Abstract
Our data-driven intelligent tutoring system presents promising results in supporting and accelerating the skills acquiring process. For example, mapping of the common latent variables enables the instructors and curricula designers to understand better the relationships between different exercise items and thus to create improved training scenarios. The case study results also reveal significant improvements in accelerating the process of training welders: participants gradually started to improve their welding skills after only 15 trials (approximately 1 hour of training using the system).
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Notes
- 1.
Parameter description
Wf= a matrix of learned weights connecting input neurons to hidden layers (ht);
ht−1 = the previous hidden state
xt = the input vector
t = timestamp
b(.) = scaling factor
Ct = cell state, \(\widetilde{{C_{t} }}\) = candidate values vector
it = input vector
- 2.
AUC = area under the receiver operating characteristic curve (ROC), representing the area under the discretized curve of precision versus recall values (estimating the probability of a binary outcome). More detailed explanations are available in [22].
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Marinescu-Muster, R., de Vries, S., Vollenbroek, W. (2021). Data-Driven Intelligent Tutoring System for Accelerating Practical Skills Development. A Deep Learning Approach. In: Mealha, Ó., Rehm, M., Rebedea, T. (eds) Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education. Smart Innovation, Systems and Technologies, vol 197. Springer, Singapore. https://doi.org/10.1007/978-981-15-7383-5_17
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