Navigating the student entrepreneurial journey: Dynamics and interplay of resourceful and innovative behavior

Manzi-Puertas, Mario A.; Agirre-Aramburu, Izaskun; López-Pérez, Sain

doi:10.1016/j.jbusres.2024.114524

Cited by 6 publications

(1 citation statement)

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“…In [11], findings suggest a preference for anonymity despite its potential to create a less productive atmosphere, prompting suggestions for optimizing anonymity features in collaborative learning tools. In [12], investigates how resourceful behaviors like financial bootstrapping and bricolage influence innovative behavior among student entrepreneurs in Spain. Findings suggest that bricolage mediates the relationship between financial bootstrapping and innovation, highlighting implications for resource management in fostering innovation among student ventures.…”

Section: Related Workmentioning

confidence: 99%

Sequence Modeling with Recurrent Neural Networks (RNNs) for Student Learning Behavior Pattern Recognition in a Flipped Classroom

Tang

2024

jes

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The flipped classroom model has become increasingly popular in education, altering the traditional methods of teaching. It is important to understand and acknowledge how students learn within this framework in order to optimize instructional strategies and promote personalized learning. This study investigates the use of Sequence Modeling with Recurrent Neural Networks (RNNs) to identify patterns in student learning behavior within a flipped classroom setting. The proposed deep learning architecture utilizes RNNs to analyze sequential patterns in students' interactions with the flipped classroom materials, while also incorporating attention mechanisms to better detect important patterns and temporal dynamics in the learning process. Multimodal learning techniques are also employed, combining data from various sources to gain a comprehensive understanding of student behavior. Additionally, clustering techniques using autoencoders are explored to group students with similar learning behaviors. Predictive models, such as RNN or LSTM networks, are developed to forecast future learning behaviors and provide insights into potential challenges or successes for individual students. The effectiveness of this framework is evaluated using real-world data from flipped classroom implementations, with performance metrics like recall, precision, and accuracy used to assess the success of the sequence modeling approach in recognizing and predicting student behavior patterns. Overall, the application of deep learning methods, specifically sequence modeling with RNNs, demonstrates potential for improving personalized learning experiences and facilitating proactive interventions to support diverse student needs.

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Section: Related Workmentioning

confidence: 99%