Developing an integrated assessment system of student learning: Innovation from education informatization in China

• In Event: Improving M&E for vocational ed, assessment systems and reading outcomes

Proposal

In China, school system and educators usually rely on limited student performance data and experience-based judgements to assess student learning and predict their learning trajectory. Such approach often suffers from inefficiency and incompleteness in data collection and analysis, which limits the capacity of tapping the full learning potential of students. Scholars have carried out many experimental studies to observe the application of plural evaluation on development of students, but the effects are not quite obvious. In 2018, the Chinese Ministry of Education released the “Education Informatization 2.0 Action Plan” promoting the use of big data and innovation-driven development to realize the promise of digital educational resources and data. The plan has provided new opportunities for the development and application of data-driven student assessment systems. The update emphasizes transforming from dedicated resources to big data resources, integrating crowdfunding and innovation, realizing the usefulness of digital resources, educational data and educational information. The efficient methods for conducting management, teaching and learning are plural education evaluation based on educational informatization as evaluation background.
This paper presents an innovative approach to student assessment using multi-dimensional data and evaluations in a Grade 1-12 school in China. In the backdrop of education informatization, an integrated assessment system can include multi-dimensional data from school management, teaching and learning, which is in contrast with the reliance on single-dimensional inputs in traditional evaluations. This study explores an integrated student assessment system that is suitable for the basic education cycle. The multidimensionality not only refers to various learning inputs but also addresses the richness of data in each input. For example, student evaluation can include self-evaluation, peer evaluation and teacher evaluation. The final evaluation would a weighted result combining quantitative and qualitative effect analysis and the weight allocation is data-driven.
This paper expounds the techniques of the integrated assessment system in detail. Specifically, this study focused on targeted homework, stratified teaching and data forecast as research subjects. For targeted homework, the assessment system breaks down information barriers and applied to different learning stages. To enrich assessment portraits for students, the system collects data on student behavior in learning preparation stage, teaching stage and post-class stage. To effectively apply the integrated evaluation, it is necessary to integrate general information about students, moral education performance, academic performance and mental health information. Through three-dimensional real-time evaluation and recording moral, intellectual, physical, aesthetic behavior, this system forms a comprehensive portrait of students to transform data into personalized teaching resources with timely feedback. Spring Boot, Spring Cloud & Alibaba, Vue and Element and other digital technology are used to build resource pools, form micro-lessons divided by knowledge point, form online resources and realize self-directed learning. The distributed ledger recording technology based on blockchain records teaching data and ensures the quality of teaching resources and traceable intellectual property rights. To build school-based teaching resources, educators need to mark attributes for every knowledge point used by the information system. Through the above resources, the system can recommend targeted homework for students automatically. The data-driven assessment system locates the actual learning status of students, analyzes the root causes of learning obstacles, then recommends knowledge points from the resource pool and provides reports and consolidation exercises from school-based resources accurately. At the same time, the assessment system tracks and monitors each student’s learning over time.
For differential teaching and learning, the integrated assessment system also provides a practical solution. In “Vision 2020: Report of the Teaching and Learning in 2020 Review Group” of the UK, differential teaching was defined as a highly organized and interactive evaluation method, which pays more attention to the individual development of students and helps each student tap their potential, and obtain learning achievements in order to actively integrate into the society and move towards success in the future (GTCE, 2020). It is challenging to implement differential teaching for every student, especially considering the large effect size in Chinese classrooms. However, the integrated assessment system in this study enables the stratification of learning into three to five manageable layers to improve the teaching quality. Blockchain technology, decentralized consensus mechanism and smart contract technology help with the delivery of teaching by suggesting teaching strategies, arranging teaching resources and tasks, which facilitates teaching without compromising teachers’ autonomy. Big data prediction method deals with data and build analysis, prejudge learning characteristics, make path and plan suitable for the growth of students and development of learning, monitor the learning process, learning state and forecast the potential of students interested in values, scientific.The forecast data provides the rationalization, personalized targets, accurately adjust learning solutions.
In conclusion, compared to traditional student assessments, this article explores an innovative integrated system of student assessment based on education informatization. The system improves the overall precision of student performance, strengthens process monitoring and explores value-added for teaching. It allocates the proportion of different evaluation subjects using a data-driven approach and adjusts the teaching cycle by combining quantitative and qualitative effect analysis. The system has been successfully implemented to assign targeted homework and facilitate differential teaching and learning in classrooms. Additionally, in the future, it could also be used to forecast students’ learning trajectories. The system takes advantage of blockchain technology with hard-to-tamper and traceability features to the forecast model. Big data prediction models process student behavior data and analyze learning characteristics, suggesting suitable growth, learning, and career development paths for students.

Author

• Kun Ma, Shandong University