Chester S. J. Huang

Taiwan Digital Learning Initiative and Big Data Analytics in Education Cloud

Taiwans Ministry of Education (MoE) started planning and promoting comprehensive Digital Learning Initiative in 2013. It is expected that teachers and students can use broadband networks in campus in 2017. Students can do online learning with different kinds of digital devices at any time. Besides, students can do ubiquitous learning by getting digital learning resources on cloud devices. Teachers can grasp innovative teaching strategies with the application of digital technology and design the learning activities that are more adaptive to individual students. Students have the information force of mobile learning, the abilities to use information tools and grasp the information, as well as Internet literacy and ethics. The learning model of education at all levels will transform into student-centered to facilitate justice, public, autonomy and adaptive learning opportunities as well as make students in the country and those in the city have equal digital learning opportunities. Digital Learning Initiative will have profound influence on education system. Education cloud (EduCloud) is the flagship of the digital learning initiative. This paper presents our research and experience of applying Big data analytics to the EduCloud. We address Big data infrastructure and analytics to cover the issues of data collection, cleaning, storage, query, analytics, and visualization in order to make EduCloud more suitable for teachers and students in Taiwan.

Look Taiwan Education Big Data Research and Development.

With the coming of 4G era, the popularity of various kinds of online equipment, the maturity of Web 2.0 skills, and the effects of increased ability of internet of students and teachers, Taiwan Education Cloud has been progressively emerging with the characteristics of large volume, velocity, and variety of education big data. This has made the currently familiarized techniques of statistical learning and data mining of all departments of MOE (Ministry of Education), such as news briefing, artificial retrieving and synthesizing information, and personnel experience judgment, unable to efficaciously analyze education big data that rapidly accumulate. We develop a new analytical steps and logical thinking so that, while facing such digital information accumulation trend, composedly extract information of valuable teachers and students feedback from such education big data mighty torrent. The paper is to aim for “Look Taiwan Education Big Data Research and Development”, and we use the currently big data techniques to analyze the learning background of individual learners in Taiwan Education Big Market. Through the collection, conduction, and analysis of the past using experience, it was further predicted, suggested, and giving feedback on the need of Taiwan Education Big Market for teachers and students.

Early-Stage Engagement: Applying Big Data Analytics on Collaborative Learning Environment for Measuring Learners' Engagement Rate

Computer-supported Collaborative Learning (CSCL) is a pedagogical strategy associated with how learners construct knowledge with a group by computer-based learning system. In recent years, most of the computer-based learning systems record the interaction log of each learner when developing course assignments. However, the recorded data is facing a challenge to expose the learners behaviors during the course and to design a computer-supported collaborative learning activity. To address those challenges in this paper, a novel collaborative programming tool called Software Project Development and Insight Learning Environment (SPDI Learning Environment) is described. The SPDI Learning environment allows learners of computer science to develop course assignments collaboratively. Besides, it allows instructors to investigate the learners behaviors by associating a web-based integrated development environment (IDE) with Big Data analysis pipeline and Visualization Dashboard. In addition to collect real data from courses, we designed learning activities to help teachers to engage the field of CSCL and Learning Analytics.

Collaborative Search Research in College Computer Courses

The Internet has become the most important information source for students; however, most students lack adequate search processes and search abilities when solving problems using Web search. Current Web search with a single-user environment does not require students to seek assistance in solving their problem. Therefore, how to use information problem solving model to help students solve problems is an emerging issue, and integrated collaborative Web technologies with a suitable model is a key solution. We propose a novel collaborative search system, and it develops the knowledge base of an expert system by organizing the problem of the students. The experimental data were conducted with 36 students at a college school in Northern Taiwan. The results show that collaborative search benefits college student information gathering and improves search processes and search abilities.