Concerns over the quality of teaching in massive open online courses devoted to language learning (LMOOCs) have prompted extensive research on learning behavior in such courses. The purpose of this study is to gain a better understanding of autonomous learning check-ins – that is, individuals sharing their own learning records and/or other information about their learning-related experience – a novel behavior that has not been studied in previous LMOOC research. Using learning analytics, we found that just 6.2% (n = 699) of a sample of 11,293 learners autonomously engaged in check-in behavior, and that the content of these learners’ check-ins varied considerably according to their contexts and the language skills they were seeking to acquire. We further found (1) a positive association between check-in behavior and LMOOC completion; (2) that students who chose to check in earned relatively low grades on unit quizzes, especially in their early stage of learning, but outperformed the non-check-in group significantly in final exam scores; and (3) that those who checked in engaged with a significantly wider array of in-LMOOC learning components than those who did not, and thus accessed a wider system of language-learning experiences. Taken together, these results confirm that check-in behavior can aid the process of learning in an LMOOC and further highlight this behavior’s wider potential to aid self-directed autonomous online learning.

In this paper, we expolore Multi-Agent Reinforcement Learning (MARL) methods for unmanned aerial vehicle (UAV) cluster. Considering that the current UAV cluster is still in the program control stage, the fully autonomous and intelligent cooperative combat has not been realised. In order to realise the autonomous planning of the UAV cluster according to the changing environment and cooperate with each other to complete the combat goal, we propose a new MARL framework. It adopts the policy of centralised training with decentralised execution, and uses Actor-Critic network to select the execution action and then to make the corresponding evaluation. The new algorithm makes three key improvements on the basis of Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. The first is to improve learning framework; it makes the calculated Q value more accurate. The second is to add collision avoidance setting, which can increase the operational safety factor. And the third is to adjust reward mechanism; it can effectively improve the cluster’s cooperative ability. Then the improved MADDPG algorithm is tested by performing two conventional combat missions. The simulation results show that the learning efficiency is obviously improved, and the operational safety factor is further increased compared with the previous algorithm.

This article reports on findings regarding the learning strategies used by a group of Chinese English as a foreign language (EFL) learners in a mobile-technology-assisted environment. The research design is a context-specific case study using Dörnyei’s (2005) categories of learning strategies as the conceptual and analytical framework to guide data collection and analysis. Both quantitative and qualitative data were collected through a questionnaire from a sample of 75 Chinese EFL learners and a small-scale follow-up interview of five participants who completed the questionnaire. Data showed that a mobile-technology-assisted environment effected changes in Chinese EFL learners’ ways of adopting a particular set of learning strategies, which differed in type and frequency from those typical of a teacher-led and examination-oriented language classroom. Metacognitive and commitment control strategies were most frequently used by the respondents in this study. The frequency of student use of metacognitive strategies moved ahead of commitment and environmental control strategies. Satiation and emotion control strategies, rarely used by Chinese students in a teacher-fronted language classroom, were also observable. These findings have implications for the understanding and designing of mobile-technology-assisted learning for EFL learners to develop appropriate strategies for autonomous learning.

This study was carried out in a language centre, in French higher education. Teachers and researchers had contrived a pedagogical system labeled guided autonomy which combined class attendance in groups and self-study in the self-study room. This kind of autonomous and technologically enhanced learning system will be referred to as CAALL (Computer Assisted Autonomous Language Learning) in this paper. To investigate and reflect critically on the students’ practices in CAALL, it was decided to carry out an extensive triangular study, cross-checking different data (Raby, 2003, 2005). The data pertained to what students did (physical behaviors while working), and to how they felt about it (verbal behaviors in the form of journals). Six students volunteered to take part in that experience. They were observed six times consecutively while working autonomously. In addition, we analyzed the journals in which they wrote about their feelings in connection with the CAALL system. From the students’ observations, three strategic models emerged (epistemic, procedural and mixed). From the journal analyses, different motivational attitudes appeared, ranging from enthusiastic appraisal to stark rejection. One unexpected result yielded by the triangular approach was the importance of internal factors (the learners’ characteristics) versus external factors (the learners' environment) in the process of appropriation of the new learning system.