This study analyzed students' online assignment submission behaviors from the perspectives of temporal learning analytics. This study aimed to model the time-dependent changes in the assignment submission behavior of university students by employing various machine learning methods. Precisely, clustering, Markov Chains, and association rule mining…

This study examines second language (L2) learners' perspectives regarding the affordances and challenges of using the Data-Driven Learning (DDL) to identify the properties of near-synonymous words. Employing a convergent mixed-method design, this study deciphers the perceptions of 40 undergraduate L2 learners majoring in English language teaching.…

Aside from learning the basic and common grammatical structures, English as foreign language learners ("hereafter EFL") have to be familiar with a wide range of lexical chunks called collocations. Being sensitive to collocations is the key of attaining a native-like fluency in English. The aim of this study is twofold: to explore the…

Data-Driven Learning (DDL) is a method for learning languages that involves analyzing language usage trends and finding patterns in language data, utilizing technology and statistics. One of the key benefits of DDL is that it allows students to focus on the most relevant and useful language data for their needs. Data-driven learning is an…

This report profiles key trends and emerging technologies and practices shaping the future of teaching and learning and envisions a number of scenarios and implications for that future. It is based on the perspectives and expertise of a global panel of leaders from across the higher education landscape. Starting in 2020, COVID-19 pandemic has…