PPTELL Conference
Taipei, Taiwan
3-5 July, 2019
Liberty Square, Taipei, Taiwan. Photo by Mark Pegrum, 2019. May be reused under CC BY 4.0 licence.
The second Pan-Pacific Technology-Enhanced Language Learning Conference took place over three days in midsummer in Taipei, with a focus on language learning within smart learning environments.
In his keynote, Smart learning approaches to improving language learning competencies, Kinshuk pointed out that education has become more inclusive, taking into account the needs of all students, and focusing on individual strengths and needs. There are various learning scenarios, both in class and outside class, which must be relevant to students’ living and work environments. There is a focus on authentic learning with physical and digital resources. The overall result is a better learning experience.
Learning should be omnipresent and highly contextual, he suggested. We need seamless learning integrated into every aspect of life; it should be immersive and always on; it should happen so natrually and in such small chunks that no conscious effort is needed to be actively engaged in it in everyday life. Technologies provide us with the means to realise this vision.
Smart learning analytics are helpful because they allow us to discover, analyse and make sense of student, instruction and environmental data from multiple sources to identify learning traces in order to facilitate instructional support in authentic learning environments. We require a past record and real-time observation of a learner’s capabilities, preferences and competencies; the learner’s location; the learner’s technology use; technologies surrounding the learner; and changes in the learner’s situational aspects. We analyse the learner’s actions, interactions with peers, instructors, physical objects, and digital information; trends in the learner’s preferences; and changes in the learner’s skill and knowledge levels. Making sense is about finding learning traces, which he defined as follows: a learning trace comprises a network of observed study activities that lead to a measurable chunk of learning. Learning traces are ‘sensed’ and supply data to learning analytics, where data is typically big, un/semi-structured, seemingly unrelated, not quite truthful (with possible gaps in data collection), and fits multiple models and theories.
In the smart language learning context, he mentioned a smart analytics tool called 21cListen, which allows learners to listen to different audio content and respond (e.g., identifying the main topic, linking essential pieces of information, locating important details, answering specific questions about the content, and paraphrasing their understanding), and analyses their level of listening comprehension depending on the nature and timing of their responses. Analytics does not replace the teacher, but gives the teacher more tools; and as teachers give feedback, the system learns from them and improves. Work is still underway on this project, with the eventual aim of producing a theory of listening skills. He went on to outline other tools taking a similar analytics approach to reading, speaking and writing.
In his presentation, Autonomous use of technology for learning English by Taiwanese students at different proficiency levels, Li-Tang Yu suggested that technology offers many opportunities for self-directed learning, which is important as students need more learning opportunities outside their English classes. In his study, he found there was no significant difference between high and low proficiency English learners in terms of the amount of autonomous technology-enhanced learning they undertook. Most students in both groups mentioned undertaking receptive skills activities, but the advanced students used more productive skills oriented activities. Teachers should familiarise students more with technology-enhanced materials for language learning, and suggest that they undertake more productive activities.
In her talk, Online revised explicit form-focused English pronunciation instruction in the exam-oriented context in China, Tian Jingxuan contrasted the traditional method of intuitive-imitative pronunciation instruction with newer and more effective form-focused instruction; in revised explicit form-focused instruction, there is a focus on both form and meaning practice. In her study, she contrasted traditional instruction (control group) with revised explicit form-focused instruction (experimental group, which also undertook after-class practice) in preparing students for the IELTS exam in China. Participants in the experimental group performed better in both the immediate and delayed post-test; she concluded that the revised form-focused instruction is more effective in preparing students for their exams, at least in the case of low-achieving students, as in her study.
In their presentation, Does watching 360 degree virtual reality videos enhance Mandarin writing of Vietnamese students?, Thi Thu Tam Van and Yu-Ju Lan described a study in which students viewed photos (control group) or viewed 360 degree videos with Google Cardboard headsets (experimental group) before engaging in writing activities. Significant differences were found in all areas assessed (content, organisation, etc) and in total; the authentic context provided by the 360 degree videos thus enhanced the level of students’ Mandarin writing. All students in the experimental group preferred using Google Cardboard compared to traditional methods in writing lessons.
