During the wake of the Covid-19 pandemic, the educational paradigm has experienced a major change from in person learning traditional to online platforms. The change of learning convention has impacted the teacher-student especially in non-verbal communication. The absent of non-verbal communication has led to a reliance on verbal feedback which diminished the efficacy of the educational experience. This paper explores the integration of sentiment analysis into learning management systems (LMS) to bridge the student-teacher's gap by offering an alternative approach to interpreting student feedback beyond its verbal context. The research involves data preparation, feature selection, and the development of a deep neural network model encompassing word embedding, LSTM, and attention mechanisms. This model is compared against a logistic regression baseline to evaluate its efficacy in understanding student feedback. The study aims to bridge the communication gap between instructors and students in online learning environments, offering insights into the emotional context of student feedback and ultimately improving the quality of online education.

In this study, we investigate the combination of indicators, including performance, behavioral engagement, and emotional engagement, to identify students experiencing difficulties. We analyzed data from two primary sources: digital traces extracted from th e Learning Management System (LMS) and images captured by students' webcams. The digital traces provided insights into students' interactions with the educational content, while the images were utilized to analyze their emotional expressions during learnin g activities. By utilizing real data collected from students at a French engineering school, recorded during the 2022 2023 academic year, we observed a correlation between positive emotional states and improved academic outcomes. These preliminary findings support the notion that emotions play a crucial role in differentiating between high achieving and low achieving students.

The widespread adoption of Internet of Things (IoT) devices in smart cities, intelligent healthcare systems, and various real-world applications have resulted in the generation of vast amounts of data, often analyzed using different Machine Learning (ML) models. Federated learning (FL) has been acknowledged as a privacy-preserving machine learning technology, where multiple parties cooperatively train ML models without exchanging raw data. However, the current FL architecture does not allow for an audit of the training process due to the various data-protection policies implemented by each FL participant. Furthermore, there is no global model verifiability available in the current architecture. This paper proposes a smart contract-based policy control for securing the Federated Learning (FL) management system. First, we develop and deploy a smart contract-based local training policy control on the FL participants' side. This policy control is used to verify the training process, ensuring that the evaluation process follows the same rules for all FL participants. We then enforce a smart contract-based aggregation policy to manage the global model aggregation process. Upon completion, the aggregated model and policy are stored on blockchain-based storage. Subsequently, we distribute the aggregated global model and the smart contract to all FL participants. Our proposed method uses smart policy control to manage access and verify the integrity of machine learning models. We conducted multiple experiments with various machine learning architectures and datasets to evaluate our proposed framework, such as MNIST and CIFAR-10.

In recent years, there is a lot of interest in modeling students' digital traces in Learning Management System (LMS) to understand students' learning behavior patterns including aspects of meta-cognition and self-regulation, with the ultimate goal to turn those insights into actionable information to support students to improve their learning outcomes. In achieving this goal, however, there are two main issues that need to be addressed given the existing literature. Firstly, most of the current work is course-centered (i.e. models are built from data for a specific course) rather than student-centered; secondly, a vast majority of the models are correlational rather than causal. Those issues make it challenging to identify the most promising actionable factors for intervention at the student level where most of the campus-wide academic support is designed for. In this paper, we explored a student-centric analytical framework for LMS activity data that can provide not only correlational but causal insights mined from observational data. We demonstrated this approach using a dataset of 1651 computing major students at a public university in the US during one semester in the Fall of 2019. This dataset includes students' fine-grained LMS interaction logs and administrative data, e.g. demographics and academic performance. In addition, we expand the repository of LMS behavior indicators to include those that can characterize the time-of-the-day of login (e.g. chronotype). Our analysis showed that student login volume, compared with other login behavior indicators, is both strongly correlated and causally linked to student academic performance, especially among students with low academic performance. We envision that those insights will provide convincing evidence for college student support groups to launch student-centered and targeted interventions that are effective and scalable.

Procrastination, the irrational delay of tasks, is a common occurrence in online learning. Potential negative consequences include higher risk of drop-outs, increased stress, and reduced mood. Due to the rise of learning management systems and learning analytics, indicators of such behavior can be detected, enabling predictions of future procrastination and other dilatory behavior. However, research focusing on such predictions is scarce. Moreover, studies involving different types of predictors and comparisons between the predictive performance of various methods are virtually non-existent. In this study, we aim to fill these research gaps by analyzing the performance of multiple machine learning algorithms when predicting the delayed or timely submission of online assignments in a higher education setting with two categories of predictors: subjective, questionnaire-based variables and objective, log-data based indicators extracted from a learning management system. The results show that models with objective predictors consistently outperform models with subjective predictors, and a combination of both variable types perform slightly better. For each of these three options, a different approach prevailed (Gradient Boosting Machines for the subjective, Bayesian multilevel models for the objective, and Random Forest for the combined predictors). We conclude that careful attention should be paid to the selection of predictors and algorithms before implementing such models in learning management systems.

VTB has launched a machine learning (ML) model management system, a new product of the Artificial Intelligence Factory.

The platform has the ability of facial recognition for the learners with 90% accuracy which is especially helpful in cheating prevention. Moreover, the platform detects student focus and engagement and alerts the instructors in case of inactivity.

Your study material will be available to you on Imarticus's Learning Management System, which is a fully integrated state-of-the-art learning management system for an extended duration of 7 months. You will need to log in to the learning portal using the credentials provided and navigate through the portal as required.

While the term Learning Analytics has been around for some time, it has been mostly restricted to data collecting from the Learning Management Systems, such as completions data. Learning Analytics has to evolve beyond simply reporting to making predictions. We discuss current trends in Learning Analytics and how xAPI, Artificial Intelligence will impact it.

As advanced machines and computers become more and more proficient at picking investments, diagnosing disease symptoms, and conversing in natural English, it is difficult not to wonder what the limits to their capabilities are. This is why many observers believe in technology's potential to disrupt our economy--and our civilization--is unprecedented. Over the past few years, my conversations with students entering the workforce and the business leaders who hire them have revealed something important: to stay relevant in this new economic reality, higher education needs a dramatic realignment. Instead of educating college students for jobs that are about to disappear under the rising tide of technology, twenty-first-century universities should liberate them from outdated career models and give them ownership of their own futures. They should equip them with the literacies and skills they need to thrive in this new economy defined by technology, as well as continue providing them with access to the learning they need to face the challenges of life in a diverse, global environment. Higher education needs a new model and a new orientation away from its dual focus on undergraduate and graduate students.