated by a check mark and coloring the answer in green. Now, the student entered tryed, and immediately after leaving the gap, the system presented the message seen in the image, puts an x next to the student answer and colors it in red.