Data-driven Learning Applications Research Articles

An Experimental Study on the Application of Data-driven Learning in Secondary Education

This experimental study investigates the impact of data-driven learning (DDL) on the acquisition of the grammar instruction among middle school students, focusing on the English passive voice, a grammatical structure that poses challenges for Chinese students due to its divergence from Chinese passives with retained objects. The findings indicate a significant improvement in the experimental group’s ability to apply the English passive voice to translate special Chinese passives, underscoring the potential of DDL as a pedagogical tool for grammar instruction.

“I WISH COCA HAD…” – LEARNER PERCEPTIONS ON HANDS-ON CORPUS USE IN EFL CONTEXT

A growing body of research has examined the integration of corpora into foreign language classrooms in the last few decades. A sub-line of research concerning the interaction between the two fields has been student perceptions. Additionally, the growing integration of online learning into curricula, triggered by the COVID-19 pandemic, has aroused a necessity to explore the application of data-driven learning in online learning environments. The current study, therefore, attempted to elicit learner perceptions on learning vocabulary targeted particularly for speaking skill through direct data-driven learning in an online learning context. The data gathered from the participants (N=28) through a questionnaire and a semi-structured interview revealed that the participants found the use of data-driven learning activities in conjunction with the coursebook and oral production tasks helpful to boost their learning. They did not evaluate corpus consultation as a very challenging task to tackle in general. Overall, the study revealed two significant findings: First, it highlighted that collaborative learning might play a significant role in data-driven learning applications. Second, the findings emphasized the necessity for integration of the audio data to the large, publicly available corpora, such as COCA, which could contribute to future research on exploring the efficiency of data-driven learning for speaking skills.

Data-Driven Learning in an EFL class: a study of Ecuadorian learners’ perceptions

Previous research has reported an increased interest in the application of Data-Driven Learning (DDL), which was derived from corpus linguistics, to aid English as a foreign or second language learning. DDL emerged as a learner-centered approach that emphasizes the discovery of linguistic patterns by analyzing natural occurrences of language. Many studies have investigated the effect of DDL on several linguistic components and have reported positive outcomes. Consequently, its application has germane importance in an Ecuadorian context as it has not been widely explored. This study seeks to document the experience of 14 EFL students from an Ecuadorian public university and examine their perceptions towards the implementation of DDL activities. The intervention included two stages: assisted guidance for students to retrieve information on their own and corpus-based materials developed by the teacher. Students' perceptions were gathered via a questionnaire, a reflection, and a follow-up interview. The findings indicate that students perceived DDL as a practical and innovative approach to support grammar discovery in an EFL class. Nevertheless, students encountered some challenges associated with the use of DDL, for example, the limited number of searches, an issue that can be resolved by a complete registration in the COCA web. This article concludes with pedagogical implications and recommendations for future DDL applications and research in Ecuadorian EFL classes.

Applying data-driven learning in self-translation of academic discourse: A case study of a Chinese medical student.

This article reports on an experiment on the use of data-driven learning (DDL) in the revision of self-translation by a Chinese medical student. The think-aloud method is employed to investigate the difficulties the student encountered in self-translation and the effectiveness of DDL in improving the quality of self-translation. Results show that difficulties in the self-translation of medical abstracts are mostly associated with markers of rhetorical moves, terminologies, and conventional academic expressions and that they can be effectively solved by such corpus consultation strategies as checking possible options in bilingual dictionaries, using the most certain keywords to find collocations, and using the most possible accompanying words to find contexts. A comparison of translations before and after the application of DDL reveals that it could help improve translation quality in lexical choices, syntactic structures, and discourse practice. An immediate interview shows that the participant holds a positive attitude toward DDL.

Using Data-Driven Approach to Teach Descriptive Text: A Study in High School

This paper aims to describe how data-driven learning can be applied in English classes when teaching descriptive text to senior high school students. This study was a self-observation involving 24 high school social studies students. The data were obtained from observation of student activities during learning, and the tasks carried out by students were in accordance with the basic competencies of the descriptive text material. The data were analyzed by conducting sorting the data and analyzing the sorted data by codifying and reporting the data. The results of this study showed that the application of data-driven learning with communicative learning methods can make students more confident in their abilities during the learning process. The use of data-driven learning is expected to increase students' motivation and learning abilities, especially in the field of writing.

The impact of concordancing on English learners’ foreign language anxiety and enjoyment: An application of data-driven learning

Over the past decades, several studies have focused on the role of data-driven learning (DDL) in language teaching and learning. Nevertheless, research on DDL is still scarce from an emotional perspective. As exposure to authentic language and discovery learning through DDL leads to increased awareness of regular language patterns, it is important to see if it affects learners' emotions, i.e., foreign language anxiety and enjoyment. To address this gap, the current study adopted a quasi-experimental comparison group pretest-posttest design within a validating quantitative data triangulation model. Ninety-six English-major university students were divided equally into two groups, i.e., one comparison and one intervention group, and were later exposed to the placebo and treatment, respectively. The placebo was 12 1-h sessions of traditional explicit instruction on how to mark important points in academic English lectures by a competent teacher, whereas the treatment was 12 1-h sessions of concordancing on how to mark important points in academic English lectures with AntConc. Drawing on foreign language anxiety and enjoyment questionnaires and free-response surveys, the findings of the study showed that concordancing did not result in any statistically significant differences between students in their foreign language anxiety. On the other hand, students found following a DDL approach with concordancing less enjoyable than traditional explicit instruction by the teacher. Altogether, the results highlighted the crucial role of the teacher in creating a supportive and enjoyable learning environment, the students’ inclination towards teacher-centered classes, and their dependence on the teacher as the sole provider of information. The study has theoretical and pedagogical implications for language teaching and learning.

