Career planning and pathway generation based on multimodal learning analytics

Concerning the growing complexity of the business environment and career growth, conventional career planning strategies have become challenging to satisfy personal needs. This paper suggests a new career planning and path generating model based on multimodal learning technology, which integrates data from many sources (e.g., educational background, work experience, social networks, emotional data, etc.) so giving individuals more personalised and accurate advice. First, by preprocessing the data with feature fusion, the study creates tailored career path recommendations by building an adaptive model architecture combining deep learning and data mining approaches. In the experimental phase, the results reveal that the model has major benefits in terms of recommendation accuracy, personalisation and flexibility, and offers a fresh technical path for the field of career planning by means of the validation of the parameter tuning of the model and the effect of career path recommendation.