On the big stage of human resources, AI has emerged as a skilled tool for analyzing the effectiveness of training. It can not only accurately measure the effect of training, but also provide strong data support for future training. Human Resources DepartmentA method to analyze the effectiveness of training with AIWhat are they?
Background
The human resources department of a large enterprise has been faced with the challenge of evaluating the effectiveness of training. Traditional assessment methods include questionnaires, interviews, and observations, but these methods have shortcomings such as strong subjectivity and lack of data support. In order to improve the accuracy and objectivity of the evaluation of training effectiveness, the human resources department decided to use AI technology for analysis.
Case Description:
The company's human resources department partnered with a professional AI technology company to develop an AI model for training effectiveness analysis. The model uses a deep learning algorithm to evaluate the training effect by conducting multi-dimensional analysis of employees' performance, achievements, satisfaction and other data after participating in the training.
Here's how
Data Collection
First, HR needs to collect data on the employee's participation in the training. This data includes:
Employee performance data in training, such as class participation, number of discussions, correct answering rate, etc.;
Employee's performance data after training, such as test scores, performance in actual work, etc.;
Data on employee satisfaction with training, such as feedback scores from surveys, etc.
Data preprocessing
For the collected data, pre-processing is required. Pre-treatment includes:
Data cleaning to remove outliers, missing values, and duplicate values;
Data standardization, which converts data from different dimensions into a unified standard to facilitate model training;
Data normalization, which normalizes the data to make model training more stable.
Model training
After the data preprocessing is complete, the AI model begins to be trained. During the training process, a variety of deep learning algorithms were used, including convolutional neural network (CNN), recurrent neural network (RNN) and long short-term memory network (LSTM). These algorithms are able to automatically learn the features and patterns in the data and generate the corresponding models.
Model evaluation and optimization
After the model is trained, the model needs to be evaluated and optimized. Evaluation methods include accuracy, recall, f1 value, and other indicators. Based on the evaluation results, the model can be optimized, such as adjusting the model parameters, increasing the dataset size, and adopting more advanced algorithms.
Model application and result presentation
The optimized AI model can be applied to real-world scenarios. Specific applications include:
Analyze the training needs of new employees, and formulate personalized training plans according to their job characteristics and personal abilities;
Evaluate the training effect of existing employees, find out the problems and deficiencies in the training, and put forward suggestions for improvement;
Optimize the overall training system of the enterprise to improve the quality and efficiency of training.
Case Study
The company's human resources department has achieved the following results by using AI technology to analyze the training effect:
Improve the accuracy and objectivity of evaluation: The AI model can automatically learn the features and rules in the data, avoiding the influence of human factors on the results in traditional evaluation methods, and improving the accuracy and objectivity of evaluation.
Optimize the training plan: Through the analysis of the training needs of new employees and the development of personalized training plans, employees can adapt to the job more quickly and improve work efficiency.
Improve the training system: Through the evaluation of the training effect of the existing employees and put forward suggestions for improvement, the problems and deficiencies in the training can be found and solved in a timely manner, and then the training system of the whole enterprise can be optimized.
Improve employee satisfaction: By collecting and analyzing employee satisfaction data on training, we can understand employees' needs and feedback in a timely manner, and then improve employee satisfaction with training.
Reduce costs: The use of AI technology for analysis can greatly reduce the workload and time cost of the human resources department and improve work efficiency. At the same time, it can also reduce the dependence on external consulting companies and reduce business costs.
Human Resources DepartmentA method to analyze the effectiveness of training with AIThe above is a detailed introduction. The use of AI has strengthened the human resources department's analysis of training effects. Not only does it improve the accuracy of your analysis, but it also brings unprecedented insights. From now on, training is no longer a vague adventure, but a scientific journey backed by data.
Yingsheng AI Application Research Institute Yang Guang).