Classroom Concordancing and Second Language Motivational Self-System: A Data-Driven Learning Approach.

Research shows that exploring language corpora through data-driven learning (DDL) plays a significant role in language learning. Nevertheless, it is not clear if using concordancing as an application of DDL affects the learners’ second language motivation. To address this gap, the current study adopted a triangulation design, validating quantitative data model, and a quasi-experimental (comparison group, pretest-posttest) design. Ninety English-major university students with an intermediate level of English language proficiency, divided into control and experimental groups, took part in the study. Drawing on a second language motivational self-system questionnaire, the findings of the study did not result in any statistically significant differences between the students in their second language motivational self-system. Altogether, the students found learning English through a DDL approach with concordancing less motivating than receiving explicit instruction. The study has implications for language teaching and learning.

Corpora as open educational resources for language teaching

AbstractCorpora, large electronic collections of texts, have been used in language teaching for several decades. Also known as Data‐Driven Learning (DDL), this method has been gaining popularity because empirical research has consistently shown its effectiveness for learning. However, corpora are still underutilized, especially with learners of languages other than English, at lower proficiency levels, and in non‐university contexts. This is regrettable because DDL has a great potential for developing modular flipped content, especially for hybrid, remote, and online courses. This article first provides an overview of DDL applications and findings of empirical research. Next, it outlines obstacles to wider DDL implementation as well as available and possible solutions. Corpus user guides and exercise collections tied to specific corpora are discussed as one promising direction, and an example of such new open educational resources for teaching German is presented. The article concludes with a discussion of implications and future directions.

Effects of Corpus‐Aided Language Learning in the EFL Grammar Classroom: A Case Study of Students' Learning Attitudes and Teachers' Perceptions in Taiwan

This study employed a blended approach to form an extensive assessment of the pedagogical suitability of data‐driven learning (DDL) in Taiwan's EFL grammar classrooms. On the one hand, the study quantitatively investigated the effects of DDL compared with that of a traditional deductive approach on the learning motivation and self‐efficacy of first‐year college students majoring in English. On the other, it qualitatively examined a group of early‐career teachers' (ECTs') hands‐on experience of teaching DDL to these students. The research results via t‐tests show that only those who received DDL treatment have enhanced learning attitudes in general. No significant differences, however, were found between the effects of these treatments in a multivariate analysis test of covariance. In contrast, qualitative inquiry shows that, despite facing technical difficulties and increased workload, the ECTs found their DDL teaching experience innovative and interesting, believed in its effectiveness in grammar learning, and judged it to transform Taiwanese students' grammar learning patterns from passivity to active engagement. This article concludes with suggestions for future DDL applications and investigation in the EFL grammar classrooms of Taiwan.

The Application of Data-Driven Learning in Business English Teaching for MBA Students in China: A Critical Assessment

This paper is aimed at laying foundation for the application of Data-Driven Learning (DDL) in the Master of Business Administration (MBA) English teaching and furthers its development and improvement in different contexts. Therefore, the paper attempts to illustrate the pilot application of an innovative teaching method, DDL, in Business English (BE) teaching for the MBA program. The survey, by using questionnaire and analysis of the data, reveals the positive effects of the DDL application in the MBA program and some disadvantageous aspects which require improvement.

A Study on the Application of Data-driven Learning in Vocabulary Teaching and Leaning in China’s EFL Class

Data-driven learning (DDL) developed from corpus linguistics plays a pioneering role in the evolution of EFL teaching, allowing the learners to indentify and induce language rules by observing numerous real corpora concordances. With the guidance of new teaching notion advocated by College English Curriculum Requirements in China, DDL model has become the tendency for college English learners' efficient and autonomous learning. It becomes urgent to take advantage of this method and apply it to college vocabulary teaching. Compared with traditional foreign language teaching and learning method, data-driven learning is characterized by “autonomic learning”, “authentic language input”, “self-discovery”, and “bottom-up inductive learning”, which will conducive to the formation of the students' personalized learning abilities and the development of their autonomic learning abilities.

In Defense of Concordancing: An Application of Data-Driven Learning in Taiwan

Data-Driven Learning (DDL) is an approach in which the language learners are also research workers whose learning is driven by access to linguistic data (Johns, 1991:2). This essay aims at evaluating the potential of the DDL approach using concordance compiling software in language teaching in Taiwan. The advantages and disadvantages of using concordancing in the classroom and the values and limitations of using small corpora are discussed. A CALL program is explored in language teaching, particularly in the teaching of J.K. Rowling's uses of the preposition in Harry Potter and the Philosopher's Stone.

The Application of Data-Driven Learning to a Small-Scale Corpus of Conversational Texts from the BNC –British National Corpus: Teaching Speech Acts

The Application of Data-Driven Learning to a Small-Scale Corpus of Conversational Texts from the BNC –British National Corpus: Teaching Speech Acts