Di Zou

Generating Incidental Word-Learning Tasks via Topic-Based and Load-Based Profiles

Compared to intentional word learning, incidental word learning better motivates learners, integrates development of more language skills, and provides richer contexts. The effectiveness of incidental word learning tasks can also be increased by employing materials that learners are more familiar with or interested in. Here, the authors present a framework to generate incidental word learning tasks via load-based profiles measured through the involvement load hypothesis, and topic-based profiles obtained from social media. They also conduct an experiment on real participants and find that the proposed framework promotes more effective and enjoyable word learning than intentional word learning. This article is part of a special issue on social media for learning.

The Load-Based Learner Profile for Incidental Word Learning Task Generation

In recent years, the popularity and prosperity of mobile technologies and e-learning applications offer brand-new learning ways for people. English, as the most widely used language and the essential communication skill for people in the ‘earth village’ nowadays, has been widely learned by speakers of other languages. The importance of word knowledge in learning a second language is broadly acknowledged in the second language research literature. However, comparing with incidental word learning, the intentional learning method has the shortages of motivating reduction, simple acquisition and contextual deficiency. To address these problems, in this paper, we therefore proposed an incidental word learning model for e-learning. In particular, we measure the load of various incidental word learning tasks from the perspective of involvement load hypothesis so as to construct load-based learner profiles. To increase the effectiveness of various word learning activities and motivate learners better, a task generation method is developed based on the load-based learner profile. Moreover, we conduct experiments on real participants, and empirical results of which have further verified the effectiveness of the task generation method and the enjoyment of word learning.

Vocabulary acquisition through cloze exercises, sentence-writing and composition-writing: Extending the evaluation component of the involvement load hypothesis:

This research inspects the allocation of involvement load to the evaluation component of the involvement load hypothesis, examining how three typical approaches to evaluation (cloze-exercises, sentence-writing, and composition-writing) promote word learning. The results of this research were partially consistent with the predictions of the hypothesis: the two writing tasks with greater involvement load led to significantly better word learning than cloze-exercises with lower load, while composition-writing was significantly more effective than sentence-writing despite the same involvement load according to the matrix of the original model. Such results are explained from the perspectives of information organization and pre-task planning, based on which evaluation induced by cloze-exercises is suggested to be allocated with ‘moderate evaluation’ as it involves no use of chunking, hierarchical organization or pre-task planning, evaluation induced by sentence-writing with ‘strong evaluation’ as it involves chunking and pre-task planning at the sentence level, and evaluation induced by composition-writing with ‘very strong evaluation’ for it involves chunking, hierarchical organization and pre-task planning at the composition level.

Discover learning path for group users : a profile-based approach

Abstract With the explosion of knowledge and information in the big data era, learning new things efficiently is of crucial significance. Despite recent development of e-learning techniques which have broken the temporal and spatial barriers for learners, it is still very difficult to meet the requirement of efficient learning, as the key issues involve not only searching for learning resources but also identification of learning paths. People from diverse backgrounds, in most cases, also need to work as a group to acquire new knowledge or skills and complete certain tasks. As these tasks are normally assigned with time constraints, employment of e-learning systems may be the optimal approach. In this research, we study the issue of identifying a suitable learning path for a group of learners rather than a single learner in an e-learning environment. Particularly, a profile-based framework for the discovery of group learning paths is proposed by taking various learning-related factors into consideration. We also conduct experiments on real learners to validate the effectiveness of the proposed approach.

Comparing Dictionary-induced Vocabulary Learning and Inferencing in the Context of Reading

This research examines dictionary-induced vocabulary learning and inferencing in the context of reading. One hundred and four intermediate English learners completed one of two word-focused tasks: reading comprehension and dictionary consultation, and reading comprehension and inferencing. In addition to performing the tasks, some subjects reported their thinking processes either during or after the completion of the tasks, and those who did not were tested both immediately and one week later for their learning of target words. The results show that dictionary-induced vocabulary learning was significantly more effective than inferencing. The researcher explains such results in terms of theories of the degree of elaboration and connectionist models, and suggests that the provision of a number of various aspects of knowledge about a target word is very facilitative for word learning.

An Explicit Learner Profiling Model for Personalized Word Learning Recommendation

Word knowledge is the foundation of language acquisition for second language learners. Due to the diversity of background knowledge and language proficiency levels of different learners, it is essential to understand and cater for various needs of users in an e-learning system. A personalized learning system which meets this requirement is therefore necessary. Users may also be concerned about the possible risk of revealing their private information and prefer controls on the personalization of a system. To leverage these two factors: personalization and control, we propose an explicit learner profiling model for word learning task recommendation in this paper. This proposed profiling model can be fully accessed and controlled by users. Moreover, the proposed system can recommend learning tasks based on explicit user profiles. The experimental results of a preliminary study further verify the effectiveness of the proposed model.

Feedback methods for student voice in the digital age

Central to the concept of Student Voice is the communication of student feedback to educators. Feedback can assume a great variety of forms, and effectiveness and appropriacy of different feedback methods may vary. This research investigates student perceptions of two traditional feedback methods-pen-and-paper questionnaires and oral question-and-answer reports-compared against feedback obtained through the use of three digital technology tools (Socrative, TodaysMeet and Google Drive). The findings suggest that the use of digital technologies in Student Voice contexts is likely to be highly effective due to the overwhelming positive attitude of students towards these tools. [ABSTRACT FROM AUTHOR]

The Augmented Hybrid Graph Framework for Multi-level E-Learning Applications

The advances in MOOCs, Web learning communities, social media platforms and mobile learning apps have been witnessed in recent few years. With the development of these applications and systems, the significant growth of learning resources with multimodalities (e.g., web pages, e-books, lecture videos) has greatly changed the way people learn new knowledge and skills. However, this results in the problem of information overload as learners are overwhelmed by the rich learning resources that accompany the ever developing technologies. In other words, it is increasingly difficult for learners to find required learning materials efficiently and effectively when they confront such a large volume of data. To tackle this problem, it is essential to build a powerful framework to organize e-learning resources and capture learning preferences. In this paper, we therefore propose a graph-based framework to achieve these intended outcomes by integrating various hidden relationships among learners, users and resources. Throughout the case studies, we have verified that the proposed framework is very flexible and powerful to support various kinds of e-learning applications in different scales.

Investigating the Effectiveness of the Uses of Electronic and Paper-Based Dictionaries in Promoting Incidental Word Learning

Although there are a numerous of studies in the facilitative effects of dictionary consultation in promoting word learning, no research has ever been conducted to investigate the effectiveness of a hybrid use of paper-based and electronic dictionaries. The present research, therefore, responds to this call and compares the effectiveness of the pure use of either paper-based or electronic dictionary and the hybrid use of both. The empirical results demonstrate the superiority of the paper-based dictionary over the electronic dictionary, the usefulness of repetition and the greater effectiveness of the hybrid use of both paper-based and electronic dictionary than the pure use of either. We further conclude that the significance of processing for constructing memory, repetition for consolidating memory and diversity for reinforcing memory should be emphasized.

A Review on Recent Development of the Involvement Load Hypothesis

The Involvement Load Hypothesis, proposed by Laufer and Hulstijn in 2001, has been widely adopted and applied to estimate effectiveness of word-focused tasks in promoting word learning. With the development and shift of learning contexts, models and technologies in the past sixteen years, the involvement load hypothesis has been researched from various aspects. This review investigates the applications and theoretical developments of the hypothesis, focusing on two main areas: examination of the three components of the hypothesis, and comparison or integration of the hypothesis with other hypothesis or theories, for example, the technique feature analysis. Future developments in related fields are also discussed.