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Chinese as a foreign language (CFL) learners generally encounter difficulty in using some special rules of Chinese grammar because such grammar points do not exist in their native languages. CFL learners require an effective learning strategy to assist them in acquiring a greater understanding of Chinese grammar. Thus, we integrated a self-explanation strategy into a Chinese-learning system. Specifically, the system includes self-explanation prompts, instructional feedback, and remedial learning materials. The aim of this study was to determine the effects of using a self-explanation strategy on Chinese sentence learning in a computer-assisted language learning environment. The participants were assigned to an experiment group with self-explanation instruction and a control group without self-explanation to practice Chinese grammar points. Learning performance, including grammar tests and cognitive loads, was measured. The results showed higher learning outcomes for Chinese grammar and sentence structure in the self-explanation group. Furthermore, no significant difference in cognitive load was observed between the two groups. Implications for using self-explanation in computer-assisted language learning are discussed.

This study investigated the influence of requiring elementary students to participate in a detective role-playing game, within which students had to be for or against a controversial issue. Three intact sixth grade classes (N = 94) participated in this study. One class of students was asked to search for supporting evidence for one side of an online ethics dilemma and to create convincing arguments (pro), while another searched for opposing evidence (con). The third other class searched for both supporting and opposing evidence (balanced). A one-way multivariate analysis of covariance shows that the balanced role-play strategy had a notable positive impact on the students' ability to construct cogent arguments.

Concept comprehension is an important foundation for more complex thoughts. To enhance concept comprehension, teachers of traditional classrooms have been using instructional strategies and specific course designs, which have been proven effective. It initiates a hypothesis that integrating instructional strategies in the course designs of an e-learning system would improve the effectiveness of concept comprehension. The purpose of this study is to propose a framework that integrates the learning cycle with variation theory in the course design of a web-based e-learning system to prove the hypothesis. A pretest and posttest quasi-experimental design is employed to test whether the framework is feasible. Two types of web-based courses are designed for the experiments: a course designed according to a science textbook and another course designed using the framework. The participants in this study were 120 fifth-grade students in Taiwan. The results of the experiments show that the average scores of the experimental group, using the course designed with the framework, are significantly higher than that of the control group, using the course designed without any instructional strategy, on comprehension of course concepts. This confirms that the coherent, explicit and systematic framework is feasible for designing web-based e-learning courses for effectively enhancing concept comprehension as well as the evidence to prove the hypothesis.

As a nearly global language, English as a Foreign Language (EFL) programs are essential for people wishing to learn English. Researchers have noted that extensive reading is an effective way to improve a person's command of English. Choosing suitable articles in accordance with a learner's needs, interests and ability using an e-learning system requires precise learner profiles. This paper proposes a personalized English article recommending system, which uses accumulated learner profiles to choose appropriate English articles for a learner. It employs fuzzy inference mechanisms, memory cycle updates, learner preferences and analytic hierarchy process (AHP) to help learners improve their English ability in an extensive reading environment. By using fuzzy inferences and personal memory cycle updates, it is possible to find an article best suited for both a learner's ability and her/his need to review vocabulary. After reading an article, a test is immediately provided to enhance a learner's memory for the words newly learned in the article. The responses of tests can be used to explicitly update memory cycles of the newly-learned vocabulary. In addition, this paper proposes a methodology that also implicitly modifies memory cycles of words that were learned before. By intensively reading articles recommended through the proposed approach, learners comprehend new words quickly and review words that they knew implicitly as well, thereby efficiently improving their vocabulary volume. Analyses of learner achievements and questionnaires have confirmed that the adaptive learning method presented in this study not only enhances the English ability of learners but also helps maintaining their learning interest. (Contains 7 tables and 10 figures.)

