Intelligent systemNeural networks applied to knowledge acquisition in the student model
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Cited by (12)
Knowledge modeling via contextualized representations for LSTM-based personalized exercise recommendation
2020, Information SciencesCitation Excerpt :This neglects the mutual effect between learning processes on these parameters by assuming each knowledge acquisition process is independent [44]. Researchers have successfully demonstrated that neural networks are effective at modeling students, at facilitating the acquisition of knowledge, and at identifying relations between skills [29,31]. Although the approach used in the cited papers is dated in terms of data availability and computing resources, they nevertheless show the effectiveness of using layered neurons and adjusted weights in modeling complex human activities such as learning.
User/tutor optimal learning path in e-learning using comprehensive neuro-fuzzy approach
2009, Educational Research ReviewMonitoring students' actions and using teachers' expertise in implementing and evaluating the neural network-based fuzzy diagnostic model
2007, Expert Systems with ApplicationsCitation Excerpt :A comprehensive review can be found in Jameson (1996). Neural networks have also been proposed in student modeling due to their abilities to learn from noisy or incomplete patterns of students’ behavior and generalize over similar examples (Beck & Woolf, 1998; Posey & Hawkes, 1996). This generalized knowledge can then be used to recognize unknown sequences.
Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis
2005, Information SciencesCitation Excerpt :However, several attempts have been made to incorporate the powerful learning abilities of neural networks in existing student modelling systems taking advantage of synergies with other AI methods. A hybrid approach, where each node and connection has symbolic meaning, has been proposed in TAPS [46]. The back-propagation algorithm has been used to modify weights that represent importance measures of attributes associated with student's performance, in order to refine and expand incomplete expert knowledge.
A New Student Modeling Technique With Convolutional Neural Networks: LearnerPrints
2021, Journal of Educational Computing ResearchAcademic performance evaluation using soft computing techniques
2014, Current Science