Protean Career Competence refers to the ability of university students to proactively design and flexibly manage their careers based on their personal values and goals in a rapidly changing labor market. In today’s society, where psychological satis...
Protean Career Competence refers to the ability of university students to proactively design and flexibly manage their careers based on their personal values and goals in a rapidly changing labor market. In today’s society, where psychological satisfaction and the realization of personal values are increasingly emphasized over traditional organizational measures of success, this competence has emerged as a crucial meta-competency for both survival and growth (Baruch & Sullivan, 2022; Hall, 1996). Encompassing dimensions such as identity, adaptability, self-direction, value orientation, change management, and reflective behavior, protean career competence enables not only successful employment but also sustainable employability and lifelong personal development. Despite its importance, current career support programs in higher education remain largely focused on short-term, standardized job placement services, falling short of providing personalized and ongoing support that reflects individual potential and value systems of students.
Conventional career development activities focused primarily on employment and short-term extracurricular programs often fall short in reflecting the diverse learning and career goals of individual students. Instructors also face significant administrative burdens in providing tailored feedback and continuous personalized support, as they often lack the time and human resources to fully understand each learner’s capabilities and motivations. To address these challenges, a new educational model is needed. This model should utilize learning data collected from multiple aspects of student activity and achievement to design and apply personalized and long-term support strategies.
This study presents the design principles for developing a Learning Data-based Support System (LDSS) aimed at systematically enhancing protean career competence. Based on these principles, the study develops the LDSS, applies it in a real university setting, and verifies its effectiveness and validity through an integrated action research approach. Specifically, the study takes the following approaches. First, it collects and integrates longitudinal learning data, such as system logs, task participation records, and interaction data, to provide both learners and instructors with objective and continuous feedback on learning status and career competence. Second, using these data flows and design procedures, the study develops the design principles and procedural model of the LDSS to support learners in independently setting and reviewing their career goals. At the same time, it helps instructors develop personalized support strategies tailored to each student’s key competencies and characteristics. Third, by integrating the theory of protean career competence into the instructional design principles, the LDSS provides students with opportunities for autonomous and creative career exploration, while also offering instructors data-driven strategies to support the development of protean career competence. Through this approach, the study aims to go beyond conventional, one-way, short-term support and to establish a mid- to long-term career support ecosystem grounded in learning data.
To achieve this, the study followed the design and development research methodology to create and validate a set of design principles and a procedural model. For systematic development, initial design principles, guidelines, and a sequence of procedures were established through a review of relevant literature, expert interviews, and case analyses. Based on this foundation, two rounds of expert validation were conducted. As a result, 13 design principles and 40 detailed sub-guidelines were developed, centered around four core design elements. These elements are support for self-exploration, support for career development activities, support for career development management, and support for change and reflection.
First, the design elements related to self-exploration include three principles. The principle of multidimensional profiling, the principle of value clarification, and the principle of ethical engagement help learners deeply explore their interests, strengths, and values while emphasizing the ethical use of data.
Second, to support career development activities, six principles were applied. These are the principle of personalized goal setting, the principle of real-time responsiveness, the principle of promoting self-direction, the principle of feedback literacy, the principle of supporting learner autonomy, and the principle of strengthening instructor and learner interaction. These principles provide continuous and personalized feedback on goal setting and the progress of career development.
Third, to support career development management, two principles were introduced. The principle of visualizing career development activities and the principle of data-informed career decision-making enable learners to monitor their career histories and achievements and to make objective and rational decisions based on data analysis.
Fourth, to support change management and reflection, the study applied two principles. The principle of enhancing adaptability and the principle of continuous self-reflection are designed to help learners actively respond to changes and grow through ongoing reflection and feedback. These principles and guidelines are intended to strengthen learners’ protean career competence in a meaningful way and to support self-directed and value-driven career design.
Reflecting these design principles and detailed guidelines, the learning data-based support system named "Career Lab" was developed as a mobile application based on the procedural model. The Career Lab app was built in collaboration with a professional system development company using the Agile methodology. This approach ensured an accurate analysis of the needs of both users and system administrators and clearly defined the scope and functionality of the system. The development was carried out in two phases. In this study, the version completed in the first phase was applied in an actual educational setting. For external validation, 16 participants were assigned to the experimental group and 19 to the control group. Both survey responses and in-depth interviews were used to derive the research findings. The results showed that the use of the learning data-based support system led to greater improvement in protean career competence among the experimental group compared to the control group.
The academic contributions of this study can be summarized in three ways. First, from the perspective of career development theory, this research integrates the protean career model with personalized support based on learning data, providing new insights into studies on career and employment support. Second, from the perspective of educational technology, the study extends the practical scope of learning data research by applying and validating the learning data-based support system design model within actual classroom settings. Third, from the perspective of innovation in higher education, the design guidelines for the LDSS offer clear directions for effectively utilizing large-scale data generated through interactions between instructors and learners. This contributes to expanding a forward-looking and flexible learning environment model. This integrated approach aligns with the needs of university education that aim to foster both self-directed learning skills and innovative career development competence. It is also expected to serve as a foundation for future studies that connect learning data with career support systems.