A Multi Hidden Recurrent Neural Network with a Modified Grey Wolf Optimizer

Identifying university students' weaknesses results in better learning and can function as an early warning system to enable students to improve. However, the satisfaction level of existing systems is not promising. New and dynamic hybrid systems are needed to imitate this mechanism. A hybrid system (a modified Recurrent Neural Network with an adapted Grey Wolf Optimizer) is used to forecast students' outcomes. This proposed system would improve instruction by the faculty and enhance the students' learning experiences. The results show that a modified recurrent neural network with an adapted Grey Wolf Optimizer has the best accuracy when compared with other models.Comment: 34 pages, published in PLoS ON

Student risk identification learning model using machine learning approach

Several challenges are associated with online based learning systems, the most important of which is the lack of student motivation in various course materials and for various course activities. Further, it is important to identify student who are at risk of failing to complete the course on time. The existing models applied machine learning approach for solving it. However, these models are not efficient as they are trained using legacy data and also failed to address imbalanced data issues for both training and testing the classification approach. Further, they are not efficient for classifying new courses. For overcoming these research challenges, this work presented a novel design by training the learning model for identifying risk using current courses. Further, we present an XGBoost classification algorithm that can classify risk for new courses. Experiments are conducted to evaluate performance of proposed model. The outcome shows the proposed model attain significant performance over stat-of-art model in terms of ROC, F-measure, Precision and Recall

A Literature Review on Intelligent Services Applied to Distance Learning

Distance learning has assumed a relevant role in the educational scenario. The use of Virtual Learning Environments contributes to obtaining a substantial amount of educational data. In this sense, the analyzed data generate knowledge used by institutions to assist managers and professors in strategic planning and teaching. The discovery of students’ behaviors enables a wide variety of intelligent services for assisting in the learning process. This article presents a literature review in order to identify the intelligent services applied in distance learning. The research covers the period from January 2010 to May 2021. The initial search found 1316 articles, among which 51 were selected for further studies. Considering the selected articles, 33% (17/51) focus on learning systems, 35% (18/51) propose recommendation systems, 26% (13/51) approach predictive systems or models, and 6% (3/51) use assessment tools. This review allowed for the observation that the principal services offered are recommendation systems and learning systems. In these services, the analysis of student profiles stands out to identify patterns of behavior, detect low performance, and identify probabilities of dropouts from courses.info:eu-repo/semantics/publishedVersio

Accurate, timely and portable: course-agnostic early prediction of student performance from LMS logs

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceLearning management systems are essential intermediaries between students and educational content in the digital era. Among other factors, the institutional adoption of such systems is meant to foster student engagement and lead to better educational outcomes in a scalable manner. However, a significant challenge facing educators and institutions is the timely identification of students who may require special attention and feedback. Early identification of students allows educators to provide necessary feedback and adopt suitable corrective measures. Therefore, a significant body of research has been dedicated to developing early warning systems with clickstream data. However, comprehensive studies that attempt prediction on multiple courses are few and far between. Moreover, most predictive models require sophisticated domain knowledge, data skills and computational power that may not be available in practice. In this work, we used an academic year’s worth of data collected from all courses at a Portuguese information management school to perform two main experiments on two binary classification problems: the first being students at risk vs students not at risk and the second being high-performing students vs not high-performing students. In the first experiment, we compared the performances obtained with traditional machine learning classifiers against majority class classifiers at multiple stages of course completion (more specifically, the 10%, 25%, 33%, 50% and 100% course completion thresholds). For both classification problems, performances on all metrics peaked when using all of the data collected throughout the course – 88.6% accuracy and 92.3% Area Under the Receiver Operating Characteristic (AUROC) using Random Forest (RF) for students at risk and 78.2% accuracy and 79.6% AUROC using ExtraTrees for high-performing students. Concerning early prediction, acceptable performances for classifying at-risk students are achieved as early as the 25% course duration threshold (72.8% AUROC using RF). Performances for high-performing students were generally lower, with AUROC at earlier stages peaking at the courses’ midway point (64.4% AUROC using RF). Our second experiment deployed long-short term memory units (LSTM) trained with a time-dependent representation of a single feature (number of total clicks). While this approach achieved inferior performances, we argue that the more straightforward data pre-processing of this approach may represent a worthwhile tradeoff against relatively small losses in model performance, especially at earlier moments of prediction. We found the best tradeoff at 33% course duration – 64% AUROC against 74% AUROC using RF to predict at-risk students. To predict high-performing students, we found the best tradeoff to occur at 25% course duration (56% AUROC against 61% using RF). Results obtained using a different set of logs validate the portability of our approach when it comes to static aggregate models. However, our deep learning approach did not generalize well on this data, which suggests that portability between courses using this approach may only be possible in specific instances

Systematic mapping review on student’s performance analysis using big data predictive model

This paper classify the various existing predicting models that are used for monitoring andimproving students’ performance at schools and higher learning institutions. It analyses all theareas within the educational data mining methodology. Two databases were chosen for thisstudy and a systematic mapping study was performed. Due to the very infant stage of thisresearch area, only 114 articles published from 2012 till 2016 were identified. Within this, atotal of 59 articles were reviewed and classified. There is an increased interest and research inthe area of educational data mining, particularly in improving students’ performance withvarious predictive and prescriptive models. Most of the models are devised for pedagogicalimprovements ultimately. It is a huge scarcity in producing portable predictive models that fitsinto any educational environment. There is more research needed in the educational big data.Keywords: predictive analysis; student’s performance; big data; big data analytics; datamining; systematic mapping study

Attention-Based LSTM for Psychological Stress Detection from Spoken Language Using Distant Supervision

We propose a Long Short-Term Memory (LSTM) with attention mechanism to classify psychological stress from self-conducted interview transcriptions. We apply distant supervision by automatically labeling tweets based on their hashtag content, which complements and expands the size of our corpus. This additional data is used to initialize the model parameters, and which it is fine-tuned using the interview data. This improves the model's robustness, especially by expanding the vocabulary size. The bidirectional LSTM model with attention is found to be the best model in terms of accuracy (74.1%) and f-score (74.3%). Furthermore, we show that distant supervision fine-tuning enhances the model's performance by 1.6% accuracy and 2.1% f-score. The attention mechanism helps the model to select informative words.Comment: Accepted in ICASSP 201