The CC2020 Report highlights the importance of transitioning from knowledge-based to competency-based CS education. Given that proficient programming is considered a foundational skill for CS majors, some researchers have developed top-down qualitative frameworks for assessing programming competency. However, the lack of quantitative competency models makes it challenging to conduct competency-oriented assessments in CS courses, especially for introductory programming courses such as CS1. To address this challenge, our study tracks the learning activities of 209 students in a CS1 course, including 10 formative tests and 44590 code submissions. The five-channel learning sequences (scores, engagement, code metrics, programming skills, coding style) are established to capture the knowledge, skill, and dispositions of the CS1 competency model for each student, with profiles in each channel characterized by five indicators: mean values, entropy, turbulence, proficiency, and resilience. This approach enables multi-dimensional competency assessment with visualization throughout the learning process, providing timely guidance for both teaching and learning. This work is a preliminary exploration in CS1 towards quantitative programming competency models in CS education via integrating multidimensional data, employing appropriate data granularity, and visualizing observable patterns.