Utilizing the Delphi Technique to Develop a Self-Regulated Learning Model

Abstract

This study combines learning process theories within the context of data science education in Sichuan Province, China, and develops a customized instructional model for the self-regulated International Higher Education (IHE) Model. In collaboration with 17 experts, selected through purposive sampling, and involving 100 instructors within Sichuan, China, this research explores an instructional model designed to foster selfregulated learning in the field of data science. The Delphi data collection method is employed to investigate the relevance of various learning theories within international higher education in Sichuan Province, China, with a specific emphasis on the data science discipline. The Self-Regulated Learning in International Higher Education (SLR-IHE) model, informed by survey questionnaires, addresses pertinent challenges encountered in data science education, including issues related to English language proficiency, faculty training, curriculum development, faculty mobility, cross-border regulations, and funding constraints. The findings of this study lead to the development of an International Higher Education (IHE) Model for Sichuan Province, China, using the Delphi Technique, which consists of four distinct instructional modules. Through a linear regression analysis of the SLR-IHE model, it becomes evident that the self-regulated learning process in data science education comprises four essential stages, each contributing to the acquisition of distinct goals. These stages include: (1) activating prior knowledge; (2) fostering idea exchange and iterative improvement; (3) building organizational knowledge through understanding, memorization, analysis, and transfer; and (4) generating innovative ideas through reflexive thinking and initiating creative thought processes. These stages collectively support the achievement of specific goals associated with Self-Managed Learning (SML), Self-Regulated Learning (SRL), Self-Paced Learning (SPL), and Self-Directed Learning (SDL) in the context of data science education. This comprehensive instructional model for data science education within the framework of international higher education development in Sichuan Province, China, emphasizes globalization, collaborative efforts, and economic growth as key drivers for enhancing the quality of education in the field of data science.

A Mixed-Methods Data Approach Integrating Importance-Performance Analysis (IPA) and Kaiser-Meyer-Olkin (KMO) in Applied Talent Cultivation

“…Amidst the backdrop of over 2,500 applied universities in China, constituting 80% of the total, a nuanced approach is warranted to cultivate applied undergraduate talents with a focus on data science capabilities. The existing talent quality assessment system, encompassing general higher education, vocational education, and engineering certification, exhibits strengths and limitations in the context of data science relevance [8]. The liberal arts higher education may emphasize theoretical understanding but could potentially overlook practical skills essential for data science.…”