The aim of this research is to measure self-regulated behavior and identify significant behavioral indicators in computer-assisted language learning courses. The behavioral measures were based on log data from 2454 freshman university students from Art and Science departments for 1 year. These measures reflected the degree of self-regulation, including anti-procrastination, irregularity of study interval, and pacing. Clustering analysis was conducted to identify typical patterns of learning pace, and hierarchical regression analysis was performed to examine significant behavioral indicators in the online course. The results of learning pace clustering analysis revealed that the final course point average in different clusters increased with the number of completed quizzes, and students who had procrastination behavior were more likely to achieve lower final course points. Furthermore, the number of completed quizzes and study interval irregularity were strong predictors of course performance in the regression model. It clearly indicated the importance of self-regulation skill, in particular completion of assigned tasks and regular learning.

Self-directed learning (SDL) ability, its usefulness in higher education and life-long learning have been highlighted in previous literature. However, there has been much less understanding of the effects of SDL ability in the school settings, specifically the effects on learners’ SDL behaviors and processes. To address this limitation, this study investigated the relations between SDL ability, SDL behaviors, and reading outcomes and further explored the process of planning behaviors in SDL. This study examined the context of SDL for extensive reading using a goal-oriented active learning system, GOAL. The results showed that the high SDL ability students demonstrated significantly more reading outcomes in terms of books completed and the number of days read than those with low SDL ability. The high SDL ability students engaged significantly more in planning behaviors, that were found to be significantly correlated with reading outcomes, than the low SDL ability students. Cluster analysis and transition analysis also differentiate groups of learners with different planning behaviors. These findings suggested that the learning behaviors and outcomes facilitated by the environment were affected to varying degrees by the levels of students’ SDL ability, and personalized feedback can be created using the SDL behavioral variables and patterns in the environment.

Self-directed learning (SDL) requires students to take initiative to learn and control their own learning process. Literature highlights the importance of SDL for lifelong learning. Yet, little understanding is known regarding how to support SDL at the school level, specifically for out-of-class learning context. To fill up this gap, this research developed a learning dashboard and integrated SDL process management in GOAL system. It was implemented to provide support for out-of-class online self-directed extensive reading (SDER) at a high school level. A two-group study conducted during a three-week spring vacation found the experimental group (N = 35, with SDL support) achieved significantly more reading outcomes than the control group (N = 12, without SDL support). Detailed GOAL interaction behavior analysis of the experimental group showed that viewing learning dashboard was significantly correlated with reading outcomes as well as interactions related to SDL process management. These findings highlights positive effect of SDL support in GOAL on students' out-of-class SDER outcomes as well as their SDL behaviors. The study provided implications for research related to extensive reading and SDL support for out-of-class learning.

Hashtags are usually adopted to highlight the topic of user-generate contents in many social media platforms. Therefore, hashtags are utilized in many topic-related applications such as user-topic opinion prediction and hashtag recommendation. Obtaining hashtag representations is usually treated as a fundamental task of these applications. These applications either learn hashtag representations from hashtag adoption or represent a hashtag by the representation of its content. However, most of existing hashtag representation learning methods fail to take full advantage of hashtag contents and the effectiveness of representing a hashtag by its content is limited in many cases. In this paper, we propose a content-enhanced hashtag embedding method called ContentHE, which introduce the semantic information of hashtag contents into hashtag representation learning by treating words which compose the hashtags as special nodes in a hashtag network. Specifically, ContentHE first introduces a word embedding space which is generated by a pre-trained language representation model and establishes a heterogeneous network. Each hashtag in the network connects with a set of user-generate contents and words if these words compose the hashtag. Then, ContentHE utilized a multi-task learning model and a sampling strategy called node sampling to map hashtags and user-generate contents to the word embedding space while preserving the network structure information. The performance of ContentHE on two real-word tweet collections demonstrates that it significantly improves the accuracy of hashtag clustering tasks and captures the relationship between the topics which hashtags belong to.