Abstract:

In South Korea, classes are currently using digital textbooks in research schools. Digital textbooks are open media which not only provide learning content and materials in a flexible manner according to individual learning levels and styles but also enable easy connection to external third-party tools, using various devices (mobile, PC, etc.) and cloud computing environment.

In the conventional educational field, most data related to learning activity disappear when education is over. However, in digital textbook-based class, various forms of learning activity data can be collected, used, and analyzed thanks to the use of on-line lesson tools. In this background, we commenced this research to improve the learning environment through analysis of data stored in this way.

IMS Global proposes the Caliper platform to measure and analyze student achievements. The Caliper platform has the structure of collecting data generated mostly from online learning system, which is central to e-Learning activity like LMS, through the standard Sensor API and assessing the data through metric that can measure the data. However, this platform is limited in its application to Korea's K12 smart education environment: In this environment, we should collect and analyze learning activity data from classes using online learning tools that are diversely dispersed in classroom-centered, not online-centered, lessons.

Therefore, we need to develop an analysis platform which will enable comprehensive assessment of various forms of data generated from in-class educational process, including the use of digital textbooks, participation in learning communities, digital textbook content, and evaluation by teachers.

Our research includes methods of collecting and storing data for analysis of teaching and learning achievements from these various and unprepared data for analysis. We design and implement a system that collects, analyzes, and visualizes data through stages of data collector, pre-processor (three-level metric generator), analyzer, and visualizer.

This paper describes methods of extracting metric data that can be used for analysis from various forms of data generated without considering learning analysis in the pre-processing phase. We divided this process into three levels in a systematic manner.

The first level metric is data collectable through queries of data to be collected. We add a timestamp to non-standard data collected from diverse systems if necessary and accumulate the data in mongoDB as log data. The first level metric refers to data set that can be generated through queries of the accumulated log data.

The second level metric refers to data that can be generated by applying simple forms of statistical functions to data set gained as the result of the first level metric. For example, such statistical functions as total sum, count, average, and min/max value can be used.

The third level metric refers to data set developed by applying multi-dimensional data conversion and data mining techniques from the data extracted not just from the first and second level metrics but also from other third level metric. Algorithm that can be used in extracting the third level metric may include similarity analysis through duration-based pattern matching and data mining. These third level metric data can be used to analyze whether students have planned patterns of learning activity based on their plans and self-directed learning skills.

Keywords:

Learning analytics, Digital textbook.