A Deep Learning Model to Predict Student Learning Outcomes in LMS Using CNN and LSTM

Abstract

Learning Management Systems (LMSs) are increasingly utilized for the administration, tracking, and reporting of educational activities. One such widely used LMS in higher education institutions around the world is Blackboard. This is due to its capabilities of aligning items of learning content, studentstudent and student-teacher interactions, and assessment tasks to specified goals and student learning outcomes. This study aimed to determine how certain Key Performance Indicators (KPIs) based on student interaction with Blackboard helped to forecast the learning outcomes of students. A mixed-methods study design was used which included an analysis of four deep learning models for predicting student performance. Data were collected from reports of seven general preparation courses. They were analyzed using a documentary analysis approach to establish possible predictive KPIs associated with the electronic Blackboard report. Correlational analyses were performed to examine the extent to which these factors are linearly correlated with the performance indicators of students. The results indicated that a predictive model that combined convolutional neural networks and long short-term memory (CNN-LSTM) was the optimal method among the four models tested. The main conclusion drawn from this finding is that the combined CNN-LSTM approach may lead to interventions that optimize and expand the use of the Blackboard LMS in universities.

Towards Finding the Impact of Deep Learning in Educational Time Series Datasets – A Systematic Literature Review

“…He and Gao [31] proposed a student performance predictive model by collecting student learning behavior information through terminal data acquisition tools to find the student concentration level in the classroom and explore the influencing factors of learning concentration. Aljaloud [32] suggested a model to predict student learning outcomes by selecting the number of essential features and evaluating the result by reducing the number of features. There are seven features(f1,f2,f3,f4,f5,f6,f7) and seven courses used in this LMS, and the final result shows the best accuracy in the more number of attribute combination.…”

“…It is an algorithm used to update the weight of each layer after each iteration (Example: Gradient descent, Adam [26,27,39,43,45,29,31,32,33])…”

“…Allowing deep learning models to learn non-linear prediction boundaries [22] (Example: Sigmoid [28,39], ReLU [24,28,32], Tanh [26,32], Leaky Relu [45])…”

“…Shin et al, carry the exact ratio in [40], but 10 % for validation from test data. In researches [25,38,31,32], the percentage used for training/testing is 70:30; the remaining studies [43,26,28,29] use slightly different ratios. www.ijacsa.thesai.org 3) Imbalanced dataset: Most of the real-time dataset is imbalanced and should be handled appropriately to avoid classification errors.…”

CNO-LSTM: A Chaotic Neural Oscillatory Long Short-Term Memory Model for Text Classification

“…It is a cross-discipline or branches of techniques to text classification. For instance, rule-based matching from pattern recognition, machine learning also known as statistic learning method with supervised learning and unsupervised learning schemes, deep learning [23] modeling in AI and its hybrid alternatives [24][25] [26]. The former two categories depend on hand-craft labeling as the training target attributed to traditional methods, despite automatic systems are built, they are unable to exercise industrial projects with billions of data by self-detection or self-judgement.…”