Research outline (purpose, methods, etc.)
What is the purpose of this research?
This research aims to develop and evaluate an IR information portal site for students to effectively utilize educational IR data to support the improvement of student learning. We specifically aim to develop a system that analyzes educational IR data, such as courses completed, grades, results of various questionnaires, logins to the learning management system; to visualize and display students’ learning status, provide advice to students, enable simulations to improve learning (Self-Tailored Educational Portal System, STEPS), and evaluate the system using actual data.
To develop this type of system, it is essential to clarify “What is the data that promotes transformation of learning? How should that data be presented?” Furthermore, STEPS is being developed to enable students to become “learners” suited to the era of lifelong learning based on their own vision of the future. Therefore, this system must promote metacognitive monitoring and promote control of learning activities based on metacognition (metacognitive control).
Why are we conducting this type of research?
■ Because learning at university level is different from learning at high school
Learning up to high school follows the government course guidelines and government-approved textbooks, with clear learning targets and timing to reach those targets. This requires Self-Regulated Learning (SRL) almost exclusively, so it is important to increase the self-regulation ability of children and students. On the other hand, learning in society (lifelong learning) is based on SDL, where the learners decide themselves “what should be learned by when,” once they have envisioned what they want to become in the future, while also being aware of their current situation. Also, as revealed by adult learning theories, learning characteristics of adult learners differ from children’s learning.
Higher education institutions such as universities link learning required during secondary education with learning required by society. Namely, the university student days correspond with a transitional learning period, shifting from children to adults (learning transition). Thus, students should learn the skills and attitudes of SDL while at school.
■ Because universities have accumulated data on student learning
Many universities are developing educational IR departments and accumulating data on students themselves and student learning. The three types of typical systems previously developed by educational IR departments are Fact Book (a system that explains the university using typical indices), Early Alert (a system that predicts low-grade students, students who will repeat a year/suspend studies), and Benchmark (a system for comparison with major indices at other universities). However, almost all these systems are used for decision-making by the university executives, board of directors, faculties, and graduate schools. Educational IR data has never been provided in a useful format for students, nor has it been used by students to improve their learning.
■ Because research has advanced in areas like Learning Analytics
The creation of departments in charge of IR, as described above, has enabled central management of educational IR data, previously managed separately within universities. Furthermore, Learning Analytics, which analyzes and visualizes educational data using a diverse range of methods, and data mining, which captures meaningful data from big educational data, has also been developed as academic fields and research on analysis methods progresses.
For these reasons, we aim to develop a system to facilitate learning support leveraging educational IR data and verify the effect on students.
How will this research progress?
■ Review the effect of the prerequisite system
We previously developed a system utilizing educational IR data. The system is called Decision Support with IR (DSIR) *, equipped with a function of matching data on student readiness predicted by the Self-Directed Learning Readiness Scale (SDLRS) and data on the class syllabus. DSIR can be incorporated as a STEPS module, so in this research, we first considered functions that were missing from this DSIR, and problems with the DSIR interface, to investigate the direction of improvement.
*Information on DSIR can be viewed from the “Deliverables” page.
■ Identification of missing data and investigation of collection methods
We will investigate how to acquire data collected in a difficult-to-use format because some data was not collected using current survey methods or was not collected to be used for student support, such as surveys for data on out-of-class learning time.
■ System design and development
At this stage, the data required for the system has been organized, the collection/input method has been identified, and the display contents are being selected. We will also develop visualization techniques that are easy for students to understand and respond to. Through this research, we will first produce a mockup and evaluate the system’s usability. The important point at this stage is to determine how much data entry can be automated, especially to reduce the burden of system usage for students. For example, the system will be set up to ensure that IR data such as grades and courses are automatically prefilled wherever possible, and other data is easy to input.
■ Measuring and evaluating the effect of the system
The anticipated effect is an increase of readiness in SDL, including accepting self-responsibility for their own learning and results, developing independence, an inquisitive mind, a positive attitude toward the future, and gaining the ability to self-regulate to continue learning by autonomously using learning strategies appropriately. Students should also be able to set learning goals suited to their situation with self-efficacy as adult learners, and to utilize various learning resources. We will evaluate the system with the cooperation of students.