Assessments, embedded with teachers' implicit (i.e. tacit) domain knowledge, play an important role in evaluating "comprehension of a subject". The knowledge on the importance of both the concepts and their relationships of a subject, if captured, made explicit, and shared around, may greatly help teachers construct more effective assessments. This study establishes a methodology to accumulate tacit knowledge of specific topics from collected assessments by using an implicit knowledge extraction mechanism and, visualises the overall importance distribution of concepts by using knowledge maps for helping teachers compile their assessments. Several two stage experiments, scheduled for one semester, were conducted in the third grade natural science courses at elementary schools in Taiwan. Eighteen teachers who actually teach the courses participated in the experiments, and thirty students were in each course. In the first stage, teachers compiled assessments without using IKMAAS's knowledge map features while in the second stage, they did use them. System usage records, questionnaires and interview results were used for evaluating the usability of the methodology and the satisfaction of using IKMAAS. The results indicate the potential of the methodology, as each of the teachers agreed that the visualised assessment knowledge helped them to comprehend the proportions of concepts they intended to test easily and, additionally, helped them to clearly notice concepts they may have ignored. Yet the results in this study also show the potential of using knowledge maps and knowledge accumulating methodology in pedagogy paradigm. (Contains 8 figures.)

Following the rapid development of Internet, particularly web page interaction technology, distant e-learning has become increasingly realistic and popular. To solve the problems associated with sharing and reusing teaching materials in different e-learning systems, several standard formats, including SCORM, IMS, LOM, and AICC, etc., recently have been proposed by several different international organizations. SCORM LOM, namely learning object metadata, facilitates the indexing and searching of learning objects in a learning object repository through extended sharing and searching features. However, LOM suffers a weakness in terms of semantic-awareness capability. Most information retrieval systems assume that users have cognitive ability regarding their needs. However, in e-learning systems, users may have no idea of what they are looking for and the learning object metadata. This study presents an ontological approach for semantic-aware learning object retrieval. This approach has two significant novel features: a fully automatic ontology-based query expansion algorithm for inferring and aggregating user intention based on their original short query, and another "ambiguity removal" procedure for correcting inappropriate user query terms. This approach is sufficiently generic to be embedded to other LOM-based search mechanisms for semantic-aware learning object retrieval. Focused on digital learning material and contrasted to other traditional keyword-based search technologies, the proposed approach has experimentally demonstrated significantly improved retrieval precision and recall rate.

With vigorous development of the Internet, especially the web page interaction technology, distant E-learning has become more and more realistic and popular. Digital courses may consist of many learning units or learning objects and, currently, many learning objects are created according to SCORM standard. It can be seen that, in the near future, a vast amount of SCORM-compliant learning objects will be published and distributed cross the Internet. Facing huge volumes of learning objects, learners may be lost in selecting suitable and favorite learning objects. In this paper, an adaptive personalized recommendation model is proposed in order to help recommend SCORM-compliant learning objects from repositories in the Internet. This model adopts an ontological approach to perform semantic discovery as well as both preference-based and correlation-based approaches to rank the degree of relevance of learning objects to a learner's intension and preference. By implementing this model, a tutoring system is able to provide easily and efficiently suitable learning objects for active learners. (Contains 1 table and 12 figures.)

Object-oriented programming skill is important for the software professionals. It has become a mandatory course in information science and computer engineering departments of universities. However, it is hard for novice learners to understand the syntax and semantics of the language while learning object-oriented programming, and that makes them feel frustrated. The purpose of this study is to build an object-oriented programming assistant system that gives syntax error feedback based the variation theory. We established the syntax correction module on the basis of the Virtual Teaching Assistant (VTA). While compiling codes, the system will display syntax errors, if any, with feedbacks that are designed according to the variation theory in different levels (the generation, contrast, separation, and fusion levels) to help them correcting the errors. The experiment design of this study splits the participants, who are university freshmen, into two groups by the S-type method based on the result of a mid-term test. The learning performances and questionnaires were used for surveying, followed by in-depth interviews, to evaluate the feasibility of the proposed assistant system. The findings indicate that the learners in the experimental group achieved better learning outcomes than their counterparts in the control group. This can also prove that the strategy of using the variation theory in implementing feedback for object-oriented programming is effective. [ABSTRACT FROM AUTHOR]