Learning words in repetition and in context may be conducive to effective vocabulary acquisition. Research in corpus linguistics and mobile learning can provide pedagogical and technical support for the strategies. DDL (data-driven learning), an approach which features concordancing through a large number of text collection, can facilitate direct and intensive exposure to authentic language in use; the ubiquitous mobile technology nowadays can enable contextual learning experience anytime, anywhere. Thus, ''mobile DDL'' may synergise DDL and mobile learning, and this combination is a proposal to enhance vocabulary learning with emerging technology. This paper reports an experiment on mobile DDL in the context of academic English. A mobile app was specially designed and developed for voluntary participants in this research to look up core academic words in authentic academic texts. Through passive data capture, questionnaire and interview, it was found that DDL could be adapted to mobile devices. However, the approach was not well acceptable to the intermediate-level students in this research, despite their familiarity with mobile technology in daily life. Major adjustments to DDL seem necessary if mobile DDL is to assist learners at large in vocabulary learning.

In the face of surging online education around the globe, it seems quite necessary and helpful for learners and teachers to have the plethora of online resources well sorted out beforehand. To some extent, the efficiency and accuracy of resource search and retrieval may determine the quality and influence of online education. In this research, based on the methodological framework of design science, the support vector machine (SVM) algorithm is chosen to optimise the design of an accurate resource classifier. The aim is to improve the unsatisfactory classification effect of traditional classification methods for online education resources, so that online learners can enjoy more accurate and convenient access to education resources they are seeking out of many more. For the purpose of performance evaluation, the proposed SVM-based classifier was compared with two other classification methods based on multiple neutral networks and deep learning respectively. Upon collection and pre-processing of online materials, the features of educational resources were extracted and output in the form of feature vectors. By calculating the similarity between the extracted feature vectors and the standard vectors of the set type, we obtained the classification results of online education resources for each of the three classifiers. It was found that, compared with those of the two traditional classification methods, the precision ratio and the recall ratio of the proposed classifier improved by 3.26% and 2.01% respectively. In the meantime, the proposed SVM-based classifier seems to more advantageous in performance balance with better F measurement.

Among the terms for technology-enhanced education, ‘e-learning' may be an umbrella one to cover various types; on the mobile platform, ‘mobile learning' or ‘m-learning' has gained a solid foothold. A historical review of the development from e-learning to m-learning may help draw a clearer picture of technology-enhanced education in history and in the future. The change of the major medium from computers to smartphones involves not only where learning may occur in the digital age but also how. M-learning shares some similarities with e-learning, for example, enhancing learner autonomy, yet facing the difficulty in assessing efficacy and effectiveness. In the meantime, with advancing mobile technology, m-learning can achieve higher portability and personalisation in three aspects: devices, materials, and learners. How to engage, retain, and motivate mobile learners in the informal and spontaneous settings merit more attention. Solid theoretical underpinnings and empirically validated practice in other disciplines may shed light on the avenues of future research.

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