MMLA research and implementation can raise severe issues among educational leaders, notably the openness, traceability, confidentiality, fairness, and prejudice of such processes in a real-world application.
There are numerous real concerns of bias and discrimination, rejection of individual autonomy and legal protections, non-transparent and morally indefensible outcomes, encroachment of teaching and learning confidentiality, and inappropriate, imbalanced, uncertain, and dangerous outcomes for people involved in the use of such interventions in education.
I think that the benefits of MMLA outweigh the potential challenges. Curkova emphasizes that MMLA research focuses on the use of physical sensors because of their ability to capture physiological and behavioral expressions of students’ emotions and characteristics that are almost impossible to identify and detect with the naked eye, such as sentiments, gazing, cognitive processes, and body reactions.
For instance, the MMLA strategy implemented on system engagement, eye movement tracking, and facial expression data estimates students’ intrinsic motivation and learning outcomes in a game-based learning environment. Incorporating gaze data into most multimodal models enhanced forecasting accuracy. Furthermore, incorporating data from multiple periods helps to improve the prognosis of academic results.