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Structured argumentation support environments have been built and used in scientific discourse in the literature. However, to the best our knowledge, there is no research work in the literature examining whether student's knowledge has grown during learning activities with asynchronous argumentation. In this work, an intelligent computer-supported collaborative argumentation-based learning platform that detects whether the learners address the expected discussion issues is proposed. After each learner presents an argument, a term weighting method is adopted to derive input parameters of a one-class support vector machines classifier which determines if the learners' arguments are related to the discussion topics. Notably, a peer review mechanism is established to improve the quality of the classifier. Besides, a feedback module is used to issue feedback messages to the learners if the learners have gone off on a tangent. The experimental results revealed that the students were benefited by the proposed learning-assistance platform.

Recent research indicated that students' ability to construct evidence-based explanations in classrooms through scientific inquiry is critical to successful science education. Structured argumentation support environments have been built and used in scientific discourse in the literature. To the best of our knowledge, no research work in the literature addressed the issue of automatically assessing the student's argumentation quality, and the teaching load of the teacher that used the online argumentation support environments is not alleviated. In this work, an intelligent argumentation assessment system based on machine learning techniques for computer supported cooperative learning is proposed. Learners' arguments on discussion board were examined by using argumentation element sequence to detect whether the learners address the expected discussion issues and to determine the argumentation skill level achieved by the learner. Learners are first assigned to heterogeneous groups based on their responses to the learning styles questionnaire given right before the beginning of learning activities on the e-learning platform. A feedback rule construction mechanism is used to issue feedback messages to the learners in case the argumentation assessment system detects that the learners go in a biased direction. The Moodle, an open source software e-learning platform, was used to establish the cooperative learning environment for this study. The experimental results exhibit that the proposed work is effective in classifying and improving student's argumentation level and assisting the students in learning the core concepts taught at a natural science course on the elementary school level.

With rapid advances in the development of information technology, information access has become central to life in the 21st century. In recent years, the development of useful learning-assistance systems has become a popular topic in literature. Learners can benefit from guidance provided by a tool that assists learning when a student has trouble using an e-learning platform. An effective learning-assistance tool can reduce teacher workload and increase the time spent giving individual guidance to learners who fall behind. However, few machine-learning techniques have been used as learning-assistance tools in literature to determine learner status or provide feedback to learners and teachers. Therefore, this work employs a learning-assistance tool that uses learning-reinforcement techniques to continuously interact with an environment and provides learners with suitable and timely feedback to guide students through difficulties, inspire them, and help them complete assigned tasks. The learning-assistance tool can promote learners' information and communication technology (ICT) literacy and assist learners in overcoming difficulties. Furthermore, teacher workload is significantly reduced because appropriate hints or feedback are automatically delivered to learners without teacher involvement. (Contains 14 tables and 3 figures.)

Recently, a lot of open source e-learning platforms have been offered for free in the Internet. We thus incorporate the intelligent diagnosis and assessment tool into an open software e-learning platform developed for programming language courses, wherein the proposed learning diagnosis assessment tools based on text mining and machine learning techniques are employed to alleviate the loading of the teachers. Experiments were conducted in two introductory-undergraduate programming courses to examine the effectiveness of the proposed diagnosis and assessment tools. The learners' work including the source code and comments were processed by the proposed text mining and machine learning techniques. This system also provides immediate feedback and high-quality evaluation results to guide the learners with poor performance. Our experimental results reveal that the proposed work can effectively assist the low-ability learners. (Contains 6 tables and 13 figures.)

In recent years, designing useful learning diagnosis systems has become a hot research topic in the literature. In order to help teachers easily analyze students' profiles in intelligent tutoring system, it is essential that students' portfolios can be transformed into some useful information to reflect the extent of students' participation in the curriculum activity. It is observed that students' portfolios seldom reflect students' actual studying behaviors in the learning diagnosis systems given in the literature; we thus propose three kinds of learning parameter improvement mechanisms in this research to establish effective parameters that are frequently used in the learning platforms. The proposed learning parameter improvement mechanisms can calculate the students' effective online learning time, extract the portion of a message in discussion section which is strongly related to the learning topics, and detect plagiarism in students' homework, respectively. The derived numeric parameters are then fed into a Support Vector Machine (SVM) classifier to predict each learner's performance in order to verify whether they mirror the student's studying behaviors. The experimental results show that the prediction rate for the SVM classifier can be increased up to 35.7% in average after the inputs to the classifier are "purified" by the learning parameter improvement mechanisms. This splendid achievement reveals that the proposed algorithms indeed produce the effective learning parameters for commonly used e-learning platforms in the literature.

This work proposes an intelligent learning diagnosis system that supports a Web-based thematic learning model, which aims to cultivate learners' ability of knowledge integration by giving the learners the opportunities to select the learning topics that they are interested, and gain knowledge on the specific topics by surfing on the Internet to search related learning courseware and discussing what they have learned with their colleagues. Based on the log files that record the learners' past online learning behavior, an intelligent diagnosis system is used to give appropriate learning guidance to assist the learners in improving their study behaviors and grade online class participation for the instructor. The achievement of the learners' final reports can also be predicted by the diagnosis system accurately. Our experimental results reveal that the proposed learning diagnosis system can efficiently help learners to expand their knowledge while surfing in cyberspace Web-based "theme-based learning" model.