Training Evaluation in a Smart Farm using Kirkpatrick Model: A Case Study of Chiang Mai

TL;DR

This study evaluates a smart farm training program in Chiang Mai using the Kirkpatrick model, showing positive farmer reactions and improved knowledge, with insights into training limitations and future improvements.

Contribution

It applies the Kirkpatrick model to assess smart farm training effectiveness among Thai farmers, integrating mobile learning and laboratory activities.

Findings

Farmers showed positive satisfaction with training

Knowledge levels improved after training

Behavioral performance increased post-training

Abstract

Farmers can now use IoT to improve farm efficiency and productivity by using sensors for farm monitoring to enhance decision-making in areas such as fertilization, irrigation, climate forecast, and harvesting information. Local farmers in Chiang Mai, Thailand, on the other hand, continue to lack knowledge and experience with smart farm technology. As a result, the 'SUNSpACe' project, funded by the European Union's Erasmus+ Program, was launched to launch a training course which improve the knowledge and performance of Thai farmers. To assess the effectiveness of the training, The Kirkpatrick model was used in this study. Eight local farmers took part in the training, which was divided into two sections: mobile learning and smart farm laboratory. During the training activities, different levels of the Kirkpatrick model were conducted and tested: reaction (satisfaction test), learning (knowledge test), and behavior (performance test). The overall result demonstrated the participants' positive reaction to the outcome. The paper also discusses the limitations and suggestions for training activities.