Due to the shortage of agricultural labor, agricultural education has become increasingly important. It consists of both classroom lectures and practical training. However, teacher observations during agricultural training are often subjective and lack objective evaluation criteria. To support teachers’ reflection, we aim to objectively record learner behaviors. In order to explore the educational applicability of this recording approach, we developed a vision-based system to estimate and visualize learners’ positions and work time during agricultural training. Learners’ positions are estimated from a \(360^\circ \) video using pose estimation, multi-object tracking, and person re-identification, while their work states are classified using features extracted from egocentric videos. To evaluate what kind of information and granularity are useful, four prototypes of visualizations were created. We collected data from actual training sessions and applied our system to the recordings. We then interviewed teachers to explore how they utilize the visualizations to reflect on and improve their agricultural training practice. In preliminary quantitative evaluations, both position estimation and work activity recognition achieved over 70% accuracy across several of our own datasets. Interview results indicated that the visualizations were generally consistent with the teachers’ observations and were regarded as useful for objectively assessing learners’ farm work.