Assessment of Student Educational Performance Analysis for Feature Extraction and Classification with LSTM-Based Deep Learning Model
Keywords:
Conditional Random Field, Data mining, Education Quality, Long Short-Term Memory, Sparse feature extraction, Student performanceAbstract
Data mining techniques are also incorporated in the model to analyze large amounts of data and extract valuable insights. Student performance is a critical aspect of education that refers to the extent to which a student has achieved the desired learning outcomes in a particular subject or course. The assessment of student performance is a complex task that involves analyzing a wide range of data related to a student's academic performance, including their grades, attendance, participation, and behavior. Effective assessment of student performance is essential for providing feedback to students, improving learning outcomes, and enhancing the overall quality of education. It helps educators to identify areas where students need additional support, provide targeted interventions to students who are struggling, and improve teaching strategies to enhance student learning. The proposed Boltzmann Sparse Probabilities - Conditional Random Field (BSP-CRF) model aims to provide an effective and accurate assessment system for analyzing students' educational performance, which can be used to identify areas for improvement and optimize learning outcomes. The present research model aims to develop an advanced assessment system for analyzing students' educational performance using data mining techniques. The proposed BSP-CRF model combines the use of a stacked voting-based model, CRF process, Bidirectional Encoder Representation model, and deep learning model. The BSP-CRF uses the Long Short-term Memory (LSTM) for the data training and testing. The feature extraction process is performed using CRF to identify patterns and key features from the student data. The features those are extracted examined with the Bidirectional Encoder Representation model to predict different classifications and assess the student’s performance. An autoencoder-based Bernoulli Boltzmann method is also used for sparse feature extraction. The deep learning model is based on the LSTM architecture. The model is trained using a large dataset of student educational performance data, and the Sparse Probabilistic Sparse Dynamic network architecture is utilized to increases the accuracy of model. The proposed BSP-CRF model achieves an accuracy of 97% to assess student performance.